From 3927600d9b9ba7a3cf50fea05f391951ee2d1fe5 Mon Sep 17 00:00:00 2001 From: exdysa <91800957+exdysa@users.noreply.github.com> Date: Wed, 3 Dec 2025 00:08:27 -0500 Subject: [PATCH 01/10] ~total refactor --- .github/workflows/mir.yml | 35 + MIR.egg-info/PKG-INFO | 146 +- MIR.egg-info/SOURCES.txt | 17 +- MIR.egg-info/requires.txt | 7 +- mir/__init__.py | 12 +- mir/automata.py | 3417 +++++++++++++ mir/config/__init__.py | 0 mir/config/constants.py | 129 + mir/config/conversion.py | 151 + mir/config/json_io.py | 44 + mir/doc_parser.py | 162 + mir/indexers.py | 425 ++ mir/inspect/__init__.py | 0 mir/inspect/classes.py | 121 + mir/inspect/metadata.py | 140 + mir/inspect/parenting.py | 32 + mir/inspect/pipes.py | 40 + mir/inspect/tasks.py | 256 + mir/maid.py | 288 ++ mir/mir.json | 8830 ++++++++++++++++++++++++++-------- mir/run.py | 68 + mir/spec/mir.py | 354 ++ mir/spec/modes.json | 7437 ++++++++++++++++++++++++++++ mir/{ => spec}/template.json | 0 mir/spec/versions.json | 30 + mir/tag.py | 113 + pyproject.toml | 25 +- uv.lock | 5458 ++++----------------- 28 files changed, 21087 insertions(+), 6650 deletions(-) create mode 100644 .github/workflows/mir.yml create mode 100644 mir/automata.py create mode 100644 mir/config/__init__.py create mode 100644 mir/config/constants.py create mode 100644 mir/config/conversion.py create mode 100644 mir/config/json_io.py create mode 100644 mir/doc_parser.py create mode 100644 mir/indexers.py create mode 100644 mir/inspect/__init__.py create mode 100644 mir/inspect/classes.py create mode 100644 mir/inspect/metadata.py create mode 100644 mir/inspect/parenting.py create mode 100644 mir/inspect/pipes.py create mode 100644 mir/inspect/tasks.py create mode 100644 mir/maid.py create mode 100644 mir/run.py create mode 100644 mir/spec/mir.py create mode 100644 mir/spec/modes.json rename mir/{ => spec}/template.json (100%) create mode 100644 mir/spec/versions.json create mode 100644 mir/tag.py diff --git a/.github/workflows/mir.yml b/.github/workflows/mir.yml new file mode 100644 index 0000000..3e3afb8 --- /dev/null +++ b/.github/workflows/mir.yml @@ -0,0 +1,35 @@ + +# This workflow will install Python dependencies, run tests and lint with a single version of Python +# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python + +name: mir pytest + +on: + push: + branches: [ "main" ] + pull_request: + branches: [ "main" ] + +permissions: + contents: read + +jobs: + + test: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: 3.13 + - name: Install dependencies and run tests + run: | + pip cache purge + rm -rf .venv + python -m venv .venv + source .venv/bin/activate + python3 -m pip install --upgrade pip + pip install ".[dev]" + cd tests + pytest -vv diff --git a/MIR.egg-info/PKG-INFO b/MIR.egg-info/PKG-INFO index b3da1cf..53d28a4 100644 --- a/MIR.egg-info/PKG-INFO +++ b/MIR.egg-info/PKG-INFO @@ -32,12 +32,17 @@ Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence Requires-Python: >=3.11 Description-Content-Type: text/markdown License-File: LICENSE -Requires-Dist: nnll[dev]@ git+https://github.com/darkshapes/nnll.git +Requires-Dist: diffusers>=0.35.2 +Requires-Dist: huggingface-hub[hf-xet]>=1.1.7 +Requires-Dist: pydantic>=2.12.5 +Requires-Dist: tokenizers>=0.22.1 +Requires-Dist: torch>=2.9.1 +Requires-Dist: transformers>=2.3.0 Dynamic: license-file --- language: -- en + - en library_name: mir license_name: MPL-2.0 + Commons Clause 1.0 --- @@ -45,29 +50,26 @@ license_name: MPL-2.0 + Commons Clause 1.0
- - - A pixellated logo of superimposed letters M I R + + + A pixellated logo of superimposed letters M I R

# MIR (Machine Intelligence Resource)
A naming schema for AIGC/ML work. - The MIR classification format seeks to standardize and complete a hyperlinked network of model information, improving accessibility and reproducibility across the AI community.
-This repo is a live development implementation, an example of autogenerating model inference parameters and code with the MIR schema. This is the sister repo to our [🤗HuggingFace MIR project](https://huggingface.co/darkshapes/MIR) which is an archive of model state dict layer information that also uses the MIR schema. +This repo is an example development implementation of autogenerated model inference parameters and code with the MIR schema. This is the sister repo to our [🤗HuggingFace MIR project](https://huggingface.co/darkshapes/MIR) which is an archive of model state dict layer information that also uses the MIR schema. -MIR is inspired by: -- [AIR-URN](https://github.com/civitai/civitai/wiki/AIR-%E2%80%90-Uniform-Resource-Names-for-AI) project by [CivitAI](https://civitai.com/) -- [Spandrel](https://github.com/chaiNNer-org/spandrel/blob/main/libs/spandrel/spandrel/__helpers/registry.py) super-resolution registry by [chaiNNer](https://github.com/chaiNNer-org/chaiNNer) -- [SDWebUI Model Toolkit](https://github.com/silveroxides/stable-diffusion-webui-model-toolkit-revisited) by [silveroxides](https://github.com/silveroxides) +## MIR Schema: > [!NOTE] +> > ## Example: -> ## mir : model . transformer . clip-l : stable-diffusion-xl > +> ## mir : model . transformer . clip-l : stable-diffusion-xl > > ``` > mir : model . lora . hyper : flux-1 @@ -75,8 +77,124 @@ MIR is inspired by: > [URI]:[Domain].[Architecture].[Series]:[Compatibility] > ``` +### DOMAIN + +↑Most Specific/Decentralized + +#### Dev + +**Pre-release or under evaluation items without an identifier in an expected format** +Anything in in-training, pre-public release, and items under evaluation + +Meant to be created by anyone, derived from code and file analysis + +- Contextual +- Layers of neural networks +- Dynamic + +#### Model + +**Publicly released machine learning models with an identifier in the database** +Model weight tensors with arbitrary locations and quantitative dimensions + +Meant to be created by file hosts, derived from research pre-prints + +- Contextual +- Layers of neural networks +- Fixed + +#### Ops + +**References to specific optimization or manipulation techniques** +Algorithms, optimizations and procedures for models + +Meant to be created by code libraries, derived from research pre-prints + +- Universal +- Attributes of neural networks +- Dynamic + +#### Info + +**Metadata of layer names or settings with an identifier in the database** +Information about the model and tensor specifications + +Meant to be created by standards community, derived from code and file analysis + +- Universal +- Attributes of neural networks +- Fixed + +↓Most General/Centralized + +### ARCHITECTURE + +| Abbreviation | Description | +| ------------------------------------- | ----------------------------------------- | +|
GRU
| Gated recurrent unit | +|
RBM
| Restricted Boltzmann machine | +|
TAE
| Tiny Autoencoder | +|
VAE
| Variable Autoencoder | +|
LSTM
| Long Short-Term Memory | +|
RESNET
| Residual Network | +|
CNN
| Convolutional Neural Network | +|
RCNN
| Region-based Convolutional Neural Network | +|
RNN
| Recurrent Neural Network | +|
BRNN
| Bi-directional Recurrent Neural Network | +|
GAN
| Generative Adversarial Model | +|
SSM
| State-Space Model | +|
DETR
| Detection Transformer | +|
VIT
| Vision Transformer | +|
MOE
| Mixture of Experts | +|
AET
| Autoencoding Transformer | +|
STST
| Sequence-to-Sequence Transformer | +|
ART
| Autoregressive Transformer | +|
LORA
| Low-Rank Adaptation | +|
CONTROLNET
| Controlnet | +|
UNCLASSIFIED
| Unknown | + +-- + +### SERIES + +#### Rules + +- Lowercase, hyphen only +- Remove parameter size, non-breaking semantic versioning, library names + +Example: `tencent-hunyuan/hunyuandiT-v1.2-diffusers`
+SERIES : `hunyuandit-v1` + +Example: `black-forest-labs/FLUX.1-dev`
+SERIES : `flux1-dev` + +In regex (roughly) + +``` +BREAKING*SUFFIX = r".*(?:-)(prior)$|.*(?:-)(diffusers)$|.\*[\*-](\d{3,4}px|-T2V$|-I2V$)" +PARAMETERS*SUFFIX = r"(\d{1,4}[KkMmBb]|[.*-]\d+[\._-]\d+[Bb][._-]).\*?$" +SEARCH*SUFFIX = r"\d+[.*-]?\d+[BbMmKk](it)?|[._-]\d+[BbMmKk](it)?" +``` + +### COMPATIBILITY + +#### Rules + +An additional SERIES label for identifying cross-compatibility + +
+ +If you would like to regenerate or update the example file here, use [nnll](https://github.com/darkshapes/nnll/tree/0.1.dev799): + +MIR is inspired by: + +- [AIR-URN](https://github.com/civitai/civitai/wiki/AIR-%E2%80%90-Uniform-Resource-Names-for-AI) project by [CivitAI](https://civitai.com/) +- [Spandrel](https://github.com/chaiNNer-org/spandrel/blob/main/libs/spandrel/spandrel/__helpers/registry.py) super-resolution registry by [chaiNNer](https://github.com/chaiNNer-org/chaiNNer) +- [SDWebUI Model Toolkit](https://github.com/silveroxides/stable-diffusion-webui-model-toolkit-revisited) by [silveroxides](https://github.com/silveroxides) + + ![commits per month](https://img.shields.io/github/commit-activity/m/darkshapes/MIR?color=indigo)
![code size](https://img.shields.io/github/languages/code-size/darkshapes/MIR?color=navy)
[](https://discord.gg/VVn9Ku74Dk)
@@ -86,6 +204,4 @@ MIR is inspired by: ### [About this project](https://github.com/darkshapes/sdbx/wiki/)
- - - +```` diff --git a/MIR.egg-info/SOURCES.txt b/MIR.egg-info/SOURCES.txt index e943593..f46776a 100644 --- a/MIR.egg-info/SOURCES.txt +++ b/MIR.egg-info/SOURCES.txt @@ -2,15 +2,24 @@ LICENSE README.md pyproject.toml -.github/workflows/mir.yml +uv.lock MIR.egg-info/PKG-INFO MIR.egg-info/SOURCES.txt MIR.egg-info/dependency_links.txt MIR.egg-info/entry_points.txt MIR.egg-info/requires.txt MIR.egg-info/top_level.txt -img_src/mir300_dark.png -img_src/mir300_light.png mir/__init__.py +mir/automata.py +mir/doc_parser.py +mir/general.py +mir/indexers.py +mir/maid.py mir/mir.json -mir/template.json \ No newline at end of file +mir/mir.py +mir/model_metadata.py +mir/parenting.py +mir/pipe.py +mir/run.py +mir/tag.py +mir/tasks.py \ No newline at end of file diff --git a/MIR.egg-info/requires.txt b/MIR.egg-info/requires.txt index d6ada9b..9f1dc9e 100644 --- a/MIR.egg-info/requires.txt +++ b/MIR.egg-info/requires.txt @@ -1 +1,6 @@ -nnll[dev]@ git+https://github.com/darkshapes/nnll.git +diffusers>=0.35.2 +huggingface-hub[hf-xet]>=1.1.7 +pydantic>=2.12.5 +tokenizers>=0.22.1 +torch>=2.9.1 +transformers>=2.3.0 diff --git a/mir/__init__.py b/mir/__init__.py index 1722ffe..fdae8c4 100644 --- a/mir/__init__.py +++ b/mir/__init__.py @@ -1,15 +1,16 @@ # SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0 # + def main(): - import nnll.mir.maid - from nnll.mir.maid import main as mir_main + import mir.maid + from mir.maid import main as mir_main mir_main() - from nnll.model_detect.tasks import main + from mir.detect_tasks import main main() - from nnll.model_detect.tasks import pipe + from mir.detect_tasks import pipe pipe() @@ -20,7 +21,8 @@ def main(): os.remove("mir.json") except FileNotFoundError: pass - shutil.copy2(os.path.join(os.path.dirname(nnll.mir.maid.__file__), "config", "mir.json"), os.path.join(os.getcwd(),"mir.json")) + shutil.copy2(os.path.join(os.path.dirname(mir.maid.__file__), "config", "mir.json"), os.path.join(os.getcwd(), "mir.json")) + if __name__ == "__main__": main() diff --git a/mir/automata.py b/mir/automata.py new file mode 100644 index 0000000..03e33e3 --- /dev/null +++ b/mir/automata.py @@ -0,0 +1,3417 @@ +# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0 +# + +"""自動化索引""" +# regex to remove \[[^\]]*\] +# (?m)^\s*"[^"]+"(?=\s*:) +# (?m)^\s*"[^"]+"\s?: +# modelspec sai https://github.com/Stability-AI/ModelSpec + +from importlib import import_module +from typing import Dict, List, Tuple +import re +import torch +from diffusers import _import_structure + +from mir.maid import MIRDatabase +from mir.spec.mir import mir_entry +from mir.tag import make_mir_tag, make_scheduler_tag, tag_pipe, tag_base_model +from mir.config.logging import nfo, dbug, dbuq +from mir.config.conversion import slice_number +from mir.indexers import diffusers_index, transformers_index + + +sd1_series, sd1_comp = make_mir_tag("stable-diffusion-v1-5/stable-diffusion-v1-5") +sdxl_series, sdxl_comp = make_mir_tag("stabilityai/stable-diffusion-xl-base-1.0") +dev_series, dev_comp = make_mir_tag("black-forest-labs/FLUX.1-dev") +schnell_series, schnell_comp = make_mir_tag("black-forest-labs/FLUX.1-schnell") +ssd_series, ssd_comp = make_mir_tag("segmind/SSD-1B") +vega_series, vega_comp = make_mir_tag("segmind/Segmind-Vega") +sd3_series, sd3_comp = make_mir_tag("stable-diffusion-3.5-medium") # + +# def gen_attention_processors(mir_db: MIRDatabase): # upstream not quite ready for this yet +# from diffusers.models.attention_processor import AttentionProcessor + +# mir_data +# for series, comp_name in mir_data.items(): +# id_segment = series.split(".") +# for compatibility in comp_name: +# dbug(id_segment) +# try: +# mir_db.add( +# mir_entry( +# domain=id_segment[0], +# arch=id_segment[1], +# series=id_segment[2], +# comp=compatibility, +# **mir_data[series][compatibility], +# ), +# ) +# except IndexError as error_log: +# nfo(f"Failed to create series: {series} compatibility: {comp_name} ") +# dbug(error_log) + + +# def gen_guiders(mir_db: MIRDatabase): # upstream not quite ready for this yet +# from nnll.metadata.helpers import snake_caseify +# from diffusers.guider import GuiderType + +# guider_type = GuiderType +# for comp_name in guider_type.items(): +# class_obj = comp_name.__name__ +# mir_data = {"pkg": {0: {"diffusers": class_obj}}} +# try: +# mir_db.add( +# mir_entry( +# domain="ops", +# arch="noise_prediction", +# series="guider", +# comp=snake_caseify(class_obj), +# **mir_data, +# ), +# ) +# except IndexError as error_log: +# nfo(f"Failed to create compatibility: {class_obj}") +# dbug(error_log) + + +# ( +# "info.unet", +# "stable-cascade", +# { +# "combined": { +# "pkg": { +# 0: { # decoder=decoder_unet +# "precision": "ops.precision.bfloat.B16", +# "generation": { +# "negative_prompt": "", +# "num_inference_steps": 20, +# "guidance_scale": 4.0, +# "num_images_per_prompt": 1, +# "width": 1024, +# "height": 1024, +# }, +# }, +# "pkg_alt": { +# 0: { +# "diffusers": { +# "StableCascadeCombinedPipeline": { +# "negative_prompt": "", +# "num_inference_steps": 10, +# "prior_num_inference_steps": 20, +# "prior_guidance_scale": 3.0, +# } +# }, +# } +# }, +# } +# } +# }, +# ), + + +def assimilate(mir_db: MIRDatabase, data_tuple: List[Tuple[Dict[str, any]]]) -> None: + """Merge new data into a pre-generated MIR database, updating while preserving existing data structures.\n + :param mir_db: The MIRDatabase instance + :param data_tuple: A list of tuples, each containing:\n + - arch (str): The architecture name + - series (str): The series name + - `new_data`: New data to be merged into the database. + :raises TypeError: If any field in `new_data` is not a dictionary. + """ + + def update_nested_dict(target, source): + for key, value in source.items(): + if isinstance(value, dict) and key in target: + if isinstance(target, dict): + update_nested_dict(target[key], value) + else: + if isinstance(source, dict): + # dbuq(target) + target.setdefault(key, value) + else: + target = {key: value} + + dbug(f"{data_tuple}, {len(data_tuple)}") + for arch, series, new_data in data_tuple: + mir_data = mir_db.database[f"{arch}.{series}"] + for comp, field_data in new_data.items(): + if not isinstance(field_data, dict): + raise TypeError(f"{field_data} <-- Cannot combine with database: Not `dict()`") + + # dbuq(f"{arch}.{series} : {comp}") + update_nested_dict(mir_data.setdefault(comp, {}), field_data) + + if series == sdxl_series: + for field, field_data in field_data.items(): + if isinstance(field_data, dict): + for definition, sub_def_data in field_data.items(): + # dbug(definition) + if isinstance(sub_def_data, dict): + mir_data[comp][field].setdefault(definition, {}) + update_nested_dict(mir_data[comp][field][definition], sub_def_data) + + +def hf_pkg_to_mir(mir_db: MIRDatabase): + """Generate MIR HF Hub model database""" + mir_data = diffusers_index() | transformers_index() + for series, comp_name in mir_data.items(): + id_segment = series.split(".") + for compatibility in comp_name: + # dbug(id_segment) + try: + mir_db.add( + mir_entry( + domain=id_segment[0], + arch=id_segment[1], + series=id_segment[2], + comp=compatibility, + **mir_data[series][compatibility], + ), + ) + except IndexError: # as error_log: + nfo(f"Failed to create series: {series} compatibility: {comp_name} ") + # dbug(error_log) + + +def add_mir_dtype(mir_db: MIRDatabase): + """Create mir info database""" + + available_dtypes: List[str] = [dtype for dtype in torch.__dict__.values() if isinstance(dtype, torch.dtype)] + series_name = "_" + for precision in available_dtypes: + dep_name, class_name = str(precision).split(".") + if "_" in class_name: + comp_name = class_name[0].upper() + "8_" + class_name.split("_")[1].upper() + if comp_name.endswith("FN"): + comp_name = comp_name[:-2] + else: + comp_name = class_name[0].upper() + str(slice_number(class_name)) + variant_name = class_name.replace("bfloat", "bf").replace("float", "fp") + dbuq(variant_name) + patterns = [r"complex", r"bits", r"quint", r"uint", r"int", r"bfloat", r"float", r"bool"] + for precision_name in patterns: + compiled = re.compile(precision_name) + dtype = re.search(compiled, class_name) + if dtype: + series_name = dtype.group() + break + + mir_db.add( + mir_entry( + domain="ops", + arch="precision", + series=series_name, + comp=comp_name, + pkg={0: {dep_name.lower(): {class_name.lower(): {"variant": variant_name}}}}, + ) + ) + + +def add_mir_schedulers(mir_db: MIRDatabase): + """Create mir info database""" + + for class_name in _import_structure["schedulers"]: + if class_name != "SchedulerMixin": + series_name, comp_name = make_scheduler_tag(class_name) + class_obj = import_module("diffusers.schedulers") + class_path = getattr(class_obj, class_name).__module__ + mir_db.add( + mir_entry( + domain="ops", + arch="scheduler", + series=series_name, + comp=comp_name.lower(), + pkg={ + 0: { + "diffusers": class_name, + "module_path": class_path, + }, + }, + ) + ) + + class_name = "KarrasDiffusionSchedulers" + series_name, comp_name = make_scheduler_tag(class_name) + class_obj = import_module("diffusers.schedulers.scheduling_utils") + class_path = getattr(class_obj, class_name).__module__ + mir_db.add( + mir_entry( + domain="ops", + arch="scheduler", + series=series_name, + comp=comp_name, + pkg={ + 0: { + "diffusers": class_name, + "module_path": class_path, + }, + }, + ), + ) + + +# def auto_gan etc etc +# ai-forever/Real-ESRGAN + + +def mir_update(mir_db: MIRDatabase, task_list: list = None, pipe_list: list = None): + """Create mir unet info database""" + + diffusers_addons = [ + ( + "stabilityai/stable-diffusion-xl-base-1.0", + "StableDiffusionXLPipeline", + { + "pkg": { + 0: { + "precision": "ops.precision.float.F16", + "generation": { + "denoising_end": 0.8, + "num_inference_steps": 40, + "output_type": "latent", + "safety_checker": False, + "width": 1024, + "height": 1024, + }, + }, + 1: {"diffusers": "DiffusionPipeline"}, + }, + "file_256": [ + "357650fbfb3c7b4d94c1f5fd7664da819ad1ff5a839430484b4ec422d03f710a", # diffusers + "83e012a805b84c7ca28e5646747c90a243c65c8ba4f070e2d7ddc9d74661e139", # fp16 diffusers + "31e35c80fc4829d14f90153f4c74cd59c90b779f6afe05a74cd6120b893f7e5b", # modelspec sai + "6f001c090fb13c0d0f8b0a5916da814712a94400b99471fabe77c1c4a51ecaaf", # onnx + ], + "layer_256": [ + "62a5ab1b5fdfa4fedb32323841298c6effe1af25be94a8583350b0a7641503ef", # any modelspec sai + "34dff8d98898baa0f10e71943e56b588cc114253b0d2f1051f3ce7a8a45fee0b", # diffusers + "56b1ccd89b0d6ab658048aa34d659788b6ed663f13ef566f4b11bccef590b9da", # diffusers fp16 + ], + "layer_b3": [ + "8be44fa13c1efa60f8bcadaa57f1d718473f9660f03c4f0e65dc037960d8cba1", # any modelspec sai + "c9ab95ed1851418b65ef99651c1eb6bbdd2e3b0715e0e435d6d1e56ce310fac3", # diffusers + "adfa260098d87616d748e3cf9c10bb2c90ff8890a84abbb2853d4aa69664070b", # diffusers fp16 + ], + "identifiers": ["logit_scale", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.k_proj.weight", "add_embedding.linear_2.bias"], + }, + ), + ( + "stabilityai/stable-diffusion-xl-refiner-1.0", + "StableDiffusionXLImg2ImgPipeline", + { + "pkg": { + 1: { + "diffusers": "DiffusionPipeline", + "generation": {"num_inference_steps": 40, "denoising_end": 0.8}, + } + }, + "identifiers": ["conditioner.embedders.0.model.transformer.resblocks.9.mlp.c_proj.bias"], + "file_256": [ + "54f9cd2f2daf3aeec0b2708fa3dbc0e84e4f8ddd1ddead42e5bc60c6572c989f", # diffusers + "7440042bbdc8a24813002c09b6b69b64dc90fded4472613437b7f55f9b7d9c5f", # modelspec sai + "3ea0376dcf065eaefd27806394a90e310001b1a71d4f1cf1f655e86c0e566ffe", # fp16 diffusers + ], + "layer_b3": [ + "6281355dbb37e5769c9460ae0ac75506d89932e2f97b09d9ade32ecf191e75ba", + "afb0639aae2eb65577c12d4a30cf7c9b3620ae63ba64a8fa632b58608c8a7a2e", + "669046014b69d98ab0f6fbb59547644436e0275f8b638f467ce2a873c3313683", + ], + "layer_256": [ + "bb9eadbfabb52c0d8645783525a3fa70b59e9d7d09d5290d742a303262e793a2", + "c5adb56fe51343af2c3d493eb9f41515c204bd91eb9f40b983d45f70a1fa3b6d", + "1f838e39ed6e916258aee6990b72c09b34aa8eb3b5342234a497b8852b3df1c6", + ], + }, + ), + ( + "lodestones/Chroma", + "ChromaPipeline", + { + "pkg": { + 1: { + "generation": {"neg_text": "", "num_steps": "28", "latent_size": [64, 64]}, + } + }, + "file_256": [ + "53adcb3b6b6005758d40e2d8058b044ed4892bc8616efb7a62cc2dd384be07de", # v1 + "2c41e8a9831f3be1eaff2c2ed590abb62e4534e814f7ec58a5fd74ff71dc2036", # v46, + "0a7b2d9699dbd22b3744ee2692900cabcfb731a43dac13729c33807f2bb7c9f6", # v37 detail + "6ddc9e2bbe3376ab5ee9f10b2d947f127b6bf6f879f06f316a2208bb0da357b8", # mlx chroma / v36 detail + ], + "layer_b3": [ + "15e227ced8a89c41abaa9cc44f84dfffdf5ead0c626035e5a2dde2bbb0935479", + ], + "layer_256": ["a4daa6ff6f45ca70c738adb8c19bc3b6f228df931e6bf2a3394463e4dd7ec882"], + }, + ), + ( + "fal/AuraFlow", + "AuraFlowPipeline", + { + "identifiers": [[8192, 3072], "mlpX.c_fc2.weight", "joint_transformer_blocks.2.ff_context.linear_2.weight"], + "file_256": [ + "ce3e475246258b94ee9dcb8b83292cb34edfffc2bbde46c74604d9c6cd7c585c", + "526be97cf581c89ad87c6b19c1f7c2378851137698f7ec436596d061a382d37b", # sai + "6a40b011f287452dbca80face78e667055904c5ad97eb2097ade3200259b2203", # diffusers fp16 + "05e5493018333d947bb5940083dbc2f071093027ff414bc5b1b1229e4836e5cb", # diffusers + ], + "layer_b3": [ + "cc6d383576c35a9709798d2e2b9e3eb31ba8c608040cf3712bc37871cfd14e21", + "ddd54c44fa28fbddecf7cfae91cfa04917fd2f2fa94fc78c528cef2356a4ec3a", # sai + "90c694e7d1e20e6da49b571e9954338d384775419790be315304103227b1051b", + "9e85aec1bdb616f52f88c80ddc7ab1eae8c16c0b5fbfcdb61a71ac02c325003d", + ], + "layer_256": [ + "3c13e6a965d03a49227d8b1606ba6a343a23772d8768407cc78d4ddb9102bc80", + "b356cc84a23bc93bda4cc0fce1d0ba1b8e3d5a521e659ffc72e9e4a2d2c7f204", + "270df7317fe01abf06333acbbd4f15f8fc7a7c56053219f42efb598454a3af24", + "7ab6aa4514dd09f3cf589587d51a81734193ce45dd51bda9db0bd62fe48ef7d5", + ], + }, + ), + ( + "Tencent-Hunyuan/HunyuanDiT-v1.2-Diffusers", + "HunyuanDiTPipeline", + { + "identifiers": ["extra_embedder", "model.blocks", "skip_norm.weight"], + "file_256": [ + "4fb84f84079cda457d171b3c6b15d1be95b5a3e5d9825703951a99ddf92d1787", # normal + "e01db5e129e8ca1117e9cf473fc5a2b096949f03ab90048aeabbc328de7ec800", # distilled + "8af691cadb78047d55721259355d708e87ddbba1b7845df9377d9a5ae917b45d", # 1.2 + ], + "layer_b3": [ + "aead6b61b17ebc77c4c186a4b82c193f11ec267b20d909726422ee9852e2e0b2", + "885a056b94f6f9844c0660be489844d63bb74cc13316f441d10968fff3dd3120", # distilled + "390d951cbdda6e2cffb690031b60f02921624651534c2effaaa7d68ab476c700", + ], + "layer_256": [ + "d4842ce2b7f927203326b25ff4d6738ec9a8b95327f06791c387e4a351ed6ed0", + "5af943f96f5dc9fecb1e92fe2b1fa17c94dd6947690201f4a5ee1a4a2721a68e", # distilled + "4a1f2b8234fa4336e263842e042d42e8d64d8a4d3941d9c0c78366b50303950c", # 1.2 + ], + }, + ), + ( + "Alpha-VLLM/Lumina-Next-SFT-diffusers", + "LuminaPipeline", + { + "pkg": { + 0: { + "precision": " ops.precision.bfloat.B16", + }, + }, + "identifiers": ["time_caption", "feed_forward"], + "file_256": [ + "371153b7c7b7a64899d4016970c7cc472039f9c9b21ebe073adf0b8525cdf1bd", + ], + "layer_b3": [ + "fa134efd6e9672e7de2965e4895fc58879bd0a6c4fdf9165c278f2748254675f", + "4d960ec35c53f72f065b94b836bcd923ea6074d38ad49881061f315d62e3c839", + ], + "layer_256": [ + "3938a85568d9df186923edf04391d79e89e6199123bc175afb520e0948d1ae05", + "c0ca51fdea051fcd042bf4b56d32e1e8bb9525a921f2e197f370f101e90527f0", + ], + }, + ), + ( + "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS", + "PixArtSigmaPipeline", + { + "identifiers": ["adaln_single", "scale_shift_table"], + "file_256": [ + "c34b520ef473329b945c2a21083cdf1337c5a468d23b3215b65576789bfd0305", + "2fa4dee9229c02b03163f57bdb8e80c7a5ee364b7161796abe9c05e8dd13f239", + ], + "layer_b3": [ + "a199930ff537994872da77391955f0dd52eddd22ab9105388f0c5852f1b8021f", + "ee6f980c32e98da6885f3e97d3f88d9158031e362cd3a49b20d1e23924b251e3", + ], + "layer_256": [ + "e0afd203aff5a1d192e325d0f59361373273d85d138b51768c3f10a75c154dc0", + "987f3c2ff5d399191e5fd7dd7b1f1f285c197dc8124ad77f05cde7f2fb677a3c", + ], + }, + ), + ( + "PixArt-alpha/PixArt-XL-2-1024-MS", + "PixArtAlphaPipeline", + { + "identifiers": ["aspect_ratio", "y_embedding", "emb.resolution", "caption_projection"], + "file_256": ["809a92d52a4a228f381a4b4f4b76051294b73285fb0cbb02f0ad24f9372217a8"], + "layer_b3": ["c5be83545ce9dbc564bcc9fd8fe4157d131347ccfc8f62adc877ec205b20acee"], + "layer_256": ["117225c0e91423746114b23d3e409708ad55c90ff52b21fa7a1c5105d2e935a5"], + }, + ), + ( + "stabilityai/stable-diffusion-3.5-medium", + "StableDiffusion3Pipeline", + { + "pkg": { + 0: {"precision": "ops.precision.float.F16"}, + }, + "identifiers": [ + "model.diffusion_model.joint_blocks.", + "transformer_blocks.21.norm1_context.linear.weight", + "transformer_blocks.31.norm1_context.linear.weight", + "blocks.11.ff.net.2.weight", + ], + "file_256": [ + "ffef7a279d9134626e6ce0d494fba84fc1c7e720b3c7df2d19a09dc3796d8f93", # large + "11fe06e22364b823dfeedc275912336b932b32a293a0b2f35ffac071990cc4de", # medium + ], + "layer_b3": [ + "e411016545785046810b29cc3999f40bc6392be134a1318386c6f1c48f98726a", + "a81e07ee67bc627e8b3c5e292ec1ca239009517a2106e8249d670ced0a88f746", # med + ], + "layer_256": [ + "13c982a6dc82d21c9f459e837d8c6f6d4696fd6e7e7b5783bdd2250b1f4fec61", + "6ee79050373337bf63ac20916596df778bb22022bb38af986128a7459eda1463", # med + ], + }, + ), + ( + "Efficient-Large-Model/Sana-1600M-1024px-BF16-diffusers", + "SanaPipeline", + { + "pkg": { + 0: { + "generation": { + "height": 1024, + "width": 1024, + "guidance_scale": 4.5, + "num_inference_steps": 20, + }, + "precision": "ops.precision.bfloat.B16", + }, + }, + "file_256": [ + "b0b50c33be8758713459aa3c760feef6315d4bea31521fb5b8c3e8fdd9841ffe", + ], + "layer_b3": [ + "461e3d83dfa7e075ef21e2138ef153922ecfadde3db464b03dff92819f3e86dd", + ], + "layer_256": [ + "b928bbcc2ce99d55d21c189e2b1c57498bc313ef5b1457036e356107d567fc4e", + ], + }, + ), + ( + "stable-diffusion-v1-5/stable-diffusion-v1-5", + "StableDiffusionPipeline", + { + "identifiers": ["up_blocks.3.attentions.0.transformer_blocks.0.norm3.weight"], + "file_256": [ + "6ce0161689b3853acaa03779ec93eafe75a02f4ced659bee03f50797806fa2fa", # pruned ema only original safetensors + "1a189f0be69d6106a48548e7626207dddd7042a418dbf372cefd05e0cdba61b6", # pruned original safetensors + "e1441589a6f3c5a53f5f54d0975a18a7feb7cdf0b0dee276dfc3331ae376a053", # ema pruned original ckpt + "cc6cb27103417325ff94f52b7a5d2dde45a7515b25c255d8e396c90014281516", # pruned ema original ckpt + "19da7aaa4b880e59d56843f1fcb4dd9b599c28a1d9d9af7c1143057c8ffae9f1", # diffusers safetensors + "cd1b6db09a81cb1d39fbd245a89c1e3db9da9fe8eba5e8f9098ea6c4994221d3", # diffusers non ema safetensors + "c83908253f9a64d08c25fc90874c9c8aef9a329ce1ca5fb909d73b0c83d1ea21", # diffusers fp16 + ], + "layer_b3": [ + "909c6ff3192ab2767e789a6125865bc23163db467ab78b1c633bad46a4293fad", + "b52807536902cabbf84f99e4fa2f8713fb4ef77e739f06367ee0d486e3222faa", # ckpt + "d31382d71a1044b636d80d861a2b4dbca51826bed34d34b5c14608b7679ccefd", # safetensors ema pruned + "5fd8b28013b7e5a64c7c235f0a93d93e48bc19a0e5dde7b646a87b429219643a", # safetensors pruned + "731f552f29edcb4f86112cc94d296377f3533a9633ccf83e202d9e1785d94a00", # diffusers + "2d2f97574a161cf01a6f6d476b141c7be06f940d94b695ffc12c4e74eca2de1c", # diffusers fp16 + ], + "layer_256": [ + "ece771354ad470a82d56eda413ae3dd6c00d2de28ab3c56a88201d08d4424b4b", + "65b084dada803461ab9ca9be9b892d211870a121dd6c555a111eea470b951c54", # st + "dc937b59892604f5a86ac96936cd7ff09e25f18ae6b758e8014a24c7fa039e91", # ckpt + "92565dec90f7c8412dc872e820f66cd0c56263bbbc392439645b6fee270f41bb", # st fp16 + ], + }, + ), + ( + "Kwai-Kolors/Kolors-diffusers", + "KolorsPipeline", + { + "pkg": { + 0: { + "precision": "ops.precision.float.F16", + "generation": { + "negative_prompt": "", + "guidance_scale": 5.0, + "num_inference_steps": 50, + "width": 1024, + "height": 1024, + }, + }, + 1: {"diffusers": "DiffusionPipeline"}, + }, + "file_256": [ + "425ff1dcbe3a70ac13d3afdd69bd4e3176b0c3260722527c80b210f11d2d966c", # fp16, + ], + "layer_b3": [ + "6eb15506fa38b4cbb26391ab1b6c9ead05f86c711e46583bfbe8fc4421571414", # fp16 + ], + "layer_256": [ + "04e3c17170b8a200481f6941b370fdc5056a00fe5a16956de01790f8a93c0dcd", # fp16 + ], + "identifiers": [".DenseReluDense.wi.weight", "encoder_hid_proj.weight"], + }, + ), + ( + "stabilityai/stable-cascade-prior", + "StableCascadePriorPipeline", + { + "pkg": { + 0: { + "precision": "ops.precision.bfloat.B16", + "generation": { + "negative_prompt": "", + "num_images_per_prompt": 1, + "num_inference_steps": 20, + "guidance_scale": 4.0, + "width": 1024, + "height": 1024, + }, + } + }, + "file_256": [ + "673b3173b037fb5f65b14fde37267390641a36726683de75dcf9df76fce2b866", # lite bf16 + "45c1eb5ce9b69efac891ad459b15c215cd90a986adbbfaf3effd3a89578cbcaf", # pretrained + "088ddf1e444abf399007b2da2bac87791df165c69f477994f6b3c745a20904b0", # stage c modelspec sai + "39cec96c7212607f9e526db719bf1df507166d09f4748676c13b0d31cd4adb07", # stage c + "31ffe2f1a3e2351d658fc7d3002a4eca22466a680f7fb3715b1e3768476f9633", # stage c lite + "dfe24009fc881011f350d08d9d13be13a1a3b3cbfed667435efe0fd419aca099", # bf16 + ], + "layer_b3": [ + "c55c83fa435ed128457f605bf1312e54727996d1c94413fc5ab5b49e9933857c", + "6fb07ed9fc6ee636e50783802754b3a37bbecfc67037813b616223aeaf6fe877", + "2ea194240e105c8962923e2baca88cb6a0c826794afc2ef82474301694711d68", + "3412c8a184805621e4595d57268ced0b5c3c1974cd221bf67b2c908eec4fd61c", + "53abfb013cfb0e41d0bc7b96bb83e42a4d4c67cb7325f9acf645b02d90efd8fe", + "34556558f680c183adc2accd493cb9888a98ba853226bbecb07d95eb2055ff4f", + ], + "layer_256": [ + "4f5e0a738b963d3d4f8413387a0966ac1ce51f0f985bcbcc124fa221a2fff467", + "8aa77e732a398b7d0dcd9a35d5682c2b5ab090ae90e915c7c91878abff0284d8", + "4bbd46ded0916de3108f0da7145a80f5c7acea26ed35b0aaa29af12008352453", + "415d1f3ecd06416708c1b83ab21e50b39c9d88d19dc33e60b977b7b7061880b9", + "f678c32815c238e14091f690c8a83c3375c8f7738dc7abff79ff086ed9b59204", + "17c8da803df7b9bbc8b1d7cc0c44916fea5b5ac0891330c4fdf0326fcd4496cb", + ], + "identifiers": ["down_blocks.0.2.kv_mapper", "previewer", "backbone"], + }, + ), + ( + "black-forest-labs/FLUX.1-dev", + "FluxPipeline", + { + "pkg": { + 0: { + "precision": "ops.precision.bfloat.B16", + "generation": { + "height": 1024, + "width": 1024, + "guidance_scale": 3.5, + "num_inference_steps": 50, + "max_sequence_length": 512, + }, + }, + 1: { + "mflux": "flux.flux.Flux1", + "generation": { + "height": 1024, + "width": 1024, + "gudance": 3.5, + "num_inference_steps": 25, + }, + }, + }, + "file_256": [ + "f6315581b7cddd450b9aba72b4e9ccf8b6580dc1a6b9538aff43ee26a1a3b6c2", # krea sai + "1b2170ac37156d4cf91909eb6834bb8adac84bc1fce8098a29cfb03738df84ad", # krea diffusers + "4610115bb0c89560703c892c59ac2742fa821e60ef5871b33493ba544683abd7", # modelspec sai + "d86a3038eacaa720682cb9b1da3c49fecf8a3ded605af4def6061eaa18903eb8", # diffusers + "b7d840eef01c27dfd72ae9143c261355a51bab3b2662263a6cb0059d55347c3d", # qwen2 + ], + "layer_b3": [ + "261559c8eaccae558f72621804a9ee188d338e45e2c622a58db709ac190198ba", + "87f5d565c66e40eb02eb96498243ad81afcbf86192db99a4fc8fff215470320e", # modelspec sai + "e61d10a394902dadca9367467b2245070f651f4553ec4a96192fbba64e820acb", # diffusers + ], + "layer_256": [ + "3db58cf834d2f81abb1e035131956da4c90451074c681d0db10810e55e60c2c4", + "ddf1a34a06b355ce2bcd0f9beb0713450d9bcdc61a03a6bc37716361735e96f1", # diffusers + "ad8763121f98e28bc4a3d5a8b494c1e8f385f14abe92fc0ca5e4ab3191f3a881", # modelspec sai + ], + "identifiers": [ + "double_blocks.12.txt_mod.lin.weight", + "add_q_proj.weight", + "single_transformer_blocks.9.norm.linear.weight", + ], + }, + ), + ( + "black-forest-labs/FLUX.1-schnell", + "FluxPipeline", + { + "pkg": { + 0: { + "precision": "ops.precision.bfloat.B16", + "generation": { + "height": 1024, + "width": 1024, + "guidance_scale": 0.0, + "num_inference_steps": 4, + "max_sequence_length": 256, + }, + }, + 1: { + "mflux": "flux.flux.Flux1", + "generation": { + "height": 1024, + "width": 1024, + "num_inference_steps": 4, + }, + }, + }, + "identifiers": [ + "double_blocks.12.txt_mod.lin.weight", + "add_q_proj.weight", + "single_transformer_blocks.9.norm.linear.weight", + ], + "file_256": [ + "9403429e0052277ac2a87ad800adece5481eecefd9ed334e1f348723621d2a0a", # sai modelspec + "9b633dbe87316385c5b1c262bd4b5a01e3d955170661d63dcec8a01e89c0d820", # diffusers + ], + "layer_b3": [ + "c65ba812ce3ce056eb1585673f62fb896afe6ec049faaf00a97bc35c9a398c44", + "03049273329fc7db2da10de6d3eb27cb03f190e379c0556cc97b3f0f29001d0c", # sai modelspec + "483c4be8ef031c56bc8450d1a3cfbe54445ed317bcd801be5abe89f1d3c48790", # diffusers + ], + "layer_256": [ + "79c07e339865fe9e22c80f723d728c778130acd07a330339c68218b92bb7b3b8", + "ef5c9cd1ebe6e3be5e8b1347eca0a6f0b138986c71220a7f1c2c14f29d01beed", # sai modelspec + "27bc71eca2d2ff7459165acc12010230911db7709a4f6a5c255befedfa6b1649", # diffusers + ], + }, + ), + ( + "stabilityai/stable-cascade", + "StableCascadeDecoderPipeline", + { + "pkg": { # prior=prior_unet + 0: { + "generation": { # image_embeddings=prior_output.image_embeddings, + "negative_prompt": "", + "guidance_scale": 0.0, + "output_type": "pil", + "num_inference_steps": 10, + }, + "precision": "ops.precision.bfloat.B16", + }, + }, + "file_256": [ + "fe92687deefcfb33bb3ec181254b55fe4e434c5084ce9d38815eaa32487ad376", # lite bf16 + "2c8d58b267678aecfa6705a0a0375c88613065a8a8d32ad3a4c3867f5461cb3a", # bf16 + "6c218dc948575e3b14b03dffe2014d7870ac505005770ce3abdc28e920a03c05", # b modelspec sai + "a6c3d534a9be308e95d2c3224af94a854bebd9b503f620f1ae3c8e6ba4a341bf", # lite + "7b431ea7d0f10e72b3eaece353bf6bf2f6bc717b6f4207411be186b40dec1f43", # b + ], + "layer_b3": [ + "9506d989de0226018de214f7ced4670eb5aad4a0c399a9229488ceccdf9a3ceb", + "6c09dcb83e0cd7ad735eb763c5e3721c579d796853f0b9d31ba74fb13cad4f94", + "e07025965cee925e31f1d617ea8baa575e7db910d40cc0482fd83df317c0812b", + "d9a42e4226fb2778aaeaf0d6bda173a4ff95aa574c6d9e27e41542aa469e40a3", + "8dcd87dc7a9b877e8e2a00abac44c4da9eadf2b8df4ae68f27415bb791381a96", + ], + "layer_256": [ + "630ec0f3adf97145316c034139836f9df952060d0237ac4e478c55d9a3a50bc8", + "80904f707c192ddd06be2cebeb2ebbec3eb0e9c99076d50824d391ef3ac67bf2", + "8ccedbe1e8cc4093f05b5f8d90e6103e688ae1ac71e0d6261fb17c42ff7c25e4", + "3524e7fa9ca6f7ef695bc2d3410934eabd5272946a05c8cacd7f329e0bd9f1dd", + "40499a8f45ae28558ed2fe4fc549a4cb469bd237434b331ccc0b1910310ed733", + ], + "identifiers": ["0.2.channelwise", "clip_mapper.bias", ".12.self_attn.k_proj.weight"], + }, + ), + ( + "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers", + "WanImageToVideoPipeline", + { + "file_256": [ + "b4602c35fa0519750a42c03e3f296c02d542291e344c4d702522cddbd1711f13", # 480 diffusers + "6d7a34b63b70eb608324e546d979167a5e787ac6bca3528e63f54a11572d66aa", # 720 fp8 scaled sai + "b2051cd29d6b2f0c924fa7a3e78a4772f0134d7b059f21590dcce416f4f6cbe8", # 720 fp8 sai + "7664fe075b3c82dcecf89012ad3429eee41ee9f10d476f60bc2d2ae3c4ca986c", # 720 fp16 sai + "8ef7ea5bf9eea636b9b3ebd84c40671b4a18ae2704cb4c8595cb5b25c1d8e8b9", # 720 bf16 sai + "b2de21b99b2e72cb0ff15253b07e926f26e7cf1b7e229efc32f94ad1f1ed9395", # 480 fp8e4m scaled sai + "0ca75338e7a47ca7cacddb7e626647e65829c497387f718ecb6ea0bae456944a", # 480 fp8 scaled + "c058a4ac5363c35d1ab4dd3bdec788c23b267fa42a0d7c68aba599f2f74600c9", # 480 bf16 sai + "27988f6b510eb8d5fdd7485671b54897f8683f2bba7a772c5671be21d3491253", # 480 fp16 sai + ], + "layer_b3": [ + "4b6c3354c9ee5694e00a78f5658fdf14129f159c3b78a57f82fb18e0f265a83d", + "c36c783559a40d22504f6c4bfb4f5aae760f3f46bbb3a595be79880935122175", # fp8 scaled + "ac62f7d5583fd2e85b738fafaf233e2cde6e2857e04351135bb9ded45f9082ce", # fp8 + "215e89e855b5e9456af9aa68bc67567dc2269002aaa6b01d849ffec425fc628d", # fp16 + "324b8b6c2d512547a2c31bafa12e20acf313fd3aad587b293334f9f629edeec6", # bf16 + ], + "layer_256": [ + "137881dad8c00063bc8bf05f93067736e419173cd171acc22f77b730db688a19", + "8c5952fd3d333d3a4b719bf7d8ce6b12d1d2e78caaa7e42d713788cfdcadd244", # fp8 scaled + "86c58bc4864c97f394ea6bccb2ecedc4aab7166f5b9bfeb313edfdcb2918164a", + "cac45f7d8f1a0628cb0738bd308689e439b1cc6206e5f887d60d5b37d30138f2", + "60e4f71a0961b1346b6f6b5ebe4c8cc93219239c5e13b4c0f1e19e9b8e1324d5", + ], + }, + ), + ( + "Qwen/Qwen-Image", + "QwenImagePipeline", + { + "file_256": [ + "9f33a59093af3abcc2836d4cf4b7bd122c238ca70a26c70f34fdde64646b3bcd", + ], + "layer_b3": [ + "c87eedda853c12844a8deb3592a90bbcbd4dff2f7a850c28755e4aa171432150", # diffusers + ], + "layer_256": [ + "fda2472d8ef6587a4c979021a2390eeb7c8fc2bcf565330ab8dc6b22f5348ec9", # diffusers + ], + }, + ), + ( + "Wan-AI/Wan2.1-VACE-1.3B-diffusers", + "WanVACEPipeline", + { + "file_256": [ + "bd8bbb8834a274525ab65cbb063f21aa58973a054bfd1638bfe395504c9d9b99", # diffusers 14 + "192804a4e10b5bb0a13f5c224bc4ec9707b3b8cc0def8eea005dbce7c9d6752a", # diffusers 1.3 + "f202a5c59b8a91ada1862c46a038214f1f7f216c61ec8350d25f69b919da4307", # 14 fp16 sai + "654693bf2a93a27cd67c3bcee238bc1d0cbb0dd9a74928ed7155fb21a2a1900a", # 1.3 preview fp16 sai + "640ccc0577e6a5d4bb15cd91b11b699ef914fc55f126c5a1c544e152130784f2", # 1.3 fp16 sai + ], + "layer_b3": [ + "5357d78799a61cd2d72a8a2824c919d63f718eb3fba624af63689e9c657db032", # diffusers 14 + "7ae67b7ccf79d1c3f4531ae138e1eb63d52dd97a66b3fcbe1d68fded8df4d5b1", # diffusers 1.3 + "ee63ecdfb3da6901853a59ec950f3e7c3f6595ac46347a03881a4a9c71425377", # 14 fp16 sai + "82762df3539021d3c0342e0da04137ddbe95ef37ea933cd0a68c09c2c650f2ac", # 1.3 fp16 sai + ], + "layer_256": [ + "2684413479030170fb3f08c1069c02957ffc386a59168d23b55d579d5c675269", # diffusers 14 + "d527680fa735e5f30ef8852aabf8a49f02a094bc4718f0787c5b85710a13c026", # diffusers 1.3 + "9677492a107b3ed827c7285db3393f5321d451cc6d922a4d0488d2a67e939446", # 14 fp16 sai + "aaef66a4f65ecf852888d160b2122753fe4c6d642b5d41db29e4ce9e6855b5a0", # 1.3 fp16 sai + ], + }, + ), + ( + "Wan-AI/Wan2.1-T2V-14B-Diffusers", + "WanPipeline", + { + "pkg": { + 0: { + "precision": "ops.precision.bfloat.B16", + "generation": { + "height": 480, + "width": 832, + "num_frames": 81, + "guidance_scale": 5.0, + }, + }, + }, + "file_256": [ + "299e6304544f2783896372fa919e755a8bb9ab8caf898ce08a678dae391e1179", # diffusers + "a9278e6e9c82d174e6c67b3c97d8b97fef30af51dcf59160f2fc241f6819f5dc", # diffusers 2 + "be531024cd9018cb5b48c40cfbb6a6191645b1c792eb8bf4f8c1c6e10f924dc5", # fp16 sai 1.3 + "6f999b0d6cb9a72b3d98ac386ed96f57f8cecae13994a69232514ea4974ad5fd", # bf16 sai 1.3 + "2e39adde59c5e0e90edbb35873126b0d67928b5c11c501e384e976d6dc597cce", # fp8 scaled sai + "2ee88ab18d7ed7691c5b7f8bdc3d0a9815e6efe75499287564830fd209d3cdfb", # fp8 sai + "46c27d3693bf2475990a912e08bf67fc6e6cd5396eab87b5e8dd1fcd3651364a", # fp16 sai + "193535c6450045f718df5f011de6d94d49bd9b13f37ca0412500f050dbbb01a8", # bf16 sai + ], + "layer_b3": [ + "32266d1c79b518adb9d21837e6a427f6ae55b68cfdd673a7dadb38820fddeb48", # diff + "3b6989856f4f05368524c1852d8660b73c84cfbe44460af017d7139c2a4641b8", # fp16 sai 1.3 + "f4d6cee3c112db93b3c9137ad102ec0e79ec7ab68b9bbc59004fbc268ccd5ddb", # bf16 sai + "e627144f41055619eb5407699c46e69ac0d87cf8873721e3e48c9e842656abf8", # fp8 scaled sai + "6c00f3fadedacb841c4b9b4321b94a11ef85a08c9dd9253e5f9ba95856715579", # fp8 sai + "a0c339253c714b05877c8fbab649ed631cf021930978f3696a46f685a07c9092", # fp16 sai + "6435da89a870fd0e88680d31de75b9a40c408a4768eff384ce9b9e99481e8e66", + ], + "layer_256": [ + "52493c23c5fc1d087a283bc4eabb151421b7ae09affa12a5bb059d62656c5766", + "058dedb3d2683a9a5b671c6302690e22722c93f6ed92281d5fa74ab190e632a1", + "5fbed4b95e7196d3626003ea9e0fbbffd074b4297ca406e01b5b6c5d881a6080", + "3a2335c8e7a4359c071b50333b5c00eef6f42a1d5206915e2ee99464a8c5eae7", + "0542780670dd75d4cd9deda123d2e150730646c0a1a8d34582460991498a77a6", + "e925b8222774905c8fbf10af77811fde7870e563eedcde2c94bd5c727e952d49", + "3d915854976284347efa7aa0a117c0fc3b415c4208e1a6c94beb4ccb9720743d", + ], + }, + ), + ( + "Wan-AI/Wan2.1-T2V-1.3B-Diffusers", + "WanVideoToVideoPipeline", + { + "pkg": { + 0: { + "diffusers": "WanPipeline", + "precision": "ops.precision.bfloat.B16", + "generation": {"height": 480, "width": 832, "num_frames": 81, "guidance_scale": 5.0}, + }, + } + }, + ), + ( + "nvidia/cosmos-predict2-text2image", + "Cosmos2TextToImagePipeline", + { + "file_256": [ + "7fbd20dae97cc26a55c7aff3024bc84e554cff8f69966c725a24c8238c5431ec", # gguf + "6d211f1c14cd793156da3a840dd5462ae072046fcd6f1dc64c613a5343bfe896", + "95a2b32ad31a271eb64d35985c7ea46f1448528af70932eb1f35d57f90c27be2", + "344e67faf333b7849fa94290c9028bdd5e40eb19700754c833cda0423bc10ad0", + "ce15ef565cbb9ef414a6f7a396c455d82d5f762d2174493da87fe009c5fee75b", + "94aa9f2b59330b88e97b6b439e2f206a51c86e6b154fb66d43ed149bfac23cf8", + "636de5388da249130d51752991a1792b90af31cbf43f021ae07f75756ee2d79a", + "472c5e4cf5056a1a59085addb5a86d801de39bf5e000d253f206a7f63c710029", + "663266ace67c22529c3b6bfa0e8bd69f0ba6e683f5f02b8e3da50881057ba142", + "21a674b314c1364d0dbb3712f5ed702996a7b7403c452835cac22709e01c2f77", + "3bf2df806c6472e039efc9e8d3181163d7faa7b385e61519b7d17d5e9c993a49", + "1de35e1603c4c30bc80b132ccea15fc0503369caf68290708f17e679e98cd41f", + "0738e559bbd71f7351ccba34b2b47362a3f829b92f3dbcffeaf1e44b0d52f42c", + ], + "layer_b3": [ + "5a18ba14c41c6601dcc1195ca180ac7744357eb15ace39272788bda1a7151e9b", # gguf + "67cc3eaf7987c89cd7ccff13de6bc03e3eec59d260d44486e2367cd946ce6f20", + "3c6fefa107742488d2e6856714198a762f2fd35c67edd50d4657eaf4b59c7ca3", + "4e1f90ee1e8959d334c9b1ea2cc5e58d0b8340e271c35f81c8a5ec26e16d9d76", + "f8171071e828524fcc2806126ad100a2198e450c82c0864c8fe8b358c5cbbfbd", + "8126101a0207ecfbd741394fd59f306bcb4c492b2a921e0921c426ca7bd38985", + "c942c5a85ff7cb602d8ca894f5d180c2224e91f0b62c3a21f6a425f9e0e8554b", + "c8c500de74da879a547875fe1046f62ab18bdfd09c09eb3da723cbc2319cb4e3", + "c0ac3f67501004e9e9a55d1658402ad97e42bf8a266edf81f6f3bb835ee476b9", + "84f5926eb4e11d826815682b076ed7d3bba4c86520859be80aa1ef92c72b26a4", + "1d4375aab5548708559b0fde150754a2163cd211eb20a5471e17afaeeb26e082", + "68bd8982f59c60d69c301d16dfb5a60f5d43d66c0b60138d48a22f5ded598e7b", + "c3e9a10cad7aebf979072092008be6e2815d03d28cbf316c15e8daf22116bd7d", + ], + "layer_256": [ + "38f2a75eab667c0cc85f3946a23ca6dc2278438c25a9f93aaaa9f79c3808e180", # gguf + "ee8434a5e9bc6fa07199de2d0c69fb87f7922c31792bafd13f527c9d92fecb0c", + "2f8382657babb4d0ae4f8e425ae33b21ad71deb6ba457fd6734f05208d52e06a", + "34b181a8291b571857cdbf67ac0081fea594a2f223bf20bd2fc8b0c889e9602d", + "d198c412b972e381acfb812304fa98ed0d97a2f072ddc195cd9a1eb83b1d8146", + "79580a13aff9859e67b0a9f4f8893236cdcfa58c3d43770641aaac8daee55a94", + "cfd48c7ad71c913fa8768167ed0c2ee8c207311b22b1e5a8761369b5a780e8d6", + "da91362ad85d4d2e80a2cb7a55e4ae0e52c9eef8b437a95894ce5ab75d36568c", + "15f84001f5205b6dd8c6f1334cb51c46f6171c7795fb2a557ea16b874f0c71e5", + "5d29179ad15a15d2561defcdda66f1d1e4d065c1e0738f9cba4db5b68b93d2ea", + "7ec489d1e461f5fb2af627b68034ca57f19c516aeccbc5d188b3bd27e3353a15", + "c8dc42fe7b411d746ebdf86286b91cd6893c5f028076b8fe4103f7ea8e1d8833", + "86df7c095aee01588e961438f322b85ca0100a9e440b8a2b6c724e00f748d8b5", + ], + }, + ), + ( + "rhymes-ai/Allegro", + "AllegroPipeline", + { + "pkg": { + 0: { + "precision": "ops.precision.bfloat.B16", + "generation": { + "guidance_scale": 7.5, + "max_sequence_length": 512, + "num_inference_steps": 100, + }, + }, + }, + "file_256": ["6927dcc812841c1da549bf11c97ddf30532aee0e708a6642fa64cf8e0dfcdef7"], + "layer_b3": ["8b20714a6af89ea4bf4ada1f805c5b9d529ef136c229e9b75392242d62d80c3e"], + "layer_256": ["9e44e6c919dc71c24a193641e6265cd9983a2a773b9bbaf527c10ac4837b29fd"], + }, + ), + ( + "audioldm-s-v2", + "AudioLDMPipeline", + { + "file_256": ["fc30d5b5a3bb8d08672736efb1fff10755ba7024dace39b2dcb579a105aa2a5a"], + "layer_b3": ["82fbcc553c1ad770d28fd1866b935249c5ebfbf75f3166ae823e1bc6ef39a95a"], + "layer_256": ["d076446a58a36bf436e37444679d62bcf2f45689d4aa3d799b3fe801c71ed2c8"], + }, + ), + ( + "zai-org/CogVideoX-2b", + "CogVideoXPipeline", + { + "pkg": { + 0: { + "precision": "ops.precision.float.F16", + "generation": {"num_videos_per_prompt": 1, "num_inference_steps": 50, "num_frames": 49, "guidance_scale": 6}, + } + }, + "file_256": ["8fbb6a5e67c70885a8ed8e33df144ac61253e45977be5035fa18cfdf77d386c7"], + "layer_b3": ["1db3439649b5362448455fb2ed6ebde0c3b973655a206832731149757ad165bb"], + "layer_256": ["edd6bd51f1236f528ff8d32dc754f0b86cfac901b800642ea497358156dc00bd"], + }, + ), + ( + "HiDream-ai/HiDream-I1-Full", + "StableDiffusion3Pipeline", + { + "file_256": ["3cb3f6d77a3fce19b90fa7f66da0cbe997b0785a38a788b559290d3062f6fd26"], + "layer_b3": ["612eb9b2676a3e7b28b10aae045a97a95de2a399fe3801c8f6369589c3a832a6"], + "layer_256": ["78fbfb7fddb9ccbdf91f22b0c3d304cbf0cc7305dbccb216982233849ec727df"], + }, + ), + ( + "cvssp/audioldm2", + "AudioLDM2Pipeline", + { + "pkg": { + 0: { + "precision": "ops.precision.float.F16", + "generation": {"num_inference_steps": 200, "audio_length_in_s": 10.0}, + }, + }, + "file_256": ["359a5ffb89a844beb2fcfac584aae2cd7cd6e87c3ab1ec4e892ef45d91db77c2"], + "layer_b3": ["eac241273f9f30982fc04aa88b4dc1c38b533430956a55b9ed4d3e5c717ec962"], + "layer_256": ["ab109d01b43788063802f00c6ecab024c830ea58d668f5c2df9e3ae5b87d86cb"], + }, + ), + ( + "tencent-hunyuan/hunyuandiT-v1.2-diffusers", + "HunyuanDiTPipeline", + { + "pkg": { + 0: { + "precision": "ops.precision.float.F16", + } + }, + "file_256": ["7d31ac8fa389ff39dd0a81430010e52c43b59f15adc00c83625a47881e16830e"], + "layer_b3": ["bccd37ecc9f85d132b46d0bb67b4facb49fc6c091428a4feba9ab9a93140f5fe"], + "layer_256": ["ed25d241d58ca298d28abd5919e70341ad194e77dce4859436b52ea4d8fcb616"], + }, + ), + ( + "Alpha-VLLM/Lumina-Image-2.0", + "Lumina2Pipeline", + { + "pkg": {}, + "file_256": [ + "132b4d213fdd3cfc14333746fc3eb8bbe6358cd73c3bc95ac4ccec230b97dca3", + "a7c09ebae62996a8289782161338a3cdba58c11d2d849c50b2d6502e152b0d6d", # pth single file + ], + "layer_b3": [ + "198bde52f09736f1fc650dcdbd0e6b0f6a5ce186582554c1d9ee8ab16ac0feb2", + "b52807536902cabbf84f99e4fa2f8713fb4ef77e739f06367ee0d486e3222faa", + ], + "layer_256": [ + "982893c99860aac8198c2e435cf85f782fce8f10732daf1f2881a26864400a4e", + "dc937b59892604f5a86ac96936cd7ff09e25f18ae6b758e8014a24c7fa039e91", + ], + }, + ), + ( + "ucsd-reach/musicldm", + "MusicLDMPipeline", + { + "pkg": { + 0: { + "generation": { + "num_inference_steps": 200, + "audio_length_in_s": 10.0, + }, + } + }, + "file_256": [ + "853d0ef1d61cbf5d682872322ea8b761ba3d2f85bfbccd58363bd6b2f837268f", # + ], + "layer_b3": [ + "82fbcc553c1ad770d28fd1866b935249c5ebfbf75f3166ae823e1bc6ef39a95a" # + ], + "layer_256": [ + "d076446a58a36bf436e37444679d62bcf2f45689d4aa3d799b3fe801c71ed2c8", # + ], + }, + ), + ( + "openai/shap-e", + "ShapEPipeline", + { + "pkg": { + 0: { + "precision": "ops.precision.float.F16", + "generation": {"num_inference_steps": 64, "size": 256, "guidance_scale": 15}, + } + }, + }, + ), + ( + "hunyuanvideo-community/HunyuanVideo", + "HunyuanVideoPipeline", + { + "file_256": [ + "bdb957b35585ea74ae42ca92865a68fa1bf1ebc6c5b7e686a889e5c977dc24c7", # + ], + "layer_b3": [ + "d31c56b4c9444d4c2f1b10120fe964e0956f6b8c7e7c1e4cc5a1f37406fc49f5" # + ], + "layer_256": [ + "fe741fdfd163bcb1e0ed81d80f79ac3576dbf6e6740674efadfeff782a48bed4", # + ], + }, + ), + ( + "zai-org/CogView3-Plus-3B", + "CogView3PlusPipeline", + { + "pkg": { + 0: { + "precision": "ops.precision.float.F16", + "generation": { + "guidance_scale": 7.0, + "num_images_per_prompt": 1, + "num_inference_steps": 50, + "width": 1024, + "height": 1024, + }, + }, + }, + }, + ), + ( + "stabilityai/stable-audio-open-1.0", + "StableAudioPipeline", + { + "pkg": { + 0: { + "precision": "ops.precision.float.F16", + "generation": { + "num_inference_steps": 200, + "audio_end_in_s": 10, + "num_waveforms_per_prompt": 3, + }, + } + } + }, + ), + ] + + transformers_addons = [ + ( + "google-t5/t5-small", + "T5Model", + { + "identifiers": [ + [4096], + "encoder.embed_tokens.weight", + "text_encoders.t5xxl.transformer.shared.weight", + "t5xxl", + "encoder.block.0.layer.1.DenseReluDense.wi.weight", # small\ + ], + "file_256": [ + "ec87bffd1923e8b2774a6d240c922a41f6143081d52cf83b8fe39e9d838c893e", # shuttle/flux diffusers# flux dev + "565cb2487351282e8e4dbeb88e63f4ad28217ce0439f5a8e6525a924807d2d9b", # bf16 modelspec sai + "6e480b09fae049a72d2a8c5fbccb8d3e92febeb233bbe9dfe7256958a9167635", # fp16 modelspec sai + "4f2751ceeb2a96edd693e539dc5d6bba0b8d3814f49a9b3798403a0cec4b2e3d", # fp16 diffusers cogvideox + "83690f3cc37cecb5e907f41ab0f7abb0855ef24a0a8aab9259f2888ce85a34e2", # flux diffusers + "7d330da4816157540d6bb7838bf63a0f02f573fc48ca4d8de34bb0cbfd514f09", # fp8_e4m3fn + "8490f7a22615c20651a63dbe7b4241929826a4de20292dc8e63bfc3c61e3654f", # qfp8_e4m34n + "d8720addef2596fef86b1b22e4b62875c9118779ba8723759a75dfcbc649ffd5", # mystic mlx + "7d0eac95abe8daae454bcd3d166b8bfc6a35fe68278f97479d62dbb6850f38c0", # mlx flex2 + "ceabd6f71c7112cfaa4dfca8711dda97b79fb9b25983f1c95532de226045f1f8", # mlx jaguar q8 + "49e139f50824fef40908ef4307c851e7adaa8b91bed44054c4829600dbedfdda", # mlx shuttle 3 q4 + "211ade1d474f5dc83190aec8be5c4baf52643777790d64de0cbd84f63613e5e9", # mlx flex1 q8 + "7894547154ba3fd6e364e66e2951ee82b4c3fc1ae0f95df6a4f9d1c5a4e98f17", # DeepFloyd/t5-v1_1-xxl sft + "eb529f693f4b17773a24e787fcba29486d5e1700dadcc20bb91e4c8b00212d08", # pixart a + "d80116f6fc39801e4eef425a584e7a7a41cbe5119797bef2dad67299909fe2ae", # Q6K + "31ebe18e901bfb6e5709a20ec1c95fce29bce2b9545073231e0f909a53239f5c", # Q3 KS + "6be2b0b7e2de7cf2919340c88cb802a103a997ce46c53131cec91958c1db1af4", # Q4 KM + "b51cbb10b1a7aac6dd1c3b62f0ed908bfd06e0b42d2f3577d43e061361f51dae", # q5 k m gguf + "9ec60f6028534b7fe5af439fcb535d75a68592a9ca3fcdeb175ef89e3ee99825", # q8 0 + "8f5ab879234384235d56732f0cda07bf8801f30a49645248c5bfdeeb1665f64b", # q3 kl + "86427a1f4dba48940e45bf78d6db5bf0d48fce8b4656f5aba27955f06af9628e", # q5ks + "88b696cfae098f03bb078cc5944ef03aec1e91ec020a6b016b723a0f0532558c", # q4ks + "1dc600961d3c5ed081f6700485cdc7ed9cfb4631f2dc385b7ac6bd3c80846d0d", # f16 gguf + "f28631189911f8d7931e8fe642a4cb2a3c51f50da7cabbfa06b89bafc19c00d0", # q3km + "de9dfdd19d7ba6859993cadec5100665dc7a4fb71e1c6c8970959cbdaf4366e3", # f32gguf + "7a68b2c8c080696a10109612a649bc69330991ecfea65930ccfdfbdb011f2686", # allegro + "2c0c539ab8e8fba3877cc94bc483e427f74c525f817a809b028ebc8d96d75a94", # hyd 1.1 + ], + "layer_b3": [ + "ca94e03b7b1fdcb0d6ff5205eac56f145d2dff8a9c489faf80935bfec8387f18", # bf16 + "c0e2b054bedd782909191b05748a88c28d1538fa91789fec63f036ba01dcc001", # fp16 sd35 + "672de9b79d14001de7d1109ffc52e4d0cccc3bfee6f45648fa347703b58e2b99", # fp16 sd35 diffusers + "abdb187a996c51cb0469630c124b14eeb0bb8f5f635aca6c71dea264f8bd61ae", # shuttle 3 aesthetic diffusers + "8926f862b7763fd9688af317eba7809aa71a478484be0c738c269de368ace4a7", # diffusers + "e616b754cf55e55b3f9f17ab7e1fff95f0607c81782822fc1223ae22fb1e9f36", # fp8 e4m3fn + "b79e5f1878a62cd726bb4f9fc1415cacb071d278440e9026290c7b36cb41e1d4", # fp8 e4m3fn sd35 + "77619d5278d9f547ddac17d4d99df56cb6a3a9e660ae31b2f896a4297907e62e", # mlx t5 jaguar + "c87c9d3cc7becc46ee34821299cf8551a6df5541582a45469a031bccdc4bd340", # mlx shuttle t5 q8 + "7e6c32c01c89fc5d1610c410135aa9708e77a7444510e5e479fa677ff2b53643", # mlx jaguar q8 + "a49c2bc301733967ddff113790e301773dc5dd71368b657af4141458de593ced", # mlx flex2 preview + "c2ea94030ea362e03d73d448fa5353ace0a449dc38c51a4a49fb148444ebb8ef", # mlx shuttle3 diff q4 + "4a90463350f08ef41479da1d561ab41b8f8b792f1603a092226a838156aebfb0", # mlx flex1 alpha q8 + "f86cd0324eebbffb81b15ad47dc8b63fedfa51dc222e44e1a958a7becce2bcb0", # df safetensors + "48c54c61c5f14e42761c6177539b2da3a22222516dab053952ca8d8e92f93d65", # pixart a + "311332d9738773669128814d944b1e860a8e3176b37abf43370bc06b43b454d0", # flux + "3f4e51dec6d542759cdea49b3bec14c090a4908f953fa3e182e2ea43b5b05402", # q5 k m gguf + "beb25461e168359108add77263ea5cc121b7584cc4aa304ffc4e134783bb1d88", # ggufs + "43313f90a359c8c1c787a7a833b1ab9f7a38204ba36d0ba587c658d0d9bf0852", + "fa9e97cdad26f55fedab83a3f114e0338c9cca3ea2bf8f1b168a6dfc5919bf8e", + "93108d67f8829a7e1e8f3773e9ce53c67f365889c2acfd69816ac80fd43f8e08", + "fc65a6cc55e89394d7bc0fa4ee952d63ce3bdc143b84b5aa4bb3edf7722a6b83", + "8163bc781a7e013dfeb806bbb828a36913cf119363ea5fcd9071d87a0c227cda", + "ad2ba63e1134bad1b15ee339313bc130708b2995e8b4b76fb44d727f28c26ad9", + "4a844772638ffed2f61d45eaac984094b92540fa1391a4098608fc73a6cd4fd8", + "76c31e1fd35da7de7cee97c1e7c5ccde640e6fac3e17a62e115ecf484c7196c3", + "a4d672e22b5bdd8f8b0885cec4a173d0466bb1dcbfbf8400cedcc41c2494f16c", # ggufs + "d1860c3f01dc9f260d98b50d3d2bbc8dc2d3eefaa93778a8de9d7adfb897fc6e", # allegro + "b8719092fc58487406211f52dc55bf40b573ccfd29933a989c33a36b694f6f0a", # cogvideox + "795e272409bc4fa55f402485acf86b607256f91aa965295c5bb771c61f8e9e74", # hyd 1.1 + ], + "layer_256": [ + "bb20f7805209379aea4d6548f17e551cf27d0f8426ca169e4df8234f718ed5ef", + "431580c2d86f9a9ed3500f776a4c997223e5644aed211f965354869ccfa4d76e", + "2ccd548c4ffe34168c60779ebd497b9b410981a2fda813c8723a24a805c94ea0", + "a608fc4e1cc9762e46187a1ce66e98e8ba4bc3a604cbfd96174bd876baea0fa1", + "dc9e74cdf535e0b7a17e1335d0d8b38a00f94facf0cb01363baee09945a25278", + "f07409710a69b2247aa4723a9b40d2225d5e5bfba7b60c51f0ea901fc2ef5ad9", + "ed28f8b6cc472f352fc840b5a9f841ff17d76ae6918f0676464dca20529aa92b", + "97c1a08f87c59b4c55ad4672841977cfce43ca7730bcd11d8c178a9330de1855", + "968972839b859a9c4457f190fad2e17e8585ce27d9ef318df4f5b4e902143944", + "4dbdeadc957c898c327197a3d8770188535672e9208beb29bbf48dfdf51c8955", + "669172c2b5e8b97774d9dd0227ede40c4d25cae3adae97d9f281d03531e7e137", + "39fff130b9ee240102c28a78ee1c4a643e9f800b734ff133f3ab2ad1357bd2f6", + "6e047ed8cb7007034ff15840dd53c92096f0e7ed5befa07808de8afa35d35874", # safetensors + "adbd0baa059074501b7686db2b0c01715f3a317275c2657c5dfbfd6ee92389b7", + "eb63790fb32b5660de34fa42c2e608df58f7aa3680b4984f0ee9008fe613729c", + "f125c20a33b0ff2dbd4e8ad9acebc34383cb2ef98668169ef79a8c06655ced35", + "e64e0ac83a785ef584a0e86b347fae8f9e2bd84324a49396ca8a9fe7532a947b", # GGUF + "70001b3ac1b66522142bb86e4c3e87e20c2bbd07276c763878e0838ef6184aad", + "f46fd1e2b5fef3b9f7ae80d183cc77f7be181117a72a0bb933bdef0bc6cd679e", + "83676d73726d101325a47c7f8a60cedf10bab99ea79a6bedad7761220cb4a625", + "a621a907586e5e270e7c7873b167364d8a935ff347d8240fa9bab319678da690", + "f0af1a089f40d8611db5c59469314f1547e2df23c6eff24860359b37ea9bd966", + "72478320b8dbfd9aeaea010dcf0896e3116fa5ab940f3b472882d9f9d2d7333f", + "9c1a88e36334a48d8482fec54b14ea1d5fd31f0dbb65d13cc616e63dc7c42be5", + "d0689f727e8ac4fef3ec4b1f29e8a3bd12e1116559eeefb2a1a457cd4e676d1e", + "fea158a4afcfaa6e95e04799bae0287de0c4fcb188f3b41768a46ce48c71c9df", + "2e5bc4e73312b5aec4c1a55631cb4ed69cf34ccaa6d1f28f7045f137a579b439", # cogvideox + "015fdecbc3b5369dbcb2302e4b79985437ac4496d1b9ad63316423a222fb0803", # hyd 1.1 + ], + }, + ), + ( + "google/umt5-small", + "UMT5Model", + { + "identifiers": ["encoder.block.1.layer.0.SelfAttention.relative_attention_bias.weight"], + "file_256": [ + "a8e861969c7433e707cc5a74065d795d36cca07ec96eb6763eb4083df7248f58", # wan t2i diffusers + "decf9b70814ed5e9965bfca9fbd0483462e2bf743790663025b7742f8c014c72", # fp16 + "0a07449cf1141c0ec86e653c00465f6f0d79c6e58a2c60c8bcf4203d0e4ec4f6", # auraflow + "c0ef3a140898e228a3520c9adec60743d2e8e5b3d229651bb37f1a3921919f99", # wan + "7b8850f1961e1cf8a77cca4c964a358d303f490833c6c087d0cff4b2f99db2af", # wan i2ixxl sai fp16 + "c3355d30191f1f066b26d93fba017ae9809dce6c627dda5f6a66eaa651204f68", # wan i2i xxl sai fp8_e4m3fn scaled sai + "fa1d36fd54f171ae60fea915c23bd77986b330bbed9729f0d2f8ecbe9168bc48", # gguf + "4a3176f32fd70c0a335b4419fcbf8c86cc875e23498c0fc06f5b4aa0930889e0", + "adbc782b9145a27e15d63dfa25057efca0ac75e2db7d372c901ddaa130ca2def", + "b7e2ca4c493c9d51fa951005e8ceba2f4b6b6877cfb4c36a8955c6cd68a1dba7", + "2521d4de0bf9e1cc6549866463ceae85e4ec3239bc6063f7488810be39033bbc", + "9209b4c77b34ad8cf3f06b04c6eaa27e7beeebb348a31f85e3b38a1d719b09ed", + "8bc12d80bc0413573fa58a93626117440b4528f640dd9cb310732e05fa9e6c3e", + "f64f8d6dc4d8a24276df69d0ccea789aae686f7417950a41e6568c30cb478a5c", + "17cf97a5bbbc60a646d6105b832b6f657ce904a8a1ad970e4b59df0c67584a40", + "eaea358bb438c5d211721a4feecc162000e3636e9cb96f51e216f1f44ebd12ce", + ], + "layer_b3": [ + "cd92b29c9099a640e3f5d4a76e64b3467f87f6c056119e0defdff94d311ad6de", # wan t2i diff + "1c943dbcb8b328a7c6c852921ddaefbd84c9df8c83bc51fe303c1f06cb734102", # fp16 + "1639a6467af0db1e15828d33b878e568cba1335947eeadd481170bcdc9ba8e33", + "72a0329740dee29a2c099eec3c320b3945590a74293356014c30249fe69652e5", # wan + "0374cba03c607ffe8ab8f04994d82f82e80901dc7578f1a9a6cb2637608be5d5", + "d75a407f873e1cfa1a0a36214b53b14bfebe9253ea263465151c07f0d57f3f29", + "621153502b985c143d304318c91dc3d10296d24268c81e3538fc336fdc84c915", # gguf + "43bb052945d38a68bec27c3d26162e88e306e6074d027d3b4b2b8ae2b1851691", + "98f50ea5d55e61c1478df47e567e48bdd036d240b9129e64d53a826406900adc", + "9400313b8eae31699473daa5f840d25a4ef660f68de9a7894f1a28f214f23384", + "9f13826b8e4ddde24d80de6a947a7868e26cea25dda52790ee6ed695ff72b9bb", + "475773ab108a537ff904b84e7f3a80129ba4983deb7170b6b52c922ece6069ce", + "5ef27b3c1eddb08cfe41b452cf9529d86dff811645d40c165bae324486d19e96", + "e170559d8551cfe651344594e54c0a9a90c0068b00f3866f6e9a3737e20925cb", + "e8dc7442a20bcdc7b6e5dd0265939d88896eab5ddd33ee16f1f09537e65914b8", + "4d3d5049857d01741780daf01e96617092973305637b435f4895499a26bbaede", + "7a2adadc2372feda23b2169337276adda6d1fdef82ba69f0d3321c4c6ba8c604", + "0a7c61a85bb3f51f75924de48ef3f5e87cbf8901f600cbfcae97f5e2919c4148", + ], + "layer_256": [ + "467916d35f3053dce1d40d998fcaf6aa03feda75aa578d964dd61461e23641a3", # wan i2i diff + "58deeef888d4ded4ffababfbf8da27227a4a6ff8adfa42016e12c0180f713816", # fp16 + "178ebd3fa3418d33a2e45a80d8b9d3662ff4a8e75f3de3f0332f82c505d8152a", + "8700dcb651465fe6c925b7ad6068b58b32951832fff0ed19819510f8d0713ee5", # wan + "954f2129ba166e746c71433f717b572d8869ec14b32b7f214d1701d3b1120047", + "32f5fc1daea014b6488b96c2a1330e0aad87e074844fa3e2e3f20b9e58440395", + "9245abaf6df8a4b5fcc828ecbcd7b21a1b19bf5f3c4388fb5c8eabc140276dce", + "172d0fbbd379ae014a7008e148813818494e9e645db802fd000d443369df9d17", # gguf + "2fa68a26b0386aaf9123d2b4067dafc8631ee724602197dd353f3ea5a61dac8a", + "16f0054014e6d07b86b0526d5bcfed7d2aa3aebe3e44e6758933d90cbd3da46e", + "fd62047f5d27ff43210c117dc0f253c101e694a5331d6b684688606c92c65ccf", + "ddc4f38db9f132fb1b736c1d693b5c039a2d6fe83bdf4f1c1e7a2745b5d79124", + "9e9ab11b3ea059b84ae2bcc5be76ab3f730a486d92a16f1fd2a959bdc2ede08f", + "bfb178b1ce27f00e122d2328c662fdef6cc239c07efc749aa61ae2d395441b02", + "50addf6a911b90194a75b0212429d1af55eb2f9d24715479b9ccc4a40adc299b", + "2e46e9f1b714d72160d3b3b775a845b3049a01396fab935f1278d9e8de2ef0c6", + "db8d2b49d9042e39d6531b33ec3bebb9cdf42b9e6ad56163f08da2a7da2a53cd", + "2d81d19ad5440422b85e0b17c71914269f6c25c9b1fa321c0dd6119ddb41d62d", + ], + }, + ), + ( + "google/gemma2-9b", + "Gemma2Model", + { + "file_256": [ + "e909230aabafad02d097c7dc02f2ae062b4e6b0593477c1f07679d277e09ce71", # sana bf16 + "d61628bc793240439e608c5ae744f55ec8770f684abb63602648a24cb6da60bc", # lumina 2 + ], + "layer_b3": [ + "55a3c812ac0832d154867f5927365bcc776926e48e65f7f35a81fc11f4bb81da", + "543572889beb25cad83a43ce70cdd255d2c82951d6595e8c97ff62fd05871c99", + ], + "layer_256": [ + "a0d820c39578cf888f398579d9a00d69b31c81e049795ba70008dad8fe5b3a33", + "abc83b04a04467579ea1952a7efbdd252b8641ac0e2a6a9be2a5a73e371111d6", + ], + }, + ), + ( + "google/gemma-7b", + "GemmaModel", + { + "file_256": ["01676b4c6e765f737a5e9854a315de3887e939c370cae116d505777729099a68"], # lumina next sft d + "layer_b3": [ + "438d82c867240f194a4e15798eef2886a911c8f57fa2d9f4ffad1d56e7bd1ccf", + "1de38e09f5f2c5345de48b8cd4dddcfff3e341cc0059752446e186b3863f0981", + ], + "layer_256": [ + "e4835a72d582b4ae066d6ff0519f2ee9f8b21fb02e8c28d8eaa317f8d1e9ea75", + "1657c7180b48672004f4463308dfdd56d92eedeb23d1408ea766985ca208e5aa", + ], + }, + ), + ( + "google/mt5-small", + "MT5Model", + { + "identifiers": [[250112, 2048], "text_encoders.mt5xl.transformer.shared.weight"], + "file_256": [ + "0524484ec81425ba9deef6fac1393a78ba9b1c9bfed704a4be5f9c7255975cc1", # fp16 + "32f70f1d187e131a5fc3e4f0edc97ce89360d8e2f1d90177a443a05296097acc", # fp16 enc + ], + "layer_b3": [ + "a1d616c37711ec7b9073d04734af2f5fd02f9035a322eb46efeace922e104c51", + # "bc71d4259f4feaa0fb27c1f288765004840f39247cddc98b3ac37329ff1354d0", # fp16 enc + ], + "layer_256": [ + "bd337daf0c1aa36896013109b406a0580aa3bb8ab9291d89df3015d737358e95", + "2e40c48c96fc7df636aad96d3e78ed0ba9f68c3059e21b7fcf917f284c569a61", # fp16 enc + ], + }, + ), + ( + "Qwen/Qwen3-15B-A2B", + "Qwen3MoeModel", + { + "file_256": [ + "c56947057481fb5e7cdf766e442da81717b34addc88bbe8f3728fd25bd03cbae", # qwen3 coder 53 a35 + ], + "layer_b3": [ + "d2d1e0875202f5c9c84c781a2105620250733bd01832f67b2c17bc981d1eb508" # qwen3 coder 53 a35 + ], + "layer_256": [ + "408c01da57c4968b7b0e36d98a74e321153e7aeb058fea63ffd140e323526476", # qwen3 coder 53 a35 + ], + }, + ), + ( + "Qwen/Qwen2-VL-7B-Instruct", + "Qwen2VLTextModel", + { + "file_256": [ + "1f48ac458d6fbd0aec53a116065a7ee3f1d34bddde544e25c16a05c9d5392b78", # orsta 32 + "0e85c7111ce849293e97aa09ce1172352ecece023a3ecea7ac8311e326b47f3a", # orsta 7 + "d725335e4ea2399be706469e4b8807716a8fa64bd03468252e9f7acf2415fee4", # qwen img + "e10bd9583a77250376d9134cd6b46799029dfa3b4d7989c1050b3ec149cc7cf5", # qwen flux + ], + "layer_b3": [ + "e4f681bde70a753f30f83495a2aa340d251bf3d818eb5a1cbe58f85fd6ea0d40", # orsta 32 + "47b062ce8ddb14845fb1a71d2fd88fd52a82e26561ba3eb05be057915a867775", # orsta 7 + "b6386f70b528ffa9e09fdd8db8a7b91a7c462ed97b06963576c6139e25fdcf31", # qwen img + "4cd449df9f9004a7e53005583a7e4cfa6de42912f03647d2ea799d489e9c1406", # qwen flux + ], + "layer_256": [ + "ed36a4a11c4ebebb10d1e010cb93e2e43fcaf975cd42bb6c9958537593d0d44d", # orsta 32 + "f7f6f64e7b6d7826400a2fc0eef942a47c47bd5914e051ad0c8cd9ff5ff7982b", # orsta 7 + "f341ed0f792cf0570ceb21d3b64ed14bf9875e9fcb90116851364eeed683a6ca", # qwen img + "ba031d0da78afe24ae63558ad29b8028244a7bd4750a5615dab9079fe32a5fd7", # qwen flux + ], + }, + ), + ( + "openai/gpt-oss-120b", + "GptOssModel", + { + "file_256": [ + "68a8dc1f8e2e5996cb702f14332a25ddf3463daeab2df68e21ca09ef181203c3", # original model + "a881aa5f561b26a22b14a8262aa61849ace349ffd73d74769e030ac90a1fcf8a", # diffusers + ], + "layer_b3": [ + "b52807536902cabbf84f99e4fa2f8713fb4ef77e739f06367ee0d486e3222faa", # gguf + "43c618018db1fd6e915dead610652da261d9058b73bc5355c85c6ac69af4d913", # "original model" + "ab27ce7391b7fbd6ce3c319faa119afdac68f746af6a0ce2c3400a132f36f6ac", # diffusers + ], + "layer_256": [ + "de5dcad822be5ed6196f0f3f6965739993118d14db97b33a94a269f4f1b7a363", # "original model" + "575f1977ed42d95a050e13dadaafc05a6d94c8aadca8364dca8a62aa4f2b146c", # diffusers + ], + }, + ), + ( + "microsoft/Phi-4-multimodal-instruct", + "Phi4MultimodalModel", + { + "file_256": [ + "bc703090b63eda16f639fa4de7ac54635c23105ab1da2f6ec4d3403151d38ee6", # mini + ], + "layer_b3": [ + "cf4add4ada6082f448788eaf2937f645b5212db88e06ee81475b8be0e99063dc", # mini + ], + "layer_256": [ + "7ff992b780b2f8993dd6bb9612207943638b2a42badc976ce80893bc205e801b", # mini + ], + }, + ), + ( + "laion/clap-htsat-fused", + "ClapModel", + { + "file_256": [ + "c92b5a2bee69ff5dd05820d9e0a5cddbc9c9b9dd19a6cb3214f0cf4f29a4d1b0", # audio ldm + "ae69f555e7f1a2333b8e684c9fa8233f44a47bbadf76d484f941b74f74d2753d", # music ldm + ], + "layer_b3": [ + "a4d26450ac399d51b9abbe37859615bb02a5cbf63521da4c7cdc549d04a2872c", + "ddf310d8eb2d4e3f61e605978675a9d3a748cad9406b9aee8335eae013e77573", # music ldm + ], + "layer_256": [ + "843ba86000971d6067bfc4f3ed6dd01bd6f6726188aaa15d86b05554f4fe8481", + "27529e30442d030a28badf9d62710f4b74e38e9c4424ed169c7e0ac072f5a771", # musicldm + ], + }, + ), + ( + "google-bert/bert-base-uncased", + "BertModel", + { + "file_256": [ + "c6c6348af2cb4d5852fe51102ce39605903dbe7925c005cf8995506cc21ea914", # hunyuandit + ], + "layer_b3": [ + "30d7d2cc3ec9e4ba45844e005d0bbcb5887b6a0976042f73da916237dc5c4c12", + ], + "layer_256": [ + "94fd2508680ff684eff57e4a5a8ca46bf338fc356a9cf6fe8db2b84543dd7971", + ], + }, + ), + ( + "llava-hf/llava-9b", + "LlavaModel", + { + "file_256": [ + "f5ad57d3eda300a3195bc9c0bb36ab76ebe88831f128e9851e63440aff4a6741", # hunyuanvideo + ], + "layer_b3": [ + "d7d6ccb9dbba90b64e4cd259b6309e56708b3f4fbd6e9f85e9f0410e549133ef", + ], + "layer_256": [ + "9969c41152aba689413b7f63888ecdc0c0badad2c2960e689ebc4c0e4a696c73", + ], + }, + ), + ] + + additional_tags = [tag_pipe(*entry) for entry in diffusers_addons] + additional_tags.extend([tag_base_model(*entry) for entry in transformers_addons]) + + assimilate( + mir_db, # format + additional_tags, + ) + + +def add_mir_diffusion(mir_db: MIRDatabase): + """Create MIR entries missing from the database""" + + repo = "microsoft/speecht5_hifigan" + series, comp = make_mir_tag(repo) + mir_db.add( + mir_entry( + domain="info", + arch="gan", + series=series, + comp=comp, + file_256=[ + "d9dc6513c30a5b86c2497712690c04fe74b4aa79fdab6d490b34fcb4e24c590c", + ], + layer_b3=[ + "85b5acdf29ad04c63f885383340d8e3445ae0055521f82cabb82bd09cfb9a956", + ], + layer_256=[ + "bd52b538e7ac05711be9321cfb7619d4056996ce32923c9c91ee02cf69154770", + ], + ) + ) + series, comp = make_mir_tag("lodestones/Chroma") + repo = "lodestones/Chroma1-HD" + mir_db.add( + mir_entry( + domain="info", + arch="dit", + series=series, + comp=make_mir_tag(repo)[0], + repo=repo, + pkg={ + "0": { + # "diffusers": "ChromaPipeline", + "generation": { + "num_inference_steps": 40, + # "guidance_scale": 3.0, + # "num_images_per_prompt": 1, + }, + } + }, + file_256=[ + "d845553f11e6afe8139c41ca73678f9f03eab2e68d2e1c6f03ae19509a4d546", # sai + "1b2993a44e63b2250496f69edce643bac2fb79833cf92ba8dd95cbd764d970c7", # annealed sai + "2dd46f08516246df1f582047cc09268ce4f747357baff05b13148e71519029fc", # diffusers + ], + # layer_b3=[ + # "8da38c3719e77a38a20356c9f92f5ca0101c17406d7a9817323cf67b74088520", # diffusers + # ], + # layer_256=[ + # "267798815e0855c2253061c6a6ab70edf9590e8ea1ba9b4621eeb0f6615ee37b", + # ], + ) + ) + repo = "lodestones/Chroma1-Flash" + mir_db.add( + mir_entry( + domain="info", + arch="dit", + series=series, + comp=make_mir_tag(repo)[0], + repo=repo, + pkg={ + "0": { + "diffusers": "ChromaPipeline", + "generation": { + "num_inference_steps": 8, + "guidance_scale": 1.0, + "num_images_per_prompt": 1, + }, + }, + }, + file_256=[ + "2c0c7d908d04418a48b453c293237a9826d54472cf0ba76e28697d1309d1021b", # sai + "c88f6794753ba23e8f6bf8c84cf220daa35a6aa16d54ea0c3e0136f52e5da7e1", # sai delta + "c759d67ca3ef50a9a1c242e3291c57f406646f226a95f43f66577996494986db", # diffusers + ], + # layer_b3= [""], + # "layer_256"= [""], + ) + ) + mir_db.add( + mir_entry( + domain="info", + arch="unet", + series=sdxl_series, + comp="pony-diffusion", + file_256=["67ab2fd8ec439a89b3fedb15cc65f54336af163c7eb5e4f2acc98f090a29b0b3"], + layer_b3=["bf4c2154daa4ece7292277b210d081f98759e9ed4d5c889564632e3ccc4a1071"], + layer_256=["465425d4420dcf5aa4b4d5b456db11a1fcc7c8f61b2e4a87e2470297c98bb96e"], + ) + ) + mir_db.add( + mir_entry( + domain="info", + arch="unet", + series=sdxl_series, + comp="pony-diffusion-turbo", + file_256=[ + "7555ac941f3a767833830ba5cc9a4508a9777cbf97b487b6baf0400ab7000587", # turbomerge + "9322f9d91b28abf09e4137bc02ec806af23510221a164e71b81778e61cc3b4b2", # turbosimple + ], + layer_b3=[ + "1e8f23fcd4be0f00eb52368b91c709fffa8a3b8e21772b92b2e0671eed9117d0", + "5c8b3f34f9d0a58135cf72fbfe9b5d75b5545a10e3d726478543fa7cc510a8bc", + ], + layer_256=[ + "7edf51ef09b39c46937a4e4141707c040cd12af0d95299a4d3cd2b7d3fabe035", + "74e4dbc89d57d61ff7e8af8b0fddcf7466ba233d53ca4ffb7777138991bc3d52", + ], + ) + ) + repo = "cagliostrolab/animagine-xl-4.0" + mir_db.add( + mir_entry( + domain="info", + arch="unet", + series=sdxl_series, + comp=make_mir_tag(repo)[0], + repo=repo, + file_256=[ + "8ece83aa1bed1fb39a2b81f1660f0ce6889218e493c1f2ed55e9f15f59a7e03f", # v4 + "6327eca98bfb6538dd7a4edce22484a1bbc57a8cff6b11d075d40da1afb847ac", # v4 opt + "1449e5b0b9de87b0f414c5f29cb11ce3b3dc61fa2b320e784c9441720bf7b766", # v3 + "e3c47aedb06418c6c331443cd89f2b3b3b34b7ed2102a3d4c4408a8d35aad6b0", # v3.1 + ], + layer_b3=[ + "268ffbb120670b9c4b25158bd474c787740884b7738b48203aa03c4c3f00028f", + "18fda1a55cad137d62c81d4328f5ece85d88b126261e06b9e14ab68055d5d484", + "bae9bc8a5c43145bcf92ee3391618d9eaddd689f626991bae202de9cf5f1e70e", + "d6bc5ccafa2b97c867b13a1e7a8c2c7ad9c4877055a66c71bb773557bc306447", + ], + layer_256=[ + "c21d1c38813e078817122e12866ab39f5aa7f56945dd4a8beee3cae1e0f139e7", + "b916c162c981155aaf74e93d5314038af6767bb5a129c51ee05a1fb6a206c6ac", + "ecc6bfc73824a2d7c3b0ca184854a235859f329c83768f017b07a19a535d17b4", + "97f6ca05de7fbdae7aacb2427a552f924492176c474a23dd252c192e1c0e9d65", + ], + ) + ) + repo = "OnomaAIResearch/Illustrious-XL-v2.0" + mir_db.add( + mir_entry( + domain="info", + arch="unet", + series=sdxl_series, + comp=make_mir_tag(repo)[0], + repo=repo, + file_256=[ + "c2a1a3eaa13d4c107dc7e00c3fe830cab427aa026362740ea094745b3422a331", # v2 + "536863e9f0c13b0ce834e2f8a19ada425ee4f722c0ad3d0051ec7e6adaa8156c", # 1.1 + "3e15ba00387db678ab4a099f75771c4f5ac67fda9e7100a01d263eaf30145aa9", # 0.1 + "e3d12d0f76d61aa31d2668a2217e5b642592193f2946842c44d7056ea5469cce", # 0.1 guided + "735cf3fefcbdc4f7817f53247e38b836ffd27c7641af6d8daa21d245242cb4bd", # 1.0 + ], + layer_b3=[ + "93b061baf21d743d592327a61f027d099d8e18da9808a76c7704ad123eba4a29", + "dc05fed2acbc73cef4c377cfa2a681c5cf6d065b88d8bf70d371bbcce6a223a8", + "8eb1c30327e5b71b35b9a4513dc5f2cac9f244667393c0eedb10a26aa9991cd8", + "3dafbe31f6ebaffa3d054e1b37049e1147faa2474ceb6dab7bc3c4cded0c845e", + "892533778ee14454938f7b50830093f58e12f1e14560a148f71927e4ccff5f5c", + ], + layer_256=[ + "397791b3d77affb7bd35c5ded7377493c6bf456920a41388ba95bd0157109803", + "b23c02b8519c6777a1f271662f4251a59468c4b3e11184a2d722fa8929b4ea48", + "a373981494f5508c124a1960bdd096bbc96935fbb54b1218f563206d3892c176", + "b709df257c40d9d981f686f2880bbe64f43b78805b7213768d659a142a593efd", + "f1e6b4cab0fce608dca6fa851384e8728202449f16270fbd1f0c4c5ec4946c10", + ], + ) + ) + repo = "playgroundai/playground-v2.5-1024px-aesthetic" + mir_db.add( + mir_entry( + domain="info", + arch="unet", + series=sdxl_series, + comp=make_mir_tag(repo)[0], + repo=repo, + file_256=[ + "11b6d7bce65674659cc6b7ea960658436edfd80e566cb240ebd4bfbc3e2076c8", # 2.5 diffusers + "bcaa7dd6780974f000b17b5a6c63e6f867a75c51ffa85c67d6b196882c69b992", # 2.5 aes sai fp16 + "956dca99114aaa5c3eb526381309d37ee96737e78ed64c8ae613409f47c3f65a", # 2.5 aes sai + "933778ce76c1fc0ca918b37e1488411b8a99bbd3279c12f527a3ac995a340864", # 2.5 fp16 diffusers + "5c7d38880d0940e6795158b7608ccef89217272b1f2a9331c5b0a2adffcd82c4", # v2 sai + "0411e988479884b1a3ecd184123efe38d051d8d0ef24270585a7d1d57499464a", # v2 sai fp16 + ], + layer_b3=[ + "d55b22740da2d5b98020ad2390cdc0a7ee08cf9e0d98c11957f16cc20c49815b", # 2.5 diffusers + "7e9be9bd9a3aed1ad7207e2f77c98c24c3a75f6adcc9b53514033c6c3365d289", # 2.5 aes sai fp16 + "5c6dfcc8d01dfb64723f8f5785caa080e2987859c0a050470bfdbe5312be9efc", # 2.5 aes sai + "703f775c6e48ed5b0eba6e847414f047bcd4adc677dbc1bf221b3ef05b2ac471", # 2.5 diffusers fp16 + "72d4ebe4af61f8a7add8fe36b8acd16602894279fb5a744ad50b5b5bac7067b8", # v2 sai + "acb757b851db12cdf9d4365a45ee0d6e64afa77ac95583bb82711baf7c4125fd", # v2 sai fp16 + ], + layer_256=[ + "adb7be228d4ee6e583c3e5ae4ddb579fef64c3987617ce4d4aff3eb7f8d6a3f7", + "d4813e9f984aa76cb4ac9bf0972d55442923292d276e97e95cb2f49a57227843", # 2.5 aes sai fp16 + "fe2e9edf7e3923a80e64c2552139d8bae926cc3b028ca4773573a6ba60e67c20", + "bc7021473a04a6de3fe0d0fed600875d852ad1ad9d47c445278f66ce9e8ec7a0" # 2.5 fp16 diffusers + "fc94481f0c52b21c5ac1fdade8d9c5b210f7239253f86ef21e6198fe393ed60e", # v2 sai + "a6f31493ceeb51c88c5239188b9078dc64ba66d3fc5958ad48c119115b06120c", # v2 sai fp16 + ], + pkg={ + 0: { + "diffusers": "DiffusionPipeline", + "precision": "ops.precision.float.F16", + "generation": {"num_inference_steps": 50, "guidance_scale": 3}, + } + }, + identifiers=[ + "edm_mean", + [1, 4, 1, 1], + 2516, + ], + ) + ) + repo = "segmind/Segmind-Vega" + mir_db.add( + mir_entry( + domain="info", + arch="unet", + series=sdxl_series, + comp=make_mir_tag(repo)[0], + repo=repo, + file_256=[ + "94762e983e5942056be73c5c1d4464b8ffa1ada500b4fef1267550e2447953ce", # modelspec sai + "1ab33e37fbb2566c55cd729e4ab79cc2f99cd9d0a578fabc7a2cf4ee47968be1", # diffusers + "8cfa375669b1222d6fecf470f41b2abb370c76a90ab9568964c4bb15b34ec8a2", # diffusers fp16 + ], + layer_b3=[ + "2f353c5e6ed0a2c05af00d014e18e65f69f1ce8c48f8eefbf8ad71b34f940fbf", + "cc34bd3135d7cafc3cb6e3f6e7cb6896c98277bad52877a952ddbd2ffe222e01", + "b90efdc848f5386d5250b6fb233ce380cf6cc299f497cfa1d2feaef22f87c9d1", + ], + layer_256=[ + "029b89ee311110c8f945dbdfc52c1d5daeb1e78c353c38aa3141ec68ce28e7cc", + "5cdb948e5f3873300679073391d48fc648171f02093d7737d078557ff75762bb", + "f73afbe43cc76571cb86ebcfced618668a2fb2252b0bc6ba88d6e942bae75741", + ], + ) + ) + repo = "segmind/SSD-1B" + + mir_db.add( + mir_entry( + domain="info", + arch="unet", + series=sdxl_series, + comp=make_mir_tag(repo)[0], + repo=repo, + file_256=[ + "7cb406ec0662e91570a79f3c4fb8f0ea5325bffe6af5d9382edae838698f72bd", # modelspec sai + "1895a00bfc769a00b0c0c43a95e433e79e9db8a85402b45a33e8448785bde94d", # a1111 aio + "0bf1ce6b065a6b969ab02dc8e8fa21eb20ee189b10935c49ce68c77a7e432c1c", + "02ed8ebd0ed55aec686fcf20946d7a1659a31f9f8d9c3798cd254ba6b67434ca", # diffusers + "40d8ea9159f3e875278dacc7879442d58c45850cf13c62f5e26681061c51829a", # diffusers fp16 + ], + layer_b3=[ + "c074dc38e8ec836816b91cbcc2ca17f80d6106de8d196d416ef9a27c8837ee45", # modelspec sai + "1d6c0216da57fe98e7ad29e9653566725f5b2a87845fdbdcda257b3be817b5f4", # a1111 aio + "c074dc38e8ec836816b91cbcc2ca17f80d6106de8d196d416ef9a27c8837ee45", + "89f86d9c846495870416b4945b6a46a517f28405e5bab666feb4057f012340be", + "535b47e9b70da6494878ca6d45af3f2e201b7f17748432911c12232e586855e6", + ], + layer_256=[ + "52267d5d327a2ba92c7a14261a9d081df621b8366819b1bb3a47d130523a813c", + "b365a3631c6c74532f3a571c84c68e088be35496d35be1e932031713ddd2a2f4", + "52267d5d327a2ba92c7a14261a9d081df621b8366819b1bb3a47d130523a813c", + "89f86d9c846495870416b4945b6a46a517f28405e5bab666feb4057f012340be", + "535b47e9b70da6494878ca6d45af3f2e201b7f17748432911c12232e586855e6", + ], + ) + ) + repo = "shuttleai/shuttle-3.1-aesthetic" + mir_db.add( + mir_entry( + domain="info", + arch="dit", + series=schnell_series, + comp=make_mir_tag(repo)[0], + repo=repo, + pkg={ + 2: { + "diffusers": "DiffusionPipeline", + "generation": {"guidance_scale": 3.5, "num_inference_steps": 4}, + } + }, + file_256=[ + "176871da1d5d2d511a52ae9b0dd70faa1f5d1b7734b7e33ed6b4bffa52050e0d", + "4b80d37681eaed07b7f5b3825a392da929d1620933ede7c2749ef3613cc53f42", + ], + layer_b3=[ + "ff422d1734abf33366e87bbf44267dc6096c5d499e695287c35558174877412e", + "5ad8034eac6b82d842311437101c52b5d35826ce34994940d9e667e702a0d45c", + ], + layer_256=[ + "e5d95de314cbfc49b79479118a1ac0b90fc95ccd6bb1a5c95803996d6cebf8fe", + "d299e8ea4a605917ab98a4a7330d4d398b4ae295efbf458eeeceb5ff1bd7959a", + ], + ) + ) + repo = "shuttleai/shuttle-3-diffusion" + mir_db.add( + mir_entry( + domain="info", + arch="dit", + series=schnell_series, + comp=make_mir_tag(repo)[0], + repo=repo, + pkg={ + 2: { + "diffusers": "DiffusionPipeline", + "generation": {"guidance_scale": 3.5, "num_inference_steps": 4}, + } + }, + file_256=[ + "a5b04df4072698395387c21e8da0176d03f6557e0c38ff1dd3bf469ebab9d0fd", # fp8 + "a91b46de2055b3511ee87523b57862648856e8c00100161d5b520543a7302755", # norm + "23a77c86189d5934da48bf44bb871cf80ba99177ffd3fd5272cdecb208c8b8be", # mlx q8 + "d3782d5a8f6e82c6676e8e26d54020934ada589d2aceb17fc5ca604b1bd55da8", # mlx q4 + ], + layer_b3=[ + "4dd3174edf6b680ce9daf3de643e33ae2c4f09a4d5968da61ea48885f3a193c0", + "9fdf191b2c58b2a6e190396e12314530593dca4f2a2bee389ec5175da5e52af8", + "ad203ad6a00d8b1315337e34069e7c41016ea407469a536de8ad6807042017fd", + ], + layer_256=[ + "14d0e1b573023deb5a4feaddf85ebca10ab2abf3452c433e2e3ae93acb216443", + "7ce8d449b32a9c959431ade729b513ee7a6457f11e1c13e3ef04dd8db3494621", + "9c3395f67a3d844483b77f0ddd5e2ea64b61732fa9d9da19845bb8ae574c1f8c", + ], + ) + ) + repo = "enhanceaiteam/Mystic" + mir_db.add( + mir_entry( + domain="info", + arch="dit", + series=dev_series, + comp=make_mir_tag(repo)[0], + repo=repo, + pkg={0: {"generation": {"num_inference_steps": 16, "guidance_scale": 7.5, "width": 768, "height": 1024}}}, + file_256=[ + "179d4000e44295f6dfadc0e4ac210146454724d46371b82657200ff9fb5c68a9", # mlx 0 + "48ca85274e3b67f07f70dd84b67725e62395c2f7b188394342716f783ea4c6ac", # mlx q8 + ], + layer_b3=[ + "91074aaebe1b5f3b2e7755d3c092af7eb240e92a192360690f1033949d3c8a68", # mlx 0 + ], + layer_256=[ + "3942e6a52dbb0abaf63b031d9c4eda0df47576b51d4c81361978a3dc27b1309e", # mlx 0 + ], + ) + ) + repo = "shuttleai/shuttle-jaguar" + mir_db.add( + mir_entry( + domain="info", + arch="dit", + series=schnell_series, + comp=make_mir_tag(repo)[0], + repo=repo, + pkg={ + 2: { + "diffusers": "DiffusionPipeline", + "generation": {"guidance_scale": 3.5, "num_inference_steps": 4}, + } + }, + file_256=[ + "dcbc4f2470b177eed12c7d7515c0e7342515a849ebd31a50c8d8d43913d7bd32", + "26a7aa64c0798a3549e1d767932da0a7fb82b49f8edcbdcde804a20d9ed1478f", # mlx q8 + ], + layer_b3=[ + "9906c29933d0c33a6ee8d9712f33fa8bd4b35b46a1c7b565ae48832b757dd980", + "89c453c4bf99220405687eed984dace4492bdae1b6fb08f3d9629145b1a11672", # mlx q8 + ], + sha_256=[ + "4eacf27e5659f5dc42f34c407cbe9e1e202290692df754eb68fe913f59fa2941", + ], + ) + ) + repo = "freepik/flux.1-lite-8b" + mir_db.add( + mir_entry( + domain="info", + arch="dit", + series=dev_series, + comp=make_mir_tag(repo)[0], + repo=repo, + pkg={0: {"generation": {"num_inference_steps": 28}}}, + file_256=[ + "09e970a7b8d1813ea7cacd48f9a944fd223882b137a8f4f3b61d864cdc20bbec", # mlx q8 + "de90e69945c2f4afcb9b6a057ce48190905c984370fce76b16ba3b97d46e2747", # mlx q4 + ], + layer_b3=[ + "9276fa4805efeb45c08cca32c5b51d490e57a2ce5c15ef476a8e468a509c5cdf", + ], + layer_256=[ + "e1afe2f9b1ca55b3c659293cf3237f6b5571f5c4e826bad025ff0f7b54dc34ee", + ], + ) + ) + repo = "freepik/f-lite-7b" + mir_db.add( + mir_entry( + domain="info", + arch="dit", + series=dev_series, + comp=make_mir_tag(repo)[0], + repo=repo, + pkg={0: {"f_lite": "FLitePipeline", "generation": {"num_inference_steps": 28}}}, + ) + ) + repo = "freepik/f-lite-texture" + mir_db.add( + mir_entry( + domain="info", + arch="dit", + series=dev_series, + comp=make_mir_tag(repo)[0], + repo=repo, + pkg={0: {"f_lite": "FLitePipeline", "generation": {"num_inference_steps": 28}}}, + ) + ) + repo = "freepik/f-lite" + mir_db.add( + mir_entry( + domain="info", + arch="dit", + series=dev_series, + comp=make_mir_tag(repo)[0], + repo=repo, + pkg={0: {"f_lite": "FLitePipeline", "generation": {"num_inference_steps": 28}}}, + ) + ) + repo = "TencentARC/flux-mini" + mir_db.add( + mir_entry( + domain="info", + arch="dit", + series=dev_series, + comp=make_mir_tag(repo)[0], + repo=repo, + file_256=["4236455adeaeb4ed444d63b253ec99805022d17e962ed7261ada9c72ce11cfee"], + layer_b3=["c1a6f83585398fe452d20596a79a522e2986f4c2c01a40e7bfd787af113735d3"], + layer_256=["e4a0d8cf2034da094518ab058da1d4aea14e00d132c6152a266ec196ffef02d0"], + ), + ) + repo = "ostris/Flex.2-preview" + mir_db.add( + mir_entry( + domain="info", + arch="dit", + series=dev_series, + comp=make_mir_tag(repo)[0], + repo=repo, + file_256=[ + "0407108e446a4f57efffc5e7518bc374876af970d3c6068dc4074de0d221c615", # modelspec sai + "df168ba94d5f96c478b24604a6beedff6189047152190509c73c162ea0d8ec02", # mlx + ], + layer_b3=[ + "7f85cdc186896da6965b57d5edb672f08663075d2b207f0e20e328c4034a8076", # mlx + ], + layer_256=[ + "5063de856be5365807d12b47ef6919b4ac611a72651739b2b4050e113bed7a83" # mlx, + ], + ), + ) + repo = "ostris/Flex.1-alpha" + mir_db.add( + mir_entry( + domain="info", + arch="dit", + series=dev_series, + comp=make_mir_tag(repo)[0], + repo=repo, + file_256=[ + "5d6dce30a266ccbf530c3a3bf253cd5486720a8fb71cdeed556c28304201dc2f", # modelspec sai + "7acf8771b80a91eaa21566abe8c7d9d3ba33d8688e6e98446827749aee7ca1ee", # mlx + ], + layer_b3=[ + "cb3d3edafd81651eefd62894b3572deb02c5304f4b5d4f7ab8654f1fb922ecd6", # mlx + ], + layer_256=[ + "a6b9af6efc25fa77cd24046b81ee66fea09a9987d2a8e56ffca9b7a1c9c9c519" # mlx, + ], + ), + ) + repo = "tensorart/stable-diffusion-3.5-medium-turbo" + mir_db.add( + mir_entry( + domain="info", + arch="dit", + series=sd3_series, + comp=make_mir_tag(repo)[0], + repo=repo, + pkg={ + 0: { + "precision": "ops.precision.bfloat.B16", + "generation": {"num_inference_steps": 8, "guidance_scale": 1.5, "height": 1024, "width": 768}, + } + }, + file_256=[ + "5b0530e8d71b49fa1358f1208047cd789a40bae5b44406c9524b0f0d88f8b246", # diffusers + "07119c77c3548a1d9eb30923df4dd55ec74914dc5ec81626804dcbe51ce17a5d", # sai + "3c379381344d2a2b3ee3d7a1bc97f7d1e58fa95c6b5187fb48b3ce446f99f17b", # q4km gguf + "6b3806cafdb4303ea2638e9e08eb186067b4a46a95ddf344ccdbe56537afaf6e", # q8km gguf + ], + layer_b3=[ + "873821614080a98e1ebfe56673bc96c2ac57379720d4ad2f97e4bca317571d48", # diffusers + "7284d2027523482af9ef47405667ca891cc518bfb6ebf1f1d4666cb0accc8cd5", + "d938ee5738c73f701760ed18acad274b074d2796123aee3f2eee1328b6c36ea4", + "c4c40056c2a77959083b5a69a1a4b205caa463ccabde057352c5c4e38b2c67b6", + ], + layer_256=[ + "3c324055a1ec6eb4ee0242e344bb2b6356afcbd2e215fdd9d160cda691a72fae", + "7284d2027523482af9ef47405667ca891cc518bfb6ebf1f1d4666cb0accc8cd5", + "d938ee5738c73f701760ed18acad274b074d2796123aee3f2eee1328b6c36ea4", + "c4c40056c2a77959083b5a69a1a4b205caa463ccabde057352c5c4e38b2c67b6", + ], + ), + ) + repo = "Wan-AI/Wan2.1-FLF2V-14B-720P-Diffusers" + series, comp = make_mir_tag(repo) + mir_db.add( + mir_entry( + domain="info", + arch="dit", + series=series, + comp=comp, + repo=repo, + file_256=[ + "", + "", + ], + layer_b3=[ + "", + ], + layer_256=[""], + ), + ) + repo = "OnomaAIResearch/Illustrious-Lumina-v0.03" + mir_db.add( + mir_entry( + domain="info", + arch="dit", + series=make_mir_tag("Alpha-VLLM/Lumina-Image-2.0")[0], + comp=make_mir_tag(repo)[0], + repo=repo, + file_256=[ + "dc6cffcfb0ccfca6332ddb5d2fe25bcb5f496f44b481627f48c42626156fa6a8", # 2b 22100 ema unified fp32 + "2ac549741fa1c6de2d6cd8be06abcdce52d472eeae2439f948e285258b66a214", # 0.03 ema + ], + layer_b3=[ + "a97b4a63e1e7678e8e7154fae55252267bd1f0ba76b03dba622d801644e657ac", + "aa6c1b2d1971cea3c4ed0963c8d68d4c50db683f8eab9f77f60ea2d04ed6ce5c", + ], + layer_256=[ + "39086c199b9ac296dcba53461ba1e113906d91fbc1b12556d92f5cc77ca11f9f", + "e51ba2ded40f1af5ca6f78c46eed8305fbd87cd6401e9d439837e10d35cc5828", + ], + ) + ) + mir_db.add( + mir_entry( + domain="ops", + arch="patch", + series="hidiffusion", + comp=sdxl_series, + pkg={ + 0: { + "hidiffusion": {"apply_hidiffusion": {"timesteps": "StableDiffusionXLTimesteps"}}, + "generation": {"height": 2048, "width": 2048, "eta": 1.0, "guidance_scale": 7.5, "num_inference_steps": 10}, + }, + }, + ) + ) + mir_db.add( + mir_entry( + domain="ops", + arch="scheduler", + series="align-your-steps", + comp=sdxl_series, + pkg={ + 0: { + "diffusers": "schedulers.scheduling_utils.AysSchedules", + "generation": {"timesteps": "StableDiffusionXLTimesteps", "num_inference_steps": 10}, + } + }, + ) + ) + # possible mixed-type architecture? + # fusion / united / universal + + +def add_mir_llm(mir_db: MIRDatabase): + base_arch, base_series, base_comp = tag_base_model(repo_path="facebook/chameleon-7b", class_name="ChameleonModel") + repo = "Alpha-VLLM/Lumina-mGPT-7B-1024" + series, comp = make_mir_tag(repo) + mir_db.add( + mir_entry( + domain="info", + arch=base_arch, + series=base_series, + comp=series, + repo=repo, + pkg={ + 0: { + "inference_solver": {"FlexARInferenceSolver": {"precision": "bf16", "target_size": 768}}, + "generation": {"images": [], "qas": [["q1", None]], "max_gen_len": 8192, "temperature": 1.0}, + }, + 1: {"inference_solver": "ChameleonXLLMXForConditionalGeneration"}, + }, + identifiers=["model.embed_tokens.weight"], + file_256=[ + "6b71408a7c574d98f00114ab770ac6addc71471770456e482e7b5ec641c02345", + "1d5d8d5532bae0f32ba35d10d411e506d61e4378dc9fc338f2b1e6af2aa322ec", # 768 + "a8fe636bbee30fef06dcd8e806ffc65b2aed0ad08a07fdc62f35717d0f851be5", # 512 multi + "6420fa13483576d46263996627ba7add2237a01f46dedd3b7750112c0cc2d95b", # 512 + ], + layer_b3=["6cd6b3caaea270feb5aff8e9fec205a27da4f48a1e740e63dc9a08f16e70a656"], + layer_256=["eaa882db6a69cf8ed0104a15b2cdbbb570a23a06ab8c8f65f4c6c21719c6ba25"], + ), + ) + repo = "openai/clip-vit-large-patch14" + series, comp = make_mir_tag(repo) + mir_db.add( + mir_entry( + domain="info", + arch="vit", + series=series, + comp=comp, + repo=repo, + pkg={0: {"transformers": "CLIPTextModel"}}, + identifiers=["text_model.encoder.layers.0.mlp.fc1.weight", "clip-l"], + file_256=[ + "cb0cba1ead482a850532ebe5ff6b5c8d4456aee32a5228acf0a31e7d9472415e", # long vit best + "39e79c916feca4ddf546d9fe923e664714b59ea61074f7228037d17c302f3d17", # vit l detail improved hit gmp + "893d67a23f4693ed42cdab4cbad7fe3e727cf59609c40da28a46b5470f9ed082", # flux/shuttle 3 aes + "778d02eb9e707c3fbaae0b67b79ea0d1399b52e624fb634f2f19375ae7c047c3", # playground 2.5 + "660c6f5b1abae9dc498ac2d21e1347d2abdb0cf6c0c0c8576cd796491d9a6cdd", # playground 2.5 fp16 + "71e183d11db0c6b6282a4d9e0abb74125edc8692393e89ed8ee5571005f35cb1", # sd3.5 fp16 + "5c3d6454dd2d23414b56aa1b5858a72487a656937847b6fea8d0606d7a42cdbc", # sdxl diffusers + "87c1c0b0894c9e9e10b962e597e8d64dd3a3a2d372c389922b335a53c250b2ae", # L + "bd289dd57fee86bc8816b55919a2b03f9c3c75af6025e21777325a6730872325", # jaguar mlx + "8377b1ca9d88fe06ec483dd7b3cfc62e5e8dbf8ddd252f455e79d659fa0553c5", # ssd-1b + "5487ea0eee9c9a9bff8abd097908d4deff3ae1fa87b3b67397f8b9538139d447", # ssd-1b fp16 + "92b998a9a64549bfa05c019bde114be6681549a0c79caee903fe30c9444d08b9", # vega + "1e090d6a828fd92401be5f83e615fd7b4fb1f4a22e9af9040a38f602e839317c", # vega fp16 + "11807cb2522cfe99240e5ee2bbeb1ccb42cecca2215102ee872567c7773b28b9", # flux + "d008943c017f0092921106440254dbbe00b6a285f7883ec8ba160c3faad88334", # sd1 + "77795e2023adcf39bc29a884661950380bd093cf0750a966d473d1718dc9ef4e", # sd1 fp16 + "b70c11ad5d7e9abf6109348908f599ea382f8019e1f36910bbc8ebecde936633", # hidream i1 + "fc42badf529dd83f2f7c3d20fe6bda1e22036162f37c4c668b9e130884e20561", + "e27bafa0b3029ad637ef3ace24ce1efe85b8d0dbd22e03a2e70bda6fc88963a1", # onnx + ], + layer_b3=[ + "f58a22a381f79985b6d38782f6110a52c2f319b40fdedd3b88b24945dfcbdf64", + "8faa00b8fd1dbd9286a7237df18caeb8c91af100a6813849b6bae272a01dd7b7", + "ab5bebc98299c155251a06deccde599ba0128038ee3ce021e8c59a45f58f72c0", + "c70e9d86a9dcbbbe7c269ef9dfac96ce9c96c46922577338cc1902e5fe936315", + "f285e9b7b70745df81adc8b558ec74b536b79b6fc02a453ecc61ea9d13f25f1a", + "7ab17bfa06ab8d65840997ef641f3f593d096860e20141f1eeb0169d131c1c23", + "2737d3f327e8176dbb549b9c5c4994821430a6c3b07e3bbc925d97511c802636", # jaguar mlx q8 + "58a826a4a5fe555b4df188a1ebc0d8d9c96cedae3a26ce84c247861dbb93388f", # sd1 + "1540fd8844898960e18ce8fd153e5f21a8c446bd8c4d6f536a7cf11418f02bf3", # sd1 + "c4c9caccdbec12b965d93688c521893f75e0bf9a5e0aad70a6a962b669e7b9d5", # vega + "e43fae8d5fd1e562607da172369cc0c5ec99b834e42502e682287ff7d12baacc", # vega fp16 + "c6f79f7416a882891957b815fbdfd6edfaa253c43970b1a25ef14e217599c7bc", # flux + "daf5e09f67ad09a909f58a01298fec0132324634cb8fca2a604c3a240c2c453f", # jaguar mlx q8 + "3f62bfb6bbde05f01435129326166c44aeb113ac0d9f735f31ed3f7dd04f6980", # hidream i1 + "22f866f3c96a92bc61e9965cf366d706db942ad047ba8cb82109edcd4e68fa40", # sd3 turbo + "f3fa9d7a8f15741621c1fe82f8a1bcc5c601c900d947ac09fba7016615a252a5", # shap-e + ], + layer_256=[ + "48daa3d8f939972e69f044533a4312a941971c18c78255f5e555fa26faf664c1", + "60f5734a74c342be8b0011fc704e718431839790bcfdc7d7004fc39d70f7fec6", + "6e76e25b4a55dddfa2eecf4b7ab189a8148658a9f6df165c00170f6ce661033c", + "2d5249df489fec9137cc3a5e9bda499dd9b72a957ddd8e7ad4e99ff3684bad99", + "3bf085e701713ed3e79775dafea375c3e2a43659ad1ee788b1b393c0aeff9f0e", + "efb7976800692772e449c81a739339f59394886590ff3f768b0f9ddd87d2a94c", + "9b0ac8d127c6c457b2eb8c7236f18c4e4ba9e8bbf27130aa8fe854d7c3f7b1e0", + "24a9ee3d60cdde6c967f08e4b2ec7088fe1bfe308c6896e73caa874860570a5c", + "5d6d9d0cc7943eb1b8c16862bfd5bee5c3766d0df027ec837e90fac715ac2bd3", + "68fb122f7d6c3cfbef320341b2af8f5916678e36a69ed36fa8cfcb19e7d5c43d", + "11807cb2522cfe99240e5ee2bbeb1ccb42cecca2215102ee872567c7773b28b9", + "50c46cdddbe9f0162278c69b9a1f818519330e3a91b994272e19b5c789670471", # jaguar mlx q8 + "ffe1c4f55e07c2010ace7b9cf35798bb9f431bc954a32784e5acbdc16acc0364", # hidream i1 + "146ea48d234e05a934db9d8988e9a9dd86b2ac70f535eaa550ecb0ee23ec135e", # sd3 turbo + "d97560cf9704cf71711f6121df2bf55e55a1eda4b574a6ddba074767420bc8c3", + ], + ) + ) + repo = "laion/CLIP-ViT-g-14-laion2B-s12B-b42K" + series, comp = make_mir_tag(repo) + mir_db.add( + mir_entry( + domain="info", + arch="vit", + series=series, + comp=comp, + repo=repo, + pkg={0: {"transformers": "CLIPTextModelWithProjection"}}, + identifiers=["31.self_attn.k_proj.weight", "text_model.encoder.layers.22.mlp.fc1.weight", "clip-g"], + file_256=[ + "ca18e0c67c1ef1e64cac22926266765b60688f692307ecc06283d987c5768134", # seaart furry g + "ec310df2af79c318e24d20511b601a591ca8cd4f1fce1d8dff822a356bcdb1f4", # modelspec sai + "fa5b2e6f4c2efc2d82e4b8312faec1a5540eabfc6415126c9a05c8436a530ef4", # playground 2.5 + "b84f413eebecbd049b72874c1df533a516510cb5a2489ae58c7e320209cf0ebe", # ssd1b + "d3df577f6e3799c8e1bd9b40e30133710e02e8e25d0ce48cdcc790e7dfe12d6d", # ssd1b fp16 + "943a2924ee888295a156dd47089d67181d633b782337890af11ef4b15af17ec5", # vega + "5b98e4a57a9292eeb819d67e2d2100f66f17db723cde4ecea27a7c3741160d0c", # vega fp16 + "4d6effa7a5e600cabf7528ed7234146a13ead1b2c151211d706b293a060b112a", # hidream i1 + "3a6032f63d37ae02bbc74ccd6a27440578cd71701f96532229d0154f55a8d3ff", # modelspec sai + "162042ac6556e73f93d4172d4c67532c1cbe4dc7a6a8fa7e44dd2e3d7cbb772b", # onnx + ], + layer_b3=[ + "d754db276f2d89d2808abb7086b3b8eccee43ac521c128d21a071f3a631474a8", + "2eb93685b34719e1d1e0541d8902b0a592d95848f80657e32816cf3b152a0f31", + "e253a5cf3a6242c58037abd6b378bf0281f278e441f28dff7ca1bcfcd3cd6bd8", # ssd1b + "16d0eec4e55b0aa63cdca4e4d36f78f66a4b1b9605ce3b1089305026f853c3d2", # ssd1b fp16 + "f606463295ecf3bae8920d3d45bb9d180793418b3d08c3e84d4c4135c7dc2aa5", # vega + "7060993a5eb32d94d1ea8aef7a7301e7be73b199c639c63f8f7cfbfcd2abf10e", # vega fp16 + "b92af95334c657371af6051a91374a41b5455907fa6622bb66a8c112dc511600", # hidream i1 + ], + layer_256=[ + "270e998633eb22145100a3889a62ca270d5080654735e5ff8dda09a7c233af8d", + "df18800c2a9d9318c4323d991a0fb24a6a9afceb41bea203812f60517c301536", + "4c228b104f6b9b383e0808c9baa1998957f5125d8f90a4d98c1a86e71edd72dc", # ssd1b + "f7fc81d8b5ae91ec28a5106ecc0d067be9a94fd3f394c4aa4686ed131ce5a5b3", # ssd1b fp16 + "61ab42bd5c0fcb9fd3db1d4014cb844ccae8dc17fd69a108cf077a573d092946", # vega + "6c64e36cdda3bec7067e94b05619f882f5d31070792acaadac60ddbef580453a", # vega fp16 + "43c9e64995b485a7f128771c48defce128640df28e65c7f79537d472f43ebe46", # hidream i1 + ], + ) + ) + repo = "laion/CLIP-ViT-H-14-laion2B-s32B-b79K" + series, comp = make_mir_tag(repo) + mir_db.add( + mir_entry( + domain="info", + arch="vit", + series=series, + comp=comp, + repo=repo, + pkg={ + 0: {"transformers": "CLIPModel"}, + }, + file_256=[ + "036e6e2bd49697511f4f8b8cb5ee465f93025f7a69a145eadeb9a881ace9b18d", + "0084e75319a50ad85ef45377bad5bc38f2f58824459eb690048d51c9f8863be5", # open clip + "64a7ef761bfccbadbaa3da77366aac4185a6c58fa5de5f589b42a65bcc21f161", # wan sai + ], + layer_b3=[ + "227f26ed63120b9034f4a0c90b6b37eede721a8260f2c1e8f7ea3ccc0d109e7e", + "3a38ffd1b60499cf2f451f3065079ff26efb9190a86f23ad1c8d993bbeb9af05", # open clip + "ce06cf1fd684269ee96631b2bf9334c6ecde6a84a55760dfa0d9d2a6411f28e4", # wan sai + ], + layer_256=[ + "130a94ed12569e099196a6ca27388181922e20148dee5bcb58c5e309acfc2352", + "cfdbd3fd2b90b64ba12d395a62dd7c3c3ea3e811f0a54593e91bae6516ca5061", # open clip + "9125ce5970c649d6f9368c25493d3aaa6b41e224d4cc427e955115f7b7e53d1c", # wan sai + ], + ) + ) + repo = "zai-org/chatglm3-6b" # formerly THUDM + series, comp = make_mir_tag(repo) + mir_db.add( + mir_entry( + domain="info", + arch="aet", + series=series, + comp=comp, + repo=repo, + pkg={ + 0: {"transformers": "AutoModel"}, + }, + file_256=[ + "0054d03310248928fdabdeef3fdc753170218dc49a1e9eb5f98323e27683f654", # kolors + "b1052386eac358a18add3d0f92521c85ab338979da8eeb08a6499555b857f80d", + ], + layer_b3=[ + "a45dfba6a9fa8739777c76deb845fc9589b40f88670d3ce4661646a7b7b1d481", # kolors + ], + layer_256=[ + "174924fd7a07f370bb6fcd1ad07a73eecb7de901f15eefb80f420c1042c47d44", # kolors + ], + ) + ) + base_arch, base_series, base_comp = tag_base_model(repo_path="Qwen/Qwen2-7B-beta", class_name="Qwen2Model") + repo = "ByteDance-Seed/BAGEL-7B-MoT" + series, comp = make_mir_tag(repo) + mir_db.add( + mir_entry( + domain="info", + arch=base_arch, + series=base_series, + comp=series, + repo=repo, + pkg={0: {"Bagel": "app"}}, + ) + ) + + +def add_mir_audio(mir_db: MIRDatabase): + """Create MIR audio modality entries""" + repo = "facebook/audiogen-medium" + series, comp = make_mir_tag(repo) + mir_db.add( + mir_entry( + domain="info", + arch="art", + series=series, + comp=comp, + repo=repo, + pkg={ + 0: { + "audiocraft": "models.AudioGen", + "generation": {"duration": 5}, + "stage_2": { + "audiocraft": ".data.audioaudio_write", + "generation": {"strategy": "loudness", "loudness_compressor": True}, + }, + } + }, + ) + ) + repo = "parler-tts/parler-tts-tiny-v1" + series, comp = make_mir_tag(repo) + mir_db.add( + mir_entry( + domain="info", + arch="art", + series=series, + comp=comp, + repo=repo, + pkg={ + 0: { + "parler_tts": "ParlerTTSForConditionalGeneration", + "generation": {"return_tensors": "pt"}, + }, + }, + ) + ) + repo = "Zuellni/snac-24khz-ST" + series, comp = make_mir_tag(repo) + ( + mir_db.add( + mir_entry( + domain="info", + arch="gan", + series=series, + comp=comp, + repo=repo, + pkg={ + 0: { + "snac": "SNAC", + }, + "1": { + "mlx_audio": "tts.generate.generate_audio", + }, + }, + file_256=["e61ae2f638f56ee07a37592cd5a6a9e7d642560ddc78a76ee4a7f96d6922f1be", "973ee1be4032319fd9685ec54eee1b93e79c7bc98c786e67f17c04669714f11d"], + layer_b3=["18307b00460a64cc4893f9061592ce8d7e15b70fc54065cc8ae0f0155381ec46", "d599b1bb36dee3cee4674b7922fcd69e5ec05b74413f611d21cfdfdf8f9b6119"], + layer_256=["35ba9aa1feb931010559a178fcac243673d2efdd1396a4b69d406c9853a88300", "5a22c4707ed6c928043f23b59f2d102a579db3a9af41cf6e60d7c3958f182841"], + ) + ), + ) + repo = "parler-tts/parler-tts-large-v1" + series, comp = make_mir_tag(repo) + mir_db.add( + mir_entry( + domain="info", + arch="art", + series=series, + comp=comp, + repo=repo, + pkg={ + 0: { + "parler_tts": "ParlerTTSForConditionalGeneration", + "generation": {"return_tensors": "pt"}, + }, + }, + ) + ) + repo = "hexgrad/Kokoro-82M" + series, comp = make_mir_tag(repo) + mir_db.add( + mir_entry( + domain="info", + arch="gan", + series=series, + comp=comp, + repo=repo, + pkg={ + 0: {"kokoro": "KPipeline"}, + 1: { + "mlx_audio": "tts.generate.generate_audio", + "generation": {"audio_format": "wav", "join_audio": True, "verbose": False}, + }, + }, + file_256=[ + "5a5cb3d87478f2e74dfca208ee52209ccfce024095e137097fd276026506e45f", + "496dba118d1a58f5f3db2efc88dbdc216e0483fc89fe6e47ee1f2c53f18ad1e4", + ], + layer_b3=[ + "3e9b5017cfe67a7804ac717b18b6add42ffc0bd3353490df2bcc520eaaef79b6", + "379660a87a64524bab69a267e3d9580f04b5eec4f7e3fbd48c6597d164d9b17d", # safetensors + "997f154f5a78879ef3ba1a1556977c40b28b9c21076b8f583f752c57ecc36e93" # pytorch + "2dc3dba29452b85ea85266084a6248f9e0efe642d5f75b43e64f25b9f2837f92", + ], + layer_256=[ + "dbedf0e2115aa309b92689f86534be4a77b91d7900365e1717879fbb19b849f6", + "2c68574571b3f9229e015a909788116ea2251142e29c1bd5c687863192124e8b", + ], + ) + ) + repo = "freddyaboulton/silero-vad" + series, comp = make_mir_tag(repo) + mir_db.add( + mir_entry( + domain="info", + arch="stst", + series=series, + comp=comp, + repo=repo, + pkg={ + 0: { + "onnx": "onnx", + }, + 1: { + "mlx_audio": "tts.generate.generate_audio", + "generation": {"audio_format": "wav", "join_audio": True, "verbose": False}, + }, + }, + file_256=["591f853590d11ddde2f2a54f9e7ccecb2533a8af7716330e8adfa6f3849787a9"], + layer_b3=[ + "41ca5931452b3ffee588c6c7e5bd327c4e914141604eaf3fd05f4a790ac83bb2", + "7dc736cd5d840182792bde4edfbf5ddc5aeaf16826a9c72d1ba8166c1e3fab9b", + "6e2c1bdbad74f56663ffb5710c7cb849a2b91ba331d81acdba47a21f69107434", # onnx + "ab5ff443aece9171af5e7603d0b4309d3ecc934e3940ccedefff10f0b54b931e", # onnx vad + # "7939427700c3b4d91428a490bde1a6d893f63ee5d79b86f68de9e89c7094d3e7" # onnx # <- clip-g ?? unet? inaccurate test at layer level + ], + layer_256=[ + "2ffef1834d5fe14ad8db58fc78d769d5dc38dda5eddbfc396786f74b326215fd", + # "94ea015f5f7f65b1d8e80f7d52859535e7761d7ed2752e24d57a8d9d9da96672", # onnx lose reliability with layer search apparently + ], + ), + ) + repo = "facebook/wav2vec2-conformer-rope-large-960h-ft" + series, comp = make_mir_tag(repo) + mir_db.add( + mir_entry( + domain="info", + arch="stst", + series=series, + comp=comp, + repo=repo, + pkg={ + 0: { + "transformers": "Wav2Vec2ConformerForCTC", + }, + }, + file_256=["97bb9761fb71ec1225100bc81ccf7d002e0d0ba3d0604c1fd2dbda7d7d491f1d"], + layer_b3=["6c9c5642aa8dce62bcb3eb577bc519619a2d868005c767c5e65371c583a8a8eb"], + layer_256=["1afcfda68307a75caa1a1c4456cf97e20c7914e8aba828006e9fe17e8675a79d"], + ), + ) + repo = "canopylabs/orpheus-3b-0.1-ft" + series, comp = make_mir_tag(repo) + mir_db.add( + mir_entry( + domain="info", + arch="art", + series=series, + comp=comp, + repo=repo, + pkg={ + 0: { + "orpheus_tts": "OrpheusModel", + "generation": {"max_model_len": 2048}, + }, + 1: { + "mlx_audio": "tts.generate.generate_audio", + "generation": {"audio_format": "wav", "join_audio": True, "verbose": False}, + }, + }, + ) + ) + repo = "OuteAI/OuteTTS-0.3-1B" + series, comp = make_mir_tag(repo) + mir_db.add( + mir_entry( + domain="info", + arch="art", + series=series, + comp=comp, + repo=repo, + pkg={ + 0: {"outetts": "InterfaceHF"}, + 1: { + "mlx_audio": "tts.generate.generate_audio", + "generation": {"audio_format": "wav", "join_audio": True, "verbose": False}, + }, + }, + ) + ) + + +def add_mir_lora(mir_db: MIRDatabase): + """Create MIR lora entries""" + mir_db.add( + mir_entry( + domain="info", + arch="lora", + series="dmd", + comp=sdxl_series, + repo="tianweiy/DMD2", + pkg={ + 0: { + "diffusers": {"load_lora_weights": {}}, + "generation": {"num_inference_steps": 4, "guidance_scale": 0, "timesteps": [999, 749, 499, 249]}, + "scheduler": {"ops.scheduler.lcm": ""}, + } + }, + file_256=[ + "b3d9173815a4b595991c3a7a0e0e63ad821080f314a0b2a3cc31ecd7fcf2cbb8", + "a374289e9446d7f14d2037c4b3770756b7b52c292142a691377c3c755010a1bb", + ], + ) + ) + mir_db.add( + mir_entry( + domain="info", + arch="lora", + series="dpo", + comp=sdxl_series, + repo="radames/sdxl-DPO-LoRA", + pkg={ + 0: { + "diffusers": {"load_lora_weights": {}}, + "generation": {"guidance_scale": 7.5, "num_inference_steps": 4}, + "scheduler": {"ops.scheduler.dpm": {"algorithm_type": "sde-dpmsolver++", "use_karras_sigmas": True, "order": 2}}, + }, + }, + file_256=[ + "666f71a833fc41229ec7e8a264fb7b0fcb8bf47a80e366ae7486c18f38ec9fc0", + "6b1dcbfb234d7b6000948b5b95ccebc8f903450ce2ba1b50bc3456987c9087ad", + ], + ) + ) + mir_db.add( + mir_entry( + domain="info", + arch="lora", + series="flash", + comp=sdxl_series, + repo="jasperai/flash-sdxl", + pkg={ + 0: { + "diffusers": {"load_lora_weights": {}}, + "scheduler": "ops.scheduler.lcm", + } + }, + file_256=["afe2ca6e27c4c6087f50ef42772c45d7b0efbc471b76e422492403f9cae724d7"], + ), + ) + mir_db.add( + mir_entry( + domain="info", + arch="lora", + series="flash", + comp="pixart-alpha", + repo="jasperai/flash-pixart", + pkg={ + 0: {"diffusers": {"load_lora_weights": {}}}, + }, + file_256=["99ef037fe3c1fb6d6bbefdbb85ad60df434fcc0577d34c768d752d60cf69681b"], + ) + ) + mir_db.add( + mir_entry( + domain="info", + arch="lora", + series="flash", + comp=sd3_series, + repo="jasperai/flash-sd3", + pkg={ + 0: {"diffusers": {"load_lora_weights": {}}}, + }, + file_256=["85fce13c36e3739aa42930f745eb9fceb6c53d53fb17e2a687e3234c1a58ee15"], + ) + ) + mir_db.add( + mir_entry( + domain="info", + arch="lora", + series="flash", + comp=sd1_series, + repo="jasperai/flash-sd", + pkg={ + 0: {"diffusers": {"load_lora_weights": {}}, "generation": {"num_inference_steps": 4, "guidance_scale": 0}}, + }, + file_256=["99353444c1a0f40719a1b3037049dbd24800317979a73c312025c05af3574a5f"], + ), + ) + mir_db.add( + mir_entry( + domain="info", + arch="lora", + series="hyper", + comp=sdxl_series, + repo="ByteDance/Hyper-SD", + pkg={0: {"diffusers": {"load_lora_weights": {"fuse": 1.0}}}}, + file_256={ + "0b97f447b5878323a28fbe7c51ba7acebd21f4d77552ba77b04b11c8911825b6": {"num_inference_steps": 12}, + "55b51334c85061afff5eff7c550b61963c8b8607a5868bbe4f26db49374719b1": {"num_inference_steps": 8}, + "c912df184c5116792d2c604d26c6bc2aa916685f4a793755255cda1c43a3c78a": {"num_inference_steps": 1, "guidance_scale": 0.0}, + "69b25c0187ced301c3603c599c0bc509ac99b8ac34db89a2aecc3d5f77a35187": {"num_inference_steps": 2, "guidance_scale": 0.0}, + "12f81a27d00a751a40d68fd15597091896c5a90f3bd632fb6c475607cbdad76e": {"num_inference_steps": 4, "guidance_scale": 0.0}, + "ca689190e8c46038550384b5675488526cfe5a40d35f82b27acb75c100f417c1": {"num_inference_steps": 8, "guidance_scale": 0.0}, + }, + ), + ) + mir_db.add( + mir_entry( + domain="info", + arch="lora", + series="hyper", + comp=dev_series, + repo="ByteDance/Hyper-SD", + pkg={0: {"diffusers": {"load_lora_weights": {"fuse": 0.125}}}}, + file_256={ + "6461f67dfc1a967ae60344c3b3f350877149ccab758c273cc37f5e8a87b5842e": {"num_inference_steps": 16, "guidance_scale": 0.0}, + "e0ab0fdf569cd01a382f19bd87681f628879dea7ad51fe5a3799b6c18c7b2d03": {"num_inference_steps": 8, "guidance_scale": 0.0}, + }, + ), + ) + mir_db.add( + mir_entry( + domain="info", + arch="lora", + series="hyper", + comp=sd3_series, + repo="ByteDance/Hyper-SD", + pkg={0: {"diffusers": {"load_lora_weights": {"fuse": 0.125}}}}, + file_256={ + "5b4d0b99d58deb811bdbbe521a06f4dbf56a2e9148ff3211c594e0502b656bc9": {"num_inference_steps": 16}, + "0ee4e529abd17b06d4295e3bb91c0d4ddae393afad86b2b43c4f5eeb9e401602": {"num_inference_steps": 4}, + "fc6a3e73e14ed11e21e4820e960d7befcffe7e333850ada9545f239e9aa6027e": {"num_inference_steps": 8}, + }, + ), + ) + mir_db.add( + mir_entry( + domain="info", + arch="lora", + series="hyper", + comp=sd1_series, + repo="ByteDance/Hyper-SD", + pkg={0: {"diffusers": {"load_lora_weights": {}}}}, + file_256={ + "64b98437383537cd968fda6f87a05c33160ece9c79ff4757949a1e212ff78361": {"num_inference_steps": 12}, + "f6123d5b950d5250ab6c33600e27f4dcf71b3099ebf888685e01e9e8117ce482": {"num_inference_steps": 8}, + "a04fd9a535c1e56d38f7590ee72a13fd5ca0409853b4fff021e5a9482cf1ca3b": {"num_inference_steps": 1, "guidance_scale": 0.0}, + "2f26dcc1d883feb07557a552315baae2ca2a04ac08556b08a355a244547e8c3a": {"num_inference_steps": 2, "guidance_scale": 0.0}, + "c5dd058616461ed5053e2b14eec4dbe3fa0eea3b13688642f6d6c80ea2ba5958": {"num_inference_steps": 4, "guidance_scale": 0.0}, + "91fc3186236e956d64dbb4357f2e120c69b968b78af7d2db9884a5ca74d3cd13": {"num_inference_steps": 8, "guidance_scale": 0.0}, + }, + ) + ) + mir_db.add( + mir_entry( + domain="info", + arch="lora", + series="lcm", + comp=sdxl_series, + repo="latent-consistency/lcm-lora-sdxl", + pkg={ + 0: { + "diffusers": {"load_lora_weights": {"fuse": 1.0}}, + "scheduler": {"ops.scheduler.lcm": {"timestep_spacing": "trailing"}}, + "generation": {"num_inference_steps": 8}, + }, + }, + file_256=["a764e6859b6e04047cd761c08ff0cee96413a8e004c9f07707530cd776b19141"], + ) + ) + mir_db.add( + mir_entry( + domain="info", + arch="lora", + series="lcm", + comp=ssd_series, + repo="latent-consistency/lcm-lora-ssd-1b", + pkg={0: {"diffusers": {"load_lora_weights": {}}, "generation": {"num_inference_steps": 8}}}, + file_256=["7adaaa69db6f011058a19fd1d5315fdf19ef79fcd513cdab30e173833fd5c59b"], + ), + ) + mir_db.add( + mir_entry( + domain="info", + arch="lora", + series="lcm", + comp=vega_series, + repo="segmind/Segmind-VegaRT", + pkg={0: {"diffusers": {"load_lora_weights": {}}, "gen_kwargs": {"num_inference_steps": 8}}}, + file_256=["9b6e8cd833fa205eaeeed391ca623a6f2546e447470bd1c5dcce3fa8d2f26afb"], + ), + ) + mir_db.add( + mir_entry( + domain="info", + arch="lora", + series="lcm", + comp=sd1_series, + repo="latent-consistency/lcm-lora-sdv1-5", + pkg={0: {"diffusers": {"load_lora_weights": {}}, "generation": {"num_inference_steps": 8}}}, + file_256=["8f90d840e075ff588a58e22c6586e2ae9a6f7922996ee6649a7f01072333afe4"], + ), + ) + mir_db.add( + mir_entry( + domain="info", + arch="lora", + series="lightning", + comp=sdxl_series, + repo="ByteDance/SDXL-Lightning", + pkg={0: {"diffusers": {"load_lora_weights": {}}, "generation": {"num_inference_steps": 4, "guidance_scale": 0}}}, + ), + ) + mir_db.add( + mir_entry( + domain="info", + arch="lora", + series="pcm", + comp=sdxl_series, + repo="wangfuyun/PCM_Weights", + pkg={0: {"diffusers": {"load_lora_weights": {}}}}, + file_256={ + "0365f6107250a4fed1b83e8ae6a070065e026a2ba54bff65f55a50284232bbe6": {"num_inference_steps": 4, "guidance_scale": 0.0}, + "04ea827435d5750e63d113dc509174b4f6e8a069ff8f91970c3d25299c10b1f8": {"num_inference_steps": 16}, + "7eb353b2abcaabab6251ba4e17d6cbe2e763feb0674b0f950555552212b44621": {"num_inference_steps": 16}, + "a85cf70ac16ed42011630a5cd6b5927722cb7c40a2107eff85e2670f9a38c893": {"num_inference_steps": 4}, # float16 + "9f7f13bb019925eacd89aeff678e4fd831f7b60245b986855dff6634aee4eba9": {"num_inference_steps": 4}, + "3b9c970a3e4c0e182931e71b3f769c1956f16c6b06db98b4d67236790d4d0b1d": {"num_inference_steps": 8}, + "7f04ba8911b4c25ef2c7cbf74abcb6daa3b4f0e4bc6a03896bdae7601f2f180b": {"num_inference_steps": 8}, + "13fb038025ce9dad93b8ee1b67fc81bac8affb59a77b67d408d286e0b0365a1d": {"num_inference_steps": 16, "guidance_scale": 0.0}, + "3442eff271aa3b60a094fd6f9169d03e49e4051044a974f6fcf690507959191f": {"num_inference_steps": 16, "guidance_scale": 0.0}, + "242cbe4695fe3f2e248faa71cf53f2ccbf248a316973e4b2f38ab9e34f35a5ab": {"num_inference_steps": 2, "guidance_scale": 0.0}, + "e1f600491bb8e0cd94f41144321e44fdb2cb346447f31e71f6e53f1c24cccfbf": {"num_inference_steps": 2, "guidance_scale": 0.0}, + "d0bf40a7f280829195563486bec7253f043a06b1f218602b20901c367641023e": {"num_inference_steps": 4, "guidance_scale": 0.0}, + "212150d7953627fb89df99aad579d6763645a1cb2ef26b19fee8b398d5e5ff4d": {"num_inference_steps": 4, "guidance_scale": 0.0}, + "e80fcf46d15f4d3821d3d9611bdb3022a4a8b647b2536833b168d317a91e4f74": {"num_inference_steps": 8, "guidance_scale": 0.0}, + "56ed9dc9f51f4bb0d6172e13b7947f215c347fc0da341c8951b2c12b9507d09e": {"num_inference_steps": 8, "guidance_scale": 0.0}, + }, + ) + ) + mir_db.add( + mir_entry( + domain="info", + arch="lora", + series="pcm", + comp=sd1_series, + repo="wangfuyun/PCM_Weights", + pkg={0: {"diffusers": {"load_lora_weights": {}}}}, + file_256={ + "b80b27dd6504f1c3a7637237dda86bc7e26fa5766da30c4fc853c0a1d46bad31": {"num_inference_steps": 4, "guidance_scale": 0.0}, + "8f605ffde3616592deb37ed8c6bacb83fe98963c1fd0883c2a4f93787098aa45": {"num_inference_steps": 16}, + "fa6acb94f11dba3bf4120af5a12e3c88cd2b9572d43ec1a6fb04eede9f32829e": {"num_inference_steps": 4}, + "bff3d4499718b61455b0757b5f8d98fe23e73a768b538c82ecf91c693b69dbcd": {"num_inference_steps": 8}, + "c7ac2fa3df3a5b7080ebe63f259ab13630014f104c93c3c706d77b05cc48506b": {"num_inference_steps": 16, "guidance_scale": 0.0}, + "4c5f27a727d12146de4b1d987cee3343bca89b085d12b03c45297af05ce88ef4": {"num_inference_steps": 2, "guidance_scale": 0.0}, + "29278bc86274fdfc840961e3c250758ff5e2dc4666d940f103e78630d5b879d3": {"num_inference_steps": 4, "guidance_scale": 0.0}, + "41a7f0b966d18f643d16c4401f0b5ef6b9ef7362c20e17128322f17874709107": {"num_inference_steps": 8, "guidance_scale": 0.0}, + }, + ) + ) + mir_db.add( + mir_entry( + domain="info", + arch="lora", + series="pcm", + comp=sd3_series, + repo="wangfuyun/PCM_Weights", + pkg={0: {"diffusers": {"load_lora_weights": {}}}}, + file_256={ + "8a45878ecc34e53855fe21146cb6ef32682053b7c4eacc013be89fb08c4c19d8": {"num_inference_steps": 2, "guidance_scale": 1.2}, + "9444a5cead551c56c4d1c455ce829ba9f96f01fbcca31294277e0862a6a15b76": {"num_inference_steps": 4, "guidance_scale": 1.2}, + "e365902c208cbc0456ca5e7c41a490f637c15f3f7b98691cbba21f96a8c960b4": {"num_inference_steps": 4, "guidance_scale": 1.2}, + "3550fa018cd0b60d9e36ac94c31b30f27e402d3855ed63e47668bb181b35a0ad": {"num_inference_steps": 4, "guidance_scale": 1.2}, + }, + ) + ) + mir_db.add( + mir_entry( + domain="info", + arch="lora", + series="slam", + comp=sdxl_series, + repo="alimama-creative/slam-lora-sdxl", + pkg={ + 0: { + "diffusers": {"load_lora_weights": {}}, + "scheduler": {"ops.scheduler.lcm": {"timestep_spacing": "trailing"}}, + "generation": {"num_inference_steps": 4, "guidance_scale": 1}, + } + }, + file_256=["22569a946b0db645aa3b8eb782c674c8e726a7cc0d655887c21fecf6dfe6ad91"], + ) + ) + mir_db.add( + mir_entry( + domain="info", + arch="lora", + series="slam", + comp=sd1_series, + repo="alimama-creative/slam-sd1.5", + pkg={0: {"diffusers": {"load_lora_weights": {}}}}, + ) + ) + mir_db.add( + mir_entry( + domain="info", + arch="lora", + series="spo", + comp=sdxl_series, + repo="SPO-Diffusion-Models/SPO-SDXL_4k-p_10ep_LoRA", + pkg={0: {"diffusers": {"load_lora_weights": {}}, "generation": {"guidance_scale": 5.0}}}, + file_256=["0b9896f30d29daa5eedcfc9e7ad03304df6efc5114508f6ca9c328c0b4f057df"], + ), + ) + mir_db.add( + mir_entry( + domain="info", + arch="lora", + series="spo", + comp=sd1_series, + repo="SPO-Diffusion-Models/SPO-SD-v1-5_4k-p_10ep_LoRA", + pkg={0: {"diffusers": {"load_lora_weights": {}}, "generation": {"guidance_scale": 7.5}}}, + file_256=["1be130c5be2de0beacadd3bf0bafe3bedd7e7a380729932a1e369fb29efa86f4"], + ), + ) + mir_db.add( + mir_entry( + domain="info", + arch="lora", + series="tcd", + comp=sdxl_series, + repo="h1t/TCD-SDXL-LoRA", + pkg={ + 0: { + "diffusers": {"load_lora_weights": {}}, + "generation": {"num_inference_steps": 4, "guidance_scale": 0, "eta": 0.3}, + "scheduler": {"ops.scheduler.tcd": {}}, + } + }, + file_256=["2c777bc60abf41d3eb0fe405d23d73c280a020eea5adf97a82a141592c33feba"], + ), + ) + mir_db.add( + mir_entry( + domain="info", + arch="lora", + series="tcd", + comp=sd1_series, + repo="h1t/TCD-SD15-LoRA", + pkg={0: {"diffusers": {"load_lora_weights": {}}}}, + file_256=["eaecb24a1cda4411eab67275b1d991071216ac93693e8fa0c9226c9df0386232"], + layer_b3=["90158259812a89beb8874216009c799f420334aac49bbf4fa1bf0ebf4bbd256b"], + layer_256=["e9825b81bca684126ac3cc8867d2ebc655f74268bc26bea4e4b7e58a52ad6c75"], + ) + ) + mir_db.add( + mir_entry( + domain="info", + arch="lora", + series="turbo", + comp=sdxl_series, + file_256=["a599c42a9f4f7494c7f410dbc0fd432cf0242720509e9d52fa41aac7a88d1b69"], + ) + ) + mir_db.add( + mir_entry( + domain="info", + arch="lora", + series="turbo", + comp=dev_series, + repo="alimama-creative/FLUX.1-Turbo-Alpha", + pkg={ + 0: { + "diffusers": {"load_lora_weights": {"fuse": 0.125}}, + "generation": {"guidance_scale": 3.5, "num_inference_steps": 8, "max_sequence_length": 512}, + } + }, + file_256=["77f7523a5e9c3da6cfc730c6b07461129fa52997ea06168e9ed5312228aa0bff"], + ) + ) + mir_db.add( + mir_entry( + domain="info", + arch="lora", + series="turbo", + comp=sd3_series, + repo="tensorart/stable-diffusion-3.5-medium-turbo", + pkg={0: {"diffusers": {"load_lora_weights": {"fuse": 1.0}}, "scheduler": {"ops.scheduler.flow-match": {"shift": 5}}}}, + file_256={"bdcbdfa3ec8ed838b77b1020eea3bc7917a2d42573688a034feb921fde8b1858": {"num_inference_steps": "4"}}, + ) + ) + mir_db.add( + mir_entry( + domain="info", + arch="lora", + series="turbo", + comp=sd3_series, + repo="tensorart/stable-diffusion-3.5-large-TurboX", + pkg={0: {"diffusers": {"load_lora_weights": {"fuse": 1.0}}, "scheduler": {"ops.scheduler.flow-match": {"shift": 5}}}}, + file_256={"fae59d1b749c0d14a8fd4c68cc94eaac92876cee7b91fa75cf8fde3160e09548": {"num_inference_steps": "8"}}, + ) + ) + + +def add_mir_vae(mir_db: MIRDatabase): + """Create MIR VAE missing from the database""" + mir_db.add( + mir_entry( + domain="info", + arch="vae", + series="tae", + comp=sd3_series, + repo="madebyollin/taesd3", + pkg={0: {"diffusers": "AutoencoderTiny"}}, + file_256=["6f79c1397cb9ce1dac363722dbe70147aee0ccca75e28338f8482fe515891399"], + ) + ) + mir_db.add( + mir_entry( + domain="info", + arch="vae", + series="tae", + comp=sdxl_series, + repo="madebyollin/taesdxl", + pkg={0: {"diffusers": "AutoencoderTiny"}}, + file_256=["ff4824aca94dd6111e0340fa749347fb74101060d9712cb5ef1ca8f1cf17502f"], + ) + ) + mir_db.add( + mir_entry( + domain="info", + arch="vae", + series="tae", + comp=sd1_series, + repo="madebyollin/taesd", + pkg={0: {"diffusers": "AutoencoderTiny"}}, + file_256=["db169d69145ec4ff064e49d99c95fa05d3eb04ee453de35824a6d0f325513549"], + ) + ) + mir_db.add( + mir_entry( + domain="info", + arch="vae", + series="tae", + comp=dev_series, + repo="madebyollin/taef1", + pkg={0: {"diffusers": "AutoencoderTiny"}}, + file_256=["927f7de7f11bbd3b2d5ce402e608d97a7649e0921a9601995b044e8efc81e449"], + ) + ) + series, comp = make_mir_tag("Qwen/Qwen-Image") + mir_db.add( + mir_entry( + domain="info", + arch="vae", + series="kl", + comp=series, + # no repo here, may conflict + pkg={ + 0: {"diffusers": "AutoencoderKLQwenImage"}, + }, + file_256=[ + "0c8bc8b758c649abef9ea407b95408389a3b2f610d0d10fcb054fe171d0a8344", # diffusers + ], + layer_b3=[ + "64af8fb08d2054c81ad2aef94965be8fb1366fcc6136cb9222ae046550af014b", # diffusers + ], + layer_256=[ + "42f255440ef1d379a8a731456bc44312a73a8568716caa6100803990cd5ea7dc", # diffusers + ], + ) + ) + series, comp = make_mir_tag("Wan-AI/Wan2.1-I2V-14B-480P-Diffusers") + sr_series_text2v, _ = make_mir_tag("Skywork/SkyReels-V2-T2V-14B-720P-Diffusers") + sr_series_image2v, _ = make_mir_tag("Skywork/SkyReels-V2-I2V-14B-720P-Diffusers") + mir_db.add( + mir_entry( + domain="info", + arch="vae", + series="wan", + comp=series, + # no repo here, may conflict + pkg={ + 0: { + "diffusers": "AutoencoderKLWan", + "precision": "ops.precision.float.F32", + } + }, + file_256=[ + "d6e524b3fffede1787a74e81b30976dce5400c4439ba64222168e607ed19e793", # diffusers + "2fc39d31359a4b0a64f55876d8ff7fa8d780956ae2cb13463b0223e15148976b", # sai + ], + layer_b3=[ + "f867543d636029ebfc05b8075e572be0b313a83b0470e56bcf4bbad07a6db010", # diffusers + "6b5b229727a2d4e37993687c62c94ff8519a371ab4103c699ff1f5969ca0b433", # sai + ], + layer_256=[ + "121b3974b39263dcca9d644d1b5c9b9251a911b6a8a8e307fcb21ca778e78ed2", + "364be43a8959012d798d3f98e17d8b5c4b99ba1e70077008dd19acca3ced395e", + ], + ) + ) + mir_db.add( + mir_entry( + domain="info", + arch="vae", + series="wan", + comp=sr_series_text2v, + # no repo here, may conflict + file_256=[], + layer_b3=[], + layer_256=[], + ) + ) + mir_db.add( + mir_entry( + domain="info", + arch="vae", + series="wan", + comp=sr_series_image2v, + # no repo here, may conflict + file_256=[], + layer_b3=[], + layer_256=[], + ) + ) + series, comp = make_mir_tag("Lightricks/LTX-Video") + mir_db.add( + mir_entry( + domain="info", + arch="vae", + series="kl", + comp=series, + # no repo here, may conflict + pkg={ + 0: {"diffusers": "AutoencoderKLLTXVideo"}, + }, + file_256=[], + layer_b3=[], + layer_256=[], + ) + ) + series, comp = make_mir_tag("rhymes-ai/Allegro") + mir_db.add( + mir_entry( + domain="info", + arch="vae", + series="kl", + comp=series, + # no repo here, may conflict + pkg={ + 0: {"diffusers": "AutoencoderKLAllegro"}, + }, + file_256=[], + layer_b3=[], + layer_256=[], + ) + ) + series, comp = make_mir_tag("zai-org/CogVideoX-5b-I2V") + series_fun, _ = make_mir_tag("alibaba-pai/CogVideoX-Fun-V1.1-5b-Pose") + series_wish, _ = make_mir_tag("BestWishYsh/ConsisID-preview") + mir_db.add( + mir_entry( + domain="info", + arch="vae", + series="cogvideox", + comp=series, + # no repo here, may conflict + pkg={ + 0: {"diffusers": "AutoencoderKLCogVideoX"}, + }, + file_256=["a410e48d988c8224cef392b68db0654485cfd41f345f4a3a81d3e6b765bb995e"], + layer_b3=["246addb8dc798240638bffee4546a3c5c83572139b4a2a602d68b4c4146226eb"], + layer_256=["43c7e9cb4364e55fd563817f01484ede8a09ff19a8e69eb61a32a12f93d6f66e"], + ) + ) + mir_db.add( + mir_entry( + domain="info", + arch="vae", + series="cogvideox", + comp=series_fun, + # no repo here, may conflict + file_256=[], + layer_b3=[], + layer_256=[], + ) + ) + mir_db.add( + mir_entry( + domain="info", + arch="vae", + series="cogvideox", + comp=series_wish, + # no repo here, may conflict + file_256=[], + layer_b3=[], + layer_256=[], + ) + ) + series, comp = make_mir_tag("nvidia/Cosmos-1.0-Diffusion-7B-Video2World") + mir_db.add( + mir_entry( + domain="info", + arch="vae", + series="kl", + comp=series, + # no repo here, may conflict + pkg={ + 0: {"diffusers": "AutoencoderKLCosmos"}, + }, + file_256=[], + layer_b3=[], + layer_256=[], + ) + ) + series, comp = make_mir_tag("alibaba-pai/EasyAnimateV5.1-7b-zh-diffusers") + mir_db.add( + mir_entry( + domain="info", + arch="vae", + series="kl", + comp=series, + # no repo here, may conflict + pkg={ + 0: {"diffusers": "AutoencoderKLMagvit"}, + }, + file_256=[], + layer_b3=[], + layer_256=[], + ) + ) + series, comp = make_mir_tag("hunyuanvideo-community/HunyuanVideo-I2V") + mir_db.add( + mir_entry( + domain="info", + arch="vae", + series="kl", + comp=series, + # no repo here, may conflict + pkg={ + 0: {"diffusers": "AutoencoderKLHunyuanVideo"}, + }, + file_256=[ + "95d1fc707c1421ccd88ea542838ab4c5d45a5babb48205bac9ce0985525f9818", # pt, + "7c68a6295f9034a88225fbafb1f3258291a08d57a1fdb938233fa57b1b8f4883", + "fbe5ea338431bc8ba20f7019b474e83379fe5763abfd562adcc04b1c0d35c728", + "019973c147e0c3462629d8d06bdbdbb83408f3ebd4ea4b4ae21a99c3cdcb54c0", + ], + # layer_b3=[], + # layer_256=[], + ) + ) + series, comp = make_mir_tag("genmo/mochi-1-preview") + mir_db.add( + mir_entry( + domain="info", + arch="vae", + series="kl", + comp=series, + # no repo here, may conflict + pkg={ + 0: {"diffusers": "AutoencoderKLMochi"}, + }, + file_256=[], + layer_b3=[], + layer_256=[], + ) + ) + series, comp = make_mir_tag("rhymes-ai/Allegro") + mir_db.add( + mir_entry( + domain="info", + arch="vae", + series="kl", + comp=series, + # no repo here, may conflict + pkg={ + 0: { + "diffusers": "AutoencoderKLAllegro", + }, + }, + file_256=["47871a698b18f92f15019d361a81cbc8af4676f8eef9a47fd2b95354a39f831a"], + layer_b3=["93654cbab7541504d2377c66e72943c7fd9947fca2eb1be01bcc8877c322c1e0"], + layer_256=["bfd496586118165a13243997101fc7cdd4f855b2d8a73ee2b771a4484c4c2f9f"], + ) + ) + series, comp = make_mir_tag("cvssp/audioldm-s-full-v2") + mir_db.add( + mir_entry( + domain="info", + arch="vae", + series="kl", + comp=series, + # no repo here, may conflict + pkg={ + 0: { + "diffusers": "AutoencoderKL", + }, + }, + file_256=["42f64f7565b23eabde68c9694e39f18b8bba5f7a14f477e7ed4b51e0ea7de8a5"], + layer_b3=["00959677dae940b9cfdbe5380c8cbb5a6b4951864cd26f8211d74a3d22b4f3de"], + layer_256=["54d075953d5253a3abac651de070736c1d5510b857a8ab24c624304f428146b6"], + ) + ) + + series, comp = make_mir_tag("Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers") + mir_db.add( + mir_entry( + domain="info", + arch="vae", + series="dc", + comp=series, + # no repo here, may conflict + pkg={ + 0: {"diffusers": "AutoencoderDC"}, + }, + file_256=["15a4b09e56d95b768a0ec9da50b702e21d920333fc9b3480d66bb5c7fad9d87f"], + layer_b3=["cf4ecc6697d18b0663e4eac58203f1dd6d9fb689cf99adfeadbc0019de0c73d0"], + layer_256=["abfc39d1a6d71f03dde7bc40fec4a90478a97d17ae1688be9aad00e0512b9bde"], + ) + ) + series, comp = make_mir_tag("stabilityai/stable-audio-open-1.0") + mir_db.add( + mir_entry( + domain="info", + arch="vae", + series="oobleck", + comp=series, + # no repo here, may conflict + pkg={ + 0: {"diffusers": "AutoencoderOobleck"}, + }, + # file_256=[], + # layer_b3=[], + # layer_256=[], + ) + ) + series, comp = make_mir_tag("stable-video-diffusion-img2vid-xt") + mir_db.add( + mir_entry( + domain="info", + arch="vae", + series="kl", + comp=series, + # no repo here, may conflict + pkg={ + 0: {"diffusers": "AutoencoderKLTemporalDecoder"}, + }, + # file_256=[], + # layer_b3=[], + # layer_256=[], + ) + ) + mir_db.add( + mir_entry( + domain="info", + arch="vae", + series="kl", + comp=sdxl_series, + repo="madebyollin/sdxl-vae-fp16-fix", + pkg={ + 0: {"diffusers": "AutoencoderKL"}, + }, + file_256=[ + "235745af8d86bf4a4c1b5b4f529868b37019a10f7c0b2e79ad0abca3a22bc6e1", # modelspec sai + "1b909373b28f2137098b0fd9dbc6f97f8410854f31f84ddc9fa04b077b0ace2c", # diffusers + "78f6189c8492013e3cac81637a1f657f790a237387f8a9dfd6bfa5fee28eb646", # ssd1b diffusers + "6353737672c94b96174cb590f711eac6edf2fcce5b6e91aa9d73c5adc589ee48", # ssd1b diffusers fp16 + "bcb60880a46b63dea58e9bc591abe15f8350bde47b405f9c38f4be70c6161e68", # kolors fp16 + "1598f3d24932bcfe6634e8b618ea1e30ab1d57f5aad13a6d2de446d2199f2341", # vega / lumina next sft d / auraflow + "703abdcd7c389316b5128faa9b750a530ea1680b453170b27afebac5e4db30c4", # pixart a + "98a14dc6fe8d71c83576f135a87c61a16561c9c080abba418d2cc976ee034f88", # hyd 1.1 + ], + layer_b3=[ + "bd5b356b509814025a9cf692710b87116d4fcd0e30a8232ed1db133e908d0e74", # modelspec sai + "9106380403dee83238af63ff1738396d2fdff9f6d78d0d9c1d0bf770ae4294d0", # diffusers + # "245070a60a25ca080cb4951220c3fb1503da43829930d5f6f7a6770b491eafe1", + # "50e65a628b5fe379798d8956e4a4e1d4b105c84b329f088d577f7f28c22abc49", # diffusers fp16 matches sd1 + ], + layer_256=[ + "c9399a4cd39a180a0bb2af96a8297b9330541e090c21e83317cebb2f7cc651da", # modelspec sai + "2240ae134a3b983abf45200c198f07e3d8068012fbbd2f658bbaa1fd6a0629c0", # diffusers + # "35641f65ad7ea600cb931dcab556f7503279f1d8d99eda170fe7976d48502a2a", # diffusers fp16 matches sd1 (incorrect) + ], + ) + ) + mir_db.add( + mir_entry( + domain="info", + arch="vae", + series="kl", + comp=sdxl_series + sdxl_comp, + pkg={ + 0: {"diffusers": "AutoencoderKL"}, + }, + file_256=[ + "235745af8d86bf4a4c1b5b4f529868b37019a10f7c0b2e79ad0abca3a22bc6e1", # modelspec sai + "27ed3b02e09638568e99d4398c67bc654dde04e6c0db61fb2d21dba630e7058a", # diffusers + "eb6516ab7e1104d5d1a174a4d65c57835ae38061531d0a2192103aecfb790cc1", # diffusers fp16 + "e6bb9ea85bbf7bf6478a7c6d18b71246f22e95d41bcdd80ed40aa212c33cfeff", # modelspec sai vae 0.9 + ], + layer_b3=[ + "bd5b356b509814025a9cf692710b87116d4fcd0e30a8232ed1db133e908d0e74", # modelspec sai + # "9106380403dee83238af63ff1738396d2fdff9f6d78d0d9c1d0bf770ae4294d0", # diffusers + # "245070a60a25ca080cb4951220c3fb1503da43829930d5f6f7a6770b491eafe1", + # "50e65a628b5fe379798d8956e4a4e1d4b105c84b329f088d577f7f28c22abc49", # diffusers fp16 matches sd1 + ], + layer_256=[ + "c9399a4cd39a180a0bb2af96a8297b9330541e090c21e83317cebb2f7cc651da", # modelspec sai + "2240ae134a3b983abf45200c198f07e3d8068012fbbd2f658bbaa1fd6a0629c0", # diffusers + # "35641f65ad7ea600cb931dcab556f7503279f1d8d99eda170fe7976d48502a2a", # diffusers fp16 matches sd1 (incorrect) + ], + ) + ) + + repo = "shuttleai/shuttle-jaguar" + mir_db.add( + mir_entry( + domain="info", + arch="vae", + series="kl", + comp=make_mir_tag(repo)[0], + # no repo here, may conflict + pkg={ + 0: {"diffusers": "AutoencoderKL"}, + }, + file_256=[ + "6fdfa2add4f04d94f36157cbb0197f97966b612e3f8eff4095315aefea74b904", + ], # q8, + layer_b3=[ + "0ebf9b7010accc44e219e355dd24bf1e3128004093c0c1dfc06f88c0a39fdbdd", + "d0e7ef3c4af06fa08b4c0485a073e2df55f7b1e9e3ba8f7b261688bc562568f0", # q8 + ], + layer_256=[ + "9b28f36873ea283905094a64e1ccb7cfc2b0f0aa166201d0ca63807ac37caa7b", # q8 + ], + ) + ) + mir_db.add( + mir_entry( + domain="info", + arch="vae", + series="kl", + comp=dev_series, + # no repo here, may conflict + pkg={ + 0: {"diffusers": "AutoencoderKL"}, + }, + file_256=[ + "afc8e28272cd15db3919bacdb6918ce9c1ed22e96cb12c4d5ed0fba823529e38", # dev + "f5b59a26851551b67ae1fe58d32e76486e1e812def4696a4bea97f16604d40a3", # dev diffusers + "8c717328c8ad41faab2ccfd52ae17332505c6833cf176aad56e7b58f2c4d4c94", # lumina2 + "8f53304a79335b55e13ec50f63e5157fee4deb2f30d5fae0654e2b2653c109dc", # sd3 turbo + ], + layer_b3=[ + "b6db93ed78c4a10d69e80831c1b8fbc1447f04e9b3d494889ee2056b98d41f17", # diffusers + "a8a3ebdec4d7b38d65b7169d3604c19b587330e5e66f69ebf0ded56a24ec6903", # lumina2 + # "245070a60a25ca080cb4951220c3fb1503da43829930d5f6f7a6770b491eafe1", + ], + layer_256=[ + "7950e4f3897c75affaa5f9f3c51c88b4d9a27bfd9b05ad41c3f71d8c1c620b89", + "79d2bfe93a2ac037cdc59ccb5576e32d00d75d4741fba49fc7e82b9724928216", # diffusers + "8f084dc91fd5b481875bc9c86a4ef05e5f176896b7d31c6a5c2ce45c2e174004", # dev diffusers + "322e01bd511e20bc2a3c27cd611f81ed85f0046b7c023b5622c2c9a5b8b34f80", # lumina2 + ], + ) + ) + mir_db.add( + mir_entry( + domain="info", + arch="vae", + series="eq", + comp=sdxl_series, + repo="KBlueLeaf/EQ-SDXL-VAE", + pkg={ + 0: {"diffusers": "AutoencoderKL"}, + }, + ) + ) + mir_db.add( + mir_entry( + domain="info", + arch="vae", + series="ms-lc-eq", + comp=sdxl_series, + repo="Anzhc/MS-LC-EQ-D-VR_VAE", + pkg={ + 0: { + "diffusers": "AutoencoderKL", + }, + }, + ) + ) + repo = "ucsd-reach/musicldm" + mir_db.add( + mir_entry( + domain="info", + arch="vae", + series="kl", + comp=make_mir_tag(repo)[0], + # no repo here, may conflict + file_256=[ + "16e0c6c7c34e459c19500cc15cf538e6331db14969ea15917caa9b0966e44fd4", + ], # q8, + layer_b3=[ + "c5c32b3fb3e73799838836ccce27d883254254daecd10f86ba8ddc55214014e0", + ], + layer_256=[ + "1610c0ce39d1379091eb9ab2a4d14a8567e0f1a5dc6cca40fc0fa6f8e4e97c0f", + ], + ) + ) + + mir_db.add( + mir_entry( + domain="info", + arch="vae", + series="kl", + comp=sd1_series, + pkg={ + 0: {"diffusers": "AutoencoderKL"}, + }, + file_256=[ + "0b204ad0cae549e0a7e298d803d57e36363760dec71c63109c1da3e1147ec520", # ckpt ema original ema pruned + "95f26a5ab04779d5467d1fcecaf93160ffa523afe399b835b3e1bb77ff2d937a", # safetensors ema original ema pruned + "32db726da04f06c1b6b14c0043ce115cc87a501482945c5add89a40d838fcb46", # safetensors ema diffusers + "c6a580b13a5bc05a5e16e4dbb80608ff2ec251a162311590c1f34c013d7f3dab", # ckpt mse original ema pruned + "735e4c3a447a3255760d7f86845f09f937809baa529c17370d83e4c3758f3c75", # safetensors mse original ema pruned + "a1d993488569e928462932c8c38a0760b874d166399b14414135bd9c42df5815", # safetensors mse diffusers + "a2b5134f4dbc140d9c11f11cba3233099e00af40f262f136c691fb7d38d2194c", # safetensors diffusers + "4fbcf0ebe55a0984f5a5e00d8c4521d52359af7229bb4d81890039d2aa16dd7c", # safetensors fp16 diffusers + ], + layer_b3=[ + "82e2dc440a23d78bb91df8c9fce069a8512da51f8f54ea29e3431f545808171e", # safetensors original + "2230487833925a104bee96e7ecfebaa4c3c43cc426c7a5b863f2584313dd4833", # safetensors diffusers + ], + layer_256=[ + "e43f3a227b5ecb43a6272fa92ed6011d2e9abcadadd1032dfa7ea7f875f9d5bd", # safetensors original + "2494154245becf98891be884f943276aa3f54e9b3f0ea1042903fc15fba488f3", # safetensors diffusers + ], + ) + ) diff --git a/mir/config/__init__.py b/mir/config/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/mir/config/constants.py b/mir/config/constants.py new file mode 100644 index 0000000..5e74dfa --- /dev/null +++ b/mir/config/constants.py @@ -0,0 +1,129 @@ +# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0 +# +from typing import List, Optional, Union +from mir.config.json_io import read_json_file +import os +from transformers.models.auto.modeling_auto import ( + MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES, + MODEL_FOR_BACKBONE_MAPPING_NAMES, + MODEL_FOR_CAUSAL_LM_MAPPING_NAMES, + MODEL_FOR_CTC_MAPPING_NAMES, + MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES, + MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES, + MODEL_FOR_IMAGE_MAPPING_NAMES, + MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES, + MODEL_FOR_MASKED_LM_MAPPING_NAMES, + MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES, + MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES, + MODEL_FOR_PRETRAINING_MAPPING_NAMES, + MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES, + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES, + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES, + MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES, + MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES, + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES, + MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES, + MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES, + MODEL_MAPPING_NAMES, +) +from transformers.modeling_utils import PretrainedConfig + + +def generate_supported_model_class_names( + model_name: type[PretrainedConfig], + supported_tasks: Optional[Union[str, list[str]]] = None, +) -> list[str]: + task_mapping = { + "default": MODEL_MAPPING_NAMES, + "pretraining": MODEL_FOR_PRETRAINING_MAPPING_NAMES, + "next-sentence-prediction": MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES, + "masked-lm": MODEL_FOR_MASKED_LM_MAPPING_NAMES, + "causal-lm": MODEL_FOR_CAUSAL_LM_MAPPING_NAMES, + "seq2seq-lm": MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES, + "speech-seq2seq": MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES, + "multiple-choice": MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES, + "document-question-answering": MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES, + "question-answering": MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES, + "sequence-classification": MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES, + "token-classification": MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES, + "masked-image-modeling": MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES, + "image-classification": MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES, + "zero-shot-image-classification": MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES, + "ctc": MODEL_FOR_CTC_MAPPING_NAMES, + "audio-classification": MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES, + "semantic-segmentation": MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES, + "backbone": MODEL_FOR_BACKBONE_MAPPING_NAMES, + "image-feature-extraction": MODEL_FOR_IMAGE_MAPPING_NAMES, + "video-classification": MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES, + } + + if supported_tasks is None: + supported_tasks = task_mapping.keys() + if isinstance(supported_tasks, str): + supported_tasks = [supported_tasks] + + model_class_names = [] + for task in supported_tasks: + class_name = task_mapping[task].get(model_name, None) + if class_name: + model_class_names.append(class_name) + + return model_class_names + + +class DocParseData: + pipe_class: str + pipe_repo: str + staged_class: Optional[str] = None + staged_repo: Optional[str] = None + + def __init__(self, pipe_class, pipe_repo, staged_class=None, staged_repo=None): + self.pipe_class: str = pipe_class + self.pipe_repo: str = pipe_repo + self.staged_class: str = staged_class + self.staged_repo: str = staged_repo + + +class DocStringParserConstants: + """Constants used by DocStringParser for parsing docstrings.""" + + pipe_prefixes: List[str] = [ + ">>> motion_adapter = ", + ">>> adapter = ", # if this moves, also change motion_adapter check + ">>> controlnet = ", + ">>> pipe_prior = ", + ">>> pipe = ", + ">>> pipeline = ", + ">>> blip_diffusion_pipe = ", + ">>> prior_pipe = ", + ">>> gen_pipe = ", + ] + repo_variables: List[str] = [ + "controlnet_model", + "controlnet_id", + "base_model", + "model_id_or_path", + "model_ckpt", + "model_id", + "repo_base", + "repo", + "motion_adapter_id", + ] + call_types: List[str] = [".from_pretrained(", ".from_single_file("] + staged_call_types: List[str] = [ + ".from_pretrain(", + ] + + +package_map = { + "diffusers": ("_import_structure", "diffusers.pipelines"), + "transformers": ("MODEL_MAPPING_NAMES", "transformers.models.auto.modeling_auto"), +} +versions = read_json_file(os.path.join(os.path.dirname(__file__), "versions.json")) +template = read_json_file(os.path.join(os.path.dirname(__file__), "template.json")) + +MIR_PATH_NAMED = os.path.join(os.path.dirname(os.path.dirname(__file__)), "mir.json") + +BREAKING_SUFFIX = r".*(?:-)(prior)$|.*(?:-)(diffusers)$|.*[_-](\d{3,4}px|-T2V$|-I2V$)" +PARAMETERS_SUFFIX = r"(\d{1,4}[KkMmBb]|[._-]\d+[\._-]\d+[Bb][._-]).*?$" +SEARCH_SUFFIX = r"\d+[._-]?\d+[BbMmKk](it)?|[._-]\d+[BbMmKk](it)?" diff --git a/mir/config/conversion.py b/mir/config/conversion.py new file mode 100644 index 0000000..f3a32d7 --- /dev/null +++ b/mir/config/conversion.py @@ -0,0 +1,151 @@ +# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0 +# + +from typing import Callable, Optional, Union, Type, List, Iterator, Tuple, Dict +from mir import dbuq, nfo + + +def import_submodules(module_name: str, pkg_name_or_abs_path: str) -> Optional[Callable]: + """Convert two strings into a callable function or property\n + :param module: The name of the module to import + :param library_path: Base package for the module + :return: The callable attribute or property + """ + from importlib import import_module + + module = module_name.strip() + library = pkg_name_or_abs_path.strip() + base_library = import_module(library, module) + try: + module = getattr(base_library, module) + return module + except AttributeError: # as error_log: + # dbuq(error_log) + return base_library + + +def code_name_to_class_name( + code_name: Optional[Union[str, Type]] = None, + pkg_name: Optional[str] = "transformers", +) -> Union[List[str], str]: + """Fetch class names from code names from Diffusers or Transformers\n + :param class_name: To return only one class, defaults to None + :param pkg_name: optional field for library, defaults to "transformers" + :return: A list of all code names, or the one corresponding to the provided class""" + from mir.config.constants import package_map + + pkg_name = pkg_name.lower() + MAPPING_NAMES = import_submodules(*package_map[pkg_name]) + if code_name: + return MAPPING_NAMES.get(code_name) + return list(MAPPING_NAMES.keys()) + + +def pkg_path_to_docstring(pkg_name: str, folder_path: bool) -> Iterator[Tuple[str, str, str]]: + """Processes package folder paths to yield example doc strings if available.\n + :param pkg_name: The name of the package under diffusers.pipelines. + :param file_specific: A flag indicating whether processing is specific to certain files. + :yield: A tuple containing (pkg_name, file_name, EXAMPLE_DOC_STRING) if found. + """ + import os + from importlib import import_module + + file_names = list(getattr(folder_path, "_import_structure").keys()) + module_path = os.path.dirname(import_module("diffusers.pipelines").__file__) + for file_name in file_names: + if file_name == "pipeline_stable_diffusion_xl_inpaint": + continue + try: + pkg_path = f"diffusers.pipelines.{str(pkg_name)}.{file_name}" + dbuq(pkg_path) + path_exists = os.path.exists(os.path.join(module_path, pkg_name, file_name + ".py")) + if path_exists: + pipe_file = import_submodules(file_name, pkg_path) + except ModuleNotFoundError: + if pkg_name != "skyreels_v2": + nfo(f"Module Not Found for {pkg_name}") + pipe_file = None + + try: + if pipe_file and hasattr(pipe_file, "EXAMPLE_DOC_STRING"): + yield (pkg_name, file_name, pipe_file.EXAMPLE_DOC_STRING) + else: + if path_exists: + pipe_file = import_module(pkg_path) + except (ModuleNotFoundError, AttributeError): + if pkg_name != "skyreels_v2": + nfo(f"Doc String Not Found for {pipe_file} {pkg_name}") + + +def file_name_to_docstring(pkg_name: str, file_specific: bool) -> Iterator[Tuple[str, str, str]]: + """Processes package using file name to yield example doc strings if available.\n + :param pkg_name: The name of the package under diffusers.pipelines. + :param file_specific: A flag indicating whether processing is specific to certain files. + :yield: A tuple containing (pkg_name, file_name, EXAMPLE_DOC_STRING) if found. + """ + from importlib import import_module + + file_name = f"pipeline_{file_specific}" + try: + pkg_path = f"diffusers.pipelines.{str(pkg_name)}" + pipe_file = import_submodules(file_name, pkg_path) + except ModuleNotFoundError: + if pkg_name != "skyreels_v2": + nfo(f"Module Not Found for {pkg_name}") + pipe_file = None + try: + if pipe_file and hasattr(pipe_file, "EXAMPLE_DOC_STRING"): + yield (pkg_name, file_name, pipe_file.EXAMPLE_DOC_STRING) + else: + pipe_file = import_module(pkg_path) + + except AttributeError: + if pkg_name != "skyreels_v2": + nfo(f"Doc String Not Found for {pipe_file} {pkg_name}") + + +def class_to_mir_tag(mir_db: Dict[str, str], code_name: str) -> Optional[str]: + """Converts a class identifier to its corresponding MIR tag.\n + :param mir_db: A dictionary mapping series-compatibility pairs to their respective data. + :param code_name: The Transformers class identifier to convert. + :return: An optional list containing the series and compatibility if found, otherwise None.""" + from transformers.models.auto.modeling_auto import MODEL_MAPPING_NAMES + from mir.config.constants import template + + template_data = template["arch"]["transformer"] + + for series, compatibility_data in mir_db.database.items(): + if any([template for template in template_data if template in series.split(".")[1]]): + for compatibility, field_data in compatibility_data.items(): + if code_name == series.split(".")[2]: + return [series, compatibility] + + class_name = MODEL_MAPPING_NAMES.get(code_name, False) + if not class_name: # second pass without separators + recoded_mapping = {code.replace("-", "").replace("_", ""): model for code, model in MODEL_MAPPING_NAMES.items()} + class_name = recoded_mapping.get(code_name, False) + if not class_name: + return None + pkg_data = field_data.get("pkg") + if pkg_data: + for _, pkg_type_data in pkg_data.items(): + maybe_class = pkg_type_data.get("transformers") + if maybe_class == class_name: + return [series, compatibility] + return None + + +def slice_number(text: str) -> Union[int, float, str]: + """Separate a numeral value appended to a string\n + :return: Converted value as int or float, or unmodified string + """ + for index, char in enumerate(text): # Traverse forwards + if char.isdigit(): + numbers = text[index:] + if "." in numbers: + return float(numbers) + try: + return int(numbers) + except ValueError: + return numbers + return text diff --git a/mir/config/json_io.py b/mir/config/json_io.py new file mode 100644 index 0000000..099834a --- /dev/null +++ b/mir/config/json_io.py @@ -0,0 +1,44 @@ +# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0 +# + +# pylint: disable=import-outside-toplevel + +from typing import Any + + +def write_json_file(folder_path_named: str, file_name: str, data: Any, mode: str = "tw") -> None: + """Save a file to disk as UTF8 JSON\n + :param folder_path_named: The location to save + :param file_name: A file name to save with + :param data: The content to save + :param mode: Type of open to use (default truncate-write) + :returns: Dictionary of json data + """ + import json + import os + + from mir import dbug + + if ".json" not in file_name: + file_name += ".json" + document = os.path.join(folder_path_named, os.path.basename(file_name)) + if os.path.exists(document): + try: + os.remove(document) + except FileNotFoundError as error_log: + dbug(f"'File was detected but not found to remove: {document}.'{error_log}", exc_info=True) + + with open(document, mode, encoding="UTF-8") as i: + json.dump(data, i, ensure_ascii=False, indent=4, sort_keys=False) + + +def read_json_file(file_path_absolute: str, mode="tr") -> dict: + """Open json file as UTF8 JSON + :param file_path_absolute: Location of the file + :param mode: Type of read to use + :returns: Dictionary of json data + """ + import json + + with open(file_path_absolute, mode, encoding="UTF-8") as f: + return json.load(f) diff --git a/mir/doc_parser.py b/mir/doc_parser.py new file mode 100644 index 0000000..e9c504e --- /dev/null +++ b/mir/doc_parser.py @@ -0,0 +1,162 @@ +# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0 +# + +from typing import List, Optional, Tuple + +from pydantic import BaseModel, field_validator +from mir import dbuq, nfo +from mir.config.constants import DocParseData, DocStringParserConstants + + +def parse_docs(doc_string: str) -> Tuple[Optional[str], Optional[str], Optional[str], Optional[str]]: + parser = DocStringParser(doc_string=doc_string) + result = parser.parse() + if result is not None: + return result + + +class DocStringValidator: + """Handles validation of docstring data and extracted values.""" + + @staticmethod + def normalize_doc_string(doc_string: str) -> str: + """Normalize docstring by joining lines with spaces.\n + :param doc_string: Raw docstring text + :returns: Normalized docstring with newlines replaced by spaces + """ + return " ".join(doc_string.splitlines()) + + @staticmethod + def is_valid_repo_path(repo_path: Optional[str]) -> bool: + """Validate that a repository path is valid.\n + :param repo_path: Repository path to validate + :returns: True if path is valid (not empty and contains '/'), False otherwise + """ + return repo_path is not None and repo_path.strip() != "" and "/" in repo_path + + @staticmethod + def validate_repo_path(repo_path: Optional[str], segment: str) -> Optional[str]: + """Validate and return repo path, or None if invalid.\n + :param repo_path: Repository path to validate + :param segment: Segment text for error reporting + :returns: Validated repo path or None if invalid + """ + if not DocStringValidator.is_valid_repo_path(repo_path): + nfo(f"Warning: Unable to resolve repo path for {segment}") + return None + return repo_path + + @staticmethod + def validate_pipe_class(pipe_class: Optional[str]) -> bool: + """Validate that a pipe class name is present.\n + :param pipe_class: Pipe class name to validate + :returns: True if class name is valid, False otherwise + """ + return pipe_class is not None and pipe_class.strip() != "" + + +class DocStringParser(BaseModel): + doc_string: str + + @field_validator("doc_string") + def normalize_doc(cls, docs: str) -> str: + return DocStringValidator.normalize_doc_string(docs) + + def doc_match(self, prefix_set: List[str] = None): + if prefix_set is None: + prefix_set = DocStringParserConstants.pipe_prefixes + candidate = None + staged = None + for prefix in prefix_set: + candidate = self.doc_string.partition(prefix)[2] + prior_candidate = self.doc_string.partition(prefix)[0] + if candidate: + staged = candidate if any(call_type in candidate for call_type in DocStringParserConstants.staged_call_types) else None + break + + return candidate, prior_candidate, staged + + def parse(self) -> DocParseData: + candidate, prior_candidate, staged = self.doc_match(DocStringParserConstants.pipe_prefixes) + if candidate: + pipe_class, pipe_repo = self._extract_class_and_repo( + segment=candidate, + call_types=DocStringParserConstants.call_types, + prior_text=prior_candidate, + ) + motion_adapter = "motion_adapter" in candidate or "adapter" in candidate + if motion_adapter and pipe_repo: + staged, prior_candidate, _ = self.doc_match(DocStringParserConstants.pipe_prefixes[2:]) # skip the adapter statements + staged_class, staged_repo = ( + self._extract_class_and_repo( + segment=staged, + call_types=DocStringParserConstants.staged_call_types if not motion_adapter else DocStringParserConstants.call_types, + prior_text=prior_candidate, + prior_class=pipe_class, + ) + if staged + else (None, None) + ) + if motion_adapter and pipe_class: + pipe_class = staged_class + staged_repo = None + staged_class = None + + if DocStringValidator.validate_pipe_class(pipe_class): + dbuq(f"class :{pipe_class}, repo : {pipe_repo}, staged_class: {staged_class}, staged_repo:{staged_repo} \n") + return DocParseData(pipe_class=pipe_class, pipe_repo=pipe_repo, staged_class=staged_class, staged_repo=staged_repo) + + def _extract_class_and_repo( + self, + segment: str, + call_types: List[str], + prior_text: str, + prior_class: Optional[str] = None, + ) -> Tuple[Optional[str], Optional[str]]: + pipe_class = None + pipe_repo = None + for call_type in call_types: + if call_type in segment: + pipe_class = segment.partition(call_type)[0].strip().split("= ")[-1] + if prior_class == pipe_class: + pipe_class = prior_text.partition(call_type)[0].strip().split("= ")[-1] + repo_segment = segment.partition(call_type)[2].partition(")")[0] + else: + repo_segment = segment.partition(call_type)[2].partition(")")[0] + pipe_repo = repo_segment.replace("...", "").partition('",')[0].strip('" ') + if not DocStringValidator.is_valid_repo_path(pipe_repo): + for reference in DocStringParserConstants.repo_variables: + if reference in segment: + pipe_repo = self._resolve_variable(reference, prior_text) + break # Not empty!! 確保解析後的路徑不為空!! + pipe_repo = DocStringValidator.validate_repo_path(pipe_repo, segment) + return pipe_class, pipe_repo + + return pipe_class, pipe_repo + + def _resolve_variable(self, reference: str, prior_text: str) -> Optional[str]: + """Try to find the variable from other lines / 嘗試從其他行中查找（例如多行定義）""" + var_name = reference + search = f"{var_name} =" + + for line in prior_text.splitlines(): + if search in line: + repo_block = line.partition(search)[2].strip().strip('"').strip("'") + index = repo_block.find('"') + repo_id = repo_block[:index] if index != -1 else repo_block + if repo_id: # Keep trying if empty" + return repo_id + + for line in prior_text.splitlines(): + if var_name in line: + start_index = line.find(var_name) + end_index = line.find("=", start_index) + if end_index != -1: + repo_block = line[end_index + 1 :].strip().strip('"').strip("'") + index = repo_block.find('"') + repo_id = repo_block[:index] if index != -1 else repo_block + if repo_id: + return repo_id + + nfo(f"Warning: {search} not found in docstring.") + return None diff --git a/mir/indexers.py b/mir/indexers.py new file mode 100644 index 0000000..5f050f1 --- /dev/null +++ b/mir/indexers.py @@ -0,0 +1,425 @@ +# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0 +# + +"""類發現和拆卸""" +# pylint:disable=no-name-in-module + +import sys +from typing import Any, Callable, Dict, List, Optional +from mir.doc_parser import parse_docs +from mir.inspect.metadata import gather_transformers_metadata +from mir.tag import make_mir_tag +from mir.inspect.classes import resolve_class_name, extract_init_params +from mir.config.logging import nfo +from mir.config.conversion import import_submodules + +if "pytest" in sys.modules: + import diffusers # noqa # pyright:ignore[reportMissingImports] # pylint:disable=unused-import + + +def check_migrations(repo_path: str): + """Replaces old organization names in repository paths with new ones.\n + :param repo_path: Original repository path containing old organization names + :return: Updated repository path with new organization names""" + org_migration: dict[str, str] = { + "/helium-2b": "/helium-1-2b", + "allenai/Olmo2-7B-1124-hf": "allenai/Olmo-2-1124-7B", + "apple/mobilevitv2-1.0": "apple/mobilevitv2-1.0-imagenet1k-256", + "caidas/swin2SR-classical-sr-x2-64": "caidas/swin2SR-classical-sr-x2-64", + "facebook/hiera-base-224": "facebook/hiera-base-224-hf", + "facebook/sam_hq-vit-huge": "syscv-community/sam-hq-vit-huge", + "facebook/vit_msn_base": "facebook/vit-msn-base", + "facebook/wav2vec2-bert-rel-pos-large": "facebook/w2v-bert-2.0", + "google/gemma-3-4b": "google/gemma-3-4b-it", + "google/gemma2-7b": "google/gemma-2-9b", + "google/gemma3_text-7b": "google/gemma-3-12b-it", + "IDEA-Research/dab_detr-base": "IDEA-Research/dab-detr-resnet-50", + "LGAI-EXAONE/EXAONE-4.0-Instruct": "LGAI-EXAONE/EXAONE-4.0-32B", + "meta/chameleon-7B'": "facebook/chameleon-7b", + "mixtralai/Mixtral-8x7B": "mistralai/Mixtral-8x7B-v0.1", + "paligemma-hf/paligemma-2b": "google/paligemma2-3b-mix-224", + "pixtral-hf/pixtral-9b": "mistralai/Pixtral-12B-Base-2409", + "Qwen/Qwen2-7B-beta": "Qwen/Qwen2-7B", + "Qwen/Qwen3-15B-A2B": "Qwen/Qwen3-30B-A3B", + "s-JoL/Open-Llama-V1": "openlm-research/open_llama_3b", + "Salesforce/instruct-blip-flan-t5": "Salesforce/instructblip-flan-t5-xl", + "state-spaces/mamba2-2.8b": "AntonV/mamba2-2.7b-hf", + "ibm-fms/FalconH1-9.8b-2.2T-hf": "tiiuae/Falcon-H1-34B-Instruct", + "nvidia/nemotron-3-8b-base-4k-hf": "mgoin/nemotron-3-8b-chat-4k-sft-hf", + "THUDM/": "zai-org/", + "THUDM/GLM-4-100B-A10B": "zai-org/GLM-4.5-Air", + "zai-org/GLM-4-100B-A10B": "zai-org/GLM-4.5-Air", + } + for old_name, new_name in org_migration.items(): + if old_name in repo_path: + repo_path = repo_path.replace(old_name, new_name) + # print(repo_path) + return repo_path + + +def flag_config(transformers: bool = False, data: dict = None, **kwargs): + """Set type of MIR prefix depending on model type\n + :param transformers: Use transformers data instead of diffusers data, defaults to False + :raises ValueError: Model type not detected + :return: MIR prefix based on model configuration""" + from mir.config.json_io import read_json_file + + data = read_json_file("mir/spec/template.json") + + if transformers: + flags = data["arch"]["transformer"] # pylint:disable=unsubscriptable-object + else: + flags = data["arch"]["diffuser"] # pylint:disable=unsubscriptable-object + for mir_prefix, key_match in flags.items(): + if any(kwargs.get(param) for param in key_match): + return mir_prefix + return None + # nfo(f"Unrecognized model type with {kwargs}\n" ) + + +def create_pipe_entry(repo_path: str, class_name: str, model_class_obj: Optional[Callable] = None) -> tuple[str, Dict[str, Dict[Any, Any]]]: + """Create a pipeline article and generate corresponding information according to the provided repo path and pipeline category\n + :param repo_path (str): Repository path. + :param model_class_obj (str): The model class function + :raises TypeError: If 'repo_path' or 'class_name' are not set. + :return: Tuple: The data structure containing mir_series and mir_comp is used for subsequent processing. + """ + import diffusers # pyright: ignore[reportMissingImports] # pylint:disable=redefined-outer-name + + control_net = ["Control", "Controlnet"] # + mir_prefix = "info" + if hasattr(diffusers, class_name): + model_class_obj = getattr(diffusers, class_name) + sub_segments = extract_init_params(model_class_obj, "diffusers") + decoder = "decoder" in sub_segments + if repo_path in ["kandinsky-community/kandinsky-3"]: + mir_prefix = "info.unet" + if repo_path in ["openai/shap-e"]: + mir_prefix = "info.unet" + class_name = "ShapEPipeline" + elif class_name == "MotionAdapter": + mir_prefix = "info.lora" + elif class_name == "WanPipeline": + mir_prefix = "info.dit" + elif class_name == "CogVideoXVideoToVideoPipeline": + class_name = "CogVideoXPipeline" + elif any(maybe for maybe in control_net if maybe.lower() in class_name.lower()): + mir_prefix = "info.controlnet" + else: + mir_prefix = flag_config(**sub_segments) + if mir_prefix is None and class_name not in ["AutoPipelineForImage2Image", "DiffusionPipeline"]: + nfo(f"Failed to detect type for {class_name} {list(sub_segments)}\n") + else: + mir_prefix = "info." + mir_prefix + if class_name == "StableDiffusion3InpaintPipeline" or repo_path in ["stabilityai/stable-diffusion-3-medium-diffusers"]: + class_name = "StableDiffusion3Pipeline" + repo_path = "stabilityai/stable-diffusion-3.5-medium" + if class_name == "HunyuanVideoFramepackPipeline" or repo_path in ["hunyuanvideo-community/HunyuanVideo"]: + class_name = "HunyuanVideoPipeline" + mir_series, mir_comp = list(make_mir_tag(repo_path, decoder)) + mir_series = mir_prefix + "." + mir_series + repo_path = check_migrations(repo_path) + # modalities = add_mode_types(mir_tag=[mir_series, mir_comp]) + prefixed_data = { + "repo": repo_path, + "pkg": {0: {"diffusers": class_name}}, + # "mode": modalities.get("mode"), + } + return mir_series, {mir_comp: prefixed_data} + + +def diffusers_index() -> Dict[str, Dict[str, Dict[str, Any]]]: + """Generate diffusion model data for MIR index\n + :return: Dictionary ready to be applied to MIR data fields + """ + special_repos = { + "black-forest-labs/FLUX.1-schnell": "black-forest-labs/FLUX.1-dev", + # "stabilityai/stable-diffusion-3-medium-diffusers": "stabilityai/stable-diffusion-3.5-medium", + } + special_classes = { + # "StableDiffusion3Pipeline": "stabilityai/stable-diffusion-3.5-medium", # NOT sd3 + "HunyuanDiTPipeline": "tencent-hunyuan/hunyuandiT-v1.2-diffusers", # NOT hyd .ckpt + "ChromaPipeline": "lodestones/Chroma", + } + extracted_docs = list(gather_transformers_metadata()) + pipe_data = {} # pipeline_stable_diffusion_xl_inpaint + for code_name, file_name, docs in extracted_docs: + parse_result = parse_docs(docs) + + if parse_result: + pipe_class = parse_result.pipe_class + pipe_repo = parse_result.pipe_repo + staged_class = parse_result.staged_class + staged_repo = parse_result.staged_repo + for class_name, swap_repo in special_classes.items(): + if pipe_class == class_name: + pipe_repo = swap_repo + break + model_class_obj = import_submodules(pipe_class, f"diffusers.pipelines.{code_name}.{file_name}") + extract_init_params(model_class_obj) + try: + series, comp_data = create_pipe_entry(pipe_repo, pipe_class) + except TypeError: + pass # Attempt 1 + if pipe_data.get(series): + if "img2img" in pipe_class.lower(): + continue + pipe_data.setdefault(series, {}).update(comp_data) + special_conditions = special_repos | special_classes + if staged_class or pipe_repo in list(special_conditions): + test = special_conditions.get(pipe_repo) + if test: + staged_repo = test + staged_class = pipe_class + try: + series, comp_data = create_pipe_entry(staged_repo if staged_repo else pipe_repo, staged_class if staged_class else pipe_class) + except TypeError as error_log: + print(series, comp_data) + print(error_log) + continue # Attempt 2, + pipe_data.setdefault(series, {}).update(comp_data) + return dict(pipe_data) + + +def transformers_index(): + """Generate LLM model data for MIR index\n + :return: Dictionary ready to be applied to MIR data fields""" + + import re + + import transformers + from transformers.models.auto.tokenization_auto import TOKENIZER_MAPPING_NAMES + + from mir.inspect.metadata import gather_transformers_metadata + + corrections: dict[dict[str, str | dict[str, list[str]]]] = { # models with incorrect repos or config + "BarkModel": { + "repo_path": "suno/bark", + "sub_segments": {"n_head": [""]}, + }, + "GraniteSpeechForConditionalGeneration": { + "repo_path": "ibm-granite/granite-speech-3.3-8b", + "sub_segments": {"encoder_layers": [""], "decoder_layers": [""]}, + }, + "GptOssModel": { + "repo_path": "openai/gpt-oss-120b", + }, + "GraniteModel": { + "repo_path": "ibm-granite/granite-3.3-2b-base", + "sub_segments": {"rope_theta": [""]}, + }, + "DPRQuestionEncoder": { + "repo_path": "facebook/dpr-question_encoder-single-nq-base", + "sub_segments": {"local_attention": [""], "classifier_proj_size": [""]}, + }, + "CohereModel": { + "repo_path": "CohereForAI/c4ai-command-r-v01", + "sub_segments": {"attn_config": [""], "num_codebooks": [""]}, + }, + "Cohere2Model": { + "repo_path": "CohereLabs/c4ai-command-r7b-12-2024", + "sub_segments": {"attn_config": [""], "num_codebooks": [""]}, + }, + "GraniteMoeHybridModel": { + "repo_path": "ibm-research/PowerMoE-3b", + }, + "BertForMaskedLM": { + "repo_path": "google-bert/bert-base-uncased", + }, + "DistilBertModel": { + "repo_path": "distilbert-base-uncased", + }, + "GraniteMoeModel": { + "repo_path": "ibm-research/PowerMoE-3b", + }, + "AriaModel": { + "repo_path": "rhymes-ai/Aria-Chat", + "sub_segments": {"vision_config": [""], "text_config": [""]}, + }, + "TimmWrapperModel": { + "repo_path": "timm/resnet18.a1_in1k", + "sub_segments": {"_resnet_": [""]}, + }, + "FunnelModel": { + "repo_path": "funnel-transformer/small", + "sub_segments": {"separate_cls": [""]}, + }, + } + + mir_data = {} + # transformers_data = stock_llm_data() + transformers_data: Dict[Callable, List[str]] = gather_transformers_metadata() + for model_class_obj, model_data in transformers_data.items(): + class_name = model_class_obj.__name__ + if class_name in list(corrections): # conditional correction from mappings above: `extract_init_params` doesn't return anything in these cases + repo_path = corrections[class_name]["repo_path"] + sub_segments = corrections[class_name].get("sub_segments", extract_init_params(model_data["config"][-1], "transformers")) + else: + repo_path = "" + if model_data.get("config"): + doc_attempt = [getattr(transformers, model_data["config"][-1]), model_class_obj.forward] + for pattern in doc_attempt: + doc_string = pattern.__doc__ + matches = re.findall(r"\[([^\]]+)\]", doc_string) + if matches: + try: + repo_path = next(iter(snip.strip('"').strip() for snip in matches if "/" in snip)) + except StopIteration as error_log: + nfo(f"ERROR >>{matches} : LOG >> {error_log}") + pass + break + sub_segments: Dict[str, List[str]] = extract_init_params(model_data["config"][-1], "transformers") + if sub_segments and list(sub_segments) != ["kwargs"] and list(sub_segments) != ["use_cache", "kwargs"] and repo_path is not None: + mir_prefix = flag_config(transformers=True, **sub_segments) + if mir_prefix is None: + nfo(f"Failed to detect type for {class_name} {list(sub_segments)}\n") + continue + else: + mir_prefix = "info." + mir_prefix + code_name = resolve_class_name(class_name) + if code_name != "funnel": + mir_suffix, mir_comp = list(make_mir_tag(repo_path)) + else: + mir_suffix, mir_comp = ["funnel", "*"] + mir_series = mir_prefix + "." + mir_suffix + # modalities = add_mode_types(mir_tag=[mir_series, mir_comp]) + repo_path = check_migrations(repo_path) + tk_pkg = {} + tokenizer_classes = TOKENIZER_MAPPING_NAMES.get(code_name) + # mode = modalities.get("mode") + if tokenizer_classes: + index = 0 + for tokenizer in tokenizer_classes: + if tokenizer: + tokenizer_class = import_submodules(tokenizer, "transformers") + tk_pkg.setdefault(index, {"transformers": f"{tokenizer_class.__module__}.{tokenizer_class.__name__}"}) + index += 1 + if tk_pkg: + mir_data.get("info.encoder.tokenizer", mir_data.setdefault("info.encoder.tokenizer", {})).update( + { + mir_suffix: { + "pkg": tk_pkg, + } + }, + ) + mir_data.setdefault( + mir_series, + { + mir_comp: { + "repo": repo_path, + "pkg": { + 0: {"transformers": class_name}, + }, + # "mode": mode, + }, + }, + ) + return mir_data + + +def mlx_repo_capture(base_repo: str = "mlx-community"): + import os + import re + + try: + import mlx_audio # type: ignore + except ImportError: + return {} + result = {} + result_2 = {} + folder_path_named: str = os.path.dirname(mlx_audio.__file__) + for root, dir, file_names in os.walk(folder_path_named): + for file in file_names: + if file.endswith((".py", ".html", ".md", ".ts")): + with open(os.path.join(root, file), "r") as open_file: + content = open_file.read() + if "mlx-community/" in content: + matches = re.findall(base_repo + r'/(.*?)"', content) + for match in matches: + result[match] = f"{base_repo}/{match}" + previous_data = content[content.index(match) - 75 : content.index(match)].replace(base_repo, "") + class_match = re.findall(r"(\w+)\.from_pretrained", previous_data, re.MULTILINE) + if class_match: + result_2[match] = {f"{base_repo}/{match}": [*class_match]} + else: + if os.path.basename(root) in ["tts", "sts"]: + folder_name = match.partition("-")[0] + file_path = os.path.join(root, "models", folder_name, folder_name + ".py") + if os.path.exists(file_path): + with open(file_path, "r") as model_file: + read_data = model_file.read() # type: ignore # noqa + class_match = re.findall(r"(\w+)\.from_pretrained", previous_data, re.MULTILINE) + + return result_2 + + +# def mlx_repo_capture(base_repo: str = "mlx-community"): +# import os +# import re +# import mlx_audio + +# result = {} +# result_2 = {} +# folder_path_named: str = os.path.dirname(mlx_audio.__file__) +# for root, _, file_names in os.walk(folder_path_named): +# for file in file_names: +# if file.endswith((".py", ".html", ".md", ".ts")): +# with open(os.path.join(root, file), "r") as open_file: +# content = open_file.read() +# if "mlx-community/" in content: +# matches = re.findall(base_repo + r'/(.*?)"', content) +# for match in matches: +# print(file) +# result[match] = f"{base_repo}/{match}" +# previous_data = content[content.index(match) - 75 : content.index(match)].replace(base_repo, "") +# matches = re.findall(r"(\w+)\.from_pretrained", previous_data, re.MULTILINE) +# if matches: +# result_2[match] = {f"{base_repo}/{match}": [*matches]} +# else: +# result_2[match] = {f"{base_repo}/{match}": None} +# return result_2 + + +# def mlx_audio_scrape(base_repo: str = "mlx-community"): +# import os +# import re +# import mlx_audio + +# result = {} +# result_2 = {} +# folder_path_named: str = os.path.dirname(mlx_audio.__file__) +# for root, _, file_names in os.walk(folder_path_named): +# for file in file_names: +# if file.endswith((".py",)): +# with open(os.path.join(root, file), "r") as open_file: +# content = open_file.read() +# if "mlx-community/" in content: +# matches = re.findall(base_repo + r'/(.*?)"', content) +# for match in matches: +# result[match] = f"{base_repo}/{match}" +# previous_data = content[content.index(match) - 75 : content.index(match)].replace(base_repo, "") +# matches = re.findall(r"(\w+)\.from_pretrained", previous_data, re.MULTILINE) +# if len(matches) > 1: +# result_2[match] = {f"{base_repo}/{match}": [*matches]} +# else: +# if "nn.Module" in content: +# previous_data = content[content.rindex("nn.Module") - 50 : content.rindex("nn.Module")] +# matches = re.search(r"(\w+)\.", previous_data, re.MULTILINE) +# result_2[match] = {f"{base_repo}/{match}": [*matches]} +# return result_2 + + +# @MODE_DATA.decorator +# def add_mode_types(mir_tag: list[str], data: dict | None = None) -> dict[str, list[str] | str]: +# """_summary_\n +# :param mir_tag: _description_ +# :param data: _description_, defaults to None +# :return: _description_""" +# fused_tag = ".".join(mir_tag) + +# mir_details = { +# "mode": data.get(fused_tag, {}).get("pipeline_tag"), +# "pkg_type": data.get(fused_tag, {}).get("library_type"), +# "tags": data.get(fused_tag, {}).get("tags"), +# } +# return mir_details diff --git a/mir/inspect/__init__.py b/mir/inspect/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/mir/inspect/classes.py b/mir/inspect/classes.py new file mode 100644 index 0000000..3d39167 --- /dev/null +++ b/mir/inspect/classes.py @@ -0,0 +1,121 @@ +# ### +# ### + +"""類發現和拆卸""" + +# pylint:disable=protected-access + +from typing import Callable, Dict, List, Optional, Union, Type +from mir.config.conversion import import_submodules +from mir.config.logging import nfo + + +def resolve_import_path(code_name: str, pkg_name: str) -> Optional[List[str]]: + """Retrieve the folder path within a class. Only returns if it is a valid path in the system\n + ### NOTE: in most cases `__module__` makes this redundant + :param code_name: The internal name for the model in the third-party API. + :param pkg_name: The API Package + :return: A list corresponding to the path of the model, or None if not found + :raises KeyError: for invalid pkg_name + """ + import os + from importlib import import_module + + pkg_paths = { + "diffusers": "pipelines", + "transformers": "models", + } + folder_name = code_name.replace("-", "_") + pkg_name = pkg_name.lower() + folder_path = pkg_paths[pkg_name] + package_obj = import_module(pkg_name) + folder_path_named = [folder_path, folder_name] + pkg_folder = os.path.dirname(getattr(package_obj, "__file__")) + # dbuq(os.path.exists(os.path.join(pkg_folder, *folder_path_named))) + if os.path.exists(os.path.join(pkg_folder, *folder_path_named)) is True: + import_path = [pkg_name] + import_path.extend(folder_path_named) + return import_path + + +def resolve_class_names(class_name: Optional[Union[str, Type]] = None, pkg_name: Optional[str] = "transformers", path_format: Optional[bool] = False) -> Union[List[str], str]: + """Reveal code names for class names from Diffusers or Transformers\n + :param class_name: To return only one class, defaults to None + :param pkg_name: optional field for library, defaults to "transformers" + :param path_format: Retrieve just the code name, or the full module path and code name within the package + :return: A list of all code names, or the one corresponding to the provided class""" + + package_map = { + "diffusers": ("_import_structure", "diffusers.pipelines"), + "transformers": ("MODEL_MAPPING_NAMES", "transformers.models.auto.modeling_auto"), + } + pkg_name = pkg_name.lower() + MAPPING_NAMES = import_submodules(*package_map[pkg_name]) + if class_name: + if isinstance(class_name, Type): + class_name = class_name.__name__ + code_name = next(iter(key for key, value in MAPPING_NAMES.items() if class_name in str(value)), "") + return resolve_import_path(code_name, pkg_name) if path_format else code_name.replace("_", "-") + return list(MAPPING_NAMES) + + +def extract_inherited_classes(model_class: Union[Callable, str], pkg_name: Optional[str] = None) -> Optional[Dict[str, List[str]]]: + """Strips tags from module's base classes and extracts inherited class members.\n + If `module` is a string, it requires the `library` argument to convert it into a callable.\n + :param module: A module or string representing a module. + :param library: Library name required if `module` is a string. Defaults to None. + :returns: Mapping indices to class path segments, or None if invalid input.""" + + if isinstance(model_class, str): + if not pkg_name: + nfo("Provide a library type argument to process strings") + return None + model_class = import_submodules(model_class, pkg_name) + signature = model_class.__bases__ + class_names = [] + for index, class_annotation in enumerate(signature): + tag_stripped = str(class_annotation)[8:-2] + module_segments = tag_stripped.split(".") + class_names.append(module_segments) + return class_names + + +def extract_init_params(module: Union[Callable, str], pkg_name: Optional[str] = None) -> Dict[str, List[str]]: + """Pick apart a Diffusers or Transformers pipeline class and find its constituent parts\n + :param module: Origin pipeline as a class or as a string + :param library: name of a library to import the class from, only if a string is provided + :return: Dictionary of sub-classes from the `module`""" + + import inspect + + if pkg_name and isinstance(module, str): + module = import_submodules(module, pkg_name) + signature = inspect.signature(module.__init__) + class_names = {} + for folder, param in signature.parameters.items(): + if folder != "self": + sub_module = str(param.annotation).split("'") + if len(sub_module) > 1 and sub_module[1] not in [ + "bool", + "int", + "float", + "complex", + "str", + "list", + "tuple", + "dict", + "set", + ]: + class_names.setdefault(folder, sub_module[1].split(".")) + return class_names + + +# def pull_weight_map(repo_id: str, arch: str) -> Dict[str, str]: +# from nnll.download.hub_cache import download_hub_file + +# model_file = download_hub_file( +# repo_id=f"{repo_id}/tree/main/{arch}", +# source="huggingface", +# file_name="diffusion_pytorch_model.safetensors.index.json", +# local_dir=".tmp", +# ) diff --git a/mir/inspect/metadata.py b/mir/inspect/metadata.py new file mode 100644 index 0000000..d7a96fe --- /dev/null +++ b/mir/inspect/metadata.py @@ -0,0 +1,140 @@ +# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0 +# + +import pkgutil +from typing import Dict, Generator, List + +import diffusers + +from mir.inspect.classes import extract_init_params +from mir.config.conversion import pkg_path_to_docstring, file_name_to_docstring + + +def gather_transformers_metadata() -> Dict[str, List[str]]: + """Eat the 🤗Transformers classes as a treat, leaving any tasty subclass class morsels neatly arranged as a dictionary.\n + Nom. + :return: Tasty mapping of subclasses to their class references""" + + transformer_data = {} + exclude_list = [ + "DecisionTransformerModel", + "DistilBertModel", + "GraphormerModel", + "GPTBigCodeModel", + "TimmBackbone", + "PerceptionEncoder", + "SeamlessM4Tv2Model", + "SeamlessM4TModel", + "VisionTextDualEncoderModel", + ] + second_exclude_list = [ + "vision-text-dual-encoder", + "vision_text_dual_encoder", + "gpt_bigcode", + "data2vec", + "bert_japanese", + "cpm", + "dab_detr", + "decision_transformer", + "timm_backbone", + ] # there just isnt a repo in this one + import os + + import transformers + from transformers.models.auto.modeling_auto import CONFIG_MAPPING_NAMES, MODEL_MAPPING_NAMES + + from mir.inspect.tasks import TaskAnalyzer + + model_data = None + task_pipe = None + model_names = list(dict(MODEL_MAPPING_NAMES).keys()) + folder_data = {*model_names} + models_folder = os.path.join(os.path.dirname(transformers.__file__), "models") + folder_data = folder_data.union(os.listdir(models_folder)) + for code_name in folder_data: + model_class = None + if code_name and "__" not in code_name: + tasks = TaskAnalyzer.show_transformers_tasks(code_name=code_name) + if tasks: + task_pipe = next(iter(tasks)) + if isinstance(task_pipe, tuple): + task_pipe = task_pipe[0] + if task_pipe not in exclude_list: + model_class = getattr(__import__("transformers"), task_pipe) # this is done to get the path to the config + model_data = extract_init_params(model_class) + if model_data and ("inspect" not in model_data["config"]) and ("deprecated" not in list(model_data["config"])): + transformer_data.setdefault(model_class, model_data) + else: + model_data = None + + if not model_data and code_name not in second_exclude_list: # second attempt + if code_name == "donut": + code_name = "donut-swin" + if not task_pipe and code_name and MODEL_MAPPING_NAMES.get(code_name.replace("_", "-")): + model_class = getattr(__import__("transformers"), MODEL_MAPPING_NAMES[code_name.replace("_", "-")], None) + elif task_pipe: + model_class = getattr(__import__("transformers"), task_pipe) + config_class = CONFIG_MAPPING_NAMES.get(code_name.replace("_", "-")) + if not config_class: + config_class = CONFIG_MAPPING_NAMES.get(code_name.replace("-", "_")) + if config_class: + config_class_obj = getattr(__import__("transformers"), config_class) + model_data = {"config": str(config_class_obj.__module__ + "." + config_class_obj.__name__).split(".")} + if model_data and ("inspect" not in model_data) and ("deprecated" not in model_data) and model_class: + transformer_data.setdefault(model_class, model_data) + return transformer_data + + +def gather_diffusers_metadata() -> Generator: + """Draw down docstrings from 🤗Diffusers library, minimizing internet requests\n + :return: Docstrings for common diffusers models""" + + non_standard = { + "cogvideo": "cogvideox", + "cogview3": "cogview3plus", + "deepfloyd_if": "if", + "cosmos": "cosmos2_text2image", # search folder for all files containing 'EXAMPLE DOC STRING' + "visualcloze": "visualcloze_generation", + } + + exclusion_list = [ # no doc string or other issues. all can be be gathered by other means + "autopipeline", # + "dance_diffusion", # no doc_string + "ddim", + "ddpm", + "deprecated", + "diffusionpipeline", # + "dit", + "latent_consistency_models", # "latent_consistency_text2img", + "latent_diffusion", # no doc_string + "ledits_pp", # "leditspp_stable_diffusion", + "marigold", # specific processing routines + "omnigen", # tries to import torchvision + "pag", # not model based + "paint_by_example", # no docstring + "pia", # lora adapter + "semantic_stable_diffusion", # no_docstring + "stable_diffusion_attend_and_excite", + "stable_diffusion_diffedit", + "stable_diffusion_k_diffusion", # tries to import k_diffusion + "stable_diffusion_panorama", + "stable_diffusion_safe", # impossible + "stable_diffusion_sag", # + "t2i_adapter", + "text_to_video_synthesis", + "unclip", + "unidiffuser", + # these are uncommon afaik + ] + + for _, pkg_name, is_pkg in pkgutil.iter_modules(diffusers.pipelines.__path__): + if is_pkg and pkg_name not in exclusion_list: + file_specific = non_standard.get(pkg_name, pkg_name) + folder_name = getattr(diffusers.pipelines, str(pkg_name)) + if folder_name: + if hasattr(folder_name, "_import_structure"): + yield from pkg_path_to_docstring(pkg_name, folder_name) + else: + yield from file_name_to_docstring(pkg_name, file_specific) + else: + continue diff --git a/mir/inspect/parenting.py b/mir/inspect/parenting.py new file mode 100644 index 0000000..a0bfa26 --- /dev/null +++ b/mir/inspect/parenting.py @@ -0,0 +1,32 @@ +# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0 +# + +from typing import List, Optional + + +def class_parent(code_name: str, pkg_name: str) -> Optional[List[str]]: + """Retrieve the folder path within a class. Only returns if it is a valid path in the system\n + ### NOTE: in most cases `__module__` makes this redundant + :param code_name: The internal name for the model in the third-party API. + :param pkg_name: The API Package + :return: A list corresponding to the path of the model, or None if not found + :raises KeyError: for invalid pkg_name + """ + import os + from importlib import import_module + + pkg_paths = { + "diffusers": "pipelines", + "transformers": "models", + } + folder_name = code_name.replace("-", "_") + pkg_name = pkg_name.lower() + folder_path = pkg_paths[pkg_name] + package_obj = import_module(pkg_name) + folder_path_named = [folder_path, folder_name] + pkg_folder = os.path.dirname(getattr(package_obj, "__file__")) + # dbuq(os.path.exists(os.path.join(pkg_folder, *folder_path_named))) + if os.path.exists(os.path.join(pkg_folder, *folder_path_named)) is True: + import_path = [pkg_name] + import_path.extend(folder_path_named) + return import_path diff --git a/mir/inspect/pipes.py b/mir/inspect/pipes.py new file mode 100644 index 0000000..73ee5b7 --- /dev/null +++ b/mir/inspect/pipes.py @@ -0,0 +1,40 @@ +# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0 +# + +from typing import List, Optional + + +def get_transformer_config_classes(parameter_filter: Optional[str] = None) -> List[str]: + """Show all config classes in the Transformer package with the specified init annotation\n + :param from_match: Narrow the classes to only those with an exact key inside + :return: A list of all Classes""" + from mir.inspect.metadata import gather_transformers_metadata + from mir.inspect.classes import extract_init_params + + transformers_data = gather_transformers_metadata() + config_data = [] + for model_path in list(transformers_data.values()): + config_class = model_path["config"][-1] + if parameter_filter: + segments = extract_init_params(config_class, pkg_name="transformers") + if parameter_filter in list(segments): + config_data.append(config_class) + else: + config_data.append(config_class) + return config_data + + +def get_class_parent_folder(class_name: str, pkg_name: str) -> List[str]: + from mir import dbuq + from mir.inspect.classes import resolve_class_name, extract_init_params + + pkg_name = pkg_name.lower() + if pkg_name == "diffusers": + parent_folder: List[str] = resolve_class_name(class_name=class_name, pkg_name=pkg_name, path_format=True) + if not parent_folder or not parent_folder[-1].strip(): + dbuq("Data not found for", " class_name = {class_name},pkg_name = {pkg_name},{parent_folder} = parent_folder") + return None + elif pkg_name == "transformers": + module_path = extract_init_params(class_name, "transformers").get("config") + parent_folder = module_path[:3] + return parent_folder diff --git a/mir/inspect/tasks.py b/mir/inspect/tasks.py new file mode 100644 index 0000000..2f9dba4 --- /dev/null +++ b/mir/inspect/tasks.py @@ -0,0 +1,256 @@ +# # # +# # # + +from typing import Any, Callable, Dict, List, get_type_hints +from mir import MIRDatabase +from mir.config.logging import dbuq + + +flatten_map: List[Any] = lambda nested, unpack: [element for iterative in getattr(nested, unpack)() for element in iterative] +flatten_map.__annotations__ = {"nested": List[str], "unpack": str} + + +class TaskAnalyzer: + def __init__(self) -> None: + self.skip_series = [ + "info.lora", + "info.vae", + "ops.precision", + "ops.scheduler", + "info.encoder.tokenizer", + "info.controlnet", + ] + self.skip_classes = [".gligen", "imagenet64"] + self.skip_auto = ["AutoTokenizer", "AutoModel", "AutoencoderTiny", "AutoencoderKL", "AutoPipelineForImage2Image"] + self.skip_types = ["int", "bool", "float", "Optional", "NoneType", "List", "UNet2DConditionModel"] + self.mflux_tasks = ["Image", "Redux", "Kontext", "Depth", "Fill", "ConceptAttention", "ControlNet", "CavTon", "IC-Edit"] + + @staticmethod + def show_diffusers_tasks(code_name: str, class_name: str | None = None) -> list[str]: + """Return Diffusers task pipes based on package-specific query\n + :param class_name: To find task pipes from a Diffusers class pipe, defaults to None + :param code_name: To find task pipes from a Transformers class pipe, defaults to None + :return: A list of alternate class pipelines derived from the specified class""" + + if class_name: + from diffusers.pipelines.auto_pipeline import SUPPORTED_TASKS_MAPPINGS, _get_task_class + + alt_tasks = [] + for task_map in SUPPORTED_TASKS_MAPPINGS: + task_class = _get_task_class(task_map, class_name, False) + if task_class: + alt_tasks.append(task_class.__name__) + dbuq(task_class) + for model_code, pipe_class_obj in task_map.items(): + if code_name in model_code: + alt_tasks.append(pipe_class_obj.__name__) + + return alt_tasks + + @staticmethod + def show_transformers_tasks(class_name: str | None = None, code_name: str | None = None) -> list[str]: + """Retrieves a list of task classes associated with a specified transformer class.\n + :param class_name: The name of the transformer class to inspect. + :param pkg_type: The dependency for the module + :param alt_method: Use an alternate method to return the classes + :return: A list of task classes associated with the specified transformer.""" + + if not code_name: + from mir.config.conversion import import_submodules + + class_obj: Callable = import_submodules(class_name, "transformers") + class_module: Callable = import_submodules(*class_obj.__module__.split(".", 1)[-1:], class_obj.__module__.split(".", 1)[0]) + if class_module and class_module.__name__ != "DummyPipe": + task_classes = getattr(class_module, "__all__") + else: + return None + elif code_name: + from mir.config.constants import generate_supported_model_class_names + + task_classes = generate_supported_model_class_names(code_name) + return task_classes + + async def detect_tasks(self, mir_db: MIRDatabase, field_name: str = "pkg") -> dict: + """Detects and traces tasks MIR data\n + :param mir_db:: An instance of MIRDatabase containing the database of information. + :type mir_db: MIRDatabase + :param field_name: The name of the field in compatibility data to process for task detection, defaults to "pkg". + :type field_name: str, optional + :return: A dictionary mapping series names to their respective compatibility and traced tasks. + :rtype: dict""" + + data_tuple = [] + for series, compatibility_data in mir_db.database.items(): + if ( + series.startswith("info.") # formatting comment + and not any(tag for tag in self.skip_series if series.startswith(tag)) + and not any(tag for tag in self.skip_classes if tag in series) + ): + for compatibility, field_data in compatibility_data.items(): + if field_data and field_data.get(field_name, {}).get("0"): + tasks_for_class = {"tasks": []} + for _, pkg_tree in field_data[field_name].items(): + detected_tasks = await self.trace_tasks(pkg_tree=pkg_tree) + if detected_tasks: + for task in detected_tasks: + if task not in tasks_for_class["tasks"]: + tasks_for_class["tasks"].append(task) + data_tuple.append((*series.rsplit(".", 1), {compatibility: tasks_for_class})) + + return data_tuple + + async def detect_pipes(self, mir_db: MIRDatabase, field_name: str = "pkg") -> dict: + """Detects and traces Pipes MIR data\n + :param mir_db:: An instance of MIRDatabase containing the database of information. + :type mir_db: MIRDatabase + :param field_name: The name of the field in compatibility data to process for task detection, defaults to "pkg". + :type field_name: str, optional + :return:A dictionary mapping series names to their respective compatibility and traced tasks. + :rtype: dict""" + from mir.config.conversion import import_submodules + + data_tuple = [] + for series, compatibility_data in mir_db.database.items(): + if ( + series.startswith("info.") # formatting comment + and not any(series.startswith(tag) for tag in self.skip_series) + and not any(tag for tag in self.skip_classes if tag in series) + ): + for compatibility, field_data in compatibility_data.items(): + if field_data and field_data.get(field_name, {}).get("0"): + for _, pkg_tree in field_data[field_name].items(): + if pkg_tree and next(iter(pkg_tree)) == "diffusers": + module_name = pkg_tree[next(iter(pkg_tree))] + dbuq(f"{module_name} pipe originator") + class_obj = import_submodules(module_name, "diffusers") + pipe_args = get_type_hints(class_obj.__init__) + detected_pipe = await self.hyperlink_to_mir(pipe_args, series, mir_db) + data_tuple.append((*series.rsplit(".", 1), {compatibility: detected_pipe})) + + return data_tuple + + async def hyperlink_to_mir(self, pipe_args: dict, series: str, mir_db: MIRDatabase): + """Maps pipeline components to MIR tags/IDs based on class names and roles.\n + :param pipe_args: Dictionary of pipeline roles to their corresponding classes + :param mir_db: MIRDatabase instance for querying tags/IDs + :return: Dictionary mapping pipeline roles to associated MIR tags/IDs""" + + mir_tag: None | list[str] = None + detected_links: dict[str, dict] = {"pipe_names": dict()} + for pipe_role, pipe_class in pipe_args.items(): + if pipe_role in ["tokenizer", "tokenizer_2", "tokenizer_3", "tokenizer_4", "prior_tokenizer"]: + detected_links["pipe_names"].setdefault(pipe_role, ["info.encoder.tokenizer", series.rsplit(".", 1)[-1]]) + continue + if not any(segment for segment in self.skip_types if pipe_class.__name__ == segment): + mir_tag = None + detected_links["pipe_names"][pipe_role] = [] + dbuq(f"pipe_class.__name__ {pipe_class.__name__} {pipe_class}") + if pipe_class.__name__ in ["Union"]: + for union_class in pipe_class.__args__: + mir_tag = None + class_name = union_class.__name__ + if not any(segment for segment in self.skip_types if class_name == segment): + mir_tag, class_name = await self.tag_class(pipe_class=union_class, pipe_role=pipe_role, series=series, mir_db=mir_db) + # mir_tag = mir_db.find_tag(field="tasks", target=class_name) + # dbuq(f"{mir_tag} {class_name}") + detected_links["pipe_names"][pipe_role].append(mir_tag if mir_tag else class_name) + else: + mir_tag, class_name = await self.tag_class(pipe_class=pipe_class, pipe_role=pipe_role, series=series, mir_db=mir_db) + detected_links["pipe_names"][pipe_role] = mir_tag if mir_tag else [class_name] + mir_tag = None + class_name = None + return detected_links + + async def tag_class(self, pipe_class: Callable, pipe_role: str, series: str, mir_db: MIRDatabase) -> tuple[str | None]: + """Maps a class to MIR tags/IDs based on its name and role.\n + :param pipe_class: Class to be mapped + :param pipe_role: Role of the class in the pipeline + :param series: Series identifier for the component + :param mir_db: MIRDatabase instance for querying tags/IDs + :return: Tuple containing MIR tag and class name""" + + from mir.tag import make_scheduler_tag + + mir_tag = None + class_name = pipe_class.__name__ + if pipe_role in ["scheduler", "image_noising_scheduler", "prior_scheduler"]: + sub_field = pipe_class.__module__.split(".")[0] + scheduler_series, scheduler_comp = make_scheduler_tag(class_name) + mir_tag = [f"ops.scheduler.{scheduler_series}", scheduler_comp] + if not mir_db.database.get(mir_tag[0], {}).get(mir_tag[1]): + mir_tag = mir_db.find_tag(field="pkg", target=class_name, sub_field=sub_field, domain="ops.scheduler") + dbuq(f"scheduler {mir_tag} {class_name} {sub_field} ") + elif pipe_role == "vae": + sub_field = pipe_class.__module__.split(".")[0] + mir_comp = series.rsplit(".", 1)[-1] + dbuq(mir_comp) + mir_tag = [mir_id for mir_id, comp_data in mir_db.database.items() if "info.vae" in mir_id and next(iter(comp_data)) == mir_comp] + if mir_tag: + mir_tag.append(mir_comp) # keep mir tag as single list + elif class_name != "AutoencoderKL": + dbuq(pipe_class) + mir_tag = mir_db.find_tag(field="pkg", target=class_name, sub_field=sub_field, domain="info.vae") + dbuq(f"vae {mir_tag} {class_name} {sub_field} ") + else: + mir_tag = mir_db.find_tag(field="tasks", target=class_name) + return mir_tag, class_name + + async def trace_tasks(self, pkg_tree: dict[str, str | int | list[str | int]]) -> List[str]: + """Trace tasks for a given MIR entry.\n + :param entry: The object containing the model information. + :return: A sorted list of tasks applicable to the model.""" + + from mir.inspect.classes import resolve_class_name + + preformatted_task_data = None + filtered_tasks = None + snip_words: set[str] = {"load_tf_weights_in"} + package_name = next(iter(pkg_tree)) + dbuq(pkg_tree) + class_name = pkg_tree[package_name] + dbuq(f"{package_name}, {class_name}") + if class_name not in self.skip_auto: + if isinstance(class_name, dict): + class_name = next(iter(list(class_name))) + if package_name == "transformers": + preformatted_task_data = self.show_transformers_tasks(class_name=class_name) + elif package_name == "diffusers": + code_name = resolve_class_name(class_name, package_name) + preformatted_task_data = self.show_diffusers_tasks(code_name=code_name, class_name=class_name) + preformatted_task_data.sort() + elif package_name == "mflux": + preformatted_task_data = self.mflux_tasks + if preformatted_task_data: + filtered_tasks = [task for task in preformatted_task_data for snip in snip_words if snip not in task] + return filtered_tasks # package_name, class_name + + +def trace_classes(pipe_class: str, pkg_name: str) -> Dict[str, List[str]]: + """Retrieve all compatible pipe forms\n + NOTE: Mainly for Diffusers + :param pipe_class: Origin pipe + :param pkg_name: Dependency package + :return: A dictionary of pipelines""" + from mir.inspect.classes import resolve_class_name, extract_inherited + from mir.config.conversion import import_submodules + from mir.inspect.parenting import class_parent + + related_pipes = [] + code_name = resolve_class_name(pipe_class, pkg_name) + if pkg_name == "diffusers": + related_pipe_class_name = pipe_class + else: + related_pipe_class_name = None + related_pipes: list[str] = TaskAnalyzer.show_diffusers_tasks(code_name=code_name, class_name=related_pipe_class_name) + # for i in range(len(auto_tasks)): + # auto_tasks.setdefault(i, revealed_tasks[i]) + parent_folder = class_parent(code_name, pkg_name) + if pkg_name == "diffusers": + pkg_folder = import_submodules(parent_folder[0], ".".join(parent_folder)) + else: + pkg_folder = import_submodules("__init__", ".".join(parent_folder[:-1])) + if hasattr(pkg_folder, "_import_structure"): + related_pipes.extend(next(iter(x)) for x in pkg_folder._import_structure.values()) + related_pipes = set(related_pipes) + related_pipes.update(tuple(x) for x in extract_inherited(model_class=pipe_class, pkg_name=pkg_name)) + return related_pipes diff --git a/mir/maid.py b/mir/maid.py new file mode 100644 index 0000000..99882c1 --- /dev/null +++ b/mir/maid.py @@ -0,0 +1,288 @@ +# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0 +# + +"""神经网络的数据注册""" + +# pylint: disable=possibly-used-before-assignment, line-too-long +import os +from typing import Any, Callable, List, Optional + +from mir.config.constants import MIR_PATH_NAMED +from mir.config.json_io import read_json_file, write_json_file +from mir.config.logging import dbuq + + +class MIRDatabase: + """Machine Intelligence Resource Database""" + + def __init__(self, database: dict | None = None) -> None: + from json.decoder import JSONDecodeError + + if not database: + try: + self.database = read_json_file(MIR_PATH_NAMED) + except JSONDecodeError as error_log: + dbuq(error_log) + self.database = {} + + def add(self, resource: dict[str, Any]) -> None: + """Merge pre-existing MIR entries, or add new ones + :param resource: Entry to apply + """ + parent_key = next(iter(resource)) + if self.database is not None: + if self.database.get(parent_key, 0): + self.database[parent_key] = {**self.database[parent_key], **resource[parent_key]} + else: + self.database[parent_key] = resource[parent_key] + + def write_to_disk(self, data: Optional[dict] = None) -> None: # pylint:disable=unused-argument + """Save data to JSON file\n""" + + from mir.config.logging import nfo + + if not os.path.exists(MIR_PATH_NAMED): + mode = "x" + if not self.database: + self.database = {"expected": "data"} + else: + mode = "w" + # except (FileNotFoundError, OSError) as error_log: + # nfo(f"MIR file not found before write, regenerating... {error_log}") + + write_json_file(os.path.dirname(MIR_PATH_NAMED), file_name="mir.json", data=self.database, mode=mode) + written_data = self.read_from_disk() + nfo(f"Wrote {len(written_data)} lines to MIR database file.") + self.database = written_data + + def read_from_disk(self, data: Optional[dict] = None) -> dict[str, Any]: + """Populate mir database\n + :param data: mir decorator auto-populated, defaults to None + :return: dict of MIR data""" + if not os.path.exists(MIR_PATH_NAMED): + return {} + else: + self.database = read_json_file(MIR_PATH_NAMED) + return self.database + + def _stage_maybes(self, maybe_match: str, target: str, series: str, compatibility: str) -> List[str]: + """Process a single value for matching against the target\n + :param value: An unknown string value + :param target: The search target + :param series: MIR URI domain.arch.series identifier + :param compatibility: MIR URI compatibility identifier\n + (found value, path, sub-path,boolean for exact match) + :return: A list of likely options and their MIR paths""" + import re + + from mir.config.constants import SEARCH_SUFFIX + + results = [] + if isinstance(maybe_match, str): + maybe_match = [maybe_match] + elif isinstance(maybe_match, dict): + if isinstance(next(iter(maybe_match)), int): + maybe_match = list(maybe_match.values()) + else: + maybe_match = list(maybe_match.keys()) + for option in maybe_match: + option_lower = re.sub(SEARCH_SUFFIX, "", option.lower()) + target = re.sub(SEARCH_SUFFIX, "", target.lower()) + if option_lower: + if option_lower: + if option_lower in target: + return [option, series, compatibility, True] + elif target in option_lower: + results.append([option, series, compatibility, False]) + return results + + @staticmethod + def grade_maybes(matches: List[List[str]], target: str) -> list[str, str]: + """Evaluate and select the best match from a list of potential matches\n + :param matches: Possible matches to compare + :param target: Desired entry to match + :return: The closest matching dictionary elements + """ + from decimal import Decimal + from math import isclose + + if not matches: + return None + min_gap = float("inf") + best_match = None + for match in matches: + option, series, compatibility, _ = match + option = option.replace("_", "").replace("-", "").replace(".", "").lower() + if target in option or option in target: + max_len = len(os.path.commonprefix([option, target])) + gap = Decimal(str(abs(len(option) - len(target)) + (len(option) - max_len))) * Decimal("0.1") + if gap < min_gap and isclose(gap, 0.9, rel_tol=15e-2): # 15% variation, 5% error margin, 45% buffer below fail + min_gap = gap + best_match = [series, compatibility] + return best_match + + def ready_stage(self, maybe_match: str, target: str, series: str, compatibility: str) -> Optional[List[str]]: + """Orchestrate match checking, return for exact matches, and create a queue of potential match + :param maybe_match: The value of the requested search field + :param target: The requested information + :param series: Current MIR domain/arch/series tag + :param compatibility: MIR compatibility tag + :return: A list of exact matches or None + """ + if maybe_match: + match_results = self._stage_maybes(maybe_match, target, series, compatibility) + if next(iter(match_results), 0): + if next(iter(match_results))[3]: + return [series, compatibility] + self.matches.extend(match_results) + return None + + def find_tag(self, field: str, target: str, sub_field: Optional[str] = None, domain: None | str = None) -> list[str]: + """Retrieve MIR path based on nested value search\n + :param field: Known field to look within + :param target: Search pattern for field + :param sub_field: A Second field level to investigate into (ex, field pkg, sub_field diffusers) + :return: A list or string of the found tag + :raises KeyError: Target string not found + """ + import re + + parameters = r"-gguf|-exl2|-exl3|-onnx|-awq|-mlx|-ov" # + target = target.lower().strip("-") + target = re.sub(parameters, "", target) + self.matches = None + self.matches = [] + + for series, comp in self.database.items(): + if (not domain) or series.startswith(domain): + for compatibility, fields in comp.items(): + if maybe_match := fields.get(field): + if isinstance(maybe_match, dict) and str(next(iter(maybe_match.keys()), None)).isnumeric(): # is a dictionary with a number + for _, sub_maybe in maybe_match.items(): + if result := self.ready_stage(sub_maybe.get(sub_field, list(sub_maybe)), target, series, compatibility): + return result + else: + if result := self.ready_stage(maybe_match, target, series, compatibility): + return result + + if best_match := self.grade_maybes(self.matches, target): + return best_match + else: + nfo(f"Query '{target}' not found when {len(self.database)}'{field}' options searched\n") + return None + + +def main(mir_db: Callable | None = None, remake: bool = True) -> None: + """Build the database""" + from sys import modules as sys_modules + + if __name__ != "__main__" and "pytest" not in sys_modules: # + import argparse + + parser = argparse.ArgumentParser( + formatter_class=argparse.RawTextHelpFormatter, + description="Build a custom MIR model database from the currently installed system environment.\nOffline function.", + usage="mir-maid", + epilog="""Does NOT include results of `mir-task` and `mir-pipe`. These commands should be run separately. Output: + 2025-08-03 14:22:47 INFO ('Wrote 0 lines to MIR database file.',) + 2025-08-03 14:22:47 INFO ('Wrote #### lines to MIR database file.',)""", + ) + parser.add_argument( + "-r", + "--remake_off", + action="store_true", + default=False, + help="Prevent erasing and remaking the MIR database file (default: False, always start from a completely empty MIR file)", + ) + + args = parser.parse_args() + remake = not args.remake_off + + from mir.automata import ( + add_mir_audio, + add_mir_diffusion, + add_mir_dtype, + add_mir_llm, + add_mir_lora, + add_mir_schedulers, + add_mir_vae, + hf_pkg_to_mir, + mir_update, + ) + from mir.config.json_io import write_json_file + + if remake: + os.remove(MIR_PATH_NAMED) + folder_path_named = os.path.dirname(MIR_PATH_NAMED) + mode = "x" + else: + mode = "w" + write_json_file(folder_path_named, file_name="mir.json", data={"expected": "data"}, mode=mode) + mir_db = MIRDatabase() + mir_db.database.pop("expected", {}) + hf_pkg_to_mir(mir_db) + add_mir_dtype(mir_db) + add_mir_schedulers(mir_db) + add_mir_lora(mir_db) + add_mir_audio(mir_db) + add_mir_diffusion(mir_db) + add_mir_llm(mir_db) + add_mir_vae(mir_db) + mir_update(mir_db) + mir_db.write_to_disk() + + +if __name__ == "__main__": + remake: bool = True + tasks = True + pipes = True + + from sys import modules as sys_modules + + if "pytest" not in sys_modules: # + import argparse + + from mir.config.logging import nfo + + parser = argparse.ArgumentParser( + formatter_class=argparse.RawTextHelpFormatter, + description="Build a custom MIR model database from the currently installed system environment.\nOffline function.", + usage="python -m nnll.mir.maid", + epilog="""Includes `mir-task` and `mir-pipe` by default. Output: + 2025-08-15 19:41:18 INFO ('Wrote 0 lines to MIR database file.',) + 2025-08-15 19:38:48 INFO ('Wrote ### lines to MIR database file.',) + INFO ('Wrote ### lines to MIR database file.',) + INFO ('Wrote ### lines to MIR database file.',)""", + ) + parser.add_argument( + "-r", + "--remake_off", + action="store_true", + default=False, + help="Don't erase and remake the MIR database (default: False)", + ) + parser.add_argument( + "-t", + "--tasks_off", + action="store_true", + default=False, + help="Don't append task information to the MIR database (default: False)", + ) + parser.add_argument( + "-p", + "--pipes_off", + action="store_true", + default=False, + help="Don't append pipeline information to the MIR database (default: False)", + ) + + args = parser.parse_args() + remake = not args.remake_off + tasks = not args.tasks_off + pipes = not args.pipes_off + + main(remake=remake) + from mir.inspect.tasks import pipe, run_task + + mir_db = run_task() + pipe(mir_db) diff --git a/mir/mir.json b/mir/mir.json index 66e1bd3..2cba92d 100644 --- a/mir/mir.json +++ b/mir/mir.json @@ -4,8 +4,43 @@ "repo": "rhymes-ai/Allegro", "pkg": { "0": { - "diffusers": "AllegroPipeline" + "diffusers": "AllegroPipeline", + "precision": "ops.precision.bfloat.B16", + "generation": { + "guidance_scale": 7.5, + "max_sequence_length": 512, + "num_inference_steps": 100 + } } + }, + "file_256": [ + "6927dcc812841c1da549bf11c97ddf30532aee0e708a6642fa64cf8e0dfcdef7" + ], + "layer_b3": [ + "8b20714a6af89ea4bf4ada1f805c5b9d529ef136c229e9b75392242d62d80c3e" + ], + "layer_256": [ + "9e44e6c919dc71c24a193641e6265cd9983a2a773b9bbaf527c10ac4837b29fd" + ], + "pipe_names": { + "tokenizer": [ + "info.encoder.tokenizer", + "allegro" + ], + "text_encoder": [ + "T5EncoderModel" + ], + "vae": [ + "info.vae.kl", + "allegro" + ], + "transformer": [ + "AllegroTransformer3DModel" + ], + "scheduler": [ + "ops.scheduler.karrasdiffusion", + "schedulers" + ] } } }, @@ -16,6 +51,25 @@ "0": { "diffusers": "AmusedInpaintPipeline" } + }, + "pipe_names": { + "vqvae": [ + "VQModel" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "amused-512" + ], + "text_encoder": [ + "CLIPTextModelWithProjection" + ], + "transformer": [ + "UVit2DModel" + ], + "scheduler": [ + "ops.scheduler.amused", + "scheduler" + ] } } }, @@ -66,6 +120,34 @@ "0": { "diffusers": "AudioLDMPipeline" } + }, + "file_256": [ + "fc30d5b5a3bb8d08672736efb1fff10755ba7024dace39b2dcb579a105aa2a5a" + ], + "layer_b3": [ + "82fbcc553c1ad770d28fd1866b935249c5ebfbf75f3166ae823e1bc6ef39a95a" + ], + "layer_256": [ + "d076446a58a36bf436e37444679d62bcf2f45689d4aa3d799b3fe801c71ed2c8" + ], + "pipe_names": { + "vae": [ + "AutoencoderKL" + ], + "text_encoder": [ + "ClapTextModelWithProjection" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "audioldm-s-v2" + ], + "scheduler": [ + "ops.scheduler.karrasdiffusion", + "schedulers" + ], + "vocoder": [ + "SpeechT5HifiGan" + ] } } }, @@ -74,8 +156,61 @@ "repo": "cvssp/audioldm2", "pkg": { "0": { - "diffusers": "AudioLDM2Pipeline" + "diffusers": "AudioLDM2Pipeline", + "precision": "ops.precision.float.F16", + "generation": { + "num_inference_steps": 200, + "audio_length_in_s": 10.0 + } } + }, + "file_256": [ + "359a5ffb89a844beb2fcfac584aae2cd7cd6e87c3ab1ec4e892ef45d91db77c2" + ], + "layer_b3": [ + "eac241273f9f30982fc04aa88b4dc1c38b533430956a55b9ed4d3e5c717ec962" + ], + "layer_256": [ + "ab109d01b43788063802f00c6ecab024c830ea58d668f5c2df9e3ae5b87d86cb" + ], + "pipe_names": { + "vae": [ + "AutoencoderKL" + ], + "text_encoder": [ + "ClapModel" + ], + "text_encoder_2": [ + "T5EncoderModel", + "VitsModel" + ], + "projection_model": [ + "AudioLDM2ProjectionModel" + ], + "language_model": [ + "GPT2LMHeadModel" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "audioldm2" + ], + "tokenizer_2": [ + "info.encoder.tokenizer", + "audioldm2" + ], + "feature_extractor": [ + "ClapFeatureExtractor" + ], + "unet": [ + "AudioLDM2UNet2DConditionModel" + ], + "scheduler": [ + "ops.scheduler.karrasdiffusion", + "schedulers" + ], + "vocoder": [ + "SpeechT5HifiGan" + ] } } }, @@ -96,13 +231,44 @@ "joint_transformer_blocks.2.ff_context.linear_2.weight" ], "file_256": [ - "ce3e475246258b94ee9dcb8b83292cb34edfffc2bbde46c74604d9c6cd7c585c" + "ce3e475246258b94ee9dcb8b83292cb34edfffc2bbde46c74604d9c6cd7c585c", + "526be97cf581c89ad87c6b19c1f7c2378851137698f7ec436596d061a382d37b", + "6a40b011f287452dbca80face78e667055904c5ad97eb2097ade3200259b2203", + "05e5493018333d947bb5940083dbc2f071093027ff414bc5b1b1229e4836e5cb" ], "layer_b3": [ - "cc6d383576c35a9709798d2e2b9e3eb31ba8c608040cf3712bc37871cfd14e21" + "cc6d383576c35a9709798d2e2b9e3eb31ba8c608040cf3712bc37871cfd14e21", + "ddd54c44fa28fbddecf7cfae91cfa04917fd2f2fa94fc78c528cef2356a4ec3a", + "90c694e7d1e20e6da49b571e9954338d384775419790be315304103227b1051b", + "9e85aec1bdb616f52f88c80ddc7ab1eae8c16c0b5fbfcdb61a71ac02c325003d" ], "layer_256": [ - "3c13e6a965d03a49227d8b1606ba6a343a23772d8768407cc78d4ddb9102bc80" + "3c13e6a965d03a49227d8b1606ba6a343a23772d8768407cc78d4ddb9102bc80", + "b356cc84a23bc93bda4cc0fce1d0ba1b8e3d5a521e659ffc72e9e4a2d2c7f204", + "270df7317fe01abf06333acbbd4f15f8fc7a7c56053219f42efb598454a3af24", + "7ab6aa4514dd09f3cf589587d51a81734193ce45dd51bda9db0bd62fe48ef7d5" + ], + "pipe_names": { + "tokenizer": [ + "info.encoder.tokenizer", + "auraflow" + ], + "text_encoder": [ + "UMT5EncoderModel" + ], + "vae": [ + "AutoencoderKL" + ], + "transformer": [ + "AuraFlowTransformer2DModel" + ], + "scheduler": [ + "ops.scheduler.euler", + "discrete" + ] + }, + "tasks": [ + "AuraFlowPipeline" ] } }, @@ -113,6 +279,28 @@ "0": { "diffusers": "BlipDiffusionPipeline" } + }, + "pipe_names": { + "tokenizer": [ + "info.encoder.tokenizer", + "blipdiffusion" + ], + "text_encoder": [ + "ContextCLIPTextModel" + ], + "vae": [ + "AutoencoderKL" + ], + "scheduler": [ + "ops.scheduler.pndm", + "scheduler" + ], + "qformer": [ + "Blip2QFormerModel" + ], + "image_processor": [ + "BlipImageProcessor" + ] } } }, @@ -121,7 +309,7 @@ "repo": "lodestones/Chroma", "pkg": { "0": { - "diffusers": "ChromaImg2ImgPipeline" + "diffusers": "ChromaPipeline" }, "1": { "generation": { @@ -145,16 +333,147 @@ ], "layer_256": [ "a4daa6ff6f45ca70c738adb8c19bc3b6f228df931e6bf2a3394463e4dd7ec882" + ], + "pipe_names": { + "scheduler": [ + "ops.scheduler.euler", + "discrete" + ], + "vae": [ + "AutoencoderKL" + ], + "text_encoder": [ + "T5EncoderModel" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "chroma" + ], + "transformer": [ + "ChromaTransformer2DModel" + ], + "image_encoder": [ + "CLIPVisionModelWithProjection" + ], + "feature_extractor": [ + "CLIPImageProcessor" + ] + }, + "tasks": [ + "ChromaPipeline" + ] + }, + "chroma1-hd": { + "repo": "lodestones/Chroma1-HD", + "pkg": { + "0": { + "generation": { + "num_inference_steps": 40 + } + } + }, + "file_256": [ + "d845553f11e6afe8139c41ca73678f9f03eab2e68d2e1c6f03ae19509a4d546", + "1b2993a44e63b2250496f69edce643bac2fb79833cf92ba8dd95cbd764d970c7", + "2dd46f08516246df1f582047cc09268ce4f747357baff05b13148e71519029fc" + ] + }, + "chroma1-flash": { + "repo": "lodestones/Chroma1-Flash", + "pkg": { + "0": { + "diffusers": "ChromaPipeline", + "generation": { + "num_inference_steps": 8, + "guidance_scale": 1.0, + "num_images_per_prompt": 1 + } + } + }, + "file_256": [ + "2c0c7d908d04418a48b453c293237a9826d54472cf0ba76e28697d1309d1021b", + "c88f6794753ba23e8f6bf8c84cf220daa35a6aa16d54ea0c3e0136f52e5da7e1", + "c759d67ca3ef50a9a1c242e3291c57f406646f226a95f43f66577996494986db" + ], + "pipe_names": { + "scheduler": [ + "ops.scheduler.euler", + "discrete" + ], + "vae": [ + "AutoencoderKL" + ], + "text_encoder": [ + "T5EncoderModel" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "chroma" + ], + "transformer": [ + "ChromaTransformer2DModel" + ], + "image_encoder": [ + "CLIPVisionModelWithProjection" + ], + "feature_extractor": [ + "CLIPImageProcessor" + ] + }, + "tasks": [ + "ChromaPipeline" ] } }, "info.dit.cogvideox": { "*": { - "repo": "zai-org/CogVideoX-5b", + "repo": "zai-org/CogVideoX-2b", "pkg": { "0": { - "diffusers": "CogVideoXVideoToVideoPipeline" + "diffusers": "CogVideoXPipeline", + "precision": "ops.precision.float.F16", + "generation": { + "num_videos_per_prompt": 1, + "num_inference_steps": 50, + "num_frames": 49, + "guidance_scale": 6 + } } + }, + "file_256": [ + "8fbb6a5e67c70885a8ed8e33df144ac61253e45977be5035fa18cfdf77d386c7" + ], + "layer_b3": [ + "1db3439649b5362448455fb2ed6ebde0c3b973655a206832731149757ad165bb" + ], + "layer_256": [ + "edd6bd51f1236f528ff8d32dc754f0b86cfac901b800642ea497358156dc00bd" + ], + "pipe_names": { + "tokenizer": [ + "info.encoder.tokenizer", + "cogvideox" + ], + "text_encoder": [ + "T5EncoderModel" + ], + "vae": [ + "info.vae.kl", + "audioldm-s-v2" + ], + "transformer": [ + "CogVideoXTransformer3DModel" + ], + "scheduler": [ + [ + "ops.scheduler.cogvideoxddim", + "scheduler" + ], + [ + "ops.scheduler.cogvideoxdpm", + "scheduler" + ] + ] } } }, @@ -175,6 +494,32 @@ "0": { "diffusers": "CogVideoXImageToVideoPipeline" } + }, + "pipe_names": { + "tokenizer": [ + "info.encoder.tokenizer", + "cogvideox-i2v" + ], + "text_encoder": [ + "T5EncoderModel" + ], + "vae": [ + "info.vae.cogvideox", + "cogvideox-i2v" + ], + "transformer": [ + "CogVideoXTransformer3DModel" + ], + "scheduler": [ + [ + "ops.scheduler.cogvideoxddim", + "scheduler" + ], + [ + "ops.scheduler.cogvideoxdpm", + "scheduler" + ] + ] } } }, @@ -183,9 +528,45 @@ "repo": "zai-org/CogView3-Plus-3B", "pkg": { "0": { - "diffusers": "CogView3PlusPipeline" + "diffusers": "CogView3PlusPipeline", + "precision": "ops.precision.float.F16", + "generation": { + "guidance_scale": 7.0, + "num_images_per_prompt": 1, + "num_inference_steps": 50, + "width": 1024, + "height": 1024 + } } - } + }, + "pipe_names": { + "tokenizer": [ + "info.encoder.tokenizer", + "cogview3" + ], + "text_encoder": [ + "T5EncoderModel" + ], + "vae": [ + "AutoencoderKL" + ], + "transformer": [ + "CogView3PlusTransformer2DModel" + ], + "scheduler": [ + [ + "ops.scheduler.cogvideoxddim", + "scheduler" + ], + [ + "ops.scheduler.cogvideoxdpm", + "scheduler" + ] + ] + }, + "tasks": [ + "CogView3PlusPipeline" + ] } }, "info.dit.cogview4": { @@ -195,7 +576,30 @@ "0": { "diffusers": "CogView4Pipeline" } - } + }, + "pipe_names": { + "tokenizer": [ + "info.encoder.tokenizer", + "cogview4" + ], + "text_encoder": [ + "GlmModel" + ], + "vae": [ + "AutoencoderKL" + ], + "transformer": [ + "CogView4Transformer2DModel" + ], + "scheduler": [ + "ops.scheduler.euler", + "discrete" + ] + }, + "tasks": [ + "CogView4ControlPipeline", + "CogView4Pipeline" + ] } }, "info.controlnet.cogview4-control": { @@ -215,6 +619,26 @@ "0": { "diffusers": "ConsisIDPipeline" } + }, + "pipe_names": { + "tokenizer": [ + "info.encoder.tokenizer", + "consisid" + ], + "text_encoder": [ + "T5EncoderModel" + ], + "vae": [ + "info.vae.kl", + "audioldm-s-v2" + ], + "transformer": [ + "ConsisIDTransformer3DModel" + ], + "scheduler": [ + "ops.scheduler.cogvideoxdpm", + "scheduler" + ] } } }, @@ -335,6 +759,74 @@ "0": { "diffusers": "Cosmos2TextToImagePipeline" } + }, + "file_256": [ + "7fbd20dae97cc26a55c7aff3024bc84e554cff8f69966c725a24c8238c5431ec", + "6d211f1c14cd793156da3a840dd5462ae072046fcd6f1dc64c613a5343bfe896", + "95a2b32ad31a271eb64d35985c7ea46f1448528af70932eb1f35d57f90c27be2", + "344e67faf333b7849fa94290c9028bdd5e40eb19700754c833cda0423bc10ad0", + "ce15ef565cbb9ef414a6f7a396c455d82d5f762d2174493da87fe009c5fee75b", + "94aa9f2b59330b88e97b6b439e2f206a51c86e6b154fb66d43ed149bfac23cf8", + "636de5388da249130d51752991a1792b90af31cbf43f021ae07f75756ee2d79a", + "472c5e4cf5056a1a59085addb5a86d801de39bf5e000d253f206a7f63c710029", + "663266ace67c22529c3b6bfa0e8bd69f0ba6e683f5f02b8e3da50881057ba142", + "21a674b314c1364d0dbb3712f5ed702996a7b7403c452835cac22709e01c2f77", + "3bf2df806c6472e039efc9e8d3181163d7faa7b385e61519b7d17d5e9c993a49", + "1de35e1603c4c30bc80b132ccea15fc0503369caf68290708f17e679e98cd41f", + "0738e559bbd71f7351ccba34b2b47362a3f829b92f3dbcffeaf1e44b0d52f42c" + ], + "layer_b3": [ + "5a18ba14c41c6601dcc1195ca180ac7744357eb15ace39272788bda1a7151e9b", + "67cc3eaf7987c89cd7ccff13de6bc03e3eec59d260d44486e2367cd946ce6f20", + "3c6fefa107742488d2e6856714198a762f2fd35c67edd50d4657eaf4b59c7ca3", + "4e1f90ee1e8959d334c9b1ea2cc5e58d0b8340e271c35f81c8a5ec26e16d9d76", + "f8171071e828524fcc2806126ad100a2198e450c82c0864c8fe8b358c5cbbfbd", + "8126101a0207ecfbd741394fd59f306bcb4c492b2a921e0921c426ca7bd38985", + "c942c5a85ff7cb602d8ca894f5d180c2224e91f0b62c3a21f6a425f9e0e8554b", + "c8c500de74da879a547875fe1046f62ab18bdfd09c09eb3da723cbc2319cb4e3", + "c0ac3f67501004e9e9a55d1658402ad97e42bf8a266edf81f6f3bb835ee476b9", + "84f5926eb4e11d826815682b076ed7d3bba4c86520859be80aa1ef92c72b26a4", + "1d4375aab5548708559b0fde150754a2163cd211eb20a5471e17afaeeb26e082", + "68bd8982f59c60d69c301d16dfb5a60f5d43d66c0b60138d48a22f5ded598e7b", + "c3e9a10cad7aebf979072092008be6e2815d03d28cbf316c15e8daf22116bd7d" + ], + "layer_256": [ + "38f2a75eab667c0cc85f3946a23ca6dc2278438c25a9f93aaaa9f79c3808e180", + "ee8434a5e9bc6fa07199de2d0c69fb87f7922c31792bafd13f527c9d92fecb0c", + "2f8382657babb4d0ae4f8e425ae33b21ad71deb6ba457fd6734f05208d52e06a", + "34b181a8291b571857cdbf67ac0081fea594a2f223bf20bd2fc8b0c889e9602d", + "d198c412b972e381acfb812304fa98ed0d97a2f072ddc195cd9a1eb83b1d8146", + "79580a13aff9859e67b0a9f4f8893236cdcfa58c3d43770641aaac8daee55a94", + "cfd48c7ad71c913fa8768167ed0c2ee8c207311b22b1e5a8761369b5a780e8d6", + "da91362ad85d4d2e80a2cb7a55e4ae0e52c9eef8b437a95894ce5ab75d36568c", + "15f84001f5205b6dd8c6f1334cb51c46f6171c7795fb2a557ea16b874f0c71e5", + "5d29179ad15a15d2561defcdda66f1d1e4d065c1e0738f9cba4db5b68b93d2ea", + "7ec489d1e461f5fb2af627b68034ca57f19c516aeccbc5d188b3bd27e3353a15", + "c8dc42fe7b411d746ebdf86286b91cd6893c5f028076b8fe4103f7ea8e1d8833", + "86df7c095aee01588e961438f322b85ca0100a9e440b8a2b6c724e00f748d8b5" + ], + "pipe_names": { + "text_encoder": [ + "T5EncoderModel" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "cosmos-predict2-text2image" + ], + "transformer": [ + "CosmosTransformer3DModel" + ], + "vae": [ + "info.vae.kl", + "audioldm-s-v2" + ], + "scheduler": [ + "ops.scheduler.euler", + "discrete" + ], + "safety_checker": [ + "CosmosSafetyChecker" + ] } } }, @@ -345,6 +837,29 @@ "0": { "diffusers": "Cosmos2VideoToWorldPipeline" } + }, + "pipe_names": { + "text_encoder": [ + "T5EncoderModel" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "cosmos-predict2-video2world" + ], + "transformer": [ + "CosmosTransformer3DModel" + ], + "vae": [ + "info.vae.kl", + "audioldm-s-v2" + ], + "scheduler": [ + "ops.scheduler.euler", + "discrete" + ], + "safety_checker": [ + "CosmosSafetyChecker" + ] } } }, @@ -355,6 +870,29 @@ "0": { "diffusers": "CosmosTextToWorldPipeline" } + }, + "pipe_names": { + "text_encoder": [ + "T5EncoderModel" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "cosmos-1-diffusion-text2world" + ], + "transformer": [ + "CosmosTransformer3DModel" + ], + "vae": [ + "info.vae.kl", + "cosmos-1-diffusion-video2world" + ], + "scheduler": [ + "ops.scheduler.edmeuler", + "scheduler" + ], + "safety_checker": [ + "CosmosSafetyChecker" + ] } } }, @@ -365,6 +903,29 @@ "0": { "diffusers": "CosmosVideoToWorldPipeline" } + }, + "pipe_names": { + "text_encoder": [ + "T5EncoderModel" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "cosmos-1-diffusion-video2world" + ], + "transformer": [ + "CosmosTransformer3DModel" + ], + "vae": [ + "info.vae.kl", + "cosmos-1-diffusion-video2world" + ], + "scheduler": [ + "ops.scheduler.edmeuler", + "scheduler" + ], + "safety_checker": [ + "CosmosSafetyChecker" + ] } } }, @@ -375,7 +936,25 @@ "0": { "diffusers": "IFPipeline" } - } + }, + "pipe_names": { + "tokenizer": [ + "info.encoder.tokenizer", + "if-i-xl-v1" + ], + "text_encoder": [ + "T5EncoderModel" + ], + "scheduler": [ + "ops.scheduler.ddpm", + "scheduler" + ] + }, + "tasks": [ + "IFImg2ImgPipeline", + "IFInpaintingPipeline", + "IFPipeline" + ] } }, "info.dit.easyanimatev5-zh": { @@ -385,6 +964,27 @@ "0": { "diffusers": "EasyAnimatePipeline" } + }, + "pipe_names": { + "vae": [ + "info.vae.kl", + "easyanimatev5-zh" + ], + "text_encoder": [ + "Qwen2VLForConditionalGeneration", + "BertModel" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "easyanimatev5-zh" + ], + "transformer": [ + "EasyAnimateTransformer3DModel" + ], + "scheduler": [ + "ops.scheduler.euler", + "discrete" + ] } } }, @@ -405,6 +1005,27 @@ "0": { "diffusers": "EasyAnimateInpaintPipeline" } + }, + "pipe_names": { + "vae": [ + "info.vae.kl", + "easyanimatev5-zh" + ], + "text_encoder": [ + "Qwen2VLForConditionalGeneration", + "BertModel" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "easyanimatev5-zh-inp" + ], + "transformer": [ + "EasyAnimateTransformer3DModel" + ], + "scheduler": [ + "ops.scheduler.euler", + "discrete" + ] } } }, @@ -414,7 +1035,7 @@ "pkg": { "0": { "diffusers": "FluxInpaintPipeline", - "precision": "ops.precision.bfloat.b16", + "precision": "ops.precision.bfloat.B16", "generation": { "height": 1024, "width": 1024, @@ -424,15 +1045,11 @@ } }, "1": { - "mflux": { - "Flux1": { - "model_name": "schnell" - } - }, + "mflux": "flux.flux.Flux1", "generation": { "height": 1024, "width": 1024, - "steps": 4 + "num_inference_steps": 4 } } }, @@ -454,6 +1071,59 @@ "79c07e339865fe9e22c80f723d728c778130acd07a330339c68218b92bb7b3b8", "ef5c9cd1ebe6e3be5e8b1347eca0a6f0b138986c71220a7f1c2c14f29d01beed", "27bc71eca2d2ff7459165acc12010230911db7709a4f6a5c255befedfa6b1649" + ], + "pipe_names": { + "scheduler": [ + "ops.scheduler.euler", + "discrete" + ], + "vae": [ + "AutoencoderKL" + ], + "text_encoder": [ + "CLIPTextModel" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "flux1-schnell" + ], + "text_encoder_2": [ + "T5EncoderModel" + ], + "tokenizer_2": [ + "info.encoder.tokenizer", + "flux1-schnell" + ], + "transformer": [ + "FluxTransformer2DModel" + ], + "image_encoder": [ + "CLIPVisionModelWithProjection" + ], + "feature_extractor": [ + "CLIPImageProcessor" + ] + }, + "tasks": [ + "FluxControlImg2ImgPipeline", + "FluxControlInpaintPipeline", + "FluxControlNetImg2ImgPipeline", + "FluxControlNetInpaintPipeline", + "FluxControlNetPipeline", + "FluxControlPipeline", + "FluxImg2ImgPipeline", + "FluxInpaintPipeline", + "FluxKontextPipeline", + "FluxPipeline", + "Image", + "Redux", + "Kontext", + "Depth", + "Fill", + "ConceptAttention", + "ControlNet", + "CavTon", + "IC-Edit" ] }, "shuttle-3-aesthetic": { @@ -535,7 +1205,7 @@ "pkg": { "0": { "diffusers": "FluxPipeline", - "precision": "ops.precision.bfloat.b16", + "precision": "ops.precision.bfloat.B16", "generation": { "height": 1024, "width": 1024, @@ -545,16 +1215,12 @@ } }, "1": { - "mflux": { - "Flux1": { - "model_name": "dev" - } - }, + "mflux": "flux.flux.Flux1", "generation": { "height": 1024, "width": 1024, "gudance": 3.5, - "steps": 25 + "num_inference_steps": 25 } } }, @@ -579,6 +1245,59 @@ "double_blocks.12.txt_mod.lin.weight", "add_q_proj.weight", "single_transformer_blocks.9.norm.linear.weight" + ], + "pipe_names": { + "scheduler": [ + "ops.scheduler.euler", + "discrete" + ], + "vae": [ + "AutoencoderKL" + ], + "text_encoder": [ + "CLIPTextModel" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "flux1-dev" + ], + "text_encoder_2": [ + "T5EncoderModel" + ], + "tokenizer_2": [ + "info.encoder.tokenizer", + "flux1-dev" + ], + "transformer": [ + "FluxTransformer2DModel" + ], + "image_encoder": [ + "CLIPVisionModelWithProjection" + ], + "feature_extractor": [ + "CLIPImageProcessor" + ] + }, + "tasks": [ + "FluxControlImg2ImgPipeline", + "FluxControlInpaintPipeline", + "FluxControlNetImg2ImgPipeline", + "FluxControlNetInpaintPipeline", + "FluxControlNetPipeline", + "FluxControlPipeline", + "FluxImg2ImgPipeline", + "FluxInpaintPipeline", + "FluxKontextPipeline", + "FluxPipeline", + "Image", + "Redux", + "Kontext", + "Depth", + "Fill", + "ConceptAttention", + "ControlNet", + "CavTon", + "IC-Edit" ] }, "mystic": { @@ -690,7 +1409,7 @@ "repo": "black-forest-labs/FLUX.1-Canny-dev", "pkg": { "0": { - "diffusers": "FluxControlImg2ImgPipeline" + "diffusers": "FluxControlPipeline" } } } @@ -722,7 +1441,45 @@ "0": { "diffusers": "FluxFillPipeline" } - } + }, + "pipe_names": { + "scheduler": [ + "ops.scheduler.euler", + "discrete" + ], + "vae": [ + "AutoencoderKL" + ], + "text_encoder": [ + "CLIPTextModel" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "flux1-fill-dev" + ], + "text_encoder_2": [ + "T5EncoderModel" + ], + "tokenizer_2": [ + "info.encoder.tokenizer", + "flux1-fill-dev" + ], + "transformer": [ + "FluxTransformer2DModel" + ] + }, + "tasks": [ + "FluxControlImg2ImgPipeline", + "FluxControlInpaintPipeline", + "FluxControlNetImg2ImgPipeline", + "FluxControlNetInpaintPipeline", + "FluxControlNetPipeline", + "FluxControlPipeline", + "FluxImg2ImgPipeline", + "FluxInpaintPipeline", + "FluxKontextPipeline", + "FluxPipeline" + ] } }, "info.dit.flux1-kontext-dev": { @@ -732,7 +1489,51 @@ "0": { "diffusers": "FluxKontextInpaintPipeline" } - } + }, + "pipe_names": { + "scheduler": [ + "ops.scheduler.euler", + "discrete" + ], + "vae": [ + "AutoencoderKL" + ], + "text_encoder": [ + "CLIPTextModel" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "flux1-kontext-dev" + ], + "text_encoder_2": [ + "T5EncoderModel" + ], + "tokenizer_2": [ + "info.encoder.tokenizer", + "flux1-kontext-dev" + ], + "transformer": [ + "FluxTransformer2DModel" + ], + "image_encoder": [ + "CLIPVisionModelWithProjection" + ], + "feature_extractor": [ + "CLIPImageProcessor" + ] + }, + "tasks": [ + "FluxControlImg2ImgPipeline", + "FluxControlInpaintPipeline", + "FluxControlNetImg2ImgPipeline", + "FluxControlNetInpaintPipeline", + "FluxControlNetPipeline", + "FluxControlPipeline", + "FluxImg2ImgPipeline", + "FluxInpaintPipeline", + "FluxKontextPipeline", + "FluxPipeline" + ] } }, "info.dit.hidream-i1": { @@ -742,6 +1543,55 @@ "0": { "diffusers": "HiDreamImagePipeline" } + }, + "file_256": [ + "3cb3f6d77a3fce19b90fa7f66da0cbe997b0785a38a788b559290d3062f6fd26" + ], + "layer_b3": [ + "612eb9b2676a3e7b28b10aae045a97a95de2a399fe3801c8f6369589c3a832a6" + ], + "layer_256": [ + "78fbfb7fddb9ccbdf91f22b0c3d304cbf0cc7305dbccb216982233849ec727df" + ], + "pipe_names": { + "scheduler": [ + "ops.scheduler.euler", + "discrete" + ], + "vae": [ + "AutoencoderKL" + ], + "text_encoder": [ + "CLIPTextModelWithProjection" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "hidream-i1" + ], + "text_encoder_2": [ + "CLIPTextModelWithProjection" + ], + "tokenizer_2": [ + "info.encoder.tokenizer", + "hidream-i1" + ], + "text_encoder_3": [ + "T5EncoderModel" + ], + "tokenizer_3": [ + "info.encoder.tokenizer", + "hidream-i1" + ], + "text_encoder_4": [ + "LlamaForCausalLM" + ], + "tokenizer_4": [ + "info.encoder.tokenizer", + "hidream-i1" + ], + "transformer": [ + "HiDreamImageTransformer2DModel" + ] } } }, @@ -750,18 +1600,84 @@ "repo": "hunyuanvideo-community/HunyuanVideo", "pkg": { "0": { - "diffusers": "HunyuanVideoFramepackPipeline" + "diffusers": "HunyuanVideoPipeline" } - } - } - }, - "info.dit.hunyuanvideo-i2v": { - "*": { - "repo": "hunyuanvideo-community/HunyuanVideo-I2V", - "pkg": { + }, + "file_256": [ + "bdb957b35585ea74ae42ca92865a68fa1bf1ebc6c5b7e686a889e5c977dc24c7" + ], + "layer_b3": [ + "d31c56b4c9444d4c2f1b10120fe964e0956f6b8c7e7c1e4cc5a1f37406fc49f5" + ], + "layer_256": [ + "fe741fdfd163bcb1e0ed81d80f79ac3576dbf6e6740674efadfeff782a48bed4" + ], + "pipe_names": { + "text_encoder": [ + "LlamaModel" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "hunyuanvideo" + ], + "transformer": [ + "HunyuanVideoTransformer3DModel" + ], + "vae": [ + "info.vae.kl", + "hunyuanvideo-i2v" + ], + "scheduler": [ + "ops.scheduler.euler", + "discrete" + ], + "text_encoder_2": [ + "CLIPTextModel" + ], + "tokenizer_2": [ + "info.encoder.tokenizer", + "hunyuanvideo" + ] + } + } + }, + "info.dit.hunyuanvideo-i2v": { + "*": { + "repo": "hunyuanvideo-community/HunyuanVideo-I2V", + "pkg": { "0": { "diffusers": "HunyuanVideoImageToVideoPipeline" } + }, + "pipe_names": { + "text_encoder": [ + "LlavaForConditionalGeneration" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "hunyuanvideo-i2v" + ], + "transformer": [ + "HunyuanVideoTransformer3DModel" + ], + "vae": [ + "info.vae.kl", + "hunyuanvideo-i2v" + ], + "scheduler": [ + "ops.scheduler.euler", + "discrete" + ], + "text_encoder_2": [ + "CLIPTextModel" + ], + "tokenizer_2": [ + "info.encoder.tokenizer", + "hunyuanvideo-i2v" + ], + "image_processor": [ + "CLIPImageProcessor" + ] } } }, @@ -770,7 +1686,8 @@ "repo": "tencent-hunyuan/hunyuandiT-v1.2-diffusers", "pkg": { "0": { - "diffusers": "HunyuanDiTPipeline" + "diffusers": "HunyuanDiTPipeline", + "precision": "ops.precision.float.F16" } }, "identifiers": [ @@ -780,15 +1697,50 @@ ], "file_256": [ "4fb84f84079cda457d171b3c6b15d1be95b5a3e5d9825703951a99ddf92d1787", - "e01db5e129e8ca1117e9cf473fc5a2b096949f03ab90048aeabbc328de7ec800" + "e01db5e129e8ca1117e9cf473fc5a2b096949f03ab90048aeabbc328de7ec800", + "8af691cadb78047d55721259355d708e87ddbba1b7845df9377d9a5ae917b45d" ], "layer_b3": [ "aead6b61b17ebc77c4c186a4b82c193f11ec267b20d909726422ee9852e2e0b2", - "885a056b94f6f9844c0660be489844d63bb74cc13316f441d10968fff3dd3120" + "885a056b94f6f9844c0660be489844d63bb74cc13316f441d10968fff3dd3120", + "390d951cbdda6e2cffb690031b60f02921624651534c2effaaa7d68ab476c700" ], "layer_256": [ "d4842ce2b7f927203326b25ff4d6738ec9a8b95327f06791c387e4a351ed6ed0", - "5af943f96f5dc9fecb1e92fe2b1fa17c94dd6947690201f4a5ee1a4a2721a68e" + "5af943f96f5dc9fecb1e92fe2b1fa17c94dd6947690201f4a5ee1a4a2721a68e", + "4a1f2b8234fa4336e263842e042d42e8d64d8a4d3941d9c0c78366b50303950c" + ], + "pipe_names": { + "vae": [ + "AutoencoderKL" + ], + "text_encoder": [ + "BertModel" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "hunyuandit-v1" + ], + "transformer": [ + "HunyuanDiT2DModel" + ], + "scheduler": [ + "ops.scheduler.ddpm", + "scheduler" + ], + "safety_checker": [ + "StableDiffusionSafetyChecker" + ], + "feature_extractor": [ + "CLIPImageProcessor" + ], + "tokenizer_2": [ + "info.encoder.tokenizer", + "hunyuandit-v1" + ] + }, + "tasks": [ + "HunyuanDiTPipeline" ] } }, @@ -799,6 +1751,31 @@ "0": { "diffusers": "I2VGenXLPipeline" } + }, + "pipe_names": { + "vae": [ + "AutoencoderKL" + ], + "text_encoder": [ + "CLIPTextModel" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "i2vgen-xl" + ], + "image_encoder": [ + "CLIPVisionModelWithProjection" + ], + "feature_extractor": [ + "CLIPImageProcessor" + ], + "unet": [ + "I2VGenXLUNet" + ], + "scheduler": [ + "ops.scheduler.ddim", + "scheduler" + ] } } }, @@ -809,7 +1786,45 @@ "0": { "diffusers": "KandinskyPriorPipeline" } - } + }, + "pipe_names": { + "prior": [ + "PriorTransformer" + ], + "image_encoder": [ + "CLIPVisionModelWithProjection" + ], + "text_encoder": [ + "CLIPTextModelWithProjection" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "kandinsky-2-1" + ], + "scheduler": [ + "ops.scheduler.unclip", + "scheduler" + ], + "image_processor": [ + "CLIPImageProcessor" + ] + }, + "tasks": [ + "Kandinsky3Img2ImgPipeline", + "Kandinsky3Pipeline", + "KandinskyCombinedPipeline", + "KandinskyImg2ImgCombinedPipeline", + "KandinskyImg2ImgPipeline", + "KandinskyInpaintCombinedPipeline", + "KandinskyInpaintPipeline", + "KandinskyPipeline", + "KandinskyV22CombinedPipeline", + "KandinskyV22Img2ImgCombinedPipeline", + "KandinskyV22Img2ImgPipeline", + "KandinskyV22InpaintCombinedPipeline", + "KandinskyV22InpaintPipeline", + "KandinskyV22Pipeline" + ] } }, "info.unet.kandinsky-2-2": { @@ -819,7 +1834,45 @@ "0": { "diffusers": "KandinskyPriorPipeline" } - } + }, + "pipe_names": { + "prior": [ + "PriorTransformer" + ], + "image_encoder": [ + "CLIPVisionModelWithProjection" + ], + "text_encoder": [ + "CLIPTextModelWithProjection" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "kandinsky-2-2" + ], + "scheduler": [ + "ops.scheduler.unclip", + "scheduler" + ], + "image_processor": [ + "CLIPImageProcessor" + ] + }, + "tasks": [ + "Kandinsky3Img2ImgPipeline", + "Kandinsky3Pipeline", + "KandinskyCombinedPipeline", + "KandinskyImg2ImgCombinedPipeline", + "KandinskyImg2ImgPipeline", + "KandinskyInpaintCombinedPipeline", + "KandinskyInpaintPipeline", + "KandinskyPipeline", + "KandinskyV22CombinedPipeline", + "KandinskyV22Img2ImgCombinedPipeline", + "KandinskyV22Img2ImgPipeline", + "KandinskyV22InpaintCombinedPipeline", + "KandinskyV22InpaintPipeline", + "KandinskyV22Pipeline" + ] } }, "info.unet.kolors": { @@ -827,8 +1880,8 @@ "repo": "Kwai-Kolors/Kolors-diffusers", "pkg": { "0": { - "diffusers": "KolorsImg2ImgPipeline", - "precision": "ops.precision.float.f16", + "diffusers": "KolorsPipeline", + "precision": "ops.precision.float.F16", "generation": { "negative_prompt": "", "guidance_scale": 5.0, @@ -853,6 +1906,33 @@ "identifiers": [ ".DenseReluDense.wi.weight", "encoder_hid_proj.weight" + ], + "pipe_names": { + "vae": [ + "AutoencoderKL" + ], + "text_encoder": [ + "ChatGLMModel" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "kolors" + ], + "scheduler": [ + "ops.scheduler.karrasdiffusion", + "schedulers" + ], + "image_encoder": [ + "CLIPVisionModelWithProjection" + ], + "feature_extractor": [ + "CLIPImageProcessor" + ] + }, + "tasks": [ + "KolorsImg2ImgPipeline", + "KolorsPAGPipeline", + "KolorsPipeline" ] } }, @@ -863,6 +1943,25 @@ "0": { "diffusers": "LattePipeline" } + }, + "pipe_names": { + "tokenizer": [ + "info.encoder.tokenizer", + "latte-1" + ], + "text_encoder": [ + "T5EncoderModel" + ], + "vae": [ + "AutoencoderKL" + ], + "transformer": [ + "LatteTransformer3DModel" + ], + "scheduler": [ + "ops.scheduler.karrasdiffusion", + "schedulers" + ] } } }, @@ -873,6 +1972,26 @@ "0": { "diffusers": "LTXImageToVideoPipeline" } + }, + "pipe_names": { + "scheduler": [ + "ops.scheduler.euler", + "discrete" + ], + "vae": [ + "info.vae.kl", + "ltx-video" + ], + "text_encoder": [ + "T5EncoderModel" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "ltx-video" + ], + "transformer": [ + "LTXVideoTransformer3DModel" + ] } } }, @@ -883,6 +2002,26 @@ "0": { "diffusers": "LTXConditionPipeline" } + }, + "pipe_names": { + "scheduler": [ + "ops.scheduler.euler", + "discrete" + ], + "vae": [ + "info.vae.kl", + "ltx-video" + ], + "text_encoder": [ + "T5EncoderModel" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "ltx-video-09" + ], + "transformer": [ + "LTXVideoTransformer3DModel" + ] } } }, @@ -891,7 +2030,8 @@ "repo": "Alpha-VLLM/Lumina-Next-SFT-diffusers", "pkg": { "0": { - "diffusers": "LuminaPipeline" + "diffusers": "LuminaPipeline", + "precision": " ops.precision.bfloat.B16" } }, "identifiers": [ @@ -908,6 +2048,29 @@ "layer_256": [ "3938a85568d9df186923edf04391d79e89e6199123bc175afb520e0948d1ae05", "c0ca51fdea051fcd042bf4b56d32e1e8bb9525a921f2e197f370f101e90527f0" + ], + "pipe_names": { + "transformer": [ + "LuminaNextDiT2DModel" + ], + "scheduler": [ + "ops.scheduler.euler", + "discrete" + ], + "vae": [ + "AutoencoderKL" + ], + "text_encoder": [ + "GemmaPreTrainedModel" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "lumina-next-sft" + ] + }, + "tasks": [ + "Lumina2Pipeline", + "LuminaPipeline" ] } }, @@ -918,7 +2081,41 @@ "0": { "diffusers": "Lumina2Pipeline" } - } + }, + "file_256": [ + "132b4d213fdd3cfc14333746fc3eb8bbe6358cd73c3bc95ac4ccec230b97dca3", + "a7c09ebae62996a8289782161338a3cdba58c11d2d849c50b2d6502e152b0d6d" + ], + "layer_b3": [ + "198bde52f09736f1fc650dcdbd0e6b0f6a5ce186582554c1d9ee8ab16ac0feb2", + "b52807536902cabbf84f99e4fa2f8713fb4ef77e739f06367ee0d486e3222faa" + ], + "layer_256": [ + "982893c99860aac8198c2e435cf85f782fce8f10732daf1f2881a26864400a4e", + "dc937b59892604f5a86ac96936cd7ff09e25f18ae6b758e8014a24c7fa039e91" + ], + "pipe_names": { + "transformer": [ + "Lumina2Transformer2DModel" + ], + "scheduler": [ + "ops.scheduler.euler", + "discrete" + ], + "vae": [ + "AutoencoderKL" + ], + "text_encoder": [ + "Gemma2PreTrainedModel" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "lumina-image-2" + ] + }, + "tasks": [ + "Lumina2Pipeline" + ] }, "illustrious-lumina-v3": { "repo": "OnomaAIResearch/Illustrious-Lumina-v0.03", @@ -943,6 +2140,26 @@ "0": { "diffusers": "MochiPipeline" } + }, + "pipe_names": { + "scheduler": [ + "ops.scheduler.euler", + "discrete" + ], + "vae": [ + "info.vae.kl", + "mochi-1" + ], + "text_encoder": [ + "T5EncoderModel" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "mochi-1" + ], + "transformer": [ + "MochiTransformer3DModel" + ] } } }, @@ -951,8 +2168,41 @@ "repo": "ucsd-reach/musicldm", "pkg": { "0": { - "diffusers": "MusicLDMPipeline" + "diffusers": "MusicLDMPipeline", + "generation": { + "num_inference_steps": 200, + "audio_length_in_s": 10.0 + } } + }, + "file_256": [ + "853d0ef1d61cbf5d682872322ea8b761ba3d2f85bfbccd58363bd6b2f837268f" + ], + "layer_b3": [ + "82fbcc553c1ad770d28fd1866b935249c5ebfbf75f3166ae823e1bc6ef39a95a" + ], + "layer_256": [ + "d076446a58a36bf436e37444679d62bcf2f45689d4aa3d799b3fe801c71ed2c8" + ], + "pipe_names": { + "vae": [ + "AutoencoderKL" + ], + "text_encoder": [ + "ClapTextModelWithProjection", + "ClapModel" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "musicldm" + ], + "scheduler": [ + "ops.scheduler.karrasdiffusion", + "schedulers" + ], + "vocoder": [ + "SpeechT5HifiGan" + ] } } }, @@ -978,6 +2228,28 @@ ], "layer_256": [ "117225c0e91423746114b23d3e409708ad55c90ff52b21fa7a1c5105d2e935a5" + ], + "pipe_names": { + "tokenizer": [ + "info.encoder.tokenizer", + "pixart-xl-2-1024-ms" + ], + "text_encoder": [ + "T5EncoderModel" + ], + "vae": [ + "AutoencoderKL" + ], + "transformer": [ + "PixArtTransformer2DModel" + ], + "scheduler": [ + "ops.scheduler.dpm", + "multistep" + ] + }, + "tasks": [ + "PixArtAlphaPipeline" ] } }, @@ -994,13 +2266,39 @@ "scale_shift_table" ], "file_256": [ - "c34b520ef473329b945c2a21083cdf1337c5a468d23b3215b65576789bfd0305" + "c34b520ef473329b945c2a21083cdf1337c5a468d23b3215b65576789bfd0305", + "2fa4dee9229c02b03163f57bdb8e80c7a5ee364b7161796abe9c05e8dd13f239" ], "layer_b3": [ - "a199930ff537994872da77391955f0dd52eddd22ab9105388f0c5852f1b8021f" + "a199930ff537994872da77391955f0dd52eddd22ab9105388f0c5852f1b8021f", + "ee6f980c32e98da6885f3e97d3f88d9158031e362cd3a49b20d1e23924b251e3" ], "layer_256": [ - "e0afd203aff5a1d192e325d0f59361373273d85d138b51768c3f10a75c154dc0" + "e0afd203aff5a1d192e325d0f59361373273d85d138b51768c3f10a75c154dc0", + "987f3c2ff5d399191e5fd7dd7b1f1f285c197dc8124ad77f05cde7f2fb677a3c" + ], + "pipe_names": { + "tokenizer": [ + "info.encoder.tokenizer", + "pixart-sigma-xl-2-1024-ms" + ], + "text_encoder": [ + "T5EncoderModel" + ], + "vae": [ + "AutoencoderKL" + ], + "transformer": [ + "PixArtTransformer2DModel" + ], + "scheduler": [ + "ops.scheduler.karrasdiffusion", + "schedulers" + ] + }, + "tasks": [ + "PixArtAlphaPipeline", + "PixArtSigmaPipeline" ] } }, @@ -1009,7 +2307,7 @@ "repo": "Qwen/Qwen-Image", "pkg": { "0": { - "diffusers": "QwenImagePipeline" + "diffusers": "QwenImageInpaintPipeline" } }, "file_256": [ @@ -1020,7 +2318,60 @@ ], "layer_256": [ "fda2472d8ef6587a4c979021a2390eeb7c8fc2bcf565330ab8dc6b22f5348ec9" - ] + ], + "pipe_names": { + "scheduler": [ + "ops.scheduler.euler", + "discrete" + ], + "vae": [ + "info.vae.kl", + "qwen-image" + ], + "text_encoder": [ + "Qwen2_5_VLForConditionalGeneration" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "qwen-image" + ], + "transformer": [ + "QwenImageTransformer2DModel" + ] + } + } + }, + "info.dit.qwen-image-edit": { + "*": { + "repo": "Qwen/Qwen-Image-Edit", + "pkg": { + "0": { + "diffusers": "QwenImageEditPipeline" + } + }, + "pipe_names": { + "scheduler": [ + "ops.scheduler.euler", + "discrete" + ], + "vae": [ + "info.vae.kl", + "qwen-image" + ], + "text_encoder": [ + "Qwen2_5_VLForConditionalGeneration" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "qwen-image-edit" + ], + "processor": [ + "Qwen2VLProcessor" + ], + "transformer": [ + "QwenImageTransformer2DModel" + ] + } } }, "info.dit.sana-1024px-bf16": { @@ -1028,9 +2379,49 @@ "repo": "Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers", "pkg": { "0": { - "diffusers": "SanaPipeline" + "diffusers": "SanaPipeline", + "generation": { + "height": 1024, + "width": 1024, + "guidance_scale": 4.5, + "num_inference_steps": 20 + }, + "precision": "ops.precision.bfloat.B16" } - } + }, + "file_256": [ + "b0b50c33be8758713459aa3c760feef6315d4bea31521fb5b8c3e8fdd9841ffe" + ], + "layer_b3": [ + "461e3d83dfa7e075ef21e2138ef153922ecfadde3db464b03dff92819f3e86dd" + ], + "layer_256": [ + "b928bbcc2ce99d55d21c189e2b1c57498bc313ef5b1457036e356107d567fc4e" + ], + "pipe_names": { + "tokenizer": [ + "info.encoder.tokenizer", + "sana-1024px-bf16" + ], + "text_encoder": [ + "Gemma2PreTrainedModel" + ], + "vae": [ + "info.vae.dc", + "sana-1024px-bf16" + ], + "transformer": [ + "SanaTransformer2DModel" + ], + "scheduler": [ + "ops.scheduler.dpm", + "multistep" + ] + }, + "tasks": [ + "SanaPAGPipeline", + "SanaPipeline" + ] } }, "info.controlnet.sana-1024px-controlnet": { @@ -1048,9 +2439,33 @@ "repo": "Efficient-Large-Model/Sana_Sprint_1.6B_1024px_diffusers", "pkg": { "0": { - "diffusers": "SanaSprintImg2ImgPipeline" + "diffusers": "SanaSprintPipeline" } - } + }, + "pipe_names": { + "tokenizer": [ + "info.encoder.tokenizer", + "sana-sprint-1024px" + ], + "text_encoder": [ + "Gemma2PreTrainedModel" + ], + "vae": [ + "info.vae.dc", + "sana-1024px-bf16" + ], + "transformer": [ + "SanaTransformer2DModel" + ], + "scheduler": [ + "ops.scheduler.dpm", + "multistep" + ] + }, + "tasks": [ + "SanaPAGPipeline", + "SanaPipeline" + ] } }, "info.unet.shap-e": { @@ -1058,8 +2473,33 @@ "repo": "openai/shap-e", "pkg": { "0": { - "diffusers": "ShapEPipeline" + "diffusers": "ShapEPipeline", + "precision": "ops.precision.float.F16", + "generation": { + "num_inference_steps": 64, + "size": 256, + "guidance_scale": 15 + } } + }, + "pipe_names": { + "prior": [ + "PriorTransformer" + ], + "text_encoder": [ + "CLIPTextModelWithProjection" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "shap-e" + ], + "scheduler": [ + "ops.scheduler.heun", + "discrete" + ], + "shap_e_renderer": [ + "ShapERenderer" + ] } } }, @@ -1070,6 +2510,56 @@ "0": { "diffusers": "SkyReelsV2Pipeline" } + }, + "pipe_names": { + "tokenizer": [ + "info.encoder.tokenizer", + "skyreels-v2-t2v-720p" + ], + "text_encoder": [ + "UMT5EncoderModel" + ], + "transformer": [ + "SkyReelsV2Transformer3DModel" + ], + "vae": [ + "info.vae.kl", + "audioldm-s-v2" + ], + "scheduler": [ + "ops.scheduler.unipc", + "multistep" + ] + } + } + }, + "info.dit.skyreels-v2-df-720p": { + "diffusers": { + "repo": "Skywork/SkyReels-V2-DF-14B-720P-Diffusers", + "pkg": { + "0": { + "diffusers": "SkyReelsV2DiffusionForcingVideoToVideoPipeline" + } + }, + "pipe_names": { + "tokenizer": [ + "info.encoder.tokenizer", + "skyreels-v2-df-720p" + ], + "text_encoder": [ + "UMT5EncoderModel" + ], + "transformer": [ + "SkyReelsV2Transformer3DModel" + ], + "vae": [ + "info.vae.kl", + "audioldm-s-v2" + ], + "scheduler": [ + "ops.scheduler.unipc", + "multistep" + ] } } }, @@ -1080,6 +2570,32 @@ "0": { "diffusers": "SkyReelsV2ImageToVideoPipeline" } + }, + "pipe_names": { + "tokenizer": [ + "info.encoder.tokenizer", + "skyreels-v2-i2v-720p" + ], + "text_encoder": [ + "UMT5EncoderModel" + ], + "image_encoder": [ + "CLIPVisionModelWithProjection" + ], + "image_processor": [ + "CLIPProcessor" + ], + "transformer": [ + "SkyReelsV2Transformer3DModel" + ], + "vae": [ + "info.vae.kl", + "audioldm-s-v2" + ], + "scheduler": [ + "ops.scheduler.unipc", + "multistep" + ] } } }, @@ -1088,8 +2604,37 @@ "repo": "stabilityai/stable-audio-open-1.0", "pkg": { "0": { - "diffusers": "StableAudioPipeline" + "diffusers": "StableAudioPipeline", + "precision": "ops.precision.float.F16", + "generation": { + "num_inference_steps": 200, + "audio_end_in_s": 10, + "num_waveforms_per_prompt": 3 + } } + }, + "pipe_names": { + "vae": [ + "info.vae.oobleck", + "stable-audio-open-1" + ], + "text_encoder": [ + "T5EncoderModel" + ], + "projection_model": [ + "StableAudioProjectionModel" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "stable-audio-open-1" + ], + "transformer": [ + "StableAudioDiTModel" + ], + "scheduler": [ + "ops.scheduler.dpm", + "multistep" + ] } } }, @@ -1099,7 +2644,7 @@ "pkg": { "0": { "diffusers": "StableCascadePriorPipeline", - "precision": "ops.precision.bfloat.b16", + "precision": "ops.precision.bfloat.B16", "generation": { "negative_prompt": "", "num_images_per_prompt": 1, @@ -1138,7 +2683,23 @@ "down_blocks.0.2.kv_mapper", "previewer", "backbone" - ] + ], + "pipe_names": { + "tokenizer": [ + "info.encoder.tokenizer", + "stable-cascade" + ], + "text_encoder": [ + "CLIPTextModelWithProjection" + ], + "prior": [ + "StableCascadeUNet" + ], + "scheduler": [ + "ops.scheduler.ddpmwuerstchen", + "scheduler" + ] + } }, "decoder": { "repo": "stabilityai/stable-cascade", @@ -1151,7 +2712,7 @@ "output_type": "pil", "num_inference_steps": 10 }, - "precision": "ops.precision.bfloat.b16" + "precision": "ops.precision.bfloat.B16" } }, "file_256": [ @@ -1179,6 +2740,29 @@ "0.2.channelwise", "clip_mapper.bias", ".12.self_attn.k_proj.weight" + ], + "pipe_names": { + "decoder": [ + "StableCascadeUNet" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "stable-cascade" + ], + "text_encoder": [ + "CLIPTextModelWithProjection" + ], + "scheduler": [ + "ops.scheduler.ddpmwuerstchen", + "scheduler" + ], + "vqgan": [ + "PaellaVQModel" + ] + }, + "tasks": [ + "StableCascadeCombinedPipeline", + "StableCascadeDecoderPipeline" ] } }, @@ -1187,7 +2771,7 @@ "repo": "stable-diffusion-v1-5/stable-diffusion-v1-5", "pkg": { "0": { - "diffusers": "StableDiffusionImg2ImgPipeline" + "diffusers": "StableDiffusionPipeline" } }, "identifiers": [ @@ -1215,6 +2799,65 @@ "65b084dada803461ab9ca9be9b892d211870a121dd6c555a111eea470b951c54", "dc937b59892604f5a86ac96936cd7ff09e25f18ae6b758e8014a24c7fa039e91", "92565dec90f7c8412dc872e820f66cd0c56263bbbc392439645b6fee270f41bb" + ], + "pipe_names": { + "vae": [ + "AutoencoderKL" + ], + "text_encoder": [ + "CLIPTextModel" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "stable-diffusion-v1-5" + ], + "scheduler": [ + "ops.scheduler.karrasdiffusion", + "schedulers" + ], + "safety_checker": [ + "StableDiffusionSafetyChecker" + ], + "feature_extractor": [ + "CLIPImageProcessor" + ], + "image_encoder": [ + "CLIPVisionModelWithProjection" + ] + }, + "tasks": [ + "StableDiffusion3ControlNetInpaintingPipeline", + "StableDiffusion3ControlNetPipeline", + "StableDiffusion3Img2ImgPipeline", + "StableDiffusion3InpaintPipeline", + "StableDiffusion3PAGImg2ImgPipeline", + "StableDiffusion3PAGPipeline", + "StableDiffusion3Pipeline", + "StableDiffusionControlNetImg2ImgPipeline", + "StableDiffusionControlNetInpaintPipeline", + "StableDiffusionControlNetPAGInpaintPipeline", + "StableDiffusionControlNetPAGPipeline", + "StableDiffusionControlNetPipeline", + "StableDiffusionImg2ImgPipeline", + "StableDiffusionInpaintPipeline", + "StableDiffusionPAGImg2ImgPipeline", + "StableDiffusionPAGInpaintPipeline", + "StableDiffusionPAGPipeline", + "StableDiffusionPipeline", + "StableDiffusionXLControlNetImg2ImgPipeline", + "StableDiffusionXLControlNetInpaintPipeline", + "StableDiffusionXLControlNetPAGImg2ImgPipeline", + "StableDiffusionXLControlNetPAGPipeline", + "StableDiffusionXLControlNetPipeline", + "StableDiffusionXLControlNetUnionImg2ImgPipeline", + "StableDiffusionXLControlNetUnionInpaintPipeline", + "StableDiffusionXLControlNetUnionPipeline", + "StableDiffusionXLImg2ImgPipeline", + "StableDiffusionXLInpaintPipeline", + "StableDiffusionXLPAGImg2ImgPipeline", + "StableDiffusionXLPAGInpaintPipeline", + "StableDiffusionXLPAGPipeline", + "StableDiffusionXLPipeline" ] } }, @@ -1225,7 +2868,78 @@ "0": { "diffusers": "StableUnCLIPPipeline" } - } + }, + "pipe_names": { + "prior_tokenizer": [ + "info.encoder.tokenizer", + "stable-unclip-2-1-l" + ], + "prior_text_encoder": [ + "CLIPTextModelWithProjection" + ], + "prior": [ + "PriorTransformer" + ], + "prior_scheduler": [ + "ops.scheduler.karrasdiffusion", + "schedulers" + ], + "image_normalizer": [ + "StableUnCLIPImageNormalizer" + ], + "image_noising_scheduler": [ + "ops.scheduler.karrasdiffusion", + "schedulers" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "stable-unclip-2-1-l" + ], + "text_encoder": [ + "CLIPTextModel" + ], + "scheduler": [ + "ops.scheduler.karrasdiffusion", + "schedulers" + ], + "vae": [ + "AutoencoderKL" + ] + }, + "tasks": [ + "StableDiffusion3ControlNetInpaintingPipeline", + "StableDiffusion3ControlNetPipeline", + "StableDiffusion3Img2ImgPipeline", + "StableDiffusion3InpaintPipeline", + "StableDiffusion3PAGImg2ImgPipeline", + "StableDiffusion3PAGPipeline", + "StableDiffusion3Pipeline", + "StableDiffusionControlNetImg2ImgPipeline", + "StableDiffusionControlNetInpaintPipeline", + "StableDiffusionControlNetPAGInpaintPipeline", + "StableDiffusionControlNetPAGPipeline", + "StableDiffusionControlNetPipeline", + "StableDiffusionImg2ImgPipeline", + "StableDiffusionInpaintPipeline", + "StableDiffusionPAGImg2ImgPipeline", + "StableDiffusionPAGInpaintPipeline", + "StableDiffusionPAGPipeline", + "StableDiffusionPipeline", + "StableDiffusionXLControlNetImg2ImgPipeline", + "StableDiffusionXLControlNetInpaintPipeline", + "StableDiffusionXLControlNetPAGImg2ImgPipeline", + "StableDiffusionXLControlNetPAGPipeline", + "StableDiffusionXLControlNetPipeline", + "StableDiffusionXLControlNetUnionImg2ImgPipeline", + "StableDiffusionXLControlNetUnionInpaintPipeline", + "StableDiffusionXLControlNetUnionPipeline", + "StableDiffusionXLImg2ImgPipeline", + "StableDiffusionXLInpaintPipeline", + "StableDiffusionXLPAGImg2ImgPipeline", + "StableDiffusionXLPAGInpaintPipeline", + "StableDiffusionXLPAGPipeline", + "StableDiffusionXLPipeline" + ] } }, "info.unet.stable-diffusion-2-1-unclip": { @@ -1235,15 +2949,79 @@ "0": { "diffusers": "StableUnCLIPImg2ImgPipeline" } - } - } - }, - "info.dit.stable-diffusion-3": { + }, + "pipe_names": { + "feature_extractor": [ + "CLIPImageProcessor" + ], + "image_encoder": [ + "CLIPVisionModelWithProjection" + ], + "image_normalizer": [ + "StableUnCLIPImageNormalizer" + ], + "image_noising_scheduler": [ + "ops.scheduler.karrasdiffusion", + "schedulers" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "stable-diffusion-2-1-unclip" + ], + "text_encoder": [ + "CLIPTextModel" + ], + "scheduler": [ + "ops.scheduler.karrasdiffusion", + "schedulers" + ], + "vae": [ + "AutoencoderKL" + ] + }, + "tasks": [ + "StableDiffusion3ControlNetInpaintingPipeline", + "StableDiffusion3ControlNetPipeline", + "StableDiffusion3Img2ImgPipeline", + "StableDiffusion3InpaintPipeline", + "StableDiffusion3PAGImg2ImgPipeline", + "StableDiffusion3PAGPipeline", + "StableDiffusion3Pipeline", + "StableDiffusionControlNetImg2ImgPipeline", + "StableDiffusionControlNetInpaintPipeline", + "StableDiffusionControlNetPAGInpaintPipeline", + "StableDiffusionControlNetPAGPipeline", + "StableDiffusionControlNetPipeline", + "StableDiffusionImg2ImgPipeline", + "StableDiffusionInpaintPipeline", + "StableDiffusionPAGImg2ImgPipeline", + "StableDiffusionPAGInpaintPipeline", + "StableDiffusionPAGPipeline", + "StableDiffusionPipeline", + "StableDiffusionXLControlNetImg2ImgPipeline", + "StableDiffusionXLControlNetInpaintPipeline", + "StableDiffusionXLControlNetPAGImg2ImgPipeline", + "StableDiffusionXLControlNetPAGPipeline", + "StableDiffusionXLControlNetPipeline", + "StableDiffusionXLControlNetUnionImg2ImgPipeline", + "StableDiffusionXLControlNetUnionInpaintPipeline", + "StableDiffusionXLControlNetUnionPipeline", + "StableDiffusionXLImg2ImgPipeline", + "StableDiffusionXLInpaintPipeline", + "StableDiffusionXLPAGImg2ImgPipeline", + "StableDiffusionXLPAGInpaintPipeline", + "StableDiffusionXLPAGPipeline", + "StableDiffusionXLPipeline" + ] + } + }, + "info.dit.stable-diffusion-3": { "*": { "repo": "stabilityai/stable-diffusion-3.5-medium", "pkg": { "0": { - "diffusers": "StableDiffusion3Pipeline" + "diffusers": "StableDiffusion3Pipeline", + "precision": "ops.precision.float.F16" } }, "identifiers": [ @@ -1263,15 +3041,88 @@ "layer_256": [ "13c982a6dc82d21c9f459e837d8c6f6d4696fd6e7e7b5783bdd2250b1f4fec61", "6ee79050373337bf63ac20916596df778bb22022bb38af986128a7459eda1463" + ], + "pipe_names": { + "transformer": [ + "SD3Transformer2DModel" + ], + "scheduler": [ + "ops.scheduler.euler", + "discrete" + ], + "vae": [ + "info.vae.tae", + "stable-diffusion-3" + ], + "text_encoder": [ + "CLIPTextModelWithProjection" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "stable-diffusion-3" + ], + "text_encoder_2": [ + "CLIPTextModelWithProjection" + ], + "tokenizer_2": [ + "info.encoder.tokenizer", + "stable-diffusion-3" + ], + "text_encoder_3": [ + "T5EncoderModel" + ], + "tokenizer_3": [ + "info.encoder.tokenizer", + "stable-diffusion-3" + ], + "image_encoder": [ + "SiglipVisionModel" + ], + "feature_extractor": [ + "SiglipImageProcessor" + ] + }, + "tasks": [ + "StableDiffusion3ControlNetInpaintingPipeline", + "StableDiffusion3ControlNetPipeline", + "StableDiffusion3Img2ImgPipeline", + "StableDiffusion3InpaintPipeline", + "StableDiffusion3PAGImg2ImgPipeline", + "StableDiffusion3PAGPipeline", + "StableDiffusion3Pipeline" ] }, - "diffusers": { - "repo": "stabilityai/stable-diffusion-3-medium-diffusers", + "stable-diffusion-3-turbo": { + "repo": "tensorart/stable-diffusion-3.5-medium-turbo", "pkg": { "0": { - "diffusers": "StableDiffusion3InpaintPipeline" + "precision": "ops.precision.bfloat.B16", + "generation": { + "num_inference_steps": 8, + "guidance_scale": 1.5, + "height": 1024, + "width": 768 + } } - } + }, + "file_256": [ + "5b0530e8d71b49fa1358f1208047cd789a40bae5b44406c9524b0f0d88f8b246", + "07119c77c3548a1d9eb30923df4dd55ec74914dc5ec81626804dcbe51ce17a5d", + "3c379381344d2a2b3ee3d7a1bc97f7d1e58fa95c6b5187fb48b3ce446f99f17b", + "6b3806cafdb4303ea2638e9e08eb186067b4a46a95ddf344ccdbe56537afaf6e" + ], + "layer_256": [ + "3c324055a1ec6eb4ee0242e344bb2b6356afcbd2e215fdd9d160cda691a72fae", + "7284d2027523482af9ef47405667ca891cc518bfb6ebf1f1d4666cb0accc8cd5", + "d938ee5738c73f701760ed18acad274b074d2796123aee3f2eee1328b6c36ea4", + "c4c40056c2a77959083b5a69a1a4b205caa463ccabde057352c5c4e38b2c67b6" + ], + "layer_b3": [ + "873821614080a98e1ebfe56673bc96c2ac57379720d4ad2f97e4bca317571d48", + "7284d2027523482af9ef47405667ca891cc518bfb6ebf1f1d4666cb0accc8cd5", + "d938ee5738c73f701760ed18acad274b074d2796123aee3f2eee1328b6c36ea4", + "c4c40056c2a77959083b5a69a1a4b205caa463ccabde057352c5c4e38b2c67b6" + ] } }, "info.unet.gligen-1-4-inpainting-text-box": { @@ -1301,7 +3152,63 @@ "0": { "diffusers": "StableDiffusionLDM3DPipeline" } - } + }, + "pipe_names": { + "vae": [ + "AutoencoderKL" + ], + "text_encoder": [ + "CLIPTextModel" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "ldm3d-4c" + ], + "scheduler": [ + "ops.scheduler.karrasdiffusion", + "schedulers" + ], + "safety_checker": [ + "StableDiffusionSafetyChecker" + ], + "feature_extractor": [ + "CLIPImageProcessor" + ] + }, + "tasks": [ + "StableDiffusion3ControlNetInpaintingPipeline", + "StableDiffusion3ControlNetPipeline", + "StableDiffusion3Img2ImgPipeline", + "StableDiffusion3InpaintPipeline", + "StableDiffusion3PAGImg2ImgPipeline", + "StableDiffusion3PAGPipeline", + "StableDiffusion3Pipeline", + "StableDiffusionControlNetImg2ImgPipeline", + "StableDiffusionControlNetInpaintPipeline", + "StableDiffusionControlNetPAGInpaintPipeline", + "StableDiffusionControlNetPAGPipeline", + "StableDiffusionControlNetPipeline", + "StableDiffusionImg2ImgPipeline", + "StableDiffusionInpaintPipeline", + "StableDiffusionPAGImg2ImgPipeline", + "StableDiffusionPAGInpaintPipeline", + "StableDiffusionPAGPipeline", + "StableDiffusionPipeline", + "StableDiffusionXLControlNetImg2ImgPipeline", + "StableDiffusionXLControlNetInpaintPipeline", + "StableDiffusionXLControlNetPAGImg2ImgPipeline", + "StableDiffusionXLControlNetPAGPipeline", + "StableDiffusionXLControlNetPipeline", + "StableDiffusionXLControlNetUnionImg2ImgPipeline", + "StableDiffusionXLControlNetUnionInpaintPipeline", + "StableDiffusionXLControlNetUnionPipeline", + "StableDiffusionXLImg2ImgPipeline", + "StableDiffusionXLInpaintPipeline", + "StableDiffusionXLPAGImg2ImgPipeline", + "StableDiffusionXLPAGInpaintPipeline", + "StableDiffusionXLPAGPipeline", + "StableDiffusionXLPipeline" + ] } }, "info.unet.stable-diffusion-xl-1": { @@ -1310,8 +3217,10 @@ "pkg": { "0": { "diffusers": "StableDiffusionXLPipeline", + "precision": "ops.precision.float.F16", "generation": { "denoising_end": 0.8, + "num_inference_steps": 40, "output_type": "latent", "safety_checker": false, "width": 1024, @@ -1323,10 +3232,10 @@ } }, "file_256": [ - "31e35c80fc4829d14f90153f4c74cd59c90b779f6afe05a74cd6120b893f7e5b", - "e6bb9ea85bbf7bf6478a7c6d18b71246f22e95d41bcdd80ed40aa212c33cfeff", "357650fbfb3c7b4d94c1f5fd7664da819ad1ff5a839430484b4ec422d03f710a", - "83e012a805b84c7ca28e5646747c90a243c65c8ba4f070e2d7ddc9d74661e139" + "83e012a805b84c7ca28e5646747c90a243c65c8ba4f070e2d7ddc9d74661e139", + "31e35c80fc4829d14f90153f4c74cd59c90b779f6afe05a74cd6120b893f7e5b", + "6f001c090fb13c0d0f8b0a5916da814712a94400b99471fabe77c1c4a51ecaaf" ], "layer_256": [ "62a5ab1b5fdfa4fedb32323841298c6effe1af25be94a8583350b0a7641503ef", @@ -1342,6 +3251,53 @@ "logit_scale", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.k_proj.weight", "add_embedding.linear_2.bias" + ], + "pipe_names": { + "vae": [ + "info.vae.eq", + "info.vae.ms-lc-eq", + "stable-diffusion-xl-1" + ], + "text_encoder": [ + "CLIPTextModel" + ], + "text_encoder_2": [ + "CLIPTextModelWithProjection" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "stable-diffusion-xl-1" + ], + "tokenizer_2": [ + "info.encoder.tokenizer", + "stable-diffusion-xl-1" + ], + "scheduler": [ + "ops.scheduler.karrasdiffusion", + "schedulers" + ], + "image_encoder": [ + "CLIPVisionModelWithProjection" + ], + "feature_extractor": [ + "CLIPImageProcessor" + ] + }, + "tasks": [ + "StableDiffusionXLControlNetImg2ImgPipeline", + "StableDiffusionXLControlNetInpaintPipeline", + "StableDiffusionXLControlNetPAGImg2ImgPipeline", + "StableDiffusionXLControlNetPAGPipeline", + "StableDiffusionXLControlNetPipeline", + "StableDiffusionXLControlNetUnionImg2ImgPipeline", + "StableDiffusionXLControlNetUnionInpaintPipeline", + "StableDiffusionXLControlNetUnionPipeline", + "StableDiffusionXLImg2ImgPipeline", + "StableDiffusionXLInpaintPipeline", + "StableDiffusionXLPAGImg2ImgPipeline", + "StableDiffusionXLPAGInpaintPipeline", + "StableDiffusionXLPAGPipeline", + "StableDiffusionXLPipeline" ] }, "pony-diffusion": { @@ -1418,7 +3374,8 @@ "repo": "playgroundai/playground-v2.5-1024px-aesthetic", "pkg": { "0": { - "precision": "ops.precision.float.f16", + "diffusers": "DiffusionPipeline", + "precision": "ops.precision.float.F16", "generation": { "num_inference_steps": 50, "guidance_scale": 3 @@ -1426,17 +3383,29 @@ } }, "file_256": [ + "11b6d7bce65674659cc6b7ea960658436edfd80e566cb240ebd4bfbc3e2076c8", "bcaa7dd6780974f000b17b5a6c63e6f867a75c51ffa85c67d6b196882c69b992", - "956dca99114aaa5c3eb526381309d37ee96737e78ed64c8ae613409f47c3f65a" + "956dca99114aaa5c3eb526381309d37ee96737e78ed64c8ae613409f47c3f65a", + "933778ce76c1fc0ca918b37e1488411b8a99bbd3279c12f527a3ac995a340864", + "5c7d38880d0940e6795158b7608ccef89217272b1f2a9331c5b0a2adffcd82c4", + "0411e988479884b1a3ecd184123efe38d051d8d0ef24270585a7d1d57499464a" ], "layer_256": [ + "adb7be228d4ee6e583c3e5ae4ddb579fef64c3987617ce4d4aff3eb7f8d6a3f7", + "d4813e9f984aa76cb4ac9bf0972d55442923292d276e97e95cb2f49a57227843", "fe2e9edf7e3923a80e64c2552139d8bae926cc3b028ca4773573a6ba60e67c20", - "d4813e9f984aa76cb4ac9bf0972d55442923292d276e97e95cb2f49a57227843" + "bc7021473a04a6de3fe0d0fed600875d852ad1ad9d47c445278f66ce9e8ec7a0fc94481f0c52b21c5ac1fdade8d9c5b210f7239253f86ef21e6198fe393ed60e", + "a6f31493ceeb51c88c5239188b9078dc64ba66d3fc5958ad48c119115b06120c" ], "layer_b3": [ + "d55b22740da2d5b98020ad2390cdc0a7ee08cf9e0d98c11957f16cc20c49815b", "7e9be9bd9a3aed1ad7207e2f77c98c24c3a75f6adcc9b53514033c6c3365d289", - "5c6dfcc8d01dfb64723f8f5785caa080e2987859c0a050470bfdbe5312be9efc" - ] + "5c6dfcc8d01dfb64723f8f5785caa080e2987859c0a050470bfdbe5312be9efc", + "703f775c6e48ed5b0eba6e847414f047bcd4adc677dbc1bf221b3ef05b2ac471", + "72d4ebe4af61f8a7add8fe36b8acd16602894279fb5a744ad50b5b5bac7067b8", + "acb757b851db12cdf9d4365a45ee0d6e64afa77ac95583bb82711baf7c4125fd" + ], + "pipe_names": {} }, "segmind-vega": { "repo": "segmind/Segmind-Vega", @@ -1497,7 +3466,7 @@ } }, "identifiers": [ - "r'conditioner.embedders.0.model.transformer.resblocks.d+.mlp.c_proj.bias'" + "conditioner.embedders.0.model.transformer.resblocks.9.mlp.c_proj.bias" ], "file_256": [ "54f9cd2f2daf3aeec0b2708fa3dbc0e84e4f8ddd1ddead42e5bc60c6572c989f", @@ -1513,6 +3482,51 @@ "bb9eadbfabb52c0d8645783525a3fa70b59e9d7d09d5290d742a303262e793a2", "c5adb56fe51343af2c3d493eb9f41515c204bd91eb9f40b983d45f70a1fa3b6d", "1f838e39ed6e916258aee6990b72c09b34aa8eb3b5342234a497b8852b3df1c6" + ], + "pipe_names": { + "vae": [ + "AutoencoderKL" + ], + "text_encoder": [ + "CLIPTextModel" + ], + "text_encoder_2": [ + "CLIPTextModelWithProjection" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "stable-diffusion-xl-refiner-1" + ], + "tokenizer_2": [ + "info.encoder.tokenizer", + "stable-diffusion-xl-refiner-1" + ], + "scheduler": [ + "ops.scheduler.karrasdiffusion", + "schedulers" + ], + "image_encoder": [ + "CLIPVisionModelWithProjection" + ], + "feature_extractor": [ + "CLIPImageProcessor" + ] + }, + "tasks": [ + "StableDiffusionXLControlNetImg2ImgPipeline", + "StableDiffusionXLControlNetInpaintPipeline", + "StableDiffusionXLControlNetPAGImg2ImgPipeline", + "StableDiffusionXLControlNetPAGPipeline", + "StableDiffusionXLControlNetPipeline", + "StableDiffusionXLControlNetUnionImg2ImgPipeline", + "StableDiffusionXLControlNetUnionInpaintPipeline", + "StableDiffusionXLControlNetUnionPipeline", + "StableDiffusionXLImg2ImgPipeline", + "StableDiffusionXLInpaintPipeline", + "StableDiffusionXLPAGImg2ImgPipeline", + "StableDiffusionXLPAGInpaintPipeline", + "StableDiffusionXLPAGPipeline", + "StableDiffusionXLPipeline" ] } }, @@ -1523,7 +3537,46 @@ "0": { "diffusers": "StableDiffusionXLInstructPix2PixPipeline" } - } + }, + "pipe_names": { + "vae": [ + "AutoencoderKL" + ], + "text_encoder": [ + "CLIPTextModel" + ], + "text_encoder_2": [ + "CLIPTextModelWithProjection" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "sdxl-pix2pix-768" + ], + "tokenizer_2": [ + "info.encoder.tokenizer", + "sdxl-pix2pix-768" + ], + "scheduler": [ + "ops.scheduler.karrasdiffusion", + "schedulers" + ] + }, + "tasks": [ + "StableDiffusionXLControlNetImg2ImgPipeline", + "StableDiffusionXLControlNetInpaintPipeline", + "StableDiffusionXLControlNetPAGImg2ImgPipeline", + "StableDiffusionXLControlNetPAGPipeline", + "StableDiffusionXLControlNetPipeline", + "StableDiffusionXLControlNetUnionImg2ImgPipeline", + "StableDiffusionXLControlNetUnionInpaintPipeline", + "StableDiffusionXLControlNetUnionPipeline", + "StableDiffusionXLImg2ImgPipeline", + "StableDiffusionXLInpaintPipeline", + "StableDiffusionXLPAGImg2ImgPipeline", + "StableDiffusionXLPAGInpaintPipeline", + "StableDiffusionXLPAGPipeline", + "StableDiffusionXLPipeline" + ] } }, "info.unet.stable-video-diffusion-img2vid-xt": { @@ -1533,6 +3586,25 @@ "0": { "diffusers": "StableVideoDiffusionPipeline" } + }, + "pipe_names": { + "vae": [ + "info.vae.kl", + "audioldm-s-v2" + ], + "image_encoder": [ + "CLIPVisionModelWithProjection" + ], + "unet": [ + "UNetSpatioTemporalConditionModel" + ], + "scheduler": [ + "ops.scheduler.euler", + "discrete" + ], + "feature_extractor": [ + "CLIPImageProcessor" + ] } } }, @@ -1543,6 +3615,32 @@ "0": { "diffusers": "VisualClozeGenerationPipeline" } + }, + "pipe_names": { + "scheduler": [ + "ops.scheduler.euler", + "discrete" + ], + "vae": [ + "AutoencoderKL" + ], + "text_encoder": [ + "CLIPTextModel" + ], + "tokenizer": [ + "info.encoder.tokenizer", + "visualclozepipeline-384" + ], + "text_encoder_2": [ + "T5EncoderModel" + ], + "tokenizer_2": [ + "info.encoder.tokenizer", + "visualclozepipeline-384" + ], + "transformer": [ + "FluxTransformer2DModel" + ] } } }, @@ -1551,7 +3649,14 @@ "repo": "Wan-AI/Wan2.1-T2V-14B-Diffusers", "pkg": { "0": { - "diffusers": "WanPipeline" + "diffusers": "WanPipeline", + "precision": "ops.precision.bfloat.B16", + "generation": { + "height": 480, + "width": 832, + "num_frames": 81, + "guidance_scale": 5.0 + } } }, "file_256": [ @@ -1581,7 +3686,24 @@ "0542780670dd75d4cd9deda123d2e150730646c0a1a8d34582460991498a77a6", "e925b8222774905c8fbf10af77811fde7870e563eedcde2c94bd5c727e952d49", "3d915854976284347efa7aa0a117c0fc3b415c4208e1a6c94beb4ccb9720743d" - ] + ], + "pipe_names": { + "tokenizer": [ + "info.encoder.tokenizer", + "wan2-t2v" + ], + "text_encoder": [ + "UMT5EncoderModel" + ], + "vae": [ + "info.vae.kl", + "audioldm-s-v2" + ], + "scheduler": [ + "ops.scheduler.euler", + "discrete" + ] + } } }, "info.dit.wan2-i2v-480p": { @@ -1616,7 +3738,36 @@ "86c58bc4864c97f394ea6bccb2ecedc4aab7166f5b9bfeb313edfdcb2918164a", "cac45f7d8f1a0628cb0738bd308689e439b1cc6206e5f887d60d5b37d30138f2", "60e4f71a0961b1346b6f6b5ebe4c8cc93219239c5e13b4c0f1e19e9b8e1324d5" - ] + ], + "pipe_names": { + "tokenizer": [ + "info.encoder.tokenizer", + "wan2-i2v-480p" + ], + "text_encoder": [ + "UMT5EncoderModel" + ], + "vae": [ + "info.vae.wan", + "wan2-i2v-480p" + ], + "scheduler": [ + "ops.scheduler.euler", + "discrete" + ], + "image_processor": [ + "CLIPImageProcessor" + ], + "image_encoder": [ + "CLIPVisionModel" + ], + "transformer": [ + "WanTransformer3DModel" + ], + "transformer_2": [ + "WanTransformer3DModel" + ] + } } }, "info.dit.wan21-vace": { @@ -1645,7 +3796,27 @@ "d527680fa735e5f30ef8852aabf8a49f02a094bc4718f0787c5b85710a13c026", "9677492a107b3ed827c7285db3393f5321d451cc6d922a4d0488d2a67e939446", "aaef66a4f65ecf852888d160b2122753fe4c6d642b5d41db29e4ce9e6855b5a0" - ] + ], + "pipe_names": { + "tokenizer": [ + "info.encoder.tokenizer", + "wan21-vace" + ], + "text_encoder": [ + "UMT5EncoderModel" + ], + "transformer": [ + "WanVACETransformer3DModel" + ], + "vae": [ + "info.vae.kl", + "audioldm-s-v2" + ], + "scheduler": [ + "ops.scheduler.euler", + "discrete" + ] + } } }, "info.dit.wan21-t2v": { @@ -1653,8 +3824,35 @@ "repo": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers", "pkg": { "0": { - "diffusers": "WanVideoToVideoPipeline" + "diffusers": "WanVideoToVideoPipeline", + "precision": "ops.precision.bfloat.B16", + "generation": { + "height": 480, + "width": 832, + "num_frames": 81, + "guidance_scale": 5.0 + } } + }, + "pipe_names": { + "tokenizer": [ + "info.encoder.tokenizer", + "wan21-t2v" + ], + "text_encoder": [ + "UMT5EncoderModel" + ], + "transformer": [ + "WanTransformer3DModel" + ], + "vae": [ + "info.vae.kl", + "audioldm-s-v2" + ], + "scheduler": [ + "ops.scheduler.euler", + "discrete" + ] } } }, @@ -1665,7 +3863,27 @@ "0": { "diffusers": "WuerstchenPriorPipeline" } - } + }, + "pipe_names": { + "tokenizer": [ + "info.encoder.tokenizer", + "wuerstchen" + ], + "text_encoder": [ + "CLIPTextModel" + ], + "prior": [ + "WuerstchenPrior" + ], + "scheduler": [ + "ops.scheduler.ddpmwuerstchen", + "scheduler" + ] + }, + "tasks": [ + "WuerstchenCombinedPipeline", + "WuerstchenDecoderPipeline" + ] }, "decoder": { "repo": "warp-ai/wuerstchen", @@ -1673,41 +3891,51 @@ "0": { "diffusers": "WuerstchenDecoderPipeline" } - } - } - }, - "info.aet.sew-d": { - "*": { - "repo": "asapp/sew-d-tiny-100k", - "pkg": { - "0": { - "transformers": "SEWDModel" - } - } + }, + "pipe_names": { + "tokenizer": [ + "info.encoder.tokenizer", + "wuerstchen" + ], + "text_encoder": [ + "CLIPTextModel" + ], + "decoder": [ + "WuerstchenDiffNeXt" + ], + "scheduler": [ + "ops.scheduler.ddpmwuerstchen", + "scheduler" + ], + "vqgan": [ + "PaellaVQModel" + ] + }, + "tasks": [ + "WuerstchenCombinedPipeline", + "WuerstchenDecoderPipeline" + ] } }, - "info.vit.sam-vit-huge": { - "*": { - "repo": "facebook/sam-vit-huge", + "info.encoder.tokenizer": { + "powermoe": { "pkg": { "0": { - "transformers": "SamModel" + "transformers": "transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer" } } - } - }, - "info.encoder.tokenizer": { - "mpnet": { + }, + "bert-uncased": { "pkg": { "0": { - "transformers": "transformers.models.mpnet.tokenization_mpnet.MPNetTokenizer" + "transformers": "transformers.models.bert.tokenization_bert.BertTokenizer" }, "1": { - "transformers": "transformers.models.mpnet.tokenization_mpnet_fast.MPNetTokenizerFast" + "transformers": "transformers.models.bert.tokenization_bert_fast.BertTokenizerFast" } } }, - "afm": { + "persimmon": { "pkg": { "0": { "transformers": "transformers.models.llama.tokenization_llama.LlamaTokenizer" @@ -1717,27 +3945,24 @@ } } }, - "yoso-4096": { + "wav2vec2-conformer-rel-pos": { "pkg": { "0": { - "transformers": "transformers.models.albert.tokenization_albert.AlbertTokenizer" - }, - "1": { - "transformers": "transformers.models.albert.tokenization_albert_fast.AlbertTokenizerFast" + "transformers": "transformers.models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizer" } } }, - "nystromformer-512": { + "xclip-patch32": { "pkg": { "0": { - "transformers": "transformers.models.albert.tokenization_albert.AlbertTokenizer" + "transformers": "transformers.models.clip.tokenization_clip.CLIPTokenizer" }, "1": { - "transformers": "transformers.models.albert.tokenization_albert_fast.AlbertTokenizerFast" + "transformers": "transformers.models.clip.tokenization_clip_fast.CLIPTokenizerFast" } } }, - "bert-uncased": { + "vilt-b32-mlm": { "pkg": { "0": { "transformers": "transformers.models.bert.tokenization_bert.BertTokenizer" @@ -1747,77 +3972,74 @@ } } }, - "mvp": { + "grounding-dino": { "pkg": { "0": { - "transformers": "transformers.models.mvp.tokenization_mvp.MvpTokenizer" + "transformers": "transformers.models.bert.tokenization_bert.BertTokenizer" }, "1": { - "transformers": "transformers.models.mvp.tokenization_mvp_fast.MvpTokenizerFast" + "transformers": "transformers.models.bert.tokenization_bert_fast.BertTokenizerFast" } } }, - "idefics2": { + "mpnet": { "pkg": { "0": { - "transformers": "transformers.models.llama.tokenization_llama.LlamaTokenizer" + "transformers": "transformers.models.mpnet.tokenization_mpnet.MPNetTokenizer" }, "1": { - "transformers": "transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast" + "transformers": "transformers.models.mpnet.tokenization_mpnet_fast.MPNetTokenizerFast" } } }, - "zamba-v1": { + "vit-patch16-224": { "pkg": { "0": { - "transformers": "transformers.models.llama.tokenization_llama.LlamaTokenizer" + "transformers": "transformers.models.clip.tokenization_clip.CLIPTokenizer" }, "1": { - "transformers": "transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast" + "transformers": "transformers.models.clip.tokenization_clip_fast.CLIPTokenizerFast" } } }, - "conv-bert": { + "starcoder2": { "pkg": { "0": { - "transformers": "transformers.models.convbert.tokenization_convbert.ConvBertTokenizer" + "transformers": "transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer" }, "1": { - "transformers": "transformers.models.convbert.tokenization_convbert_fast.ConvBertTokenizerFast" + "transformers": "transformers.models.gpt2.tokenization_gpt2_fast.GPT2TokenizerFast" } } }, - "splinter": { + "glm-4v-thinking": { "pkg": { "0": { - "transformers": "transformers.models.splinter.tokenization_splinter.SplinterTokenizer" - }, - "1": { - "transformers": "transformers.models.splinter.tokenization_splinter_fast.SplinterTokenizerFast" + "transformers": "transformers.tokenization_utils_fast.PreTrainedTokenizerFast" } } }, - "megatron-bert-uncased": { + "jamba-v0": { "pkg": { "0": { - "transformers": "transformers.models.bert.tokenization_bert.BertTokenizer" + "transformers": "transformers.models.llama.tokenization_llama.LlamaTokenizer" }, "1": { - "transformers": "transformers.models.bert.tokenization_bert_fast.BertTokenizerFast" + "transformers": "transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast" } } }, - "mm-grounding-dino-o365v1-goldg-v3det": { + "lilt-roberta-en": { "pkg": { "0": { - "transformers": "transformers.models.bert.tokenization_bert.BertTokenizer" + "transformers": "transformers.models.layoutlmv3.tokenization_layoutlmv3.LayoutLMv3Tokenizer" }, "1": { - "transformers": "transformers.models.bert.tokenization_bert_fast.BertTokenizerFast" + "transformers": "transformers.models.layoutlmv3.tokenization_layoutlmv3_fast.LayoutLMv3TokenizerFast" } } }, - "llava": { + "aria-chat": { "pkg": { "0": { "transformers": "transformers.models.llama.tokenization_llama.LlamaTokenizer" @@ -1827,141 +4049,128 @@ } } }, - "glm-4-chat": { + "helium": { "pkg": { "0": { "transformers": "transformers.tokenization_utils_fast.PreTrainedTokenizerFast" } } }, - "bart": { + "mvp": { "pkg": { "0": { - "transformers": "transformers.models.bart.tokenization_bart.BartTokenizer" + "transformers": "transformers.models.mvp.tokenization_mvp.MvpTokenizer" }, "1": { - "transformers": "transformers.models.bart.tokenization_bart_fast.BartTokenizerFast" + "transformers": "transformers.models.mvp.tokenization_mvp_fast.MvpTokenizerFast" } } }, - "t5": { + "wmt19-en-ru": { "pkg": { "0": { - "transformers": "transformers.models.t5.tokenization_t5.T5Tokenizer" - }, - "1": { - "transformers": "transformers.models.t5.tokenization_t5_fast.T5TokenizerFast" + "transformers": "transformers.models.fsmt.tokenization_fsmt.FSMTTokenizer" } } }, - "blip-flan-t5": { + "vip-llava-hf": { "pkg": { "0": { - "transformers": "transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer" + "transformers": "transformers.models.llama.tokenization_llama.LlamaTokenizer" }, "1": { - "transformers": "transformers.models.gpt2.tokenization_gpt2_fast.GPT2TokenizerFast" + "transformers": "transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast" } } }, - "aria-chat": { + "owlv2-patch16": { "pkg": { "0": { - "transformers": "transformers.models.llama.tokenization_llama.LlamaTokenizer" + "transformers": "transformers.models.clip.tokenization_clip.CLIPTokenizer" }, "1": { - "transformers": "transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast" + "transformers": "transformers.models.clip.tokenization_clip_fast.CLIPTokenizerFast" } } }, - "xclip-patch32": { + "falcon": { "pkg": { "0": { - "transformers": "transformers.models.clip.tokenization_clip.CLIPTokenizer" - }, - "1": { - "transformers": "transformers.models.clip.tokenization_clip_fast.CLIPTokenizerFast" + "transformers": "transformers.tokenization_utils_fast.PreTrainedTokenizerFast" } } }, - "internvl3-hf": { + "llama-3-vision": { "pkg": { "0": { - "transformers": "transformers.models.qwen2.tokenization_qwen2.Qwen2Tokenizer" + "transformers": "transformers.models.llama.tokenization_llama.LlamaTokenizer" }, "1": { - "transformers": "transformers.models.qwen2.tokenization_qwen2_fast.Qwen2TokenizerFast" + "transformers": "transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast" } } }, - "mra-512-4": { + "hf-moshiko": { "pkg": { "0": { - "transformers": "transformers.models.roberta.tokenization_roberta.RobertaTokenizer" - }, - "1": { - "transformers": "transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast" + "transformers": "transformers.tokenization_utils_fast.PreTrainedTokenizerFast" } } }, - "owlvit-patch32": { + "layoutlmv2-uncased": { "pkg": { "0": { - "transformers": "transformers.models.clip.tokenization_clip.CLIPTokenizer" + "transformers": "transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer" }, "1": { - "transformers": "transformers.models.clip.tokenization_clip_fast.CLIPTokenizerFast" + "transformers": "transformers.models.layoutlmv2.tokenization_layoutlmv2_fast.LayoutLMv2TokenizerFast" } } }, - "idefics": { + "canine-s": { "pkg": { "0": { - "transformers": "transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast" + "transformers": "transformers.models.canine.tokenization_canine.CanineTokenizer" } } }, - "vilt-b32-mlm": { + "idefics3-llama3": { "pkg": { "0": { - "transformers": "transformers.models.bert.tokenization_bert.BertTokenizer" + "transformers": "transformers.models.llama.tokenization_llama.LlamaTokenizer" }, "1": { - "transformers": 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"transformers.tokenization_utils_fast.PreTrainedTokenizerFast" + "transformers": "transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast" } } }, - "moe-active-shared-experts": { + "deberta": { "pkg": { "0": { - "transformers": "transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer" + "transformers": "transformers.models.deberta.tokenization_deberta.DebertaTokenizer" + }, + "1": { + "transformers": "transformers.models.deberta.tokenization_deberta_fast.DebertaTokenizerFast" } } }, @@ -1972,184 +4181,172 @@ } } }, - "speecht5-asr": { + "xlm-mlm-en-2048": { "pkg": { "0": { - "transformers": "transformers.models.speecht5.tokenization_speecht5.SpeechT5Tokenizer" + "transformers": "transformers.models.xlm.tokenization_xlm.XLMTokenizer" } } }, - "kosmos-2-patch14-224": { + "llava": { "pkg": { "0": { - "transformers": "transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer" + "transformers": "transformers.models.llama.tokenization_llama.LlamaTokenizer" }, 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"transformers.models.layoutlm.tokenization_layoutlm.LayoutLMTokenizer" }, "1": { - "transformers": "transformers.models.bert.tokenization_bert_fast.BertTokenizerFast" - } - } - }, - "bert-for-seq-generation-l-24-bbc-encoder": { - "pkg": { - "0": { - "transformers": "transformers.models.bert_generation.tokenization_bert_generation.BertGenerationTokenizer" + "transformers": "transformers.models.layoutlm.tokenization_layoutlm_fast.LayoutLMTokenizerFast" } } }, - "rwkv-4-pile": { + "align": { "pkg": { "0": { - "transformers": "transformers.models.gpt_neox.tokenization_gpt_neox_fast.GPTNeoXTokenizerFast" + "transformers": "transformers.models.bert.tokenization_bert.BertTokenizer" + }, + "1": { + "transformers": "transformers.models.bert.tokenization_bert_fast.BertTokenizerFast" } } }, - "efficient-mlm-m0-0": { + "retribert-uncased": { "pkg": { "0": { - "transformers": "transformers.models.roberta.tokenization_roberta.RobertaTokenizer" + "transformers": "transformers.models.deprecated.retribert.tokenization_retribert.RetriBertTokenizer" }, "1": { - "transformers": "transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast" + "transformers": "transformers.models.deprecated.retribert.tokenization_retribert_fast.RetriBertTokenizerFast" } } }, - "powermoe": { + "ctrl": { "pkg": { "0": { - "transformers": "transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer" + "transformers": "transformers.models.ctrl.tokenization_ctrl.CTRLTokenizer" } } }, - "long-t5-local": { + "smollm3": { "pkg": { "0": { - "transformers": "transformers.models.t5.tokenization_t5.T5Tokenizer" - }, - "1": { - "transformers": "transformers.models.t5.tokenization_t5_fast.T5TokenizerFast" + "transformers": "transformers.tokenization_utils_fast.PreTrainedTokenizerFast" } } }, - "led-16384": { + "layoutlmv3": { "pkg": { "0": { - "transformers": "transformers.models.led.tokenization_led.LEDTokenizer" + "transformers": "transformers.models.layoutlmv3.tokenization_layoutlmv3.LayoutLMv3Tokenizer" }, "1": { - "transformers": "transformers.models.led.tokenization_led_fast.LEDTokenizerFast" - } - } - }, - "glm-4-0414": { - "pkg": { - "0": { - "transformers": "transformers.tokenization_utils_fast.PreTrainedTokenizerFast" + "transformers": "transformers.models.layoutlmv3.tokenization_layoutlmv3_fast.LayoutLMv3TokenizerFast" } } }, - "llama-4-scout-16e": { + "openai-gpt": { "pkg": { "0": { - "transformers": "transformers.models.llama.tokenization_llama.LlamaTokenizer" + "transformers": "transformers.models.openai.tokenization_openai.OpenAIGPTTokenizer" }, "1": { - "transformers": "transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast" + "transformers": "transformers.models.openai.tokenization_openai_fast.OpenAIGPTTokenizerFast" } } }, - "mgp-str": { + "blip-flan-t5": { "pkg": { "0": { - "transformers": "transformers.models.mgp_str.tokenization_mgp_str.MgpstrTokenizer" + "transformers": "transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer" + }, + "1": { + "transformers": "transformers.models.gpt2.tokenization_gpt2_fast.GPT2TokenizerFast" } } }, - "canine-s": { + "siglip-patch16-224": { "pkg": { "0": { - "transformers": "transformers.models.canine.tokenization_canine.CanineTokenizer" + "transformers": "transformers.models.siglip.tokenization_siglip.SiglipTokenizer" } } }, - "layoutlm-uncased": { + "omdet-turbo-swin-hf": { "pkg": { "0": { - "transformers": "transformers.models.layoutlm.tokenization_layoutlm.LayoutLMTokenizer" + "transformers": "transformers.models.clip.tokenization_clip.CLIPTokenizer" }, "1": { - "transformers": "transformers.models.layoutlm.tokenization_layoutlm_fast.LayoutLMTokenizerFast" - } - } - }, - "mms-tts-eng": { - "pkg": { - "0": { - "transformers": "transformers.models.vits.tokenization_vits.VitsTokenizer" + "transformers": "transformers.models.clip.tokenization_clip_fast.CLIPTokenizerFast" } } }, - "max-text-01-hf": { + "funnel": { "pkg": { "0": { - "transformers": "transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer" + "transformers": "transformers.models.funnel.tokenization_funnel.FunnelTokenizer" }, "1": { - "transformers": "transformers.models.gpt2.tokenization_gpt2_fast.GPT2TokenizerFast" + "transformers": "transformers.models.funnel.tokenization_funnel_fast.FunnelTokenizerFast" } } }, - "lilt-roberta-en": { + "qwen2": { "pkg": { "0": { - "transformers": "transformers.models.layoutlmv3.tokenization_layoutlmv3.LayoutLMv3Tokenizer" + "transformers": "transformers.models.qwen2.tokenization_qwen2.Qwen2Tokenizer" }, "1": { - "transformers": "transformers.models.layoutlmv3.tokenization_layoutlmv3_fast.LayoutLMv3TokenizerFast" + "transformers": "transformers.models.qwen2.tokenization_qwen2_fast.Qwen2TokenizerFast" } } }, - "janus": { + "aimv2-patch14-224-lit": { "pkg": { "0": { - "transformers": "transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast" + "transformers": "transformers.models.clip.tokenization_clip.CLIPTokenizer" + }, + "1": { + "transformers": "transformers.models.clip.tokenization_clip_fast.CLIPTokenizerFast" } } }, - "mpt": { + "data2vec-text": { "pkg": { "0": { - "transformers": "transformers.models.gpt_neox.tokenization_gpt_neox_fast.GPTNeoXTokenizerFast" + "transformers": "transformers.models.roberta.tokenization_roberta.RobertaTokenizer" + }, + "1": { + "transformers": "transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast" } } }, - "mega-wikitext": { + "efficient-mlm-m0-0": { "pkg": { "0": { "transformers": "transformers.models.roberta.tokenization_roberta.RobertaTokenizer" @@ -2159,48 +4356,51 @@ } } }, - "bloom": { + "gemma2": { "pkg": { "0": { - "transformers": "transformers.models.bloom.tokenization_bloom_fast.BloomTokenizerFast" + "transformers": "transformers.models.gemma.tokenization_gemma.GemmaTokenizer" + }, + "1": { + "transformers": "transformers.models.gemma.tokenization_gemma_fast.GemmaTokenizerFast" } } }, - "jetmoe": { + "yoso-4096": { "pkg": { "0": { - "transformers": "transformers.models.llama.tokenization_llama.LlamaTokenizer" + "transformers": "transformers.models.albert.tokenization_albert.AlbertTokenizer" }, "1": { - "transformers": "transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast" + "transformers": "transformers.models.albert.tokenization_albert_fast.AlbertTokenizerFast" } } }, - "fnet": { + "llama-4-scout-16e": { "pkg": { "0": { - "transformers": "transformers.models.fnet.tokenization_fnet.FNetTokenizer" + "transformers": "transformers.models.llama.tokenization_llama.LlamaTokenizer" }, "1": { - "transformers": "transformers.models.fnet.tokenization_fnet_fast.FNetTokenizerFast" + "transformers": "transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast" } } }, - "hubert-ls960": { + "olmo-hf": { "pkg": { "0": { - "transformers": "transformers.models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizer" + "transformers": "transformers.models.gpt_neox.tokenization_gpt_neox_fast.GPTNeoXTokenizerFast" } } }, - "pixtral": { + "plbart": { "pkg": { "0": { - "transformers": "transformers.tokenization_utils_fast.PreTrainedTokenizerFast" + "transformers": "transformers.models.plbart.tokenization_plbart.PLBartTokenizer" } } }, - "phi-3-moe": { + "mixtral-8x": { "pkg": { "0": { "transformers": "transformers.models.llama.tokenization_llama.LlamaTokenizer" @@ -2210,31 +4410,24 @@ } } }, - "olmo2-1124-hf": { - "pkg": { - "0": { - "transformers": "transformers.models.gpt_neox.tokenization_gpt_neox_fast.GPTNeoXTokenizerFast" - } - } - }, - "smollm3": { + "bitnet-b18-4t": { "pkg": { "0": { "transformers": "transformers.tokenization_utils_fast.PreTrainedTokenizerFast" } } }, - "opt": { + "lxmert-uncased": { "pkg": { "0": { - "transformers": "transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer" + "transformers": "transformers.models.lxmert.tokenization_lxmert.LxmertTokenizer" }, "1": { - "transformers": "transformers.models.gpt2.tokenization_gpt2_fast.GPT2TokenizerFast" + "transformers": "transformers.models.lxmert.tokenization_lxmert_fast.LxmertTokenizerFast" } } }, - "llama-3-vision": { + "chameleon": { "pkg": { "0": { "transformers": "transformers.models.llama.tokenization_llama.LlamaTokenizer" @@ -2244,24 +4437,17 @@ } } }, - "plbart": { - "pkg": { - "0": { - "transformers": "transformers.models.plbart.tokenization_plbart.PLBartTokenizer" - } - } - }, - "deberta-v2-x": { + "albert-xx-v2": { "pkg": { "0": { - "transformers": "transformers.models.deberta_v2.tokenization_deberta_v2.DebertaV2Tokenizer" + "transformers": "transformers.models.albert.tokenization_albert.AlbertTokenizer" }, "1": { - "transformers": "transformers.models.deberta_v2.tokenization_deberta_v2_fast.DebertaV2TokenizerFast" + "transformers": "transformers.models.albert.tokenization_albert_fast.AlbertTokenizerFast" } } }, - "bark": { + "ernie-3-zh": { "pkg": { "0": { "transformers": "transformers.models.bert.tokenization_bert.BertTokenizer" @@ -2271,426 +4457,400 @@ } } }, - "voxtral-2507": { + "clvp-dev": { "pkg": { "0": { - "transformers": "transformers.models.llama.tokenization_llama.LlamaTokenizer" - }, - "1": { - "transformers": "transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast" - } - } - }, - "stablelm-4e1t": { - "pkg": { - "0": { - "transformers": "transformers.models.gpt_neox.tokenization_gpt_neox_fast.GPTNeoXTokenizerFast" + "transformers": "transformers.models.clvp.tokenization_clvp.ClvpTokenizer" } } }, - "clvp-dev": { + "esm": { "pkg": { "0": { - "transformers": "transformers.models.clvp.tokenization_clvp.ClvpTokenizer" + "transformers": "transformers.models.esm.tokenization_esm.EsmTokenizer" } } }, - "gemma": { + "rembert": { "pkg": { "0": { - "transformers": "transformers.models.gemma.tokenization_gemma.GemmaTokenizer" + "transformers": "transformers.models.rembert.tokenization_rembert.RemBertTokenizer" }, "1": { - "transformers": "transformers.models.gemma.tokenization_gemma_fast.GemmaTokenizerFast" + "transformers": "transformers.models.rembert.tokenization_rembert_fast.RemBertTokenizerFast" } } }, - "granite": { + "c4ai-command-r-v01": { "pkg": { "0": { - "transformers": "transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer" + "transformers": "transformers.models.cohere.tokenization_cohere_fast.CohereTokenizerFast" } } }, - "groupvit-gcc-yfcc": { + "rwkv-4-pile": { "pkg": { "0": { - "transformers": "transformers.models.clip.tokenization_clip.CLIPTokenizer" - }, - "1": { - "transformers": "transformers.models.clip.tokenization_clip_fast.CLIPTokenizerFast" + "transformers": "transformers.models.gpt_neox.tokenization_gpt_neox_fast.GPTNeoXTokenizerFast" } } }, - "glm-4v-thinking": { + "umt5": { "pkg": { "0": { - "transformers": "transformers.tokenization_utils_fast.PreTrainedTokenizerFast" + "transformers": "transformers.models.t5.tokenization_t5.T5Tokenizer" + }, + "1": { + "transformers": "transformers.models.t5.tokenization_t5_fast.T5TokenizerFast" } } }, - "jukebox-lyrics": { + "gemma": { "pkg": { "0": { - "transformers": "transformers.models.deprecated.jukebox.tokenization_jukebox.JukeboxTokenizer" + "transformers": "transformers.models.gemma.tokenization_gemma.GemmaTokenizer" + }, + "1": { + "transformers": "transformers.models.gemma.tokenization_gemma_fast.GemmaTokenizerFast" } } }, - "gptsan-japanese": { + "reformer-crime-and-punishment": { "pkg": { "0": { - "transformers": "transformers.models.deprecated.gptsan_japanese.tokenization_gptsan_japanese.GPTSanJapaneseTokenizer" + "transformers": "transformers.models.reformer.tokenization_reformer.ReformerTokenizer" + }, + "1": { + "transformers": "transformers.models.reformer.tokenization_reformer_fast.ReformerTokenizerFast" } } }, - "bitnet-b18-4t": { + "wav2vec2-960h": { "pkg": { "0": { - "transformers": "transformers.tokenization_utils_fast.PreTrainedTokenizerFast" + "transformers": "transformers.models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizer" } } }, - "xlm-roberta": { + "nezha-cn": { "pkg": { "0": { - "transformers": "transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer" + "transformers": "transformers.models.bert.tokenization_bert.BertTokenizer" }, "1": { - "transformers": "transformers.models.xlm_roberta.tokenization_xlm_roberta_fast.XLMRobertaTokenizerFast" + "transformers": "transformers.models.bert.tokenization_bert_fast.BertTokenizerFast" } } }, - "vit-patch16-224": { + "git": { "pkg": { "0": { - "transformers": "transformers.models.clip.tokenization_clip.CLIPTokenizer" + "transformers": "transformers.models.bert.tokenization_bert.BertTokenizer" }, "1": { - "transformers": "transformers.models.clip.tokenization_clip_fast.CLIPTokenizerFast" + "transformers": "transformers.models.bert.tokenization_bert_fast.BertTokenizerFast" } } }, - 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"transformers.models.codegen.tokenization_codegen.CodeGenTokenizer" }, "1": { - "transformers": "transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast" + "transformers": "transformers.models.codegen.tokenization_codegen_fast.CodeGenTokenizerFast" } } }, - "gemma2": { + "codegen-mono": { "pkg": { "0": { - "transformers": "transformers.models.gemma.tokenization_gemma.GemmaTokenizer" + "transformers": "transformers.models.codegen.tokenization_codegen.CodeGenTokenizer" }, "1": { - "transformers": "transformers.models.gemma.tokenization_gemma_fast.GemmaTokenizerFast" + "transformers": "transformers.models.codegen.tokenization_codegen_fast.CodeGenTokenizerFast" } } }, - "wmt19-en-ru": { + "data2vec-audio-960h": { "pkg": { "0": { - "transformers": "transformers.models.fsmt.tokenization_fsmt.FSMTTokenizer" + "transformers": "transformers.models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizer" } } }, - "mbart-cc25": { + "musicgen": { "pkg": { "0": { - "transformers": 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"transformers.models.udop.tokenization_udop_fast.UdopTokenizerFast" + "transformers": "transformers.models.t5.tokenization_t5_fast.T5TokenizerFast" } } }, - "siglip-patch16-224": { + "nemotron-3-hf": { "pkg": { "0": { - "transformers": "transformers.models.siglip.tokenization_siglip.SiglipTokenizer" + "transformers": "transformers.tokenization_utils_fast.PreTrainedTokenizerFast" } } }, - "xlnet-cased": { + "wav2vec2-bert-rel-pos": { "pkg": { "0": { - "transformers": "transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer" - }, - "1": { - "transformers": "transformers.models.xlnet.tokenization_xlnet_fast.XLNetTokenizerFast" + "transformers": "transformers.models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizer" } } }, - "lxmert-uncased": { + "led-16384": { "pkg": { "0": { - "transformers": "transformers.models.lxmert.tokenization_lxmert.LxmertTokenizer" + "transformers": "transformers.models.led.tokenization_led.LEDTokenizer" }, "1": { - "transformers": "transformers.models.lxmert.tokenization_lxmert_fast.LxmertTokenizerFast" - } - } - }, - "luke": { - "pkg": { - "0": { - "transformers": "transformers.models.luke.tokenization_luke.LukeTokenizer" + "transformers": "transformers.models.led.tokenization_led_fast.LEDTokenizerFast" } } }, - "albert-xx-v2": { + "idefics2": { "pkg": { "0": { - "transformers": "transformers.models.albert.tokenization_albert.AlbertTokenizer" + "transformers": "transformers.models.llama.tokenization_llama.LlamaTokenizer" }, "1": { - "transformers": "transformers.models.albert.tokenization_albert_fast.AlbertTokenizerFast" + "transformers": "transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast" } } }, - "qwen2": { + "squeezebert-uncased": { "pkg": { "0": { - "transformers": "transformers.models.qwen2.tokenization_qwen2.Qwen2Tokenizer" + "transformers": "transformers.models.squeezebert.tokenization_squeezebert.SqueezeBertTokenizer" }, "1": { - "transformers": "transformers.models.qwen2.tokenization_qwen2_fast.Qwen2TokenizerFast" + "transformers": "transformers.models.squeezebert.tokenization_squeezebert_fast.SqueezeBertTokenizerFast" } } }, - "idefics3-llama3": { + "mms-tts-eng": { "pkg": { "0": { - "transformers": "transformers.models.llama.tokenization_llama.LlamaTokenizer" - }, - "1": { - "transformers": "transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast" + "transformers": "transformers.models.vits.tokenization_vits.VitsTokenizer" } } }, - "mixtral-8x": { + "mt5": { "pkg": { "0": { - "transformers": "transformers.models.llama.tokenization_llama.LlamaTokenizer" + "transformers": "transformers.models.mt5.tokenization_mt5.MT5Tokenizer" }, "1": { - "transformers": "transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast" + "transformers": "transformers.models.mt5.tokenization_mt5_fast.MT5TokenizerFast" } } }, - "nllb-moe": { + "paligemma": { "pkg": { "0": { - "transformers": "transformers.models.nllb.tokenization_nllb.NllbTokenizer" + "transformers": "transformers.models.llama.tokenization_llama.LlamaTokenizer" }, "1": { - "transformers": "transformers.models.nllb.tokenization_nllb_fast.NllbTokenizerFast" + "transformers": "transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast" } } }, - "c4ai-command-r-v01": { + "speecht5-asr": { "pkg": { "0": { - "transformers": "transformers.models.cohere.tokenization_cohere_fast.CohereTokenizerFast" + "transformers": "transformers.models.speecht5.tokenization_speecht5.SpeechT5Tokenizer" } } }, - "roformer-chinese": { + "conv-bert": { "pkg": { "0": { - "transformers": "transformers.models.roformer.tokenization_roformer.RoFormerTokenizer" + "transformers": "transformers.models.convbert.tokenization_convbert.ConvBertTokenizer" }, "1": { - "transformers": "transformers.models.roformer.tokenization_roformer_fast.RoFormerTokenizerFast" + "transformers": "transformers.models.convbert.tokenization_convbert_fast.ConvBertTokenizerFast" } } }, - "data2vec-audio-960h": { + "mamba2": { "pkg": { "0": { - "transformers": "transformers.models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizer" + "transformers": "transformers.models.gpt_neox.tokenization_gpt_neox_fast.GPTNeoXTokenizerFast" } } }, - "electra-discriminator": { + "c4ai-command-r-12-2024": { "pkg": { "0": { - "transformers": "transformers.models.electra.tokenization_electra.ElectraTokenizer" - }, - "1": { - "transformers": "transformers.models.electra.tokenization_electra_fast.ElectraTokenizerFast" + "transformers": "transformers.models.cohere.tokenization_cohere_fast.CohereTokenizerFast" } } }, - "umt5": { + "xmod": { "pkg": { "0": { - "transformers": "transformers.models.t5.tokenization_t5.T5Tokenizer" + "transformers": "transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer" }, "1": { - "transformers": "transformers.models.t5.tokenization_t5_fast.T5TokenizerFast" + "transformers": "transformers.models.xlm_roberta.tokenization_xlm_roberta_fast.XLMRobertaTokenizerFast" } } }, - "nemotron-3-hf": { + "bart": { "pkg": { "0": { - "transformers": "transformers.tokenization_utils_fast.PreTrainedTokenizerFast" + "transformers": "transformers.models.bart.tokenization_bart.BartTokenizer" + }, + "1": { + "transformers": "transformers.models.bart.tokenization_bart_fast.BartTokenizerFast" } } }, - "camembert": { + "voxtral-2507": { "pkg": { "0": { - "transformers": "transformers.models.camembert.tokenization_camembert.CamembertTokenizer" + "transformers": "transformers.models.llama.tokenization_llama.LlamaTokenizer" }, "1": { - "transformers": "transformers.models.camembert.tokenization_camembert_fast.CamembertTokenizerFast" + "transformers": "transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast" } } }, - "transfo-xl-wt103": { + "pixtral": { "pkg": { "0": { - "transformers": "transformers.models.deprecated.transfo_xl.tokenization_transfo_xl.TransfoXLTokenizer" + "transformers": "transformers.tokenization_utils_fast.PreTrainedTokenizerFast" } } }, - "gpt-j": { + "whisper": { "pkg": { "0": { - "transformers": "transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer" + "transformers": "transformers.models.whisper.tokenization_whisper.WhisperTokenizer" }, "1": { - "transformers": "transformers.models.gpt2.tokenization_gpt2_fast.GPT2TokenizerFast" + "transformers": "transformers.models.whisper.tokenization_whisper_fast.WhisperTokenizerFast" } } }, - "biogpt": { + "blip-vqa": { "pkg": { "0": { - "transformers": "transformers.models.biogpt.tokenization_biogpt.BioGptTokenizer" + "transformers": "transformers.models.bert.tokenization_bert.BertTokenizer" + }, + "1": { + "transformers": "transformers.models.bert.tokenization_bert_fast.BertTokenizerFast" } } }, - "tapas-finetuned-sqa": { + "flaubert-uncased": { "pkg": { "0": { - "transformers": "transformers.models.tapas.tokenization_tapas.TapasTokenizer" + "transformers": "transformers.models.flaubert.tokenization_flaubert.FlaubertTokenizer" } } }, - "blenderbot": { + "transfo-xl-wt103": { "pkg": { "0": { - "transformers": "transformers.models.blenderbot_small.tokenization_blenderbot_small.BlenderbotSmallTokenizer" + "transformers": "transformers.models.deprecated.transfo_xl.tokenization_transfo_xl.TransfoXLTokenizer" } } }, - "olmoe-0924": { + "moonshine": { "pkg": { "0": { - "transformers": "transformers.models.gpt_neox.tokenization_gpt_neox_fast.GPTNeoXTokenizerFast" + "transformers": "transformers.tokenization_utils_fast.PreTrainedTokenizerFast" } } }, - "vip-llava-hf": { + "tvp": { "pkg": { "0": { - "transformers": "transformers.models.llama.tokenization_llama.LlamaTokenizer" + "transformers": "transformers.models.bert.tokenization_bert.BertTokenizer" }, "1": { - "transformers": "transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast" + "transformers": "transformers.models.bert.tokenization_bert_fast.BertTokenizerFast" } } }, - "exaone-4": { + "biogpt": { "pkg": { "0": { - "transformers": "transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer" - }, - "1": { - "transformers": "transformers.models.gpt2.tokenization_gpt2_fast.GPT2TokenizerFast" + "transformers": "transformers.models.biogpt.tokenization_biogpt.BioGptTokenizer" } } }, - "siglip2-patch16-224": { + "opt": { "pkg": { "0": { - "transformers": "transformers.models.gemma.tokenization_gemma.GemmaTokenizer" + "transformers": "transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer" }, "1": { - "transformers": "transformers.models.gemma.tokenization_gemma_fast.GemmaTokenizerFast" + "transformers": "transformers.models.gpt2.tokenization_gpt2_fast.GPT2TokenizerFast" } } }, @@ -2704,37 +4864,41 @@ } } }, - "aimv2-patch14-224-lit": { + "gptsan-japanese": { "pkg": { "0": { - "transformers": "transformers.models.clip.tokenization_clip.CLIPTokenizer" - }, - "1": { - "transformers": "transformers.models.clip.tokenization_clip_fast.CLIPTokenizerFast" + "transformers": "transformers.models.deprecated.gptsan_japanese.tokenization_gptsan_japanese.GPTSanJapaneseTokenizer" } } }, - "phi-1": { + "dpr-question-encoder-single-nq": { "pkg": { "0": { - "transformers": "transformers.models.codegen.tokenization_codegen.CodeGenTokenizer" + "transformers": "transformers.models.dpr.tokenization_dpr.DPRQuestionEncoderTokenizer" }, "1": { - "transformers": "transformers.models.codegen.tokenization_codegen_fast.CodeGenTokenizerFast" + "transformers": "transformers.models.dpr.tokenization_dpr_fast.DPRQuestionEncoderTokenizerFast" } } }, - "xglm": { + "mbart-cc25": { "pkg": { "0": { - "transformers": "transformers.models.xglm.tokenization_xglm.XGLMTokenizer" + "transformers": "transformers.models.mbart.tokenization_mbart.MBartTokenizer" }, "1": { - "transformers": "transformers.models.xglm.tokenization_xglm_fast.XGLMTokenizerFast" + "transformers": "transformers.models.mbart.tokenization_mbart_fast.MBartTokenizerFast" } } }, - "phi-3": { + "idefics": { + "pkg": { + "0": { + "transformers": "transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast" + } + } + }, + "diffllama-handcut": { "pkg": { "0": { "transformers": "transformers.models.llama.tokenization_llama.LlamaTokenizer" @@ -2744,14 +4908,24 @@ } } }, - "modernbert": { + "deberta-v2-x": { "pkg": { "0": { - "transformers": "transformers.tokenization_utils_fast.PreTrainedTokenizerFast" + "transformers": "transformers.models.deberta_v2.tokenization_deberta_v2.DebertaV2Tokenizer" + }, + "1": { + "transformers": "transformers.models.deberta_v2.tokenization_deberta_v2_fast.DebertaV2TokenizerFast" } } }, - "qwen3": { + "granite": { + "pkg": { + "0": { + "transformers": "transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer" + } + } + }, + "internvl3-hf": { "pkg": { "0": { "transformers": "transformers.models.qwen2.tokenization_qwen2.Qwen2Tokenizer" @@ -2761,31 +4935,34 @@ } } }, - "opus-mt-en-de": { + "fnet": { "pkg": { "0": { - "transformers": "transformers.models.marian.tokenization_marian.MarianTokenizer" + "transformers": "transformers.models.fnet.tokenization_fnet.FNetTokenizer" + }, + "1": { + "transformers": "transformers.models.fnet.tokenization_fnet_fast.FNetTokenizerFast" } } }, - "xlstm": { + "blenderbot": { "pkg": { "0": { - "transformers": "transformers.models.gpt_neox.tokenization_gpt_neox_fast.GPTNeoXTokenizerFast" + "transformers": "transformers.models.blenderbot_small.tokenization_blenderbot_small.BlenderbotSmallTokenizer" } } }, - "bridgetower": { + "clipseg-rd64": { "pkg": { "0": { - "transformers": "transformers.models.roberta.tokenization_roberta.RobertaTokenizer" + "transformers": "transformers.models.clip.tokenization_clip.CLIPTokenizer" }, "1": { - "transformers": "transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast" + "transformers": "transformers.models.clip.tokenization_clip_fast.CLIPTokenizerFast" } } }, - "chameleon": { + "jetmoe": { "pkg": { "0": { "transformers": "transformers.models.llama.tokenization_llama.LlamaTokenizer" @@ -2795,108 +4972,95 @@ } } }, - "mamba2": { - "pkg": { - "0": { - "transformers": "transformers.models.gpt_neox.tokenization_gpt_neox_fast.GPTNeoXTokenizerFast" - } - } - }, - "rembert": { + "mobilebert-uncased": { "pkg": { "0": { - "transformers": "transformers.models.rembert.tokenization_rembert.RemBertTokenizer" + "transformers": "transformers.models.mobilebert.tokenization_mobilebert.MobileBertTokenizer" }, "1": { - "transformers": "transformers.models.rembert.tokenization_rembert_fast.RemBertTokenizerFast" + "transformers": "transformers.models.mobilebert.tokenization_mobilebert_fast.MobileBertTokenizerFast" } } }, - "mistral-v0": { + "groupvit-gcc-yfcc": { "pkg": { "0": { - "transformers": "transformers.models.llama.tokenization_llama.LlamaTokenizer" + "transformers": "transformers.models.clip.tokenization_clip.CLIPTokenizer" }, "1": { - "transformers": "transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast" + "transformers": "transformers.models.clip.tokenization_clip_fast.CLIPTokenizerFast" } } }, - "ernie-3-zh": { + "ibert-roberta": { "pkg": { "0": { - "transformers": "transformers.models.bert.tokenization_bert.BertTokenizer" + "transformers": "transformers.models.roberta.tokenization_roberta.RobertaTokenizer" }, "1": { - "transformers": "transformers.models.bert.tokenization_bert_fast.BertTokenizerFast" + "transformers": "transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast" } } }, - "funnel": { + "nystromformer-512": { "pkg": { "0": { - "transformers": "transformers.models.funnel.tokenization_funnel.FunnelTokenizer" + "transformers": "transformers.models.albert.tokenization_albert.AlbertTokenizer" }, "1": { - "transformers": "transformers.models.funnel.tokenization_funnel_fast.FunnelTokenizerFast" + "transformers": "transformers.models.albert.tokenization_albert_fast.AlbertTokenizerFast" } } }, - "musicgen": { + "gpt-j": { "pkg": { "0": { - "transformers": "transformers.models.t5.tokenization_t5.T5Tokenizer" + "transformers": "transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer" }, "1": { - "transformers": "transformers.models.t5.tokenization_t5_fast.T5TokenizerFast" + "transformers": "transformers.models.gpt2.tokenization_gpt2_fast.GPT2TokenizerFast" } } }, - "nezha-cn": { + "stablelm-4e1t": { "pkg": { "0": { - "transformers": "transformers.models.bert.tokenization_bert.BertTokenizer" - }, - "1": { - "transformers": "transformers.models.bert.tokenization_bert_fast.BertTokenizerFast" + "transformers": "transformers.models.gpt_neox.tokenization_gpt_neox_fast.GPTNeoXTokenizerFast" } } }, - "moonshine": { + "hubert-ls960": { "pkg": { "0": { - "transformers": "transformers.tokenization_utils_fast.PreTrainedTokenizerFast" + "transformers": "transformers.models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizer" } } }, - "xmod": { + "mpt": { "pkg": { "0": { - "transformers": "transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer" - }, - "1": { - "transformers": "transformers.models.xlm_roberta.tokenization_xlm_roberta_fast.XLMRobertaTokenizerFast" + "transformers": "transformers.models.gpt_neox.tokenization_gpt_neox_fast.GPTNeoXTokenizerFast" } } }, - "ctrl": { + "xlstm": { "pkg": { "0": { - "transformers": "transformers.models.ctrl.tokenization_ctrl.CTRLTokenizer" + "transformers": "transformers.models.gpt_neox.tokenization_gpt_neox_fast.GPTNeoXTokenizerFast" } } }, - "layoutlmv2-uncased": { + "xglm": { "pkg": { "0": { - "transformers": "transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer" + "transformers": "transformers.models.xglm.tokenization_xglm.XGLMTokenizer" }, "1": { - "transformers": "transformers.models.layoutlmv2.tokenization_layoutlmv2_fast.LayoutLMv2TokenizerFast" + "transformers": "transformers.models.xglm.tokenization_xglm_fast.XGLMTokenizerFast" } } }, - "paligemma": { + "afm": { "pkg": { "0": { "transformers": "transformers.models.llama.tokenization_llama.LlamaTokenizer" @@ -2906,102 +5070,105 @@ } } }, - "whisper": { + "clap-htsat-fused": { "pkg": { "0": { - "transformers": "transformers.models.whisper.tokenization_whisper.WhisperTokenizer" + "transformers": "transformers.models.roberta.tokenization_roberta.RobertaTokenizer" }, "1": { - "transformers": "transformers.models.whisper.tokenization_whisper_fast.WhisperTokenizerFast" + "transformers": "transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast" } } }, - "wav2vec2-960h": { + "zamba2": { "pkg": { "0": { - "transformers": "transformers.models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizer" + "transformers": "transformers.models.llama.tokenization_llama.LlamaTokenizer" + }, + "1": { + "transformers": "transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast" } } }, - "data2vec-text": { + "xlnet-cased": { "pkg": { "0": { - "transformers": "transformers.models.roberta.tokenization_roberta.RobertaTokenizer" + "transformers": "transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer" }, "1": { - "transformers": "transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast" + "transformers": "transformers.models.xlnet.tokenization_xlnet_fast.XLNetTokenizerFast" } } }, - "mt5": { + "nllb-moe": { "pkg": { "0": { - "transformers": "transformers.models.mt5.tokenization_mt5.MT5Tokenizer" + "transformers": "transformers.models.nllb.tokenization_nllb.NllbTokenizer" }, "1": { - "transformers": "transformers.models.mt5.tokenization_mt5_fast.MT5TokenizerFast" + "transformers": "transformers.models.nllb.tokenization_nllb_fast.NllbTokenizerFast" } } }, - "olmo-hf": { + "jukebox-lyrics": { "pkg": { "0": { - "transformers": "transformers.models.gpt_neox.tokenization_gpt_neox_fast.GPTNeoXTokenizerFast" + "transformers": "transformers.models.deprecated.jukebox.tokenization_jukebox.JukeboxTokenizer" } } }, - "helium": { + "mistral-v0": { "pkg": { "0": { - "transformers": "transformers.tokenization_utils_fast.PreTrainedTokenizerFast" + "transformers": "transformers.models.llama.tokenization_llama.LlamaTokenizer" + }, + "1": { + "transformers": "transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast" } } }, - "squeezebert-uncased": { + "xlm-roberta-xl": { "pkg": { "0": { - "transformers": "transformers.models.squeezebert.tokenization_squeezebert.SqueezeBertTokenizer" + "transformers": "transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer" }, "1": { - "transformers": "transformers.models.squeezebert.tokenization_squeezebert_fast.SqueezeBertTokenizerFast" + "transformers": "transformers.models.xlm_roberta.tokenization_xlm_roberta_fast.XLMRobertaTokenizerFast" } } }, - 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"GPTNeoXForTokenClassification", + "GPTNeoXLayer", + "GPTNeoXModel", + "GPTNeoXPreTrainedModel" + ] } }, - "info.vit.retribert-uncased": { + "info.ssm.zamba-v1": { "*": { - "repo": "yjernite/retribert-base-uncased", + "repo": "Zyphra/Zamba-7B-v1", "pkg": { "0": { - "transformers": "RetriBertModel" + "transformers": "ZambaModel" } - } + }, + "tasks": [ + "ZambaForCausalLM", + "ZambaForSequenceClassification", + "ZambaModel", + "ZambaPreTrainedModel" + ] } }, - "info.aet.roberta": { + "info.aet.markuplm": { "*": { - "repo": "FacebookAI/roberta-base", + "repo": "microsoft/markuplm-base", "pkg": { "0": { - "transformers": "RobertaModel" + "transformers": "MarkupLMModel" } - } + }, + "tasks": [ + "MarkupLMForQuestionAnswering", + "MarkupLMForSequenceClassification", + "MarkupLMForTokenClassification", + "MarkupLMModel", + "MarkupLMPreTrainedModel" + ] } }, - "info.vit.blip-vqa": { + "info.art.bark": { "*": { - "repo": "Salesforce/blip-vqa-base", + "repo": "suno/bark", "pkg": { "0": { - "transformers": "BlipModel" + "transformers": "BarkModel" } - } + }, + "tasks": [ + "BarkFineModel", + "BarkSemanticModel", + "BarkCoarseModel", + "BarkModel", + "BarkPreTrainedModel", + "BarkCausalModel" + ] } }, - "info.vit.language-perceiver": { + "info.aet.roberta": { "*": { - "repo": "deepmind/language-perceiver", + "repo": "FacebookAI/roberta-base", "pkg": { "0": { - "transformers": "PerceiverModel" + "transformers": "RobertaModel" } - } + }, + "tasks": [ + "RobertaForCausalLM", + "RobertaForMaskedLM", + "RobertaForMultipleChoice", + "RobertaForQuestionAnswering", + "RobertaForSequenceClassification", + "RobertaForTokenClassification", + "RobertaModel", + "RobertaPreTrainedModel" + ] } }, - "info.aet.unispeech-1500h-cv": { + "info.aet.sew-d": { "*": { - "repo": "microsoft/unispeech-large-1500h-cv", + "repo": "asapp/sew-d-tiny-100k", "pkg": { "0": { - "transformers": "UniSpeechModel" + "transformers": "SEWDModel" } - } + }, + "tasks": [ + "SEWDForCTC", + "SEWDForSequenceClassification", + "SEWDModel", + "SEWDPreTrainedModel" + ] } }, - "info.aet.xlm-roberta-xl": { + "info.vit.dinov2-patch16-224": { "*": { - "repo": "facebook/xlm-roberta-xl", + "repo": "google/dinov2-base-patch16-224", "pkg": { "0": { - "transformers": "XLMRobertaXLModel" + "transformers": "Dinov2Model" } - } + }, + "tasks": [ + "Dinov2ForImageClassification", + "Dinov2Model", + "Dinov2PreTrainedModel", + "Dinov2Backbone" + ] } }, - "info.vit.deit-distilled-patch16-224": { + "info.aet.electra-discriminator": { "*": { - "repo": "facebook/deit-base-distilled-patch16-224", + "repo": "google/electra-small-discriminator", "pkg": { "0": { - "transformers": "DeiTModel" + "transformers": "ElectraModel" } - } + }, + "tasks": [ + "ElectraForCausalLM", + "ElectraForMaskedLM", + "ElectraForMultipleChoice", + "ElectraForPreTraining", + "ElectraForQuestionAnswering", + "ElectraForSequenceClassification", + "ElectraForTokenClassification", + "ElectraModel", + "ElectraPreTrainedModel" + ] } }, - "info.vit.mistral-3-2503": { + "info.vit.language-perceiver": { "*": { - "repo": "mistralai/Mistral-Small-3.1-24B-Instruct-2503", + "repo": "deepmind/language-perceiver", "pkg": { "0": { - "transformers": "Mistral3Model" + "transformers": "PerceiverModel" } - } + }, + "tasks": [ + "PerceiverForImageClassificationConvProcessing", + "PerceiverForImageClassificationFourier", + "PerceiverForImageClassificationLearned", + "PerceiverForMaskedLM", + "PerceiverForMultimodalAutoencoding", + "PerceiverForOpticalFlow", + "PerceiverForSequenceClassification", + "PerceiverLayer", + "PerceiverModel", + "PerceiverPreTrainedModel" + ] } }, - "info.ssm.jamba-v0": { + "info.vit.glpn-kitti": { "*": { - "repo": "ai21labs/Jamba-v0.1", + "repo": "vinvino02/glpn-kitti", "pkg": { "0": { - "transformers": "JambaModel" + "transformers": "GLPNModel" } - } + }, + "tasks": [ + "GLPNForDepthEstimation", + "GLPNLayer", + "GLPNModel", + "GLPNPreTrainedModel" + ] } }, - "info.ssm.zamba2": { + "info.vit.segformer-b0-finetuned-ade-512-512": { "*": { - "repo": "Zyphra/Zamba2-2.7B", + "repo": "nvidia/segformer-b0-finetuned-ade-512-512", "pkg": { "0": { - "transformers": "Zamba2Model" + "transformers": "SegformerModel" } - } + }, + "tasks": [ + "SegformerDecodeHead", + "SegformerForImageClassification", + "SegformerForSemanticSegmentation", + "SegformerLayer", + "SegformerModel", + "SegformerPreTrainedModel" + ] } }, - "info.moe.glm-4-a": { + "info.detr.mm-grounding-dino-o365v1-goldg-v3det": { "*": { - "repo": "zai-org/GLM-4.5-Air", + "repo": "openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg_v3det", "pkg": { "0": { - "transformers": "Glm4MoeModel" + "transformers": "MMGroundingDinoModel" } - } + }, + "tasks": [ + "MMGroundingDinoForObjectDetection", + "MMGroundingDinoModel", + "MMGroundingDinoPreTrainedModel" + ] } }, - "info.art.hf-moshiko": { + "info.vit.llava-next-video-hf": { "*": { - "repo": "kmhf/hf-moshiko", + "repo": "llava-hf/LLaVA-NeXT-Video-7B-hf", "pkg": { "0": { - "transformers": "MoshiModel" + "transformers": "LlavaNextVideoModel" } - } + }, + "tasks": [ + "LlavaNextVideoForConditionalGeneration", + "LlavaNextVideoModel", + "LlavaNextVideoPreTrainedModel" + ] } }, - "info.vit.resnet-50": { + "info.gan.encodec": { "*": { - "repo": "microsoft/resnet-50", + "repo": "facebook/encodec_24khz", "pkg": { "0": { - "transformers": "ResNetModel" + "transformers": "EncodecModel" } - } + }, + "tasks": [ + "EncodecModel", + "EncodecPreTrainedModel" + ] } }, - "info.vit.mobilevit": { + "info.aet.camembert": { "*": { - "repo": "apple/mobilevit-small", + "repo": "almanach/camembert-base", "pkg": { "0": { - "transformers": "MobileViTModel" + "transformers": "CamembertModel" } - } + }, + "tasks": [ + "CamembertForCausalLM", + "CamembertForMaskedLM", + "CamembertForMultipleChoice", + "CamembertForQuestionAnswering", + "CamembertForSequenceClassification", + "CamembertForTokenClassification", + "CamembertModel", + "CamembertPreTrainedModel" + ] } }, - "info.vit.owlv2-patch16": { + "info.art.exaone-4": { "*": { - "repo": "google/owlv2-base-patch16", + "repo": "LGAI-EXAONE/EXAONE-4.0-32B", "pkg": { "0": { - "transformers": "Owlv2Model" + "transformers": "Exaone4Model" } - } + }, + "tasks": [ + "Exaone4PreTrainedModel", + "Exaone4Model", + "Exaone4ForCausalLM", + "Exaone4ForSequenceClassification", + "Exaone4ForTokenClassification", + "Exaone4ForQuestionAnswering" + ] } }, - "info.detr.table-transformer-detection": { + "info.art.bloom": { "*": { - "repo": "microsoft/table-transformer-detection", + "repo": "bigscience/bloom", "pkg": { "0": { - "transformers": "TableTransformerModel" + "transformers": "BloomModel" } - } + }, + "tasks": [ + "BloomForCausalLM", + "BloomModel", + "BloomPreTrainedModel", + "BloomForSequenceClassification", + "BloomForTokenClassification", + "BloomForQuestionAnswering" + ] } }, - "info.vit.colqwen2-v1-hf": { + "info.ssm.bamba-t-hf": { "*": { - "repo": "vidore/colqwen2-v1.0-hf", + "repo": "ibm-fms/Bamba-9.8b-2.2T-hf", "pkg": { "0": { - "transformers": "ColQwen2ForRetrieval" + "transformers": "BambaModel" } - } + }, + "tasks": [ + "BambaModel", + "BambaForCausalLM", + "BambaPreTrainedModel" + ] } }, - "info.aet.wav2vec2-bert-rel-pos": { + "info.vit.seggpt-vit": { "*": { - "repo": "facebook/w2v-bert-2.0", + "repo": "BAAI/seggpt-vit-large", "pkg": { "0": { - "transformers": "Wav2Vec2BertModel" + "transformers": "SegGptModel" } - } + }, + "tasks": [ + "SegGptModel", + "SegGptPreTrainedModel", + "SegGptForImageSegmentation" + ] } }, - "info.art.reformer-crime-and-punishment": { + "info.art.bros-uncased": { "*": { - "repo": "google/reformer-crime-and-punishment", + "repo": "jinho8345/bros-base-uncased", "pkg": { "0": { - "transformers": "ReformerModel" + "transformers": "BrosModel" } - } + }, + "tasks": [ + "BrosPreTrainedModel", + "BrosModel", + "BrosForTokenClassification", + "BrosSpadeEEForTokenClassification", + "BrosSpadeELForTokenClassification" + ] } }, - "info.art.mobilebert-uncased": { + "info.vit.mistral-3-2503": { "*": { - "repo": "google/mobilebert-uncased", + "repo": "mistralai/Mistral-Small-3.1-24B-Instruct-2503", "pkg": { "0": { - "transformers": "MobileBertModel" + "transformers": "Mistral3Model" } - } + }, + "tasks": [ + "Mistral3Model", + "Mistral3PreTrainedModel", + "Mistral3ForConditionalGeneration" + ] } }, "info.vit.phi-4": { @@ -6334,137 +10698,209 @@ ], "layer_256": [ "7ff992b780b2f8993dd6bb9612207943638b2a42badc976ce80893bc205e801b" + ], + "tasks": [ + "Phi4MultimodalAudioPreTrainedModel", + "Phi4MultimodalAudioModel", + "Phi4MultimodalVisionPreTrainedModel", + "Phi4MultimodalVisionModel", + "Phi4MultimodalPreTrainedModel", + "Phi4MultimodalModel", + "Phi4MultimodalForCausalLM" ] } }, - "info.moe.llama-4-scout-16e": { - "*": { - "repo": "meta-llama/Llama-4-Scout-17B-16E", - "pkg": { - "0": { - "transformers": "Llama4TextModel" - } - } - } - }, - "info.vit.align": { - "*": { - "repo": "kakaobrain/align-base", - "pkg": { - "0": { - "transformers": "AlignModel" - } - } - } - }, - "info.art.diffllama-handcut": { + "info.aet.megatron-bert-uncased": { "*": { - "repo": "kajuma/DiffLlama-0.3B-handcut", + "repo": "nvidia/megatron-bert-uncased-345m", "pkg": { "0": { - "transformers": "DiffLlamaModel" + "transformers": "MegatronBertModel" } - } + }, + "tasks": [ + "MegatronBertForCausalLM", + "MegatronBertForMaskedLM", + "MegatronBertForMultipleChoice", + "MegatronBertForNextSentencePrediction", + "MegatronBertForPreTraining", + "MegatronBertForQuestionAnswering", + "MegatronBertForSequenceClassification", + "MegatronBertForTokenClassification", + "MegatronBertModel", + "MegatronBertPreTrainedModel" + ] } }, - "info.aet.esm": { + "info.gan.levit-128s": { "*": { - "repo": "facebook/esm-1b", + "repo": "facebook/levit-128S", "pkg": { "0": { - "transformers": "EsmModel" + "transformers": "LevitModel" } - } + }, + "tasks": [ + "LevitForImageClassification", + "LevitForImageClassificationWithTeacher", + "LevitModel", + "LevitPreTrainedModel" + ] } }, - "info.art.persimmon": { + "info.art.gpt-neo": { "*": { - "repo": "adept/persimmon-8b-base", + "repo": "EleutherAI/gpt-neo-1.3B", "pkg": { "0": { - "transformers": "PersimmonModel" + "transformers": "GPTNeoModel" } - } + }, + "tasks": [ + "GPTNeoForCausalLM", + "GPTNeoForQuestionAnswering", + "GPTNeoForSequenceClassification", + "GPTNeoForTokenClassification", + "GPTNeoModel", + "GPTNeoPreTrainedModel" + ] } }, - "info.detr.tvp": { + "info.aet.bert-for-seq-generation-l-24-bbc-encoder": { "*": { - "repo": "Intel/tvp-base", + "repo": "google/bert_for_seq_generation_L-24_bbc_encoder", "pkg": { "0": { - "transformers": "TvpModel" + "transformers": "BertGenerationEncoder" } - } + }, + "tasks": [ + "BertGenerationDecoder", + "BertGenerationEncoder", + "BertGenerationPreTrainedModel" + ] } }, - "info.vit.vitpose-simple": { + "info.aet.modernbert": { "*": { - "repo": "usyd-community/vitpose-base-simple", + "repo": "answerdotai/ModernBERT-base", "pkg": { "0": { - "transformers": "VitPoseBackbone" + "transformers": "ModernBertModel" } - } + }, + "tasks": [ + "ModernBertModel", + "ModernBertPreTrainedModel", + "ModernBertForMaskedLM", + "ModernBertForSequenceClassification", + "ModernBertForTokenClassification", + "ModernBertForQuestionAnswering", + "ModernBertForMultipleChoice" + ] } }, - "info.aet.ibert-roberta": { + "info.moe.max-text-01-hf": { "*": { - "repo": "kssteven/ibert-roberta-base", + "repo": "MiniMaxAI/MiniMax-Text-01-hf", "pkg": { "0": { - "transformers": "IBertModel" + "transformers": "MiniMaxModel" } - } + }, + "tasks": [ + "MiniMaxPreTrainedModel", + "MiniMaxModel", + "MiniMaxForCausalLM", + "MiniMaxForSequenceClassification", + "MiniMaxForTokenClassification", + "MiniMaxForQuestionAnswering" + ] } }, - "info.vit.textnet": { + "info.vit.bridgetower": { "*": { - "repo": "czczup/textnet-base", + "repo": "BridgeTower/bridgetower-base", "pkg": { "0": { - "transformers": "TextNetModel" + "transformers": "BridgeTowerModel" } - } + }, + "tasks": [ + "BridgeTowerForContrastiveLearning", + "BridgeTowerForImageAndTextRetrieval", + "BridgeTowerForMaskedLM", + "BridgeTowerModel", + "BridgeTowerPreTrainedModel" + ] } }, - "info.vit.data2vec-vision": { + "info.vit.deit-distilled-patch16-224": { "*": { - "repo": "facebook/data2vec-vision-base", + "repo": "facebook/deit-base-distilled-patch16-224", "pkg": { "0": { - "transformers": "Data2VecVisionModel" + "transformers": "DeiTModel" } - } + }, + "tasks": [ + "DeiTForImageClassification", + "DeiTForImageClassificationWithTeacher", + "DeiTForMaskedImageModeling", + "DeiTModel", + "DeiTPreTrainedModel" + ] } }, - "info.detr.dab-detr": { + "info.art.phi-3": { "*": { - "repo": "IDEA-Research/dab-detr-resnet-50", + "repo": "microsoft/Phi-3-mini-4k-instruct", "pkg": { "0": { - "transformers": "DabDetrModel" + "transformers": "Phi3Model" } - } + }, + "tasks": [ + "Phi3PreTrainedModel", + "Phi3Model", + "Phi3ForCausalLM", + "Phi3ForSequenceClassification", + "Phi3ForTokenClassification" + ] } }, - "info.vit.swiftformer-xs": { + "info.vit.colqwen2-v1-hf": { "*": { - "repo": "MBZUAI/swiftformer-xs", + "repo": "vidore/colqwen2-v1.0-hf", "pkg": { "0": { - "transformers": "SwiftFormerModel" + "transformers": "ColQwen2ForRetrieval" } - } + }, + "tasks": [ + "ColQwen2ForRetrieval", + "ColQwen2PreTrainedModel" + ] } }, - "info.vit.git": { + "info.aet.xlm-roberta": { "*": { - "repo": "microsoft/git-base", + "repo": "FacebookAI/xlm-roberta-base", "pkg": { "0": { - "transformers": "GitModel" + "transformers": "XLMRobertaModel" } - } + }, + "tasks": [ + "XLMRobertaForCausalLM", + "XLMRobertaForMaskedLM", + "XLMRobertaForMultipleChoice", + "XLMRobertaForQuestionAnswering", + "XLMRobertaForSequenceClassification", + "XLMRobertaForTokenClassification", + "XLMRobertaModel", + "XLMRobertaPreTrainedModel" + ] } }, "ops.precision.uint": { @@ -6925,7 +11361,7 @@ "0": { "torch": { "bfloat16": { - "variant": "bfp16" + "variant": "bf16" } } } @@ -6993,7 +11429,8 @@ "scheduler": { "pkg": { "0": { - "diffusers": "AmusedScheduler" + "diffusers": "AmusedScheduler", + "module_path": "diffusers.schedulers.scheduling_amused" } } } @@ -7002,7 +11439,8 @@ "scheduler": { "pkg": { "0": { - "diffusers": "CMStochasticIterativeScheduler" + "diffusers": "CMStochasticIterativeScheduler", + "module_path": "diffusers.schedulers.scheduling_consistency_models" } } } @@ -7011,7 +11449,8 @@ "scheduler": { "pkg": { "0": { - "diffusers": "CogVideoXDDIMScheduler" + "diffusers": "CogVideoXDDIMScheduler", + "module_path": "diffusers.schedulers.scheduling_ddim_cogvideox" } } } @@ -7020,7 +11459,8 @@ "scheduler": { "pkg": { "0": { - "diffusers": "CogVideoXDPMScheduler" + "diffusers": "CogVideoXDPMScheduler", + "module_path": "diffusers.schedulers.scheduling_dpm_cogvideox" } } } @@ -7029,7 +11469,8 @@ "scheduler": { "pkg": { "0": { - "diffusers": "DDIMInverseScheduler" + "diffusers": "DDIMInverseScheduler", + "module_path": "diffusers.schedulers.scheduling_ddim_inverse" } } } @@ -7038,7 +11479,8 @@ "scheduler": { "pkg": { "0": { - "diffusers": "DDIMParallelScheduler" + "diffusers": "DDIMParallelScheduler", + "module_path": "diffusers.schedulers.scheduling_ddim_parallel" } } } @@ -7047,7 +11489,8 @@ "scheduler": { "pkg": { "0": { - "diffusers": "DDIMScheduler" + "diffusers": "DDIMScheduler", + "module_path": "diffusers.schedulers.scheduling_ddim" } } } @@ -7056,7 +11499,8 @@ "scheduler": { "pkg": { "0": { - "diffusers": "DDPMParallelScheduler" + "diffusers": "DDPMParallelScheduler", + "module_path": "diffusers.schedulers.scheduling_ddpm_parallel" } } } @@ -7065,7 +11509,8 @@ "scheduler": { "pkg": { "0": { - "diffusers": "DDPMScheduler" + "diffusers": "DDPMScheduler", + "module_path": "diffusers.schedulers.scheduling_ddpm" } } } @@ -7074,7 +11519,8 @@ "scheduler": { "pkg": { "0": { - "diffusers": "DDPMWuerstchenScheduler" + "diffusers": "DDPMWuerstchenScheduler", + "module_path": "diffusers.schedulers.scheduling_ddpm_wuerstchen" } } } @@ -7083,7 +11529,8 @@ "multistep": { "pkg": { "0": { - "diffusers": "DEISMultistepScheduler" + "diffusers": "DEISMultistepScheduler", + "module_path": "diffusers.schedulers.scheduling_deis_multistep" } } } @@ -7092,7 +11539,8 @@ "multistep": { "pkg": { "0": { - "diffusers": "DPMSolverMultistepInverseScheduler" + "diffusers": "DPMSolverMultistepInverseScheduler", + "module_path": "diffusers.schedulers.scheduling_dpmsolver_multistep_inverse" } } } @@ -7101,7 +11549,8 @@ "multistep": { "pkg": { "0": { - "diffusers": "DPMSolverMultistepScheduler" + "diffusers": "DPMSolverMultistepScheduler", + "module_path": "diffusers.schedulers.scheduling_dpmsolver_multistep" } } } @@ -7110,7 +11559,8 @@ "solver": { "pkg": { "0": { - "diffusers": "DPMSolverSinglestepScheduler" + "diffusers": "DPMSolverSinglestepScheduler", + "module_path": "diffusers.schedulers.scheduling_dpmsolver_singlestep" } } } @@ -7119,7 +11569,8 @@ "multistep": { "pkg": { "0": { - "diffusers": "EDMDPMSolverMultistepScheduler" + "diffusers": "EDMDPMSolverMultistepScheduler", + "module_path": "diffusers.schedulers.scheduling_edm_dpmsolver_multistep" } } } @@ -7128,7 +11579,8 @@ "scheduler": { "pkg": { "0": { - "diffusers": "EDMEulerScheduler" + "diffusers": "EDMEulerScheduler", + "module_path": "diffusers.schedulers.scheduling_edm_euler" } } } @@ -7137,7 +11589,8 @@ "discrete": { "pkg": { "0": { - "diffusers": "EulerAncestralDiscreteScheduler" + "diffusers": "EulerAncestralDiscreteScheduler", + "module_path": "diffusers.schedulers.scheduling_euler_ancestral_discrete" } } } @@ -7146,7 +11599,8 @@ "discrete": { "pkg": { "0": { - "diffusers": "EulerDiscreteScheduler" + "diffusers": "EulerDiscreteScheduler", + "module_path": "diffusers.schedulers.scheduling_euler_discrete" } } } @@ -7155,7 +11609,8 @@ "discrete": { "pkg": { "0": { - "diffusers": "FlowMatchEulerDiscreteScheduler" + "diffusers": "FlowMatchEulerDiscreteScheduler", + "module_path": "diffusers.schedulers.scheduling_flow_match_euler_discrete" } } } @@ -7164,7 +11619,8 @@ "discrete": { "pkg": { "0": { - "diffusers": "FlowMatchHeunDiscreteScheduler" + "diffusers": "FlowMatchHeunDiscreteScheduler", + "module_path": "diffusers.schedulers.scheduling_flow_match_heun_discrete" } } } @@ -7173,7 +11629,8 @@ "scheduler": { "pkg": { "0": { - "diffusers": "FlowMatchLCMScheduler" + "diffusers": "FlowMatchLCMScheduler", + "module_path": "diffusers.schedulers.scheduling_flow_match_lcm" } } } @@ -7182,7 +11639,8 @@ "discrete": { "pkg": { "0": { - "diffusers": "HeunDiscreteScheduler" + "diffusers": "HeunDiscreteScheduler", + "module_path": "diffusers.schedulers.scheduling_heun_discrete" } } } @@ -7191,7 +11649,8 @@ "scheduler": { "pkg": { "0": { - "diffusers": "IPNDMScheduler" + "diffusers": "IPNDMScheduler", + "module_path": "diffusers.schedulers.scheduling_ipndm" } } } @@ -7200,7 +11659,8 @@ "scheduler": { "pkg": { "0": { - "diffusers": "KarrasVeScheduler" + "diffusers": "KarrasVeScheduler", + "module_path": "diffusers.schedulers.deprecated.scheduling_karras_ve" } } } @@ -7209,7 +11669,8 @@ "discrete": { "pkg": { "0": { - "diffusers": "KDPM2AncestralDiscreteScheduler" + "diffusers": "KDPM2AncestralDiscreteScheduler", + "module_path": "diffusers.schedulers.scheduling_k_dpm_2_ancestral_discrete" } } } @@ -7218,7 +11679,8 @@ "discrete": { "pkg": { "0": { - "diffusers": "KDPM2DiscreteScheduler" + "diffusers": "KDPM2DiscreteScheduler", + "module_path": "diffusers.schedulers.scheduling_k_dpm_2_discrete" } } } @@ -7227,7 +11689,8 @@ "scheduler": { "pkg": { "0": { - "diffusers": "LCMScheduler" + "diffusers": "LCMScheduler", + "module_path": "diffusers.schedulers.scheduling_lcm" } } } @@ -7236,7 +11699,8 @@ "scheduler": { "pkg": { "0": { - "diffusers": "PNDMScheduler" + "diffusers": "PNDMScheduler", + "module_path": "diffusers.schedulers.scheduling_pndm" } } } @@ -7245,7 +11709,8 @@ "scheduler": { "pkg": { "0": { - "diffusers": "RePaintScheduler" + "diffusers": "RePaintScheduler", + "module_path": "diffusers.schedulers.scheduling_repaint" } } } @@ -7254,7 +11719,8 @@ "solver": { "pkg": { "0": { - "diffusers": "SASolverScheduler" + "diffusers": "SASolverScheduler", + "module_path": "diffusers.schedulers.scheduling_sasolver" } } } @@ -7263,7 +11729,8 @@ "scheduler": { "pkg": { "0": { - "diffusers": "SCMScheduler" + "diffusers": "SCMScheduler", + "module_path": "diffusers.schedulers.scheduling_scm" } } } @@ -7272,7 +11739,8 @@ "scheduler": { "pkg": { "0": { - "diffusers": "ScoreSdeVeScheduler" + "diffusers": "ScoreSdeVeScheduler", + "module_path": "diffusers.schedulers.scheduling_sde_ve" } } } @@ -7281,7 +11749,8 @@ "scheduler": { "pkg": { "0": { - "diffusers": "TCDScheduler" + "diffusers": "TCDScheduler", + "module_path": "diffusers.schedulers.scheduling_tcd" } } } @@ -7290,7 +11759,8 @@ "scheduler": { "pkg": { "0": { - "diffusers": "UnCLIPScheduler" + "diffusers": "UnCLIPScheduler", + "module_path": "diffusers.schedulers.scheduling_unclip" } } } @@ -7299,7 +11769,8 @@ "multistep": { "pkg": { "0": { - "diffusers": "UniPCMultistepScheduler" + "diffusers": "UniPCMultistepScheduler", + "module_path": "diffusers.schedulers.scheduling_unipc_multistep" } } } @@ -7308,7 +11779,8 @@ "scheduler": { "pkg": { "0": { - "diffusers": "VQDiffusionScheduler" + "diffusers": "VQDiffusionScheduler", + "module_path": "diffusers.schedulers.scheduling_vq_diffusion" } } } @@ -7317,7 +11789,28 @@ "discrete": { "pkg": { "0": { - "diffusers": "LMSDiscreteScheduler" + "diffusers": "LMSDiscreteScheduler", + "module_path": "diffusers.schedulers.scheduling_lms_discrete" + } + } + } + }, + "ops.scheduler.cosinedpm": { + "multistep": { + "pkg": { + "0": { + "diffusers": "CosineDPMSolverMultistepScheduler", + "module_path": "diffusers.schedulers.scheduling_cosine_dpmsolver_multistep" + } + } + } + }, + "ops.scheduler.dpmsde": { + "solver": { + "pkg": { + "0": { + "diffusers": "DPMSolverSDEScheduler", + "module_path": "diffusers.schedulers.scheduling_dpmsolver_sde" } } } @@ -7326,7 +11819,8 @@ "schedulers": { "pkg": { "0": { - "diffusers": "KarrasDiffusionSchedulers" + "diffusers": "KarrasDiffusionSchedulers", + "module_path": "diffusers.schedulers.scheduling_utils" } } } @@ -8057,11 +12551,10 @@ "591f853590d11ddde2f2a54f9e7ccecb2533a8af7716330e8adfa6f3849787a9" ], "layer_256": [ - "2ffef1834d5fe14ad8db58fc78d769d5dc38dda5eddbfc396786f74b326215fd", - "94ea015f5f7f65b1d8e80f7d52859535e7761d7ed2752e24d57a8d9d9da96672" + "2ffef1834d5fe14ad8db58fc78d769d5dc38dda5eddbfc396786f74b326215fd" ], "layer_b3": [ - "7939427700c3b4d91428a490bde1a6d893f63ee5d79b86f68de9e89c7094d3e741ca5931452b3ffee588c6c7e5bd327c4e914141604eaf3fd05f4a790ac83bb2", + "41ca5931452b3ffee588c6c7e5bd327c4e914141604eaf3fd05f4a790ac83bb2", "7dc736cd5d840182792bde4edfbf5ddc5aeaf16826a9c72d1ba8166c1e3fab9b", "6e2c1bdbad74f56663ffb5710c7cb849a2b91ba331d81acdba47a21f69107434", "ab5ff443aece9171af5e7603d0b4309d3ecc934e3940ccedefff10f0b54b931e" @@ -8084,6 +12577,15 @@ ], "layer_b3": [ "6c9c5642aa8dce62bcb3eb577bc519619a2d868005c767c5e65371c583a8a8eb" + ], + "tasks": [ + "Wav2Vec2ConformerForAudioFrameClassification", + "Wav2Vec2ConformerForCTC", + "Wav2Vec2ConformerForPreTraining", + "Wav2Vec2ConformerForSequenceClassification", + "Wav2Vec2ConformerForXVector", + "Wav2Vec2ConformerModel", + "Wav2Vec2ConformerPreTrainedModel" ] } }, @@ -8126,6 +12628,19 @@ } } }, + "info.gan.speecht5-hifigan": { + "*": { + "file_256": [ + "d9dc6513c30a5b86c2497712690c04fe74b4aa79fdab6d490b34fcb4e24c590c" + ], + "layer_256": [ + "bd52b538e7ac05711be9321cfb7619d4056996ce32923c9c91ee02cf69154770" + ], + "layer_b3": [ + "85b5acdf29ad04c63f885383340d8e3445ae0055521f82cabb82bd09cfb9a956" + ] + } + }, "info.dit.wan2-flf2v-720p": { "diffusers": { "repo": "Wan-AI/Wan2.1-FLF2V-14B-720P-Diffusers", @@ -8198,7 +12713,10 @@ "1e090d6a828fd92401be5f83e615fd7b4fb1f4a22e9af9040a38f602e839317c", "11807cb2522cfe99240e5ee2bbeb1ccb42cecca2215102ee872567c7773b28b9", "d008943c017f0092921106440254dbbe00b6a285f7883ec8ba160c3faad88334", - "77795e2023adcf39bc29a884661950380bd093cf0750a966d473d1718dc9ef4e" + "77795e2023adcf39bc29a884661950380bd093cf0750a966d473d1718dc9ef4e", + "b70c11ad5d7e9abf6109348908f599ea382f8019e1f36910bbc8ebecde936633", + "fc42badf529dd83f2f7c3d20fe6bda1e22036162f37c4c668b9e130884e20561", + "e27bafa0b3029ad637ef3ace24ce1efe85b8d0dbd22e03a2e70bda6fc88963a1" ], "layer_256": [ "48daa3d8f939972e69f044533a4312a941971c18c78255f5e555fa26faf664c1", @@ -8212,7 +12730,10 @@ "5d6d9d0cc7943eb1b8c16862bfd5bee5c3766d0df027ec837e90fac715ac2bd3", "68fb122f7d6c3cfbef320341b2af8f5916678e36a69ed36fa8cfcb19e7d5c43d", "11807cb2522cfe99240e5ee2bbeb1ccb42cecca2215102ee872567c7773b28b9", - "50c46cdddbe9f0162278c69b9a1f818519330e3a91b994272e19b5c789670471" + "50c46cdddbe9f0162278c69b9a1f818519330e3a91b994272e19b5c789670471", + "ffe1c4f55e07c2010ace7b9cf35798bb9f431bc954a32784e5acbdc16acc0364", + "146ea48d234e05a934db9d8988e9a9dd86b2ac70f535eaa550ecb0ee23ec135e", + "d97560cf9704cf71711f6121df2bf55e55a1eda4b574a6ddba074767420bc8c3" ], "layer_b3": [ "f58a22a381f79985b6d38782f6110a52c2f319b40fdedd3b88b24945dfcbdf64", @@ -8227,7 +12748,19 @@ "c4c9caccdbec12b965d93688c521893f75e0bf9a5e0aad70a6a962b669e7b9d5", "e43fae8d5fd1e562607da172369cc0c5ec99b834e42502e682287ff7d12baacc", "c6f79f7416a882891957b815fbdfd6edfaa253c43970b1a25ef14e217599c7bc", - "daf5e09f67ad09a909f58a01298fec0132324634cb8fca2a604c3a240c2c453f" + "daf5e09f67ad09a909f58a01298fec0132324634cb8fca2a604c3a240c2c453f", + "3f62bfb6bbde05f01435129326166c44aeb113ac0d9f735f31ed3f7dd04f6980", + "22f866f3c96a92bc61e9965cf366d706db942ad047ba8cb82109edcd4e68fa40", + "f3fa9d7a8f15741621c1fe82f8a1bcc5c601c900d947ac09fba7016615a252a5" + ], + "tasks": [ + "CLIPModel", + "CLIPPreTrainedModel", + "CLIPTextModel", + "CLIPTextModelWithProjection", + "CLIPVisionModel", + "CLIPVisionModelWithProjection", + "CLIPForImageClassification" ] } }, @@ -8246,7 +12779,10 @@ "b84f413eebecbd049b72874c1df533a516510cb5a2489ae58c7e320209cf0ebe", "d3df577f6e3799c8e1bd9b40e30133710e02e8e25d0ce48cdcc790e7dfe12d6d", "943a2924ee888295a156dd47089d67181d633b782337890af11ef4b15af17ec5", - "5b98e4a57a9292eeb819d67e2d2100f66f17db723cde4ecea27a7c3741160d0c" + "5b98e4a57a9292eeb819d67e2d2100f66f17db723cde4ecea27a7c3741160d0c", + "4d6effa7a5e600cabf7528ed7234146a13ead1b2c151211d706b293a060b112a", + "3a6032f63d37ae02bbc74ccd6a27440578cd71701f96532229d0154f55a8d3ff", + "162042ac6556e73f93d4172d4c67532c1cbe4dc7a6a8fa7e44dd2e3d7cbb772b" ], "layer_256": [ "270e998633eb22145100a3889a62ca270d5080654735e5ff8dda09a7c233af8d", @@ -8254,7 +12790,8 @@ "4c228b104f6b9b383e0808c9baa1998957f5125d8f90a4d98c1a86e71edd72dc", "f7fc81d8b5ae91ec28a5106ecc0d067be9a94fd3f394c4aa4686ed131ce5a5b3", "61ab42bd5c0fcb9fd3db1d4014cb844ccae8dc17fd69a108cf077a573d092946", - "6c64e36cdda3bec7067e94b05619f882f5d31070792acaadac60ddbef580453a" + "6c64e36cdda3bec7067e94b05619f882f5d31070792acaadac60ddbef580453a", + "43c9e64995b485a7f128771c48defce128640df28e65c7f79537d472f43ebe46" ], "layer_b3": [ "d754db276f2d89d2808abb7086b3b8eccee43ac521c128d21a071f3a631474a8", @@ -8262,7 +12799,17 @@ "e253a5cf3a6242c58037abd6b378bf0281f278e441f28dff7ca1bcfcd3cd6bd8", "16d0eec4e55b0aa63cdca4e4d36f78f66a4b1b9605ce3b1089305026f853c3d2", "f606463295ecf3bae8920d3d45bb9d180793418b3d08c3e84d4c4135c7dc2aa5", - "7060993a5eb32d94d1ea8aef7a7301e7be73b199c639c63f8f7cfbfcd2abf10e" + "7060993a5eb32d94d1ea8aef7a7301e7be73b199c639c63f8f7cfbfcd2abf10e", + "b92af95334c657371af6051a91374a41b5455907fa6622bb66a8c112dc511600" + ], + "tasks": [ + "CLIPModel", + "CLIPPreTrainedModel", + "CLIPTextModel", + "CLIPTextModelWithProjection", + "CLIPVisionModel", + "CLIPVisionModelWithProjection", + "CLIPForImageClassification" ] } }, @@ -8288,6 +12835,15 @@ "227f26ed63120b9034f4a0c90b6b37eede721a8260f2c1e8f7ea3ccc0d109e7e", "3a38ffd1b60499cf2f451f3065079ff26efb9190a86f23ad1c8d993bbeb9af05", "ce06cf1fd684269ee96631b2bf9334c6ecde6a84a55760dfa0d9d2a6411f28e4" + ], + "tasks": [ + "CLIPModel", + "CLIPPreTrainedModel", + "CLIPTextModel", + "CLIPTextModelWithProjection", + "CLIPVisionModel", + "CLIPVisionModelWithProjection", + "CLIPForImageClassification" ] } }, @@ -8390,9 +12946,15 @@ "diffusers": "AutoencoderKLAllegro" } }, - "file_256": [], - "layer_256": [], - "layer_b3": [] + "file_256": [ + "47871a698b18f92f15019d361a81cbc8af4676f8eef9a47fd2b95354a39f831a" + ], + "layer_256": [ + "bfd496586118165a13243997101fc7cdd4f855b2d8a73ee2b771a4484c4c2f9f" + ], + "layer_b3": [ + "93654cbab7541504d2377c66e72943c7fd9947fca2eb1be01bcc8877c322c1e0" + ] }, "cosmos-1-diffusion-video2world": { "pkg": { @@ -8421,10 +12983,11 @@ } }, "file_256": [ - "95d1fc707c1421ccd88ea542838ab4c5d45a5babb48205bac9ce0985525f9818" - ], - "layer_256": [], - "layer_b3": [] + "95d1fc707c1421ccd88ea542838ab4c5d45a5babb48205bac9ce0985525f9818", + "7c68a6295f9034a88225fbafb1f3258291a08d57a1fdb938233fa57b1b8f4883", + "fbe5ea338431bc8ba20f7019b474e83379fe5763abfd562adcc04b1c0d35c728", + "019973c147e0c3462629d8d06bdbdbb83408f3ebd4ea4b4ae21a99c3cdcb54c0" + ] }, "mochi-1": { "pkg": { @@ -8436,15 +12999,28 @@ "layer_256": [], "layer_b3": [] }, + "audioldm-s-v2": { + "pkg": { + "0": { + "diffusers": "AutoencoderKL" + } + }, + "file_256": [ + "42f64f7565b23eabde68c9694e39f18b8bba5f7a14f477e7ed4b51e0ea7de8a5" + ], + "layer_256": [ + "54d075953d5253a3abac651de070736c1d5510b857a8ab24c624304f428146b6" + ], + "layer_b3": [ + "00959677dae940b9cfdbe5380c8cbb5a6b4951864cd26f8211d74a3d22b4f3de" + ] + }, "stable-video-diffusion-img2vid-xt": { "pkg": { "0": { "diffusers": "AutoencoderKLTemporalDecoder" } - }, - "file_256": [], - "layer_256": [], - "layer_b3": [] + } }, "stable-diffusion-xl-1": { "repo": "madebyollin/sdxl-vae-fp16-fix", @@ -8460,7 +13036,8 @@ "6353737672c94b96174cb590f711eac6edf2fcce5b6e91aa9d73c5adc589ee48", "bcb60880a46b63dea58e9bc591abe15f8350bde47b405f9c38f4be70c6161e68", "1598f3d24932bcfe6634e8b618ea1e30ab1d57f5aad13a6d2de446d2199f2341", - "703abdcd7c389316b5128faa9b750a530ea1680b453170b27afebac5e4db30c4" + "703abdcd7c389316b5128faa9b750a530ea1680b453170b27afebac5e4db30c4", + "98a14dc6fe8d71c83576f135a87c61a16561c9c080abba418d2cc976ee034f88" ], "layer_256": [ "c9399a4cd39a180a0bb2af96a8297b9330541e090c21e83317cebb2f7cc651da", @@ -8471,6 +13048,26 @@ "9106380403dee83238af63ff1738396d2fdff9f6d78d0d9c1d0bf770ae4294d0" ] }, + "stable-diffusion-xl-1*": { + "pkg": { + "0": { + "diffusers": "AutoencoderKL" + } + }, + "file_256": [ + "235745af8d86bf4a4c1b5b4f529868b37019a10f7c0b2e79ad0abca3a22bc6e1", + "27ed3b02e09638568e99d4398c67bc654dde04e6c0db61fb2d21dba630e7058a", + "eb6516ab7e1104d5d1a174a4d65c57835ae38061531d0a2192103aecfb790cc1", + "e6bb9ea85bbf7bf6478a7c6d18b71246f22e95d41bcdd80ed40aa212c33cfeff" + ], + "layer_256": [ + "c9399a4cd39a180a0bb2af96a8297b9330541e090c21e83317cebb2f7cc651da", + "2240ae134a3b983abf45200c198f07e3d8068012fbbd2f658bbaa1fd6a0629c0" + ], + "layer_b3": [ + "bd5b356b509814025a9cf692710b87116d4fcd0e30a8232ed1db133e908d0e74" + ] + }, "shuttle-jaguar": { "pkg": { "0": { @@ -8496,15 +13093,30 @@ }, "file_256": [ "afc8e28272cd15db3919bacdb6918ce9c1ed22e96cb12c4d5ed0fba823529e38", - "f5b59a26851551b67ae1fe58d32e76486e1e812def4696a4bea97f16604d40a3" + "f5b59a26851551b67ae1fe58d32e76486e1e812def4696a4bea97f16604d40a3", + "8c717328c8ad41faab2ccfd52ae17332505c6833cf176aad56e7b58f2c4d4c94", + "8f53304a79335b55e13ec50f63e5157fee4deb2f30d5fae0654e2b2653c109dc" ], "layer_256": [ "7950e4f3897c75affaa5f9f3c51c88b4d9a27bfd9b05ad41c3f71d8c1c620b89", "79d2bfe93a2ac037cdc59ccb5576e32d00d75d4741fba49fc7e82b9724928216", - "8f084dc91fd5b481875bc9c86a4ef05e5f176896b7d31c6a5c2ce45c2e174004" + "8f084dc91fd5b481875bc9c86a4ef05e5f176896b7d31c6a5c2ce45c2e174004", + "322e01bd511e20bc2a3c27cd611f81ed85f0046b7c023b5622c2c9a5b8b34f80" + ], + "layer_b3": [ + "b6db93ed78c4a10d69e80831c1b8fbc1447f04e9b3d494889ee2056b98d41f17", + "a8a3ebdec4d7b38d65b7169d3604c19b587330e5e66f69ebf0ded56a24ec6903" + ] + }, + "musicldm": { + "file_256": [ + "16e0c6c7c34e459c19500cc15cf538e6331db14969ea15917caa9b0966e44fd4" + ], + "layer_256": [ + "1610c0ce39d1379091eb9ab2a4d14a8567e0f1a5dc6cca40fc0fa6f8e4e97c0f" ], "layer_b3": [ - "b6db93ed78c4a10d69e80831c1b8fbc1447f04e9b3d494889ee2056b98d41f17" + "c5c32b3fb3e73799838836ccce27d883254254daecd10f86ba8ddc55214014e0" ] }, "stable-diffusion-v1-5": { @@ -8537,7 +13149,8 @@ "wan2-i2v-480p": { "pkg": { "0": { - "diffusers": "AutoencoderKLWan" + "diffusers": "AutoencoderKLWan", + "precision": "ops.precision.float.F32" } }, "file_256": [ @@ -8571,9 +13184,15 @@ "diffusers": "AutoencoderKLCogVideoX" } }, - "file_256": [], - "layer_256": [], - "layer_b3": [] + "file_256": [ + "a410e48d988c8224cef392b68db0654485cfd41f345f4a3a81d3e6b765bb995e" + ], + "layer_256": [ + "43c7e9cb4364e55fd563817f01484ede8a09ff19a8e69eb61a32a12f93d6f66e" + ], + "layer_b3": [ + "246addb8dc798240638bffee4546a3c5c83572139b4a2a602d68b4c4146226eb" + ] }, "cogvideox-fun-v-pose": { "file_256": [], @@ -8593,9 +13212,15 @@ "diffusers": "AutoencoderDC" } }, - "file_256": [], - "layer_256": [], - "layer_b3": [] + "file_256": [ + "15a4b09e56d95b768a0ec9da50b702e21d920333fc9b3480d66bb5c7fad9d87f" + ], + "layer_256": [ + "abfc39d1a6d71f03dde7bc40fec4a90478a97d17ae1688be9aad00e0512b9bde" + ], + "layer_b3": [ + "cf4ecc6697d18b0663e4eac58203f1dd6d9fb689cf99adfeadbc0019de0c73d0" + ] } }, "info.vae.oobleck": { @@ -8604,10 +13229,7 @@ "0": { "diffusers": "AutoencoderOobleck" } - }, - "file_256": [], - "layer_256": [], - "layer_b3": [] + } } }, "info.vae.eq": { diff --git a/mir/run.py b/mir/run.py new file mode 100644 index 0000000..ab1a1aa --- /dev/null +++ b/mir/run.py @@ -0,0 +1,68 @@ +# # # +# # # + + +from mir.maid import MIRDatabase +from mir.inspect.tasks import TaskAnalyzer + + +def main(mir_db: MIRDatabase = None): + """Parse arguments to feed to dict header reader""" + import argparse + import asyncio + from mir.automata import assimilate + from sys import modules as sys_modules + + if "pytest" not in sys_modules: + parser = argparse.ArgumentParser( + formatter_class=argparse.RawTextHelpFormatter, + description="Scrape the task classes from currently installed libraries and attach them to an existing MIR database.\nOffline function.", + usage="mir-tasks", + epilog="Can be run automatically with `python -m nnll.mir.maid` Should only be used after `mir-maid`.\n\nOutput:\n INFO ('Wrote #### lines to MIR database file.',)", + ) + parser.parse_args() + + if not mir_db: + mir_db = MIRDatabase() + + tasker = TaskAnalyzer() + task_tuple = asyncio.run(tasker.detect_tasks(mir_db)) + + assimilate(mir_db, [task for task in task_tuple]) + + mir_db.write_to_disk() + return mir_db + + +def run_task(): + main() + + +def pipe(mir_db: MIRDatabase = None): + import argparse + import asyncio + from sys import modules as sys_modules + + if "pytest" not in sys_modules: + parser = argparse.ArgumentParser( + formatter_class=argparse.RawTextHelpFormatter, + description="Infer pipe components from Diffusers library and attach them to an existing MIR database.\nOffline function.", + usage="mir-pipe", + epilog="Can be run automatically with `python -m nnll.mir.maid` Should only be used after `mir-maid`.\n\nOutput:\n INFO ('Wrote #### lines to MIR database file.',)", + ) + parser.parse_args() + + from mir.automata import assimilate + + if not mir_db: + mir_db = MIRDatabase() + + tasker = TaskAnalyzer() + pipe_tuple = asyncio.run(tasker.detect_pipes(mir_db)) + assimilate(mir_db, [pipe for pipe in pipe_tuple]) + mir_db.write_to_disk() + return mir_db + + +# if __name__ == "__main__": +# pipe() diff --git a/mir/spec/mir.py b/mir/spec/mir.py new file mode 100644 index 0000000..618a5cc --- /dev/null +++ b/mir/spec/mir.py @@ -0,0 +1,354 @@ +# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0 +# + + +import urllib.parse +from collections import defaultdict +from dataclasses import dataclass +from logging import INFO, Logger +from pathlib import Path +from typing import Any, Callable, Dict, List, Optional, TypeVar, Union + +from pydantic import BaseModel, create_model + +nfo_obj = Logger(INFO) +nfo = nfo_obj.info + +T = TypeVar("T") + +PackageIndex = int +TimestepType = List[int] # never none +ParameterField = Union[str, bool, int, float, TimestepType] # never none + +PkgType = Dict[ # Outer key is an integer (e.g., 0, otherwise information gets mixed between package names and their attributes) + PackageIndex, + Dict[ParameterField, Union[Dict, ParameterField]], +] + +HashType = Optional[Union[List[str], Optional[Dict[str, Union[Any]]]]] + + +class Info(BaseModel): + """ + Static global neural network attributes, metadata with an identifier in the database\n + :param file_256: Canonical hash calculation for known model files + :param gen_kwargs: OEM arguments to pass to the generator + :param init_kwargs: OEM arguments to pass to constructor + :param module_alt: Third-party library module support for the resource + :param layer_256: Canonical hash calculation for list of model layer names, if applicable + :param repo: A dedicated remote origin + :param repo_alt: Secondary remote sources + :param scheduler_kwargs: OEM arguments to pass to constructor function + :param scheduler: OEM noise scheduler (mir:type str) + :param tasks: Supported modalities + :param weight_map: Remote location of the weight map for the model + """ + + repo: Optional[str] = None + pkg: Optional[PkgType] = None + file_256: Optional[HashType] = None + layer_256: Optional[HashType] = None + file_b3: Optional[HashType] = None + layer_b3: Optional[HashType] = None + identifier: Optional[Union[List[int], int, str]] = None # numbers preceding str + + +class Ops(BaseModel): + """ + Varying global neural network attributes, algorithms, optimizations and procedures on models\n + info: str # Immutable metadata with an identifier in the database\n + dev: str # Any pre-release or under evaluation items without an identifier in an expected format\n + :param info: Static global neural network attributes, metadata with an identifier in the database\n + `info` domain attributes + :param dtype: Model datatype (ie F16,F32,F8_E4M3,I64) name if applicable + :param gen_kwargs: OEM arguments to pass to the generator + :param init_kwargs: OEM arguments to pass to the constructor + :param repo: A dedicated remote origin + :param scheduler_kwargs: OEM arguments to pass to constructor function + :param dtype: Alternate datatype name + """ + + pkg: PkgType + repo: Optional[List[str]] = None + + +class Model(BaseModel): + """ + Static local neural network layers. Publicly released machine learning models with an identifier in the database\n + :param dtype: Model datatype (ie F16,F32,F8_E4M3,I64) name if applicable + :param file_ext: The last file extension in the filename + :param file_name: The basename of the file + :param file_path: Absolute location of the file on disk + :param file_size: Total size of the file in bytes + :param layer_type: The format and compatibility of the model structure (e.g., 'diffusers') + """ + + dtype: Optional[str] = None + file_ext: Optional[str] = None + file_name: Optional[str] = None + file_path: Optional[Path] = None + file_size: Optional[int] = None + format: Optional[str] = None + layer_type: Optional[str] = None + + +@dataclass +class Dev(Info, Ops, Model): + """ + Varying local neural network layers, in-training, pre-release, items under evaluation, likely in unexpected formats\n + Inheriting attributes from Info, Ops, and Model to reduce duplication. + """ + + # Experimental/Optional/Deprecated, maybe useful later + stage: str # Where item fits in a chain + dtype: Optional[str] = None + dep_pkg: Optional[Dict[str, list[str]]] = None + gen_kwargs: Optional[Dict[str, Any]] = None + lora_kwargs: Optional[str] = None + module_alt: Optional[List[str]] = None + module_path: Optional[list[str]] = None + repo_pkg: Optional[List[str]] = None + requires: Optional[Dict[str, list[str]]] = None + scheduler_alt: Optional[str] = None + scheduler_kwargs_alt: Optional[Dict[str, Any]] = None + scheduler_kwargs: Optional[Dict[str, Any]] = None + scheduler: Optional[str] = None + tasks: Optional[List[str]] = None + weight_map: Optional[Union[urllib.parse.ParseResult, str]] = None + # gen_kwargs: Optional[Dict[str, int | str | float | list]] = None + # init_kwargs: Optional[Dict[str, int | str | float | list]] = None + + +def add_mir_fields(domain: str, **kwargs): + """Build MIR dataclasses for each domain type\n + :param domain: Class type field constructor + :raises ValueError: An invalid domain type was entered + :return: A class of the designated type constructed with all fields added + """ + if domain.lower() == "info": + return Info(**{k: v for k, v in kwargs.items() if k in Info.__pydantic_fields__ and v is not None}) # pylint: disable=no-member + elif domain.lower() == "model": + return Model(**{k: v for k, v in kwargs.items() if k in Model.__pydantic_fields__ and v is not None}) # pylint: disable=no-member + elif domain.lower() == "ops": + return Ops(**{k: v for k, v in kwargs.items() if k in Ops.__pydantic_fields__ and v is not None}) # pylint: disable=no-member + elif domain.lower() == "dev": + return Dev(**{k: v for k, v in kwargs.items() if k in Dev.__dataclass_fields__ and v is not None}) # pylint: disable=no-member + raise ValueError(f"Unsupported domain: {domain}") + + +def build_comp(comp: str, domain: str, kwargs: dict) -> Callable: + """Create a dynamic Compatibility class and return the defined class object with a flattening function\n + :param comp: Name for the class field (and subsequent flattened `key`) + :param domain: Class type for the `value` data + :param kwargs: Key/value data pairs within `value` + :return: A Compatibility object with `comp` name mapped to `kwargs` data + """ + field = { + comp: (Union[Info, Model, Ops, Dev], ...), + } + data = {} + data.setdefault(comp, add_mir_fields(domain=domain, **kwargs)) + + DynamicModel = create_model("Compatibility", **field) # pylint: disable=invalid-name + + class Compatibility(DynamicModel): # pylint: disable=too-few-public-methods + """Dynamically created model attributes (to create key structure)""" + + def to_dict(self, _) -> Dict[str, Any]: + """Flatten the Compatibility class structure\n + :return: A dictionary of the structure + """ + setattr(self, comp, getattr(self, comp).__dict__) # add the data to an attribute if its not already made + return { + comp_name: { + inner_key: inner_value + for inner_key, inner_value in comp_value.items() + if inner_value is not None # Comment to force formatting + } + for comp_name, comp_value in self.__dict__.items() + if isinstance(comp_value, dict) + } + + return Compatibility(**data) + + +class Series: + """ + Specific seriess of generative and deep learning architectures.\n + The release name of models, in short. + mir.json contains the lengthy key list of supported seriess\n + **add_compat** Add a Compatibility object to this class + **to_dict** flatten the compatibility object + """ + + # @debug_monitor + def __init__(self, series: str) -> None: + """Constructor""" + self.series = series + self.compatibility = defaultdict(dict) + self.flat_dict = defaultdict(dict) + + def add_compat(self, compat_label: str, compat_obj: Dict[str, int | float | list | str]) -> None: + """Add compatibility: Attribute an object to a sub-class of the Series""" + self.compatibility[compat_label] = compat_obj + + def to_dict(self, prefix: str) -> Dict[str, Any]: # , prefix: str = "compatibility") + """Flatten the Architecture class structure\n + :param prefix: Prepended identifying tag""" + for _comp_name, comp_obj in self.compatibility.items(): + path = f"{prefix}" # .{comp_name}" # so that we can combine last keys into one dict + self.flat_dict[path].update(comp_obj.to_dict(path)) # path + return self.flat_dict + + +class Architecture: + """ + Known generative and deep learning architecture.\n + :param art: Autoregressive transformer, typically LLMs + :param dit: Diffusion transformer, typically Vision Synthesis + :param lora: Low-Rank Adapter (may work with dit or transformer) + :param unet: Unet diffusion structure + :param vae: Variational Autoencoder, roughly + + **add_impl** Add an Series object to the Architecture + **to_dict** Flatten the Architecture class structure + """ + + # @debug_monitor + def __init__(self, architecture: str) -> None: + """Constructor""" + self.architecture = architecture + self.series = defaultdict(dict) + self.flat_dict = defaultdict(dict) + + def add_impl(self, impl_label: str, impl_obj: Series) -> None: + """Add_component: Attribute an object to a sub-class of the Architecture""" + self.series[impl_label] = impl_obj + + def to_dict(self, prefix: str) -> Dict[str, Any]: + """Flatten the Architecture class structure\n + :param prefix: Prepended identifying tag""" + for comp_name, comp_obj in self.series.items(): + path = f"{prefix}.{comp_name}" + self.flat_dict.update(comp_obj.to_dict(path)) + return self.flat_dict + + +class Domain: + """ + Define a set of AI/ML related data\n + :param dev: `Dev()` + :param model: `Model()` + :param Ops: `Ops()` + :param info: `Info()` + + **add_arch** Create a sub-class of Domain\n + **to_dict** Flatten the Domain class structure + """ + + dev: Dev # Pre-release or under evaluation items without an identifier in an expected format + info: Info # Metadata of layer names or settings with an identifier in the database + model: Model # Model weight specifics of shifting locations and practical dimensions + ops: Ops # References to specific optimization or manipulation techniques + + # @debug_monitor + def __init__(self, domain_name: str) -> None: + """Constructor""" + self.domain_name = domain_name + self.architectures = defaultdict(dict) + self.flat_dict = defaultdict(dict) + + # @debug_monitor + def add_arch(self, arch_label: str, arch_obj: Architecture) -> None: + """ + Add an architecture to subclass this Domain\n + :param architecture_name: A valid architecture type + :param architecture_obj: Data to store + """ + self.architectures[arch_label] = arch_obj + + # @debug_monitor + def to_dict(self) -> Dict[str, Any]: + """Flatten the Architecture class structure\n + :return: A dictionary of the structure + """ + for arc_name, arc_obj in self.architectures.items(): + path = f"{self.domain_name}.{arc_name}" + self.flat_dict.update(arc_obj.to_dict(path)) + return self.flat_dict + + +def mir_entry(domain: str, arch: str, series: str, comp: str, **kwargs) -> None: + """Define a new Machine Intelligence Resource\n + :param domain: Broad name of the type of data (model/ops/info/dev) + :param arch: Common name of the neural network structure being referenced + :param series: Specific release name or technique + :param comp: Details about purpose, tasks + :param kwargs: Specific key/value data related to location and execution + """ + + domain_inst = Domain(domain.lower()) + arch_inst = Architecture(arch.lower()) + series_inst = Series(series.lower()) + comp_inst = build_comp(comp, domain, kwargs) + series_inst.add_compat(comp, comp_inst) + arch_inst.add_impl(series_inst.series, series_inst) + domain_inst.add_arch(arch_inst.architecture, arch_inst) + return domain_inst.to_dict() + + +# def create_model_tag(model_header,metadata_dict): +# parse_file = parse_model_header(model_header) +# reconstructed_file_path = os.path.join(disk_path,each_file) +# attribute_dict = metadata_dict | {"disk_path": reconstructed_file_path} +# file_metadata = parse_file | attribute_dict +# index_tag = create_model_tag(file_metadata) +# + + +def main(): + """Add a single entry to MIR database\n""" + import argparse + from mir.maid import MIRDatabase + + parser = argparse.ArgumentParser( + formatter_class=argparse.RawDescriptionHelpFormatter, + description="Manually add entries to MIR database.\nOffline function.", + usage="""mir-add --domain info --arch lora --series slam --compatibility sd1_series \\ + -k {'repo':'alimama-creative/slam-sd1.5', 'pkg':{0: {'diffusers': 'load_lora_weights'}}}""", + epilog=f"MIR Class attributes:\n \ + Domain: {[k for k in Domain.__annotations__ if not k.startswith('_')]}\n \ + Ops: {[k for k in Ops.__annotations__ if not k.startswith('_')]}\n \ + Info: {[k for k in Info.__annotations__ if not k.startswith('_')]}\n \ + Dev: {[k for k in Dev.__annotations__ if not k.startswith('_')]}", + ) + parser = argparse.ArgumentParser( + formatter_class=argparse.RawDescriptionHelpFormatter, + description="Manually add entries to MIR database.\nOffline function.", + usage="""mir-add --domain info --arch lora --series slam --compatibility sd1_series \\ + -k {'repo':'alimama-creative/slam-sd1.5', 'pkg':{0: {'diffusers': 'load_lora_weights'}}}""", + epilog=f"MIR Class attributes:\n \ + Domain: {[k for k in Domain.__annotations__ if not k.startswith('_')]}\n \ + Ops: {[k for k in Ops.__annotations__ if not k.startswith('_')]}\n \ + Info: {[k for k in Info.__annotations__ if not k.startswith('_')]}\n \ + Dev: {[k for k in Dev.__annotations__ if not k.startswith('_')]}", + ) + parser.add_argument("-d", "--domain", type=str, help=" Broad name of the type of data (model/ops/info/dev)") + parser.add_argument("-a", "--arch", type=str, help=" Common name of the neural network structure being referenced") + parser.add_argument("-s", "--series", type=str, help="Specific release title or technique") + parser.add_argument("-c", "--comp", "--compatibility", type=str, help="Details about purpose, tasks") + parser.add_argument( + "-k", "--kwargs", "--keyword-arguments", type=dict[str | int, str | int | dict | list], help="Keyword arguments to pass to function constructors (default: NOne)" + ) + + args = parser.parse_args() + + mir_db = MIRDatabase() + mir_db.add( + mir_entry(domain=args.domain, arch=args.arch, series=args.series, comp=args.compatibility, **args.kwargs), + ) + mir_db.write_to_disk() + + +if __name__ == "__main__": + main() diff --git a/mir/spec/modes.json b/mir/spec/modes.json new file mode 100644 index 0000000..24a95e3 --- /dev/null +++ b/mir/spec/modes.json @@ -0,0 +1,7437 @@ +{ + "info.dit.allegro.*": { + "pipeline": "text-to-video", + "library": "diffusers", + "tags": [ + "diffusers", + "safetensors", + "text-to-video", + "en", + "arxiv:2410.15458", + "license:apache-2.0", + "diffusers:AllegroPipeline", + "region:us" + ] + }, + "info.lora.animatediff-motion-adapter-v1-5-2.*": { + "pipeline": "text-to-video", + "library": "diffusers", + "tags": [ + "diffusers", + "safetensors", + "text-to-video", + "region:us" + ] + }, + "info.controlnet.animatediff-sparsectrl-scribble.*": { + "pipeline": "text-to-video", + "library": "diffusers", + "tags": [ + "diffusers", + "safetensors", + "animatediff", + "text-to-video", + "arxiv:2307.04725", + "arxiv:2302.05543", + "arxiv:2311.16933", + "region:us" + ] + }, + "info.controlnet.animatelcm.*": { + "pipeline": "text-to-video", + "library": "diffusers", + "tags": [ + "diffusers", + "safetensors", + "text-to-video", + "arxiv:2402.00769", + "region:us" + ] + }, + "info.dit.auraflow.*": { + "pipeline": "text-to-image", + "library": "diffusers", + "tags": [ + "diffusers", + "safetensors", + "text-to-image", + "license:apache-2.0", + "diffusers:AuraFlowPipeline", + "region:us" + ] + }, + "info.dit.chroma.*": { + "pipeline": "text-to-image", + "library": "pytorch", + "tags": [ + "pytorch", + "diffusers", + "safetensors", + "text-to-image", + "image-generation", + "chroma", + "not-for-all-audiences", + "en", + "license:apache-2.0", + "region:us" + ] + }, + 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a/mir/template.json b/mir/spec/template.json similarity index 100% rename from mir/template.json rename to mir/spec/template.json diff --git a/mir/spec/versions.json b/mir/spec/versions.json new file mode 100644 index 0000000..0fe7908 --- /dev/null +++ b/mir/spec/versions.json @@ -0,0 +1,30 @@ +{ + "semantic": [ + "-?\\d+[bBmMkK]", + "-?v\\d+", + "(?<=\\d)[.-](?=\\d)", + "-prior$", + "-diffusers$", + "-large$", + "-medium$" + ], + "suffixes": [ + "-\\d{1,2}[bBmMkK]", + "-\\d[1-9][bBmMkK]", + "-v\\d{1,2}", + "-\\d{3,}$", + "-\\d{4,}.*", + "-\\d{4,}[px].*" + ], + "ignore": [ + "-xt$", + "-box$", + "-preview$", + "-base.*", + "-Tiny$", + "-full$", + "-mini.*", + "-multimodal.*", + "-instruct.*" + ] +} \ No newline at end of file diff --git a/mir/tag.py b/mir/tag.py new file mode 100644 index 0000000..e869ad3 --- /dev/null +++ b/mir/tag.py @@ -0,0 +1,113 @@ +# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0 +# + +from typing import List +from mir.config.constants import PARAMETERS_SUFFIX, BREAKING_SUFFIX + + +def make_mir_tag(repo_title: str, decoder=False, data: dict = None) -> List[str]: + """Create a mir label from a repo path\n + :param mir_prefix: Known period-separated prefix and model type + :param repo_path: Typical remote source repo path, A URL without domain + :return: The assembled mir tag with compatibility pre-separated""" + import re + + # print(repo_title) + + root = "decoder" if decoder else "*" + repo_title = repo_title.split(":latest")[0] + repo_title = repo_title.split(":Q")[0] + repo_title = repo_title.split(r"/")[-1].lower() + pattern = r"^.*[v]?(\d{1}+\.\d).*" + match = re.findall(pattern, repo_title) + if match: + if next(iter(match)): + repo_title = repo_title.replace(next(iter(match))[-1], "") + parts = repo_title.replace(".", "").split("-") + if len(parts) == 1: + parts = repo_title.split("_") + subtraction_prefixes = r"\d.b-|\-rl|tiny|large|mlx|onnx|gguf|medium|base|multimodal|mini|instruct|full|:latest|preview|small|pro|beta|hybrid|plus|dpo|community" + + pattern_2 = re.compile(PARAMETERS_SUFFIX) + clean_parts = [re.sub(pattern_2, "", segment.lower()) for segment in parts] + cleaned_string = "-".join([x for x in clean_parts if x]) + cleaned_string = re.sub(subtraction_prefixes, "", cleaned_string) + cleaned_string = re.sub("-it", "", cleaned_string.replace("-bit", "")).replace("--", "-") + cleaned_string = cleaned_string.replace("-b-", "") + # print(cleaned_string) + suffix_match = re.findall(BREAKING_SUFFIX, cleaned_string) # Check for breaking suffixes first + if suffix_match: + suffix = next(iter(suffix for suffix in suffix_match[0] if suffix)) + cleaned_string = re.sub(suffix.lower(), "-", cleaned_string).rstrip("-,") + else: + suffix = root + cleaned_string = re.sub(r"[._]+", "-", cleaned_string.lower()).strip("-_") + return (cleaned_string, suffix) + + +def make_scheduler_tag(series_name: str) -> tuple[str]: + """Create a mir label from a scheduler operation\n + :param class_name: Known period-separated prefix and model type + :return: The assembled mir tag with compatibility pre-separated""" + + import re + + comp_name = None + patterns = [r"Schedulers", r"Multistep", r"Solver", r"Discrete", r"Scheduler"] + for scheduler in patterns: + compiled = re.compile(scheduler) + match = re.search(compiled, series_name) + if match: + comp_name = match.group() + comp_name = comp_name.lower() + break + for pattern in patterns: + series_name = re.sub(pattern, "", series_name) + series_name.lower() + # if not comp_name: + # comp_name = "*" + return series_name, comp_name + + +def tag_base_model(repo_path: str, class_name: str, addendum: dict | None = None) -> tuple[str]: + """Convert model repo paths to MIR tags, classifying by feature\n + :param name: Repo path + :param class_name: The HF transformers class for the model + :return: A segmented MIR tag useful for appending index entries""" + + from mir.inspect.classes import extract_init_params + from mir.indexers import flag_config + + annotations = extract_init_params(class_name.replace("Model", "Config"), "transformers") + mir_prefix = flag_config(transformers=True, **annotations) + base_series, base_comp = make_mir_tag(repo_path) + if not addendum: + return mir_prefix, base_series, base_comp + else: + mir_prefix = f"info.{mir_prefix}" + return mir_prefix, base_series, {base_comp: addendum} + + +def tag_pipe(repo_path: str, class_name: str, addendum: dict) -> tuple: + """Convert model repo pipes to MIR tags, classifying by feature\n + :param name: Repo path + :param class_name: The HF Diffusers class for the model + :return: A segmented MIR tag useful for appending index entries""" + + from mir.indexers import create_pipe_entry + + mir_series, mir_data = create_pipe_entry(repo_path=repo_path, class_name=class_name) + mir_prefix, mir_series = mir_series.rsplit(".", 1) + mir_comp = list(mir_data)[0] + return mir_prefix, mir_series, {mir_comp: addendum} + + +# def tag_mlx_model(repo_path: str, class_name: str, addendum: dict) -> tuple[str]: +# dev_series, dev_comp = make_mir_tag("black-forest-labs/FLUX.1-dev") +# schnell_series, schnell_comp = make_mir_tag("black-forest-labs/FLUX.1-schnell") +# series, comp = make_mir_tag(repo_path) +# if class_name == "Flux1": +# mir_prefix = "info.dit" +# base_series = dev_series +# mir_comp = series +# return mir_prefix, base_series, {base_comp: addendum} diff --git a/pyproject.toml b/pyproject.toml index 348c955..bd53514 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,7 +23,14 @@ classifiers = [ "Operating System :: OS Independent", "Topic :: Scientific/Engineering :: Artificial Intelligence", ] -dependencies = ["nnll[dev] @ git+https://github.com/darkshapes/nnll.git"] +dependencies = [ + "diffusers>=0.35.2", + "huggingface-hub[hf-xet]>=1.1.7", + "pydantic>=2.12.5", + "tokenizers>=0.22.1", + "torch>=2.9.1", + "transformers>=2.3.0", +] 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exdysa <91800957+exdysa@users.noreply.github.com> Date: Wed, 3 Dec 2025 00:22:20 -0500 Subject: [PATCH 02/10] ~restore and autodoc --- MIR.egg-info/SOURCES.txt | 22 +- docs/index.html | 7 + docs/mir.html | 315 ++ docs/mir/automata.html | 7179 +++++++++++++++++++++++++++++++ docs/mir/config.html | 245 ++ docs/mir/config/constants.html | 786 ++++ docs/mir/config/conversion.html | 719 ++++ docs/mir/config/json_io.html | 381 ++ docs/mir/doc_parser.html | 925 ++++ docs/mir/indexers.html | 1183 +++++ docs/mir/inspect.html | 246 ++ docs/mir/inspect/classes.html | 602 +++ docs/mir/inspect/metadata.html | 565 +++ docs/mir/inspect/parenting.html | 342 ++ docs/mir/inspect/pipes.html | 367 ++ docs/mir/inspect/tasks.html | 1331 ++++++ docs/mir/maid.html | 1130 +++++ docs/mir/run.html | 423 ++ docs/mir/tag.html | 582 +++ docs/search.js | 46 + mir/__init__.py | 6 +- mir/automata.py | 4 +- mir/config/constants.py | 9 +- mir/config/conversion.py | 2 +- mir/doc_parser.py | 2 +- mir/indexers.py | 4 +- mir/inspect/tasks.py | 2 +- pyproject.toml | 14 +- uv.lock | 4 +- 29 files changed, 17417 insertions(+), 26 deletions(-) create mode 100644 docs/index.html create mode 100644 docs/mir.html create mode 100644 docs/mir/automata.html create mode 100644 docs/mir/config.html create mode 100644 docs/mir/config/constants.html create mode 100644 docs/mir/config/conversion.html create mode 100644 docs/mir/config/json_io.html create mode 100644 docs/mir/doc_parser.html create mode 100644 docs/mir/indexers.html create mode 100644 docs/mir/inspect.html create mode 100644 docs/mir/inspect/classes.html create mode 100644 docs/mir/inspect/metadata.html create mode 100644 docs/mir/inspect/parenting.html create mode 100644 docs/mir/inspect/pipes.html create mode 100644 docs/mir/inspect/tasks.html create mode 100644 docs/mir/maid.html create mode 100644 docs/mir/run.html create mode 100644 docs/mir/tag.html create mode 100644 docs/search.js diff --git a/MIR.egg-info/SOURCES.txt b/MIR.egg-info/SOURCES.txt index f46776a..2b2566e 100644 --- a/MIR.egg-info/SOURCES.txt +++ b/MIR.egg-info/SOURCES.txt @@ -3,6 +3,7 @@ LICENSE README.md pyproject.toml uv.lock +.github/workflows/mir.yml MIR.egg-info/PKG-INFO MIR.egg-info/SOURCES.txt MIR.egg-info/dependency_links.txt @@ -12,14 +13,23 @@ MIR.egg-info/top_level.txt mir/__init__.py mir/automata.py mir/doc_parser.py -mir/general.py mir/indexers.py mir/maid.py mir/mir.json -mir/mir.py -mir/model_metadata.py -mir/parenting.py -mir/pipe.py mir/run.py mir/tag.py -mir/tasks.py \ No newline at end of file +mir/config/__init__.py +mir/config/constants.py +mir/config/conversion.py +mir/config/json_io.py +mir/config/logging.py +mir/inspect/__init__.py +mir/inspect/classes.py +mir/inspect/metadata.py +mir/inspect/parenting.py +mir/inspect/pipes.py +mir/inspect/tasks.py +mir/spec/mir.py +mir/spec/modes.json +mir/spec/template.json +mir/spec/versions.json \ No newline at end of file diff --git a/docs/index.html b/docs/index.html new file mode 100644 index 0000000..6142692 --- /dev/null +++ b/docs/index.html @@ -0,0 +1,7 @@ + + + + + + + diff --git a/docs/mir.html b/docs/mir.html new file mode 100644 index 0000000..a59c9f2 --- /dev/null +++ b/docs/mir.html @@ -0,0 +1,315 @@ + + + + + + + mir API documentation + + + + + + + + + +
+
+

+mir

+ + + + + + + 
 1# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0
+ 2# <!-- // /*  d a r k s h a p e s */ -->
+ 3
+ 4
+ 5def main():
+ 6    import mir.maid
+ 7    from mir.maid import main as mir_main
+ 8
+ 9    mir_main()
+10    from mir.inspect.tasks import main
+11
+12    main()
+13    from mir.inspect.tasks import pipe
+14
+15    pipe()
+16
+17    import os
+18    import shutil
+19
+20    try:
+21        os.remove("mir.json")
+22    except FileNotFoundError:
+23        pass
+24    shutil.copy2(os.path.join(os.path.dirname(mir.maid.__file__), "mir.json"), os.path.join(os.getcwd(), "mir.json"))
+25
+26
+27if __name__ == "__main__":
+28    main()
+
+ + +
+
+ +
+ + def + main(): + + + +
+ + 
 6def main():
+ 7    import mir.maid
+ 8    from mir.maid import main as mir_main
+ 9
+10    mir_main()
+11    from mir.inspect.tasks import main
+12
+13    main()
+14    from mir.inspect.tasks import pipe
+15
+16    pipe()
+17
+18    import os
+19    import shutil
+20
+21    try:
+22        os.remove("mir.json")
+23    except FileNotFoundError:
+24        pass
+25    shutil.copy2(os.path.join(os.path.dirname(mir.maid.__file__), "mir.json"), os.path.join(os.getcwd(), "mir.json"))
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/docs/mir/automata.html b/docs/mir/automata.html new file mode 100644 index 0000000..7138ce5 --- /dev/null +++ b/docs/mir/automata.html @@ -0,0 +1,7179 @@ + + + + + + + mir.automata API documentation + + + + + + + + + +
+
+

+mir.automata

+ +

自動化索引

+
+ + + + + + 
   1# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0
+   2# <!-- // /*  d a r k s h a p e s */ -->
+   3
+   4"""自動化索引"""
+   5# regex to remove \[[^\]]*\]
+   6# (?m)^\s*"[^"]+"(?=\s*:)
+   7# (?m)^\s*"[^"]+"\s?:
+   8# modelspec sai https://github.com/Stability-AI/ModelSpec
+   9
+  10from importlib import import_module
+  11from typing import Dict, List, Tuple
+  12import re
+  13import torch
+  14from diffusers import _import_structure
+  15
+  16from mir.maid import MIRDatabase
+  17from mir.spec.mir import mir_entry
+  18from mir.tag import make_mir_tag, make_scheduler_tag, tag_pipe, tag_base_model
+  19from mir.config.logging import nfo, dbuq
+  20from mir.config.conversion import slice_number
+  21from mir.indexers import diffusers_index, transformers_index
+  22
+  23
+  24sd1_series, sd1_comp = make_mir_tag("stable-diffusion-v1-5/stable-diffusion-v1-5")
+  25sdxl_series, sdxl_comp = make_mir_tag("stabilityai/stable-diffusion-xl-base-1.0")
+  26dev_series, dev_comp = make_mir_tag("black-forest-labs/FLUX.1-dev")
+  27schnell_series, schnell_comp = make_mir_tag("black-forest-labs/FLUX.1-schnell")
+  28ssd_series, ssd_comp = make_mir_tag("segmind/SSD-1B")
+  29vega_series, vega_comp = make_mir_tag("segmind/Segmind-Vega")
+  30sd3_series, sd3_comp = make_mir_tag("stable-diffusion-3.5-medium")  #
+  31
+  32# def gen_attention_processors(mir_db: MIRDatabase): # upstream not quite ready for this yet
+  33#     from diffusers.models.attention_processor import AttentionProcessor
+  34
+  35#     mir_data
+  36#     for series, comp_name in mir_data.items():
+  37#     id_segment = series.split(".")
+  38#     for compatibility in comp_name:
+  39#         dbug(id_segment)
+  40#         try:
+  41#             mir_db.add(
+  42#                 mir_entry(
+  43#                     domain=id_segment[0],
+  44#                     arch=id_segment[1],
+  45#                     series=id_segment[2],
+  46#                     comp=compatibility,
+  47#                     **mir_data[series][compatibility],
+  48#                 ),
+  49#             )
+  50#         except IndexError as error_log:
+  51#             nfo(f"Failed to create series: {series}  compatibility: {comp_name}  ")
+  52#             dbug(error_log)
+  53
+  54
+  55# def gen_guiders(mir_db: MIRDatabase):  # upstream not quite ready for this yet
+  56#     from nnll.metadata.helpers import snake_caseify
+  57#     from diffusers.guider import GuiderType
+  58
+  59#     guider_type = GuiderType
+  60#     for comp_name in guider_type.items():
+  61#         class_obj = comp_name.__name__
+  62#         mir_data = {"pkg": {0: {"diffusers": class_obj}}}
+  63#         try:
+  64#             mir_db.add(
+  65#                 mir_entry(
+  66#                     domain="ops",
+  67#                     arch="noise_prediction",
+  68#                     series="guider",
+  69#                     comp=snake_caseify(class_obj),
+  70#                     **mir_data,
+  71#                 ),
+  72#             )
+  73#         except IndexError as error_log:
+  74#             nfo(f"Failed to create compatibility: {class_obj}")
+  75#             dbug(error_log)
+  76
+  77
+  78# (
+  79#     "info.unet",
+  80#     "stable-cascade",
+  81#     {
+  82#         "combined": {
+  83#             "pkg": {
+  84#                 0: {  # decoder=decoder_unet
+  85#                     "precision": "ops.precision.bfloat.B16",
+  86#                     "generation": {
+  87#                         "negative_prompt": "",
+  88#                         "num_inference_steps": 20,
+  89#                         "guidance_scale": 4.0,
+  90#                         "num_images_per_prompt": 1,
+  91#                         "width": 1024,
+  92#                         "height": 1024,
+  93#                     },
+  94#                 },
+  95#                 "pkg_alt": {
+  96#                     0: {
+  97#                         "diffusers": {
+  98#                             "StableCascadeCombinedPipeline": {
+  99#                                 "negative_prompt": "",
+ 100#                                 "num_inference_steps": 10,
+ 101#                                 "prior_num_inference_steps": 20,
+ 102#                                 "prior_guidance_scale": 3.0,
+ 103#                             }
+ 104#                         },
+ 105#                     }
+ 106#                 },
+ 107#             }
+ 108#         }
+ 109#     },
+ 110# ),
+ 111
+ 112
+ 113def assimilate(mir_db: MIRDatabase, data_tuple: List[Tuple[Dict[str, any]]]) -> None:
+ 114    """Merge new data into a pre-generated MIR database, updating while preserving existing data structures.\n
+ 115    :param mir_db: The MIRDatabase instance
+ 116    :param data_tuple: A list of tuples, each containing:\n
+ 117            - arch (str): The architecture name
+ 118            - series (str): The series name
+ 119            - `new_data`: New data to be merged into the database.
+ 120    :raises TypeError: If any field in `new_data` is not a dictionary.
+ 121    """
+ 122
+ 123    def update_nested_dict(target, source):
+ 124        for key, value in source.items():
+ 125            if isinstance(value, dict) and key in target:
+ 126                if isinstance(target, dict):
+ 127                    update_nested_dict(target[key], value)
+ 128            else:
+ 129                if isinstance(source, dict):
+ 130                    # dbuq(target)
+ 131                    target.setdefault(key, value)
+ 132                else:
+ 133                    target = {key: value}
+ 134
+ 135    dbuq(f"{data_tuple}, {len(data_tuple)}")
+ 136    for arch, series, new_data in data_tuple:
+ 137        mir_data = mir_db.database[f"{arch}.{series}"]
+ 138        for comp, field_data in new_data.items():
+ 139            if not isinstance(field_data, dict):
+ 140                raise TypeError(f"{field_data} <-- Cannot combine with database: Not `dict()`")
+ 141
+ 142            # dbuq(f"{arch}.{series} : {comp}")
+ 143            update_nested_dict(mir_data.setdefault(comp, {}), field_data)
+ 144
+ 145            if series == sdxl_series:
+ 146                for field, field_data in field_data.items():
+ 147                    if isinstance(field_data, dict):
+ 148                        for definition, sub_def_data in field_data.items():
+ 149                            # dbug(definition)
+ 150                            if isinstance(sub_def_data, dict):
+ 151                                mir_data[comp][field].setdefault(definition, {})
+ 152                                update_nested_dict(mir_data[comp][field][definition], sub_def_data)
+ 153
+ 154
+ 155def hf_pkg_to_mir(mir_db: MIRDatabase):
+ 156    """Generate MIR HF Hub model database"""
+ 157    mir_data = diffusers_index() | transformers_index()
+ 158    for series, comp_name in mir_data.items():
+ 159        id_segment = series.split(".")
+ 160        for compatibility in comp_name:
+ 161            # dbug(id_segment)
+ 162            try:
+ 163                mir_db.add(
+ 164                    mir_entry(
+ 165                        domain=id_segment[0],
+ 166                        arch=id_segment[1],
+ 167                        series=id_segment[2],
+ 168                        comp=compatibility,
+ 169                        **mir_data[series][compatibility],
+ 170                    ),
+ 171                )
+ 172            except IndexError:  # as error_log:
+ 173                nfo(f"Failed to create series: {series}  compatibility: {comp_name}  ")
+ 174                # dbug(error_log)
+ 175
+ 176
+ 177def add_mir_dtype(mir_db: MIRDatabase):
+ 178    """Create mir info database"""
+ 179
+ 180    available_dtypes: List[str] = [dtype for dtype in torch.__dict__.values() if isinstance(dtype, torch.dtype)]
+ 181    series_name = "_"
+ 182    for precision in available_dtypes:
+ 183        dep_name, class_name = str(precision).split(".")
+ 184        if "_" in class_name:
+ 185            comp_name = class_name[0].upper() + "8_" + class_name.split("_")[1].upper()
+ 186            if comp_name.endswith("FN"):
+ 187                comp_name = comp_name[:-2]
+ 188        else:
+ 189            comp_name = class_name[0].upper() + str(slice_number(class_name))
+ 190        variant_name = class_name.replace("bfloat", "bf").replace("float", "fp")
+ 191        dbuq(variant_name)
+ 192        patterns = [r"complex", r"bits", r"quint", r"uint", r"int", r"bfloat", r"float", r"bool"]
+ 193        for precision_name in patterns:
+ 194            compiled = re.compile(precision_name)
+ 195            dtype = re.search(compiled, class_name)
+ 196            if dtype:
+ 197                series_name = dtype.group()
+ 198                break
+ 199
+ 200        mir_db.add(
+ 201            mir_entry(
+ 202                domain="ops",
+ 203                arch="precision",
+ 204                series=series_name,
+ 205                comp=comp_name,
+ 206                pkg={0: {dep_name.lower(): {class_name.lower(): {"variant": variant_name}}}},
+ 207            )
+ 208        )
+ 209
+ 210
+ 211def add_mir_schedulers(mir_db: MIRDatabase):
+ 212    """Create mir info database"""
+ 213
+ 214    for class_name in _import_structure["schedulers"]:
+ 215        if class_name != "SchedulerMixin":
+ 216            series_name, comp_name = make_scheduler_tag(class_name)
+ 217            class_obj = import_module("diffusers.schedulers")
+ 218            class_path = getattr(class_obj, class_name).__module__
+ 219            mir_db.add(
+ 220                mir_entry(
+ 221                    domain="ops",
+ 222                    arch="scheduler",
+ 223                    series=series_name,
+ 224                    comp=comp_name.lower(),
+ 225                    pkg={
+ 226                        0: {
+ 227                            "diffusers": class_name,
+ 228                            "module_path": class_path,
+ 229                        },
+ 230                    },
+ 231                )
+ 232            )
+ 233
+ 234    class_name = "KarrasDiffusionSchedulers"
+ 235    series_name, comp_name = make_scheduler_tag(class_name)
+ 236    class_obj = import_module("diffusers.schedulers.scheduling_utils")
+ 237    class_path = getattr(class_obj, class_name).__module__
+ 238    mir_db.add(
+ 239        mir_entry(
+ 240            domain="ops",
+ 241            arch="scheduler",
+ 242            series=series_name,
+ 243            comp=comp_name,
+ 244            pkg={
+ 245                0: {
+ 246                    "diffusers": class_name,
+ 247                    "module_path": class_path,
+ 248                },
+ 249            },
+ 250        ),
+ 251    )
+ 252
+ 253
+ 254# def auto_gan etc etc
+ 255# ai-forever/Real-ESRGAN
+ 256
+ 257
+ 258def mir_update(mir_db: MIRDatabase, task_list: list = None, pipe_list: list = None):
+ 259    """Create mir unet info database"""
+ 260
+ 261    diffusers_addons = [
+ 262        (
+ 263            "stabilityai/stable-diffusion-xl-base-1.0",
+ 264            "StableDiffusionXLPipeline",
+ 265            {
+ 266                "pkg": {
+ 267                    0: {
+ 268                        "precision": "ops.precision.float.F16",
+ 269                        "generation": {
+ 270                            "denoising_end": 0.8,
+ 271                            "num_inference_steps": 40,
+ 272                            "output_type": "latent",
+ 273                            "safety_checker": False,
+ 274                            "width": 1024,
+ 275                            "height": 1024,
+ 276                        },
+ 277                    },
+ 278                    1: {"diffusers": "DiffusionPipeline"},
+ 279                },
+ 280                "file_256": [
+ 281                    "357650fbfb3c7b4d94c1f5fd7664da819ad1ff5a839430484b4ec422d03f710a",  # diffusers
+ 282                    "83e012a805b84c7ca28e5646747c90a243c65c8ba4f070e2d7ddc9d74661e139",  # fp16 diffusers
+ 283                    "31e35c80fc4829d14f90153f4c74cd59c90b779f6afe05a74cd6120b893f7e5b",  # modelspec sai
+ 284                    "6f001c090fb13c0d0f8b0a5916da814712a94400b99471fabe77c1c4a51ecaaf",  # onnx
+ 285                ],
+ 286                "layer_256": [
+ 287                    "62a5ab1b5fdfa4fedb32323841298c6effe1af25be94a8583350b0a7641503ef",  # any modelspec sai
+ 288                    "34dff8d98898baa0f10e71943e56b588cc114253b0d2f1051f3ce7a8a45fee0b",  # diffusers
+ 289                    "56b1ccd89b0d6ab658048aa34d659788b6ed663f13ef566f4b11bccef590b9da",  # diffusers fp16
+ 290                ],
+ 291                "layer_b3": [
+ 292                    "8be44fa13c1efa60f8bcadaa57f1d718473f9660f03c4f0e65dc037960d8cba1",  # any modelspec sai
+ 293                    "c9ab95ed1851418b65ef99651c1eb6bbdd2e3b0715e0e435d6d1e56ce310fac3",  # diffusers
+ 294                    "adfa260098d87616d748e3cf9c10bb2c90ff8890a84abbb2853d4aa69664070b",  # diffusers fp16
+ 295                ],
+ 296                "identifiers": ["logit_scale", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.k_proj.weight", "add_embedding.linear_2.bias"],
+ 297            },
+ 298        ),
+ 299        (
+ 300            "stabilityai/stable-diffusion-xl-refiner-1.0",
+ 301            "StableDiffusionXLImg2ImgPipeline",
+ 302            {
+ 303                "pkg": {
+ 304                    1: {
+ 305                        "diffusers": "DiffusionPipeline",
+ 306                        "generation": {"num_inference_steps": 40, "denoising_end": 0.8},
+ 307                    }
+ 308                },
+ 309                "identifiers": ["conditioner.embedders.0.model.transformer.resblocks.9.mlp.c_proj.bias"],
+ 310                "file_256": [
+ 311                    "54f9cd2f2daf3aeec0b2708fa3dbc0e84e4f8ddd1ddead42e5bc60c6572c989f",  # diffusers
+ 312                    "7440042bbdc8a24813002c09b6b69b64dc90fded4472613437b7f55f9b7d9c5f",  # modelspec sai
+ 313                    "3ea0376dcf065eaefd27806394a90e310001b1a71d4f1cf1f655e86c0e566ffe",  # fp16 diffusers
+ 314                ],
+ 315                "layer_b3": [
+ 316                    "6281355dbb37e5769c9460ae0ac75506d89932e2f97b09d9ade32ecf191e75ba",
+ 317                    "afb0639aae2eb65577c12d4a30cf7c9b3620ae63ba64a8fa632b58608c8a7a2e",
+ 318                    "669046014b69d98ab0f6fbb59547644436e0275f8b638f467ce2a873c3313683",
+ 319                ],
+ 320                "layer_256": [
+ 321                    "bb9eadbfabb52c0d8645783525a3fa70b59e9d7d09d5290d742a303262e793a2",
+ 322                    "c5adb56fe51343af2c3d493eb9f41515c204bd91eb9f40b983d45f70a1fa3b6d",
+ 323                    "1f838e39ed6e916258aee6990b72c09b34aa8eb3b5342234a497b8852b3df1c6",
+ 324                ],
+ 325            },
+ 326        ),
+ 327        (
+ 328            "lodestones/Chroma",
+ 329            "ChromaPipeline",
+ 330            {
+ 331                "pkg": {
+ 332                    1: {
+ 333                        "generation": {"neg_text": "", "num_steps": "28", "latent_size": [64, 64]},
+ 334                    }
+ 335                },
+ 336                "file_256": [
+ 337                    "53adcb3b6b6005758d40e2d8058b044ed4892bc8616efb7a62cc2dd384be07de",  # v1
+ 338                    "2c41e8a9831f3be1eaff2c2ed590abb62e4534e814f7ec58a5fd74ff71dc2036",  # v46,
+ 339                    "0a7b2d9699dbd22b3744ee2692900cabcfb731a43dac13729c33807f2bb7c9f6",  # v37 detail
+ 340                    "6ddc9e2bbe3376ab5ee9f10b2d947f127b6bf6f879f06f316a2208bb0da357b8",  # mlx chroma / v36 detail
+ 341                ],
+ 342                "layer_b3": [
+ 343                    "15e227ced8a89c41abaa9cc44f84dfffdf5ead0c626035e5a2dde2bbb0935479",
+ 344                ],
+ 345                "layer_256": ["a4daa6ff6f45ca70c738adb8c19bc3b6f228df931e6bf2a3394463e4dd7ec882"],
+ 346            },
+ 347        ),
+ 348        (
+ 349            "fal/AuraFlow",
+ 350            "AuraFlowPipeline",
+ 351            {
+ 352                "identifiers": [[8192, 3072], "mlpX.c_fc2.weight", "joint_transformer_blocks.2.ff_context.linear_2.weight"],
+ 353                "file_256": [
+ 354                    "ce3e475246258b94ee9dcb8b83292cb34edfffc2bbde46c74604d9c6cd7c585c",
+ 355                    "526be97cf581c89ad87c6b19c1f7c2378851137698f7ec436596d061a382d37b",  # sai
+ 356                    "6a40b011f287452dbca80face78e667055904c5ad97eb2097ade3200259b2203",  # diffusers fp16
+ 357                    "05e5493018333d947bb5940083dbc2f071093027ff414bc5b1b1229e4836e5cb",  # diffusers
+ 358                ],
+ 359                "layer_b3": [
+ 360                    "cc6d383576c35a9709798d2e2b9e3eb31ba8c608040cf3712bc37871cfd14e21",
+ 361                    "ddd54c44fa28fbddecf7cfae91cfa04917fd2f2fa94fc78c528cef2356a4ec3a",  # sai
+ 362                    "90c694e7d1e20e6da49b571e9954338d384775419790be315304103227b1051b",
+ 363                    "9e85aec1bdb616f52f88c80ddc7ab1eae8c16c0b5fbfcdb61a71ac02c325003d",
+ 364                ],
+ 365                "layer_256": [
+ 366                    "3c13e6a965d03a49227d8b1606ba6a343a23772d8768407cc78d4ddb9102bc80",
+ 367                    "b356cc84a23bc93bda4cc0fce1d0ba1b8e3d5a521e659ffc72e9e4a2d2c7f204",
+ 368                    "270df7317fe01abf06333acbbd4f15f8fc7a7c56053219f42efb598454a3af24",
+ 369                    "7ab6aa4514dd09f3cf589587d51a81734193ce45dd51bda9db0bd62fe48ef7d5",
+ 370                ],
+ 371            },
+ 372        ),
+ 373        (
+ 374            "Tencent-Hunyuan/HunyuanDiT-v1.2-Diffusers",
+ 375            "HunyuanDiTPipeline",
+ 376            {
+ 377                "identifiers": ["extra_embedder", "model.blocks", "skip_norm.weight"],
+ 378                "file_256": [
+ 379                    "4fb84f84079cda457d171b3c6b15d1be95b5a3e5d9825703951a99ddf92d1787",  # normal
+ 380                    "e01db5e129e8ca1117e9cf473fc5a2b096949f03ab90048aeabbc328de7ec800",  # distilled
+ 381                    "8af691cadb78047d55721259355d708e87ddbba1b7845df9377d9a5ae917b45d",  # 1.2
+ 382                ],
+ 383                "layer_b3": [
+ 384                    "aead6b61b17ebc77c4c186a4b82c193f11ec267b20d909726422ee9852e2e0b2",
+ 385                    "885a056b94f6f9844c0660be489844d63bb74cc13316f441d10968fff3dd3120",  # distilled
+ 386                    "390d951cbdda6e2cffb690031b60f02921624651534c2effaaa7d68ab476c700",
+ 387                ],
+ 388                "layer_256": [
+ 389                    "d4842ce2b7f927203326b25ff4d6738ec9a8b95327f06791c387e4a351ed6ed0",
+ 390                    "5af943f96f5dc9fecb1e92fe2b1fa17c94dd6947690201f4a5ee1a4a2721a68e",  # distilled
+ 391                    "4a1f2b8234fa4336e263842e042d42e8d64d8a4d3941d9c0c78366b50303950c",  # 1.2
+ 392                ],
+ 393            },
+ 394        ),
+ 395        (
+ 396            "Alpha-VLLM/Lumina-Next-SFT-diffusers",
+ 397            "LuminaPipeline",
+ 398            {
+ 399                "pkg": {
+ 400                    0: {
+ 401                        "precision": " ops.precision.bfloat.B16",
+ 402                    },
+ 403                },
+ 404                "identifiers": ["time_caption", "feed_forward"],
+ 405                "file_256": [
+ 406                    "371153b7c7b7a64899d4016970c7cc472039f9c9b21ebe073adf0b8525cdf1bd",
+ 407                ],
+ 408                "layer_b3": [
+ 409                    "fa134efd6e9672e7de2965e4895fc58879bd0a6c4fdf9165c278f2748254675f",
+ 410                    "4d960ec35c53f72f065b94b836bcd923ea6074d38ad49881061f315d62e3c839",
+ 411                ],
+ 412                "layer_256": [
+ 413                    "3938a85568d9df186923edf04391d79e89e6199123bc175afb520e0948d1ae05",
+ 414                    "c0ca51fdea051fcd042bf4b56d32e1e8bb9525a921f2e197f370f101e90527f0",
+ 415                ],
+ 416            },
+ 417        ),
+ 418        (
+ 419            "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS",
+ 420            "PixArtSigmaPipeline",
+ 421            {
+ 422                "identifiers": ["adaln_single", "scale_shift_table"],
+ 423                "file_256": [
+ 424                    "c34b520ef473329b945c2a21083cdf1337c5a468d23b3215b65576789bfd0305",
+ 425                    "2fa4dee9229c02b03163f57bdb8e80c7a5ee364b7161796abe9c05e8dd13f239",
+ 426                ],
+ 427                "layer_b3": [
+ 428                    "a199930ff537994872da77391955f0dd52eddd22ab9105388f0c5852f1b8021f",
+ 429                    "ee6f980c32e98da6885f3e97d3f88d9158031e362cd3a49b20d1e23924b251e3",
+ 430                ],
+ 431                "layer_256": [
+ 432                    "e0afd203aff5a1d192e325d0f59361373273d85d138b51768c3f10a75c154dc0",
+ 433                    "987f3c2ff5d399191e5fd7dd7b1f1f285c197dc8124ad77f05cde7f2fb677a3c",
+ 434                ],
+ 435            },
+ 436        ),
+ 437        (
+ 438            "PixArt-alpha/PixArt-XL-2-1024-MS",
+ 439            "PixArtAlphaPipeline",
+ 440            {
+ 441                "identifiers": ["aspect_ratio", "y_embedding", "emb.resolution", "caption_projection"],
+ 442                "file_256": ["809a92d52a4a228f381a4b4f4b76051294b73285fb0cbb02f0ad24f9372217a8"],
+ 443                "layer_b3": ["c5be83545ce9dbc564bcc9fd8fe4157d131347ccfc8f62adc877ec205b20acee"],
+ 444                "layer_256": ["117225c0e91423746114b23d3e409708ad55c90ff52b21fa7a1c5105d2e935a5"],
+ 445            },
+ 446        ),
+ 447        (
+ 448            "stabilityai/stable-diffusion-3.5-medium",
+ 449            "StableDiffusion3Pipeline",
+ 450            {
+ 451                "pkg": {
+ 452                    0: {"precision": "ops.precision.float.F16"},
+ 453                },
+ 454                "identifiers": [
+ 455                    "model.diffusion_model.joint_blocks.",
+ 456                    "transformer_blocks.21.norm1_context.linear.weight",
+ 457                    "transformer_blocks.31.norm1_context.linear.weight",
+ 458                    "blocks.11.ff.net.2.weight",
+ 459                ],
+ 460                "file_256": [
+ 461                    "ffef7a279d9134626e6ce0d494fba84fc1c7e720b3c7df2d19a09dc3796d8f93",  # large
+ 462                    "11fe06e22364b823dfeedc275912336b932b32a293a0b2f35ffac071990cc4de",  # medium
+ 463                ],
+ 464                "layer_b3": [
+ 465                    "e411016545785046810b29cc3999f40bc6392be134a1318386c6f1c48f98726a",
+ 466                    "a81e07ee67bc627e8b3c5e292ec1ca239009517a2106e8249d670ced0a88f746",  # med
+ 467                ],
+ 468                "layer_256": [
+ 469                    "13c982a6dc82d21c9f459e837d8c6f6d4696fd6e7e7b5783bdd2250b1f4fec61",
+ 470                    "6ee79050373337bf63ac20916596df778bb22022bb38af986128a7459eda1463",  # med
+ 471                ],
+ 472            },
+ 473        ),
+ 474        (
+ 475            "Efficient-Large-Model/Sana-1600M-1024px-BF16-diffusers",
+ 476            "SanaPipeline",
+ 477            {
+ 478                "pkg": {
+ 479                    0: {
+ 480                        "generation": {
+ 481                            "height": 1024,
+ 482                            "width": 1024,
+ 483                            "guidance_scale": 4.5,
+ 484                            "num_inference_steps": 20,
+ 485                        },
+ 486                        "precision": "ops.precision.bfloat.B16",
+ 487                    },
+ 488                },
+ 489                "file_256": [
+ 490                    "b0b50c33be8758713459aa3c760feef6315d4bea31521fb5b8c3e8fdd9841ffe",
+ 491                ],
+ 492                "layer_b3": [
+ 493                    "461e3d83dfa7e075ef21e2138ef153922ecfadde3db464b03dff92819f3e86dd",
+ 494                ],
+ 495                "layer_256": [
+ 496                    "b928bbcc2ce99d55d21c189e2b1c57498bc313ef5b1457036e356107d567fc4e",
+ 497                ],
+ 498            },
+ 499        ),
+ 500        (
+ 501            "stable-diffusion-v1-5/stable-diffusion-v1-5",
+ 502            "StableDiffusionPipeline",
+ 503            {
+ 504                "identifiers": ["up_blocks.3.attentions.0.transformer_blocks.0.norm3.weight"],
+ 505                "file_256": [
+ 506                    "6ce0161689b3853acaa03779ec93eafe75a02f4ced659bee03f50797806fa2fa",  # pruned ema only original safetensors
+ 507                    "1a189f0be69d6106a48548e7626207dddd7042a418dbf372cefd05e0cdba61b6",  # pruned original safetensors
+ 508                    "e1441589a6f3c5a53f5f54d0975a18a7feb7cdf0b0dee276dfc3331ae376a053",  # ema pruned original ckpt
+ 509                    "cc6cb27103417325ff94f52b7a5d2dde45a7515b25c255d8e396c90014281516",  # pruned ema original ckpt
+ 510                    "19da7aaa4b880e59d56843f1fcb4dd9b599c28a1d9d9af7c1143057c8ffae9f1",  # diffusers safetensors
+ 511                    "cd1b6db09a81cb1d39fbd245a89c1e3db9da9fe8eba5e8f9098ea6c4994221d3",  # diffusers non ema safetensors
+ 512                    "c83908253f9a64d08c25fc90874c9c8aef9a329ce1ca5fb909d73b0c83d1ea21",  # diffusers fp16
+ 513                ],
+ 514                "layer_b3": [
+ 515                    "909c6ff3192ab2767e789a6125865bc23163db467ab78b1c633bad46a4293fad",
+ 516                    "b52807536902cabbf84f99e4fa2f8713fb4ef77e739f06367ee0d486e3222faa",  # ckpt
+ 517                    "d31382d71a1044b636d80d861a2b4dbca51826bed34d34b5c14608b7679ccefd",  # safetensors ema pruned
+ 518                    "5fd8b28013b7e5a64c7c235f0a93d93e48bc19a0e5dde7b646a87b429219643a",  # safetensors pruned
+ 519                    "731f552f29edcb4f86112cc94d296377f3533a9633ccf83e202d9e1785d94a00",  # diffusers
+ 520                    "2d2f97574a161cf01a6f6d476b141c7be06f940d94b695ffc12c4e74eca2de1c",  # diffusers fp16
+ 521                ],
+ 522                "layer_256": [
+ 523                    "ece771354ad470a82d56eda413ae3dd6c00d2de28ab3c56a88201d08d4424b4b",
+ 524                    "65b084dada803461ab9ca9be9b892d211870a121dd6c555a111eea470b951c54",  # st
+ 525                    "dc937b59892604f5a86ac96936cd7ff09e25f18ae6b758e8014a24c7fa039e91",  # ckpt
+ 526                    "92565dec90f7c8412dc872e820f66cd0c56263bbbc392439645b6fee270f41bb",  # st fp16
+ 527                ],
+ 528            },
+ 529        ),
+ 530        (
+ 531            "Kwai-Kolors/Kolors-diffusers",
+ 532            "KolorsPipeline",
+ 533            {
+ 534                "pkg": {
+ 535                    0: {
+ 536                        "precision": "ops.precision.float.F16",
+ 537                        "generation": {
+ 538                            "negative_prompt": "",
+ 539                            "guidance_scale": 5.0,
+ 540                            "num_inference_steps": 50,
+ 541                            "width": 1024,
+ 542                            "height": 1024,
+ 543                        },
+ 544                    },
+ 545                    1: {"diffusers": "DiffusionPipeline"},
+ 546                },
+ 547                "file_256": [
+ 548                    "425ff1dcbe3a70ac13d3afdd69bd4e3176b0c3260722527c80b210f11d2d966c",  # fp16,
+ 549                ],
+ 550                "layer_b3": [
+ 551                    "6eb15506fa38b4cbb26391ab1b6c9ead05f86c711e46583bfbe8fc4421571414",  # fp16
+ 552                ],
+ 553                "layer_256": [
+ 554                    "04e3c17170b8a200481f6941b370fdc5056a00fe5a16956de01790f8a93c0dcd",  # fp16
+ 555                ],
+ 556                "identifiers": [".DenseReluDense.wi.weight", "encoder_hid_proj.weight"],
+ 557            },
+ 558        ),
+ 559        (
+ 560            "stabilityai/stable-cascade-prior",
+ 561            "StableCascadePriorPipeline",
+ 562            {
+ 563                "pkg": {
+ 564                    0: {
+ 565                        "precision": "ops.precision.bfloat.B16",
+ 566                        "generation": {
+ 567                            "negative_prompt": "",
+ 568                            "num_images_per_prompt": 1,
+ 569                            "num_inference_steps": 20,
+ 570                            "guidance_scale": 4.0,
+ 571                            "width": 1024,
+ 572                            "height": 1024,
+ 573                        },
+ 574                    }
+ 575                },
+ 576                "file_256": [
+ 577                    "673b3173b037fb5f65b14fde37267390641a36726683de75dcf9df76fce2b866",  # lite bf16
+ 578                    "45c1eb5ce9b69efac891ad459b15c215cd90a986adbbfaf3effd3a89578cbcaf",  # pretrained
+ 579                    "088ddf1e444abf399007b2da2bac87791df165c69f477994f6b3c745a20904b0",  # stage c modelspec sai
+ 580                    "39cec96c7212607f9e526db719bf1df507166d09f4748676c13b0d31cd4adb07",  # stage c
+ 581                    "31ffe2f1a3e2351d658fc7d3002a4eca22466a680f7fb3715b1e3768476f9633",  # stage c lite
+ 582                    "dfe24009fc881011f350d08d9d13be13a1a3b3cbfed667435efe0fd419aca099",  # bf16
+ 583                ],
+ 584                "layer_b3": [
+ 585                    "c55c83fa435ed128457f605bf1312e54727996d1c94413fc5ab5b49e9933857c",
+ 586                    "6fb07ed9fc6ee636e50783802754b3a37bbecfc67037813b616223aeaf6fe877",
+ 587                    "2ea194240e105c8962923e2baca88cb6a0c826794afc2ef82474301694711d68",
+ 588                    "3412c8a184805621e4595d57268ced0b5c3c1974cd221bf67b2c908eec4fd61c",
+ 589                    "53abfb013cfb0e41d0bc7b96bb83e42a4d4c67cb7325f9acf645b02d90efd8fe",
+ 590                    "34556558f680c183adc2accd493cb9888a98ba853226bbecb07d95eb2055ff4f",
+ 591                ],
+ 592                "layer_256": [
+ 593                    "4f5e0a738b963d3d4f8413387a0966ac1ce51f0f985bcbcc124fa221a2fff467",
+ 594                    "8aa77e732a398b7d0dcd9a35d5682c2b5ab090ae90e915c7c91878abff0284d8",
+ 595                    "4bbd46ded0916de3108f0da7145a80f5c7acea26ed35b0aaa29af12008352453",
+ 596                    "415d1f3ecd06416708c1b83ab21e50b39c9d88d19dc33e60b977b7b7061880b9",
+ 597                    "f678c32815c238e14091f690c8a83c3375c8f7738dc7abff79ff086ed9b59204",
+ 598                    "17c8da803df7b9bbc8b1d7cc0c44916fea5b5ac0891330c4fdf0326fcd4496cb",
+ 599                ],
+ 600                "identifiers": ["down_blocks.0.2.kv_mapper", "previewer", "backbone"],
+ 601            },
+ 602        ),
+ 603        (
+ 604            "black-forest-labs/FLUX.1-dev",
+ 605            "FluxPipeline",
+ 606            {
+ 607                "pkg": {
+ 608                    0: {
+ 609                        "precision": "ops.precision.bfloat.B16",
+ 610                        "generation": {
+ 611                            "height": 1024,
+ 612                            "width": 1024,
+ 613                            "guidance_scale": 3.5,
+ 614                            "num_inference_steps": 50,
+ 615                            "max_sequence_length": 512,
+ 616                        },
+ 617                    },
+ 618                    1: {
+ 619                        "mflux": "flux.flux.Flux1",
+ 620                        "generation": {
+ 621                            "height": 1024,
+ 622                            "width": 1024,
+ 623                            "gudance": 3.5,
+ 624                            "num_inference_steps": 25,
+ 625                        },
+ 626                    },
+ 627                },
+ 628                "file_256": [
+ 629                    "f6315581b7cddd450b9aba72b4e9ccf8b6580dc1a6b9538aff43ee26a1a3b6c2",  # krea sai
+ 630                    "1b2170ac37156d4cf91909eb6834bb8adac84bc1fce8098a29cfb03738df84ad",  # krea diffusers
+ 631                    "4610115bb0c89560703c892c59ac2742fa821e60ef5871b33493ba544683abd7",  # modelspec sai
+ 632                    "d86a3038eacaa720682cb9b1da3c49fecf8a3ded605af4def6061eaa18903eb8",  # diffusers
+ 633                    "b7d840eef01c27dfd72ae9143c261355a51bab3b2662263a6cb0059d55347c3d",  # qwen2
+ 634                ],
+ 635                "layer_b3": [
+ 636                    "261559c8eaccae558f72621804a9ee188d338e45e2c622a58db709ac190198ba",
+ 637                    "87f5d565c66e40eb02eb96498243ad81afcbf86192db99a4fc8fff215470320e",  # modelspec sai
+ 638                    "e61d10a394902dadca9367467b2245070f651f4553ec4a96192fbba64e820acb",  # diffusers
+ 639                ],
+ 640                "layer_256": [
+ 641                    "3db58cf834d2f81abb1e035131956da4c90451074c681d0db10810e55e60c2c4",
+ 642                    "ddf1a34a06b355ce2bcd0f9beb0713450d9bcdc61a03a6bc37716361735e96f1",  # diffusers
+ 643                    "ad8763121f98e28bc4a3d5a8b494c1e8f385f14abe92fc0ca5e4ab3191f3a881",  # modelspec sai
+ 644                ],
+ 645                "identifiers": [
+ 646                    "double_blocks.12.txt_mod.lin.weight",
+ 647                    "add_q_proj.weight",
+ 648                    "single_transformer_blocks.9.norm.linear.weight",
+ 649                ],
+ 650            },
+ 651        ),
+ 652        (
+ 653            "black-forest-labs/FLUX.1-schnell",
+ 654            "FluxPipeline",
+ 655            {
+ 656                "pkg": {
+ 657                    0: {
+ 658                        "precision": "ops.precision.bfloat.B16",
+ 659                        "generation": {
+ 660                            "height": 1024,
+ 661                            "width": 1024,
+ 662                            "guidance_scale": 0.0,
+ 663                            "num_inference_steps": 4,
+ 664                            "max_sequence_length": 256,
+ 665                        },
+ 666                    },
+ 667                    1: {
+ 668                        "mflux": "flux.flux.Flux1",
+ 669                        "generation": {
+ 670                            "height": 1024,
+ 671                            "width": 1024,
+ 672                            "num_inference_steps": 4,
+ 673                        },
+ 674                    },
+ 675                },
+ 676                "identifiers": [
+ 677                    "double_blocks.12.txt_mod.lin.weight",
+ 678                    "add_q_proj.weight",
+ 679                    "single_transformer_blocks.9.norm.linear.weight",
+ 680                ],
+ 681                "file_256": [
+ 682                    "9403429e0052277ac2a87ad800adece5481eecefd9ed334e1f348723621d2a0a",  # sai modelspec
+ 683                    "9b633dbe87316385c5b1c262bd4b5a01e3d955170661d63dcec8a01e89c0d820",  # diffusers
+ 684                ],
+ 685                "layer_b3": [
+ 686                    "c65ba812ce3ce056eb1585673f62fb896afe6ec049faaf00a97bc35c9a398c44",
+ 687                    "03049273329fc7db2da10de6d3eb27cb03f190e379c0556cc97b3f0f29001d0c",  # sai modelspec
+ 688                    "483c4be8ef031c56bc8450d1a3cfbe54445ed317bcd801be5abe89f1d3c48790",  # diffusers
+ 689                ],
+ 690                "layer_256": [
+ 691                    "79c07e339865fe9e22c80f723d728c778130acd07a330339c68218b92bb7b3b8",
+ 692                    "ef5c9cd1ebe6e3be5e8b1347eca0a6f0b138986c71220a7f1c2c14f29d01beed",  # sai modelspec
+ 693                    "27bc71eca2d2ff7459165acc12010230911db7709a4f6a5c255befedfa6b1649",  # diffusers
+ 694                ],
+ 695            },
+ 696        ),
+ 697        (
+ 698            "stabilityai/stable-cascade",
+ 699            "StableCascadeDecoderPipeline",
+ 700            {
+ 701                "pkg": {  # prior=prior_unet
+ 702                    0: {
+ 703                        "generation": {  # image_embeddings=prior_output.image_embeddings,
+ 704                            "negative_prompt": "",
+ 705                            "guidance_scale": 0.0,
+ 706                            "output_type": "pil",
+ 707                            "num_inference_steps": 10,
+ 708                        },
+ 709                        "precision": "ops.precision.bfloat.B16",
+ 710                    },
+ 711                },
+ 712                "file_256": [
+ 713                    "fe92687deefcfb33bb3ec181254b55fe4e434c5084ce9d38815eaa32487ad376",  # lite bf16
+ 714                    "2c8d58b267678aecfa6705a0a0375c88613065a8a8d32ad3a4c3867f5461cb3a",  # bf16
+ 715                    "6c218dc948575e3b14b03dffe2014d7870ac505005770ce3abdc28e920a03c05",  # b modelspec sai
+ 716                    "a6c3d534a9be308e95d2c3224af94a854bebd9b503f620f1ae3c8e6ba4a341bf",  # lite
+ 717                    "7b431ea7d0f10e72b3eaece353bf6bf2f6bc717b6f4207411be186b40dec1f43",  # b
+ 718                ],
+ 719                "layer_b3": [
+ 720                    "9506d989de0226018de214f7ced4670eb5aad4a0c399a9229488ceccdf9a3ceb",
+ 721                    "6c09dcb83e0cd7ad735eb763c5e3721c579d796853f0b9d31ba74fb13cad4f94",
+ 722                    "e07025965cee925e31f1d617ea8baa575e7db910d40cc0482fd83df317c0812b",
+ 723                    "d9a42e4226fb2778aaeaf0d6bda173a4ff95aa574c6d9e27e41542aa469e40a3",
+ 724                    "8dcd87dc7a9b877e8e2a00abac44c4da9eadf2b8df4ae68f27415bb791381a96",
+ 725                ],
+ 726                "layer_256": [
+ 727                    "630ec0f3adf97145316c034139836f9df952060d0237ac4e478c55d9a3a50bc8",
+ 728                    "80904f707c192ddd06be2cebeb2ebbec3eb0e9c99076d50824d391ef3ac67bf2",
+ 729                    "8ccedbe1e8cc4093f05b5f8d90e6103e688ae1ac71e0d6261fb17c42ff7c25e4",
+ 730                    "3524e7fa9ca6f7ef695bc2d3410934eabd5272946a05c8cacd7f329e0bd9f1dd",
+ 731                    "40499a8f45ae28558ed2fe4fc549a4cb469bd237434b331ccc0b1910310ed733",
+ 732                ],
+ 733                "identifiers": ["0.2.channelwise", "clip_mapper.bias", ".12.self_attn.k_proj.weight"],
+ 734            },
+ 735        ),
+ 736        (
+ 737            "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers",
+ 738            "WanImageToVideoPipeline",
+ 739            {
+ 740                "file_256": [
+ 741                    "b4602c35fa0519750a42c03e3f296c02d542291e344c4d702522cddbd1711f13",  # 480 diffusers
+ 742                    "6d7a34b63b70eb608324e546d979167a5e787ac6bca3528e63f54a11572d66aa",  # 720 fp8 scaled sai
+ 743                    "b2051cd29d6b2f0c924fa7a3e78a4772f0134d7b059f21590dcce416f4f6cbe8",  # 720 fp8 sai
+ 744                    "7664fe075b3c82dcecf89012ad3429eee41ee9f10d476f60bc2d2ae3c4ca986c",  # 720 fp16 sai
+ 745                    "8ef7ea5bf9eea636b9b3ebd84c40671b4a18ae2704cb4c8595cb5b25c1d8e8b9",  # 720 bf16 sai
+ 746                    "b2de21b99b2e72cb0ff15253b07e926f26e7cf1b7e229efc32f94ad1f1ed9395",  # 480 fp8e4m scaled sai
+ 747                    "0ca75338e7a47ca7cacddb7e626647e65829c497387f718ecb6ea0bae456944a",  # 480 fp8 scaled
+ 748                    "c058a4ac5363c35d1ab4dd3bdec788c23b267fa42a0d7c68aba599f2f74600c9",  # 480 bf16 sai
+ 749                    "27988f6b510eb8d5fdd7485671b54897f8683f2bba7a772c5671be21d3491253",  # 480 fp16 sai
+ 750                ],
+ 751                "layer_b3": [
+ 752                    "4b6c3354c9ee5694e00a78f5658fdf14129f159c3b78a57f82fb18e0f265a83d",
+ 753                    "c36c783559a40d22504f6c4bfb4f5aae760f3f46bbb3a595be79880935122175",  # fp8 scaled
+ 754                    "ac62f7d5583fd2e85b738fafaf233e2cde6e2857e04351135bb9ded45f9082ce",  # fp8
+ 755                    "215e89e855b5e9456af9aa68bc67567dc2269002aaa6b01d849ffec425fc628d",  # fp16
+ 756                    "324b8b6c2d512547a2c31bafa12e20acf313fd3aad587b293334f9f629edeec6",  # bf16
+ 757                ],
+ 758                "layer_256": [
+ 759                    "137881dad8c00063bc8bf05f93067736e419173cd171acc22f77b730db688a19",
+ 760                    "8c5952fd3d333d3a4b719bf7d8ce6b12d1d2e78caaa7e42d713788cfdcadd244",  # fp8 scaled
+ 761                    "86c58bc4864c97f394ea6bccb2ecedc4aab7166f5b9bfeb313edfdcb2918164a",
+ 762                    "cac45f7d8f1a0628cb0738bd308689e439b1cc6206e5f887d60d5b37d30138f2",
+ 763                    "60e4f71a0961b1346b6f6b5ebe4c8cc93219239c5e13b4c0f1e19e9b8e1324d5",
+ 764                ],
+ 765            },
+ 766        ),
+ 767        (
+ 768            "Qwen/Qwen-Image",
+ 769            "QwenImagePipeline",
+ 770            {
+ 771                "file_256": [
+ 772                    "9f33a59093af3abcc2836d4cf4b7bd122c238ca70a26c70f34fdde64646b3bcd",
+ 773                ],
+ 774                "layer_b3": [
+ 775                    "c87eedda853c12844a8deb3592a90bbcbd4dff2f7a850c28755e4aa171432150",  # diffusers
+ 776                ],
+ 777                "layer_256": [
+ 778                    "fda2472d8ef6587a4c979021a2390eeb7c8fc2bcf565330ab8dc6b22f5348ec9",  # diffusers
+ 779                ],
+ 780            },
+ 781        ),
+ 782        (
+ 783            "Wan-AI/Wan2.1-VACE-1.3B-diffusers",
+ 784            "WanVACEPipeline",
+ 785            {
+ 786                "file_256": [
+ 787                    "bd8bbb8834a274525ab65cbb063f21aa58973a054bfd1638bfe395504c9d9b99",  # diffusers 14
+ 788                    "192804a4e10b5bb0a13f5c224bc4ec9707b3b8cc0def8eea005dbce7c9d6752a",  # diffusers 1.3
+ 789                    "f202a5c59b8a91ada1862c46a038214f1f7f216c61ec8350d25f69b919da4307",  # 14 fp16 sai
+ 790                    "654693bf2a93a27cd67c3bcee238bc1d0cbb0dd9a74928ed7155fb21a2a1900a",  # 1.3 preview fp16 sai
+ 791                    "640ccc0577e6a5d4bb15cd91b11b699ef914fc55f126c5a1c544e152130784f2",  # 1.3 fp16 sai
+ 792                ],
+ 793                "layer_b3": [
+ 794                    "5357d78799a61cd2d72a8a2824c919d63f718eb3fba624af63689e9c657db032",  # diffusers 14
+ 795                    "7ae67b7ccf79d1c3f4531ae138e1eb63d52dd97a66b3fcbe1d68fded8df4d5b1",  # diffusers 1.3
+ 796                    "ee63ecdfb3da6901853a59ec950f3e7c3f6595ac46347a03881a4a9c71425377",  # 14 fp16 sai
+ 797                    "82762df3539021d3c0342e0da04137ddbe95ef37ea933cd0a68c09c2c650f2ac",  # 1.3 fp16 sai
+ 798                ],
+ 799                "layer_256": [
+ 800                    "2684413479030170fb3f08c1069c02957ffc386a59168d23b55d579d5c675269",  # diffusers 14
+ 801                    "d527680fa735e5f30ef8852aabf8a49f02a094bc4718f0787c5b85710a13c026",  # diffusers 1.3
+ 802                    "9677492a107b3ed827c7285db3393f5321d451cc6d922a4d0488d2a67e939446",  # 14 fp16 sai
+ 803                    "aaef66a4f65ecf852888d160b2122753fe4c6d642b5d41db29e4ce9e6855b5a0",  # 1.3 fp16 sai
+ 804                ],
+ 805            },
+ 806        ),
+ 807        (
+ 808            "Wan-AI/Wan2.1-T2V-14B-Diffusers",
+ 809            "WanPipeline",
+ 810            {
+ 811                "pkg": {
+ 812                    0: {
+ 813                        "precision": "ops.precision.bfloat.B16",
+ 814                        "generation": {
+ 815                            "height": 480,
+ 816                            "width": 832,
+ 817                            "num_frames": 81,
+ 818                            "guidance_scale": 5.0,
+ 819                        },
+ 820                    },
+ 821                },
+ 822                "file_256": [
+ 823                    "299e6304544f2783896372fa919e755a8bb9ab8caf898ce08a678dae391e1179",  # diffusers
+ 824                    "a9278e6e9c82d174e6c67b3c97d8b97fef30af51dcf59160f2fc241f6819f5dc",  # diffusers 2
+ 825                    "be531024cd9018cb5b48c40cfbb6a6191645b1c792eb8bf4f8c1c6e10f924dc5",  # fp16 sai 1.3
+ 826                    "6f999b0d6cb9a72b3d98ac386ed96f57f8cecae13994a69232514ea4974ad5fd",  # bf16 sai 1.3
+ 827                    "2e39adde59c5e0e90edbb35873126b0d67928b5c11c501e384e976d6dc597cce",  # fp8 scaled sai
+ 828                    "2ee88ab18d7ed7691c5b7f8bdc3d0a9815e6efe75499287564830fd209d3cdfb",  # fp8 sai
+ 829                    "46c27d3693bf2475990a912e08bf67fc6e6cd5396eab87b5e8dd1fcd3651364a",  # fp16 sai
+ 830                    "193535c6450045f718df5f011de6d94d49bd9b13f37ca0412500f050dbbb01a8",  # bf16 sai
+ 831                ],
+ 832                "layer_b3": [
+ 833                    "32266d1c79b518adb9d21837e6a427f6ae55b68cfdd673a7dadb38820fddeb48",  # diff
+ 834                    "3b6989856f4f05368524c1852d8660b73c84cfbe44460af017d7139c2a4641b8",  # fp16 sai 1.3
+ 835                    "f4d6cee3c112db93b3c9137ad102ec0e79ec7ab68b9bbc59004fbc268ccd5ddb",  # bf16 sai
+ 836                    "e627144f41055619eb5407699c46e69ac0d87cf8873721e3e48c9e842656abf8",  # fp8 scaled sai
+ 837                    "6c00f3fadedacb841c4b9b4321b94a11ef85a08c9dd9253e5f9ba95856715579",  # fp8 sai
+ 838                    "a0c339253c714b05877c8fbab649ed631cf021930978f3696a46f685a07c9092",  # fp16 sai
+ 839                    "6435da89a870fd0e88680d31de75b9a40c408a4768eff384ce9b9e99481e8e66",
+ 840                ],
+ 841                "layer_256": [
+ 842                    "52493c23c5fc1d087a283bc4eabb151421b7ae09affa12a5bb059d62656c5766",
+ 843                    "058dedb3d2683a9a5b671c6302690e22722c93f6ed92281d5fa74ab190e632a1",
+ 844                    "5fbed4b95e7196d3626003ea9e0fbbffd074b4297ca406e01b5b6c5d881a6080",
+ 845                    "3a2335c8e7a4359c071b50333b5c00eef6f42a1d5206915e2ee99464a8c5eae7",
+ 846                    "0542780670dd75d4cd9deda123d2e150730646c0a1a8d34582460991498a77a6",
+ 847                    "e925b8222774905c8fbf10af77811fde7870e563eedcde2c94bd5c727e952d49",
+ 848                    "3d915854976284347efa7aa0a117c0fc3b415c4208e1a6c94beb4ccb9720743d",
+ 849                ],
+ 850            },
+ 851        ),
+ 852        (
+ 853            "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
+ 854            "WanVideoToVideoPipeline",
+ 855            {
+ 856                "pkg": {
+ 857                    0: {
+ 858                        "diffusers": "WanPipeline",
+ 859                        "precision": "ops.precision.bfloat.B16",
+ 860                        "generation": {"height": 480, "width": 832, "num_frames": 81, "guidance_scale": 5.0},
+ 861                    },
+ 862                }
+ 863            },
+ 864        ),
+ 865        (
+ 866            "nvidia/cosmos-predict2-text2image",
+ 867            "Cosmos2TextToImagePipeline",
+ 868            {
+ 869                "file_256": [
+ 870                    "7fbd20dae97cc26a55c7aff3024bc84e554cff8f69966c725a24c8238c5431ec",  # gguf
+ 871                    "6d211f1c14cd793156da3a840dd5462ae072046fcd6f1dc64c613a5343bfe896",
+ 872                    "95a2b32ad31a271eb64d35985c7ea46f1448528af70932eb1f35d57f90c27be2",
+ 873                    "344e67faf333b7849fa94290c9028bdd5e40eb19700754c833cda0423bc10ad0",
+ 874                    "ce15ef565cbb9ef414a6f7a396c455d82d5f762d2174493da87fe009c5fee75b",
+ 875                    "94aa9f2b59330b88e97b6b439e2f206a51c86e6b154fb66d43ed149bfac23cf8",
+ 876                    "636de5388da249130d51752991a1792b90af31cbf43f021ae07f75756ee2d79a",
+ 877                    "472c5e4cf5056a1a59085addb5a86d801de39bf5e000d253f206a7f63c710029",
+ 878                    "663266ace67c22529c3b6bfa0e8bd69f0ba6e683f5f02b8e3da50881057ba142",
+ 879                    "21a674b314c1364d0dbb3712f5ed702996a7b7403c452835cac22709e01c2f77",
+ 880                    "3bf2df806c6472e039efc9e8d3181163d7faa7b385e61519b7d17d5e9c993a49",
+ 881                    "1de35e1603c4c30bc80b132ccea15fc0503369caf68290708f17e679e98cd41f",
+ 882                    "0738e559bbd71f7351ccba34b2b47362a3f829b92f3dbcffeaf1e44b0d52f42c",
+ 883                ],
+ 884                "layer_b3": [
+ 885                    "5a18ba14c41c6601dcc1195ca180ac7744357eb15ace39272788bda1a7151e9b",  # gguf
+ 886                    "67cc3eaf7987c89cd7ccff13de6bc03e3eec59d260d44486e2367cd946ce6f20",
+ 887                    "3c6fefa107742488d2e6856714198a762f2fd35c67edd50d4657eaf4b59c7ca3",
+ 888                    "4e1f90ee1e8959d334c9b1ea2cc5e58d0b8340e271c35f81c8a5ec26e16d9d76",
+ 889                    "f8171071e828524fcc2806126ad100a2198e450c82c0864c8fe8b358c5cbbfbd",
+ 890                    "8126101a0207ecfbd741394fd59f306bcb4c492b2a921e0921c426ca7bd38985",
+ 891                    "c942c5a85ff7cb602d8ca894f5d180c2224e91f0b62c3a21f6a425f9e0e8554b",
+ 892                    "c8c500de74da879a547875fe1046f62ab18bdfd09c09eb3da723cbc2319cb4e3",
+ 893                    "c0ac3f67501004e9e9a55d1658402ad97e42bf8a266edf81f6f3bb835ee476b9",
+ 894                    "84f5926eb4e11d826815682b076ed7d3bba4c86520859be80aa1ef92c72b26a4",
+ 895                    "1d4375aab5548708559b0fde150754a2163cd211eb20a5471e17afaeeb26e082",
+ 896                    "68bd8982f59c60d69c301d16dfb5a60f5d43d66c0b60138d48a22f5ded598e7b",
+ 897                    "c3e9a10cad7aebf979072092008be6e2815d03d28cbf316c15e8daf22116bd7d",
+ 898                ],
+ 899                "layer_256": [
+ 900                    "38f2a75eab667c0cc85f3946a23ca6dc2278438c25a9f93aaaa9f79c3808e180",  # gguf
+ 901                    "ee8434a5e9bc6fa07199de2d0c69fb87f7922c31792bafd13f527c9d92fecb0c",
+ 902                    "2f8382657babb4d0ae4f8e425ae33b21ad71deb6ba457fd6734f05208d52e06a",
+ 903                    "34b181a8291b571857cdbf67ac0081fea594a2f223bf20bd2fc8b0c889e9602d",
+ 904                    "d198c412b972e381acfb812304fa98ed0d97a2f072ddc195cd9a1eb83b1d8146",
+ 905                    "79580a13aff9859e67b0a9f4f8893236cdcfa58c3d43770641aaac8daee55a94",
+ 906                    "cfd48c7ad71c913fa8768167ed0c2ee8c207311b22b1e5a8761369b5a780e8d6",
+ 907                    "da91362ad85d4d2e80a2cb7a55e4ae0e52c9eef8b437a95894ce5ab75d36568c",
+ 908                    "15f84001f5205b6dd8c6f1334cb51c46f6171c7795fb2a557ea16b874f0c71e5",
+ 909                    "5d29179ad15a15d2561defcdda66f1d1e4d065c1e0738f9cba4db5b68b93d2ea",
+ 910                    "7ec489d1e461f5fb2af627b68034ca57f19c516aeccbc5d188b3bd27e3353a15",
+ 911                    "c8dc42fe7b411d746ebdf86286b91cd6893c5f028076b8fe4103f7ea8e1d8833",
+ 912                    "86df7c095aee01588e961438f322b85ca0100a9e440b8a2b6c724e00f748d8b5",
+ 913                ],
+ 914            },
+ 915        ),
+ 916        (
+ 917            "rhymes-ai/Allegro",
+ 918            "AllegroPipeline",
+ 919            {
+ 920                "pkg": {
+ 921                    0: {
+ 922                        "precision": "ops.precision.bfloat.B16",
+ 923                        "generation": {
+ 924                            "guidance_scale": 7.5,
+ 925                            "max_sequence_length": 512,
+ 926                            "num_inference_steps": 100,
+ 927                        },
+ 928                    },
+ 929                },
+ 930                "file_256": ["6927dcc812841c1da549bf11c97ddf30532aee0e708a6642fa64cf8e0dfcdef7"],
+ 931                "layer_b3": ["8b20714a6af89ea4bf4ada1f805c5b9d529ef136c229e9b75392242d62d80c3e"],
+ 932                "layer_256": ["9e44e6c919dc71c24a193641e6265cd9983a2a773b9bbaf527c10ac4837b29fd"],
+ 933            },
+ 934        ),
+ 935        (
+ 936            "audioldm-s-v2",
+ 937            "AudioLDMPipeline",
+ 938            {
+ 939                "file_256": ["fc30d5b5a3bb8d08672736efb1fff10755ba7024dace39b2dcb579a105aa2a5a"],
+ 940                "layer_b3": ["82fbcc553c1ad770d28fd1866b935249c5ebfbf75f3166ae823e1bc6ef39a95a"],
+ 941                "layer_256": ["d076446a58a36bf436e37444679d62bcf2f45689d4aa3d799b3fe801c71ed2c8"],
+ 942            },
+ 943        ),
+ 944        (
+ 945            "zai-org/CogVideoX-2b",
+ 946            "CogVideoXPipeline",
+ 947            {
+ 948                "pkg": {
+ 949                    0: {
+ 950                        "precision": "ops.precision.float.F16",
+ 951                        "generation": {"num_videos_per_prompt": 1, "num_inference_steps": 50, "num_frames": 49, "guidance_scale": 6},
+ 952                    }
+ 953                },
+ 954                "file_256": ["8fbb6a5e67c70885a8ed8e33df144ac61253e45977be5035fa18cfdf77d386c7"],
+ 955                "layer_b3": ["1db3439649b5362448455fb2ed6ebde0c3b973655a206832731149757ad165bb"],
+ 956                "layer_256": ["edd6bd51f1236f528ff8d32dc754f0b86cfac901b800642ea497358156dc00bd"],
+ 957            },
+ 958        ),
+ 959        (
+ 960            "HiDream-ai/HiDream-I1-Full",
+ 961            "StableDiffusion3Pipeline",
+ 962            {
+ 963                "file_256": ["3cb3f6d77a3fce19b90fa7f66da0cbe997b0785a38a788b559290d3062f6fd26"],
+ 964                "layer_b3": ["612eb9b2676a3e7b28b10aae045a97a95de2a399fe3801c8f6369589c3a832a6"],
+ 965                "layer_256": ["78fbfb7fddb9ccbdf91f22b0c3d304cbf0cc7305dbccb216982233849ec727df"],
+ 966            },
+ 967        ),
+ 968        (
+ 969            "cvssp/audioldm2",
+ 970            "AudioLDM2Pipeline",
+ 971            {
+ 972                "pkg": {
+ 973                    0: {
+ 974                        "precision": "ops.precision.float.F16",
+ 975                        "generation": {"num_inference_steps": 200, "audio_length_in_s": 10.0},
+ 976                    },
+ 977                },
+ 978                "file_256": ["359a5ffb89a844beb2fcfac584aae2cd7cd6e87c3ab1ec4e892ef45d91db77c2"],
+ 979                "layer_b3": ["eac241273f9f30982fc04aa88b4dc1c38b533430956a55b9ed4d3e5c717ec962"],
+ 980                "layer_256": ["ab109d01b43788063802f00c6ecab024c830ea58d668f5c2df9e3ae5b87d86cb"],
+ 981            },
+ 982        ),
+ 983        (
+ 984            "tencent-hunyuan/hunyuandiT-v1.2-diffusers",
+ 985            "HunyuanDiTPipeline",
+ 986            {
+ 987                "pkg": {
+ 988                    0: {
+ 989                        "precision": "ops.precision.float.F16",
+ 990                    }
+ 991                },
+ 992                "file_256": ["7d31ac8fa389ff39dd0a81430010e52c43b59f15adc00c83625a47881e16830e"],
+ 993                "layer_b3": ["bccd37ecc9f85d132b46d0bb67b4facb49fc6c091428a4feba9ab9a93140f5fe"],
+ 994                "layer_256": ["ed25d241d58ca298d28abd5919e70341ad194e77dce4859436b52ea4d8fcb616"],
+ 995            },
+ 996        ),
+ 997        (
+ 998            "Alpha-VLLM/Lumina-Image-2.0",
+ 999            "Lumina2Pipeline",
+1000            {
+1001                "pkg": {},
+1002                "file_256": [
+1003                    "132b4d213fdd3cfc14333746fc3eb8bbe6358cd73c3bc95ac4ccec230b97dca3",
+1004                    "a7c09ebae62996a8289782161338a3cdba58c11d2d849c50b2d6502e152b0d6d",  # pth single file
+1005                ],
+1006                "layer_b3": [
+1007                    "198bde52f09736f1fc650dcdbd0e6b0f6a5ce186582554c1d9ee8ab16ac0feb2",
+1008                    "b52807536902cabbf84f99e4fa2f8713fb4ef77e739f06367ee0d486e3222faa",
+1009                ],
+1010                "layer_256": [
+1011                    "982893c99860aac8198c2e435cf85f782fce8f10732daf1f2881a26864400a4e",
+1012                    "dc937b59892604f5a86ac96936cd7ff09e25f18ae6b758e8014a24c7fa039e91",
+1013                ],
+1014            },
+1015        ),
+1016        (
+1017            "ucsd-reach/musicldm",
+1018            "MusicLDMPipeline",
+1019            {
+1020                "pkg": {
+1021                    0: {
+1022                        "generation": {
+1023                            "num_inference_steps": 200,
+1024                            "audio_length_in_s": 10.0,
+1025                        },
+1026                    }
+1027                },
+1028                "file_256": [
+1029                    "853d0ef1d61cbf5d682872322ea8b761ba3d2f85bfbccd58363bd6b2f837268f",  #
+1030                ],
+1031                "layer_b3": [
+1032                    "82fbcc553c1ad770d28fd1866b935249c5ebfbf75f3166ae823e1bc6ef39a95a"  #
+1033                ],
+1034                "layer_256": [
+1035                    "d076446a58a36bf436e37444679d62bcf2f45689d4aa3d799b3fe801c71ed2c8",  #
+1036                ],
+1037            },
+1038        ),
+1039        (
+1040            "openai/shap-e",
+1041            "ShapEPipeline",
+1042            {
+1043                "pkg": {
+1044                    0: {
+1045                        "precision": "ops.precision.float.F16",
+1046                        "generation": {"num_inference_steps": 64, "size": 256, "guidance_scale": 15},
+1047                    }
+1048                },
+1049            },
+1050        ),
+1051        (
+1052            "hunyuanvideo-community/HunyuanVideo",
+1053            "HunyuanVideoPipeline",
+1054            {
+1055                "file_256": [
+1056                    "bdb957b35585ea74ae42ca92865a68fa1bf1ebc6c5b7e686a889e5c977dc24c7",  #
+1057                ],
+1058                "layer_b3": [
+1059                    "d31c56b4c9444d4c2f1b10120fe964e0956f6b8c7e7c1e4cc5a1f37406fc49f5"  #
+1060                ],
+1061                "layer_256": [
+1062                    "fe741fdfd163bcb1e0ed81d80f79ac3576dbf6e6740674efadfeff782a48bed4",  #
+1063                ],
+1064            },
+1065        ),
+1066        (
+1067            "zai-org/CogView3-Plus-3B",
+1068            "CogView3PlusPipeline",
+1069            {
+1070                "pkg": {
+1071                    0: {
+1072                        "precision": "ops.precision.float.F16",
+1073                        "generation": {
+1074                            "guidance_scale": 7.0,
+1075                            "num_images_per_prompt": 1,
+1076                            "num_inference_steps": 50,
+1077                            "width": 1024,
+1078                            "height": 1024,
+1079                        },
+1080                    },
+1081                },
+1082            },
+1083        ),
+1084        (
+1085            "stabilityai/stable-audio-open-1.0",
+1086            "StableAudioPipeline",
+1087            {
+1088                "pkg": {
+1089                    0: {
+1090                        "precision": "ops.precision.float.F16",
+1091                        "generation": {
+1092                            "num_inference_steps": 200,
+1093                            "audio_end_in_s": 10,
+1094                            "num_waveforms_per_prompt": 3,
+1095                        },
+1096                    }
+1097                }
+1098            },
+1099        ),
+1100    ]
+1101
+1102    transformers_addons = [
+1103        (
+1104            "google-t5/t5-small",
+1105            "T5Model",
+1106            {
+1107                "identifiers": [
+1108                    [4096],
+1109                    "encoder.embed_tokens.weight",
+1110                    "text_encoders.t5xxl.transformer.shared.weight",
+1111                    "t5xxl",
+1112                    "encoder.block.0.layer.1.DenseReluDense.wi.weight",  # small\
+1113                ],
+1114                "file_256": [
+1115                    "ec87bffd1923e8b2774a6d240c922a41f6143081d52cf83b8fe39e9d838c893e",  # shuttle/flux diffusers# flux dev
+1116                    "565cb2487351282e8e4dbeb88e63f4ad28217ce0439f5a8e6525a924807d2d9b",  # bf16 modelspec sai
+1117                    "6e480b09fae049a72d2a8c5fbccb8d3e92febeb233bbe9dfe7256958a9167635",  # fp16 modelspec sai
+1118                    "4f2751ceeb2a96edd693e539dc5d6bba0b8d3814f49a9b3798403a0cec4b2e3d",  # fp16 diffusers  cogvideox
+1119                    "83690f3cc37cecb5e907f41ab0f7abb0855ef24a0a8aab9259f2888ce85a34e2",  # flux diffusers
+1120                    "7d330da4816157540d6bb7838bf63a0f02f573fc48ca4d8de34bb0cbfd514f09",  # fp8_e4m3fn
+1121                    "8490f7a22615c20651a63dbe7b4241929826a4de20292dc8e63bfc3c61e3654f",  # qfp8_e4m34n
+1122                    "d8720addef2596fef86b1b22e4b62875c9118779ba8723759a75dfcbc649ffd5",  # mystic mlx
+1123                    "7d0eac95abe8daae454bcd3d166b8bfc6a35fe68278f97479d62dbb6850f38c0",  # mlx flex2
+1124                    "ceabd6f71c7112cfaa4dfca8711dda97b79fb9b25983f1c95532de226045f1f8",  # mlx jaguar q8
+1125                    "49e139f50824fef40908ef4307c851e7adaa8b91bed44054c4829600dbedfdda",  # mlx shuttle 3 q4
+1126                    "211ade1d474f5dc83190aec8be5c4baf52643777790d64de0cbd84f63613e5e9",  # mlx flex1 q8
+1127                    "7894547154ba3fd6e364e66e2951ee82b4c3fc1ae0f95df6a4f9d1c5a4e98f17",  # DeepFloyd/t5-v1_1-xxl sft
+1128                    "eb529f693f4b17773a24e787fcba29486d5e1700dadcc20bb91e4c8b00212d08",  # pixart a
+1129                    "d80116f6fc39801e4eef425a584e7a7a41cbe5119797bef2dad67299909fe2ae",  # Q6K
+1130                    "31ebe18e901bfb6e5709a20ec1c95fce29bce2b9545073231e0f909a53239f5c",  # Q3 KS
+1131                    "6be2b0b7e2de7cf2919340c88cb802a103a997ce46c53131cec91958c1db1af4",  # Q4 KM
+1132                    "b51cbb10b1a7aac6dd1c3b62f0ed908bfd06e0b42d2f3577d43e061361f51dae",  # q5 k m gguf
+1133                    "9ec60f6028534b7fe5af439fcb535d75a68592a9ca3fcdeb175ef89e3ee99825",  # q8 0
+1134                    "8f5ab879234384235d56732f0cda07bf8801f30a49645248c5bfdeeb1665f64b",  # q3 kl
+1135                    "86427a1f4dba48940e45bf78d6db5bf0d48fce8b4656f5aba27955f06af9628e",  # q5ks
+1136                    "88b696cfae098f03bb078cc5944ef03aec1e91ec020a6b016b723a0f0532558c",  # q4ks
+1137                    "1dc600961d3c5ed081f6700485cdc7ed9cfb4631f2dc385b7ac6bd3c80846d0d",  # f16 gguf
+1138                    "f28631189911f8d7931e8fe642a4cb2a3c51f50da7cabbfa06b89bafc19c00d0",  # q3km
+1139                    "de9dfdd19d7ba6859993cadec5100665dc7a4fb71e1c6c8970959cbdaf4366e3",  # f32gguf
+1140                    "7a68b2c8c080696a10109612a649bc69330991ecfea65930ccfdfbdb011f2686",  # allegro
+1141                    "2c0c539ab8e8fba3877cc94bc483e427f74c525f817a809b028ebc8d96d75a94",  # hyd 1.1
+1142                ],
+1143                "layer_b3": [
+1144                    "ca94e03b7b1fdcb0d6ff5205eac56f145d2dff8a9c489faf80935bfec8387f18",  # bf16
+1145                    "c0e2b054bedd782909191b05748a88c28d1538fa91789fec63f036ba01dcc001",  # fp16 sd35
+1146                    "672de9b79d14001de7d1109ffc52e4d0cccc3bfee6f45648fa347703b58e2b99",  # fp16 sd35 diffusers
+1147                    "abdb187a996c51cb0469630c124b14eeb0bb8f5f635aca6c71dea264f8bd61ae",  # shuttle 3 aesthetic diffusers
+1148                    "8926f862b7763fd9688af317eba7809aa71a478484be0c738c269de368ace4a7",  # diffusers
+1149                    "e616b754cf55e55b3f9f17ab7e1fff95f0607c81782822fc1223ae22fb1e9f36",  # fp8 e4m3fn
+1150                    "b79e5f1878a62cd726bb4f9fc1415cacb071d278440e9026290c7b36cb41e1d4",  # fp8 e4m3fn sd35
+1151                    "77619d5278d9f547ddac17d4d99df56cb6a3a9e660ae31b2f896a4297907e62e",  # mlx t5 jaguar
+1152                    "c87c9d3cc7becc46ee34821299cf8551a6df5541582a45469a031bccdc4bd340",  # mlx shuttle t5 q8
+1153                    "7e6c32c01c89fc5d1610c410135aa9708e77a7444510e5e479fa677ff2b53643",  # mlx jaguar q8
+1154                    "a49c2bc301733967ddff113790e301773dc5dd71368b657af4141458de593ced",  # mlx flex2 preview
+1155                    "c2ea94030ea362e03d73d448fa5353ace0a449dc38c51a4a49fb148444ebb8ef",  # mlx shuttle3 diff q4
+1156                    "4a90463350f08ef41479da1d561ab41b8f8b792f1603a092226a838156aebfb0",  # mlx flex1 alpha q8
+1157                    "f86cd0324eebbffb81b15ad47dc8b63fedfa51dc222e44e1a958a7becce2bcb0",  # df safetensors
+1158                    "48c54c61c5f14e42761c6177539b2da3a22222516dab053952ca8d8e92f93d65",  # pixart a
+1159                    "311332d9738773669128814d944b1e860a8e3176b37abf43370bc06b43b454d0",  # flux
+1160                    "3f4e51dec6d542759cdea49b3bec14c090a4908f953fa3e182e2ea43b5b05402",  #  q5 k m gguf
+1161                    "beb25461e168359108add77263ea5cc121b7584cc4aa304ffc4e134783bb1d88",  # ggufs
+1162                    "43313f90a359c8c1c787a7a833b1ab9f7a38204ba36d0ba587c658d0d9bf0852",
+1163                    "fa9e97cdad26f55fedab83a3f114e0338c9cca3ea2bf8f1b168a6dfc5919bf8e",
+1164                    "93108d67f8829a7e1e8f3773e9ce53c67f365889c2acfd69816ac80fd43f8e08",
+1165                    "fc65a6cc55e89394d7bc0fa4ee952d63ce3bdc143b84b5aa4bb3edf7722a6b83",
+1166                    "8163bc781a7e013dfeb806bbb828a36913cf119363ea5fcd9071d87a0c227cda",
+1167                    "ad2ba63e1134bad1b15ee339313bc130708b2995e8b4b76fb44d727f28c26ad9",
+1168                    "4a844772638ffed2f61d45eaac984094b92540fa1391a4098608fc73a6cd4fd8",
+1169                    "76c31e1fd35da7de7cee97c1e7c5ccde640e6fac3e17a62e115ecf484c7196c3",
+1170                    "a4d672e22b5bdd8f8b0885cec4a173d0466bb1dcbfbf8400cedcc41c2494f16c",  # ggufs
+1171                    "d1860c3f01dc9f260d98b50d3d2bbc8dc2d3eefaa93778a8de9d7adfb897fc6e",  # allegro
+1172                    "b8719092fc58487406211f52dc55bf40b573ccfd29933a989c33a36b694f6f0a",  # cogvideox
+1173                    "795e272409bc4fa55f402485acf86b607256f91aa965295c5bb771c61f8e9e74",  # hyd 1.1
+1174                ],
+1175                "layer_256": [
+1176                    "bb20f7805209379aea4d6548f17e551cf27d0f8426ca169e4df8234f718ed5ef",
+1177                    "431580c2d86f9a9ed3500f776a4c997223e5644aed211f965354869ccfa4d76e",
+1178                    "2ccd548c4ffe34168c60779ebd497b9b410981a2fda813c8723a24a805c94ea0",
+1179                    "a608fc4e1cc9762e46187a1ce66e98e8ba4bc3a604cbfd96174bd876baea0fa1",
+1180                    "dc9e74cdf535e0b7a17e1335d0d8b38a00f94facf0cb01363baee09945a25278",
+1181                    "f07409710a69b2247aa4723a9b40d2225d5e5bfba7b60c51f0ea901fc2ef5ad9",
+1182                    "ed28f8b6cc472f352fc840b5a9f841ff17d76ae6918f0676464dca20529aa92b",
+1183                    "97c1a08f87c59b4c55ad4672841977cfce43ca7730bcd11d8c178a9330de1855",
+1184                    "968972839b859a9c4457f190fad2e17e8585ce27d9ef318df4f5b4e902143944",
+1185                    "4dbdeadc957c898c327197a3d8770188535672e9208beb29bbf48dfdf51c8955",
+1186                    "669172c2b5e8b97774d9dd0227ede40c4d25cae3adae97d9f281d03531e7e137",
+1187                    "39fff130b9ee240102c28a78ee1c4a643e9f800b734ff133f3ab2ad1357bd2f6",
+1188                    "6e047ed8cb7007034ff15840dd53c92096f0e7ed5befa07808de8afa35d35874",  # safetensors
+1189                    "adbd0baa059074501b7686db2b0c01715f3a317275c2657c5dfbfd6ee92389b7",
+1190                    "eb63790fb32b5660de34fa42c2e608df58f7aa3680b4984f0ee9008fe613729c",
+1191                    "f125c20a33b0ff2dbd4e8ad9acebc34383cb2ef98668169ef79a8c06655ced35",
+1192                    "e64e0ac83a785ef584a0e86b347fae8f9e2bd84324a49396ca8a9fe7532a947b",  # GGUF
+1193                    "70001b3ac1b66522142bb86e4c3e87e20c2bbd07276c763878e0838ef6184aad",
+1194                    "f46fd1e2b5fef3b9f7ae80d183cc77f7be181117a72a0bb933bdef0bc6cd679e",
+1195                    "83676d73726d101325a47c7f8a60cedf10bab99ea79a6bedad7761220cb4a625",
+1196                    "a621a907586e5e270e7c7873b167364d8a935ff347d8240fa9bab319678da690",
+1197                    "f0af1a089f40d8611db5c59469314f1547e2df23c6eff24860359b37ea9bd966",
+1198                    "72478320b8dbfd9aeaea010dcf0896e3116fa5ab940f3b472882d9f9d2d7333f",
+1199                    "9c1a88e36334a48d8482fec54b14ea1d5fd31f0dbb65d13cc616e63dc7c42be5",
+1200                    "d0689f727e8ac4fef3ec4b1f29e8a3bd12e1116559eeefb2a1a457cd4e676d1e",
+1201                    "fea158a4afcfaa6e95e04799bae0287de0c4fcb188f3b41768a46ce48c71c9df",
+1202                    "2e5bc4e73312b5aec4c1a55631cb4ed69cf34ccaa6d1f28f7045f137a579b439",  # cogvideox
+1203                    "015fdecbc3b5369dbcb2302e4b79985437ac4496d1b9ad63316423a222fb0803",  # hyd 1.1
+1204                ],
+1205            },
+1206        ),
+1207        (
+1208            "google/umt5-small",
+1209            "UMT5Model",
+1210            {
+1211                "identifiers": ["encoder.block.1.layer.0.SelfAttention.relative_attention_bias.weight"],
+1212                "file_256": [
+1213                    "a8e861969c7433e707cc5a74065d795d36cca07ec96eb6763eb4083df7248f58",  # wan t2i diffusers
+1214                    "decf9b70814ed5e9965bfca9fbd0483462e2bf743790663025b7742f8c014c72",  # fp16
+1215                    "0a07449cf1141c0ec86e653c00465f6f0d79c6e58a2c60c8bcf4203d0e4ec4f6",  # auraflow
+1216                    "c0ef3a140898e228a3520c9adec60743d2e8e5b3d229651bb37f1a3921919f99",  # wan
+1217                    "7b8850f1961e1cf8a77cca4c964a358d303f490833c6c087d0cff4b2f99db2af",  # wan i2ixxl sai fp16
+1218                    "c3355d30191f1f066b26d93fba017ae9809dce6c627dda5f6a66eaa651204f68",  # wan i2i xxl sai fp8_e4m3fn scaled sai
+1219                    "fa1d36fd54f171ae60fea915c23bd77986b330bbed9729f0d2f8ecbe9168bc48",  # gguf
+1220                    "4a3176f32fd70c0a335b4419fcbf8c86cc875e23498c0fc06f5b4aa0930889e0",
+1221                    "adbc782b9145a27e15d63dfa25057efca0ac75e2db7d372c901ddaa130ca2def",
+1222                    "b7e2ca4c493c9d51fa951005e8ceba2f4b6b6877cfb4c36a8955c6cd68a1dba7",
+1223                    "2521d4de0bf9e1cc6549866463ceae85e4ec3239bc6063f7488810be39033bbc",
+1224                    "9209b4c77b34ad8cf3f06b04c6eaa27e7beeebb348a31f85e3b38a1d719b09ed",
+1225                    "8bc12d80bc0413573fa58a93626117440b4528f640dd9cb310732e05fa9e6c3e",
+1226                    "f64f8d6dc4d8a24276df69d0ccea789aae686f7417950a41e6568c30cb478a5c",
+1227                    "17cf97a5bbbc60a646d6105b832b6f657ce904a8a1ad970e4b59df0c67584a40",
+1228                    "eaea358bb438c5d211721a4feecc162000e3636e9cb96f51e216f1f44ebd12ce",
+1229                ],
+1230                "layer_b3": [
+1231                    "cd92b29c9099a640e3f5d4a76e64b3467f87f6c056119e0defdff94d311ad6de",  # wan t2i diff
+1232                    "1c943dbcb8b328a7c6c852921ddaefbd84c9df8c83bc51fe303c1f06cb734102",  # fp16
+1233                    "1639a6467af0db1e15828d33b878e568cba1335947eeadd481170bcdc9ba8e33",
+1234                    "72a0329740dee29a2c099eec3c320b3945590a74293356014c30249fe69652e5",  # wan
+1235                    "0374cba03c607ffe8ab8f04994d82f82e80901dc7578f1a9a6cb2637608be5d5",
+1236                    "d75a407f873e1cfa1a0a36214b53b14bfebe9253ea263465151c07f0d57f3f29",
+1237                    "621153502b985c143d304318c91dc3d10296d24268c81e3538fc336fdc84c915",  # gguf
+1238                    "43bb052945d38a68bec27c3d26162e88e306e6074d027d3b4b2b8ae2b1851691",
+1239                    "98f50ea5d55e61c1478df47e567e48bdd036d240b9129e64d53a826406900adc",
+1240                    "9400313b8eae31699473daa5f840d25a4ef660f68de9a7894f1a28f214f23384",
+1241                    "9f13826b8e4ddde24d80de6a947a7868e26cea25dda52790ee6ed695ff72b9bb",
+1242                    "475773ab108a537ff904b84e7f3a80129ba4983deb7170b6b52c922ece6069ce",
+1243                    "5ef27b3c1eddb08cfe41b452cf9529d86dff811645d40c165bae324486d19e96",
+1244                    "e170559d8551cfe651344594e54c0a9a90c0068b00f3866f6e9a3737e20925cb",
+1245                    "e8dc7442a20bcdc7b6e5dd0265939d88896eab5ddd33ee16f1f09537e65914b8",
+1246                    "4d3d5049857d01741780daf01e96617092973305637b435f4895499a26bbaede",
+1247                    "7a2adadc2372feda23b2169337276adda6d1fdef82ba69f0d3321c4c6ba8c604",
+1248                    "0a7c61a85bb3f51f75924de48ef3f5e87cbf8901f600cbfcae97f5e2919c4148",
+1249                ],
+1250                "layer_256": [
+1251                    "467916d35f3053dce1d40d998fcaf6aa03feda75aa578d964dd61461e23641a3",  # wan i2i diff
+1252                    "58deeef888d4ded4ffababfbf8da27227a4a6ff8adfa42016e12c0180f713816",  # fp16
+1253                    "178ebd3fa3418d33a2e45a80d8b9d3662ff4a8e75f3de3f0332f82c505d8152a",
+1254                    "8700dcb651465fe6c925b7ad6068b58b32951832fff0ed19819510f8d0713ee5",  # wan
+1255                    "954f2129ba166e746c71433f717b572d8869ec14b32b7f214d1701d3b1120047",
+1256                    "32f5fc1daea014b6488b96c2a1330e0aad87e074844fa3e2e3f20b9e58440395",
+1257                    "9245abaf6df8a4b5fcc828ecbcd7b21a1b19bf5f3c4388fb5c8eabc140276dce",
+1258                    "172d0fbbd379ae014a7008e148813818494e9e645db802fd000d443369df9d17",  # gguf
+1259                    "2fa68a26b0386aaf9123d2b4067dafc8631ee724602197dd353f3ea5a61dac8a",
+1260                    "16f0054014e6d07b86b0526d5bcfed7d2aa3aebe3e44e6758933d90cbd3da46e",
+1261                    "fd62047f5d27ff43210c117dc0f253c101e694a5331d6b684688606c92c65ccf",
+1262                    "ddc4f38db9f132fb1b736c1d693b5c039a2d6fe83bdf4f1c1e7a2745b5d79124",
+1263                    "9e9ab11b3ea059b84ae2bcc5be76ab3f730a486d92a16f1fd2a959bdc2ede08f",
+1264                    "bfb178b1ce27f00e122d2328c662fdef6cc239c07efc749aa61ae2d395441b02",
+1265                    "50addf6a911b90194a75b0212429d1af55eb2f9d24715479b9ccc4a40adc299b",
+1266                    "2e46e9f1b714d72160d3b3b775a845b3049a01396fab935f1278d9e8de2ef0c6",
+1267                    "db8d2b49d9042e39d6531b33ec3bebb9cdf42b9e6ad56163f08da2a7da2a53cd",
+1268                    "2d81d19ad5440422b85e0b17c71914269f6c25c9b1fa321c0dd6119ddb41d62d",
+1269                ],
+1270            },
+1271        ),
+1272        (
+1273            "google/gemma2-9b",
+1274            "Gemma2Model",
+1275            {
+1276                "file_256": [
+1277                    "e909230aabafad02d097c7dc02f2ae062b4e6b0593477c1f07679d277e09ce71",  # sana bf16
+1278                    "d61628bc793240439e608c5ae744f55ec8770f684abb63602648a24cb6da60bc",  # lumina 2
+1279                ],
+1280                "layer_b3": [
+1281                    "55a3c812ac0832d154867f5927365bcc776926e48e65f7f35a81fc11f4bb81da",
+1282                    "543572889beb25cad83a43ce70cdd255d2c82951d6595e8c97ff62fd05871c99",
+1283                ],
+1284                "layer_256": [
+1285                    "a0d820c39578cf888f398579d9a00d69b31c81e049795ba70008dad8fe5b3a33",
+1286                    "abc83b04a04467579ea1952a7efbdd252b8641ac0e2a6a9be2a5a73e371111d6",
+1287                ],
+1288            },
+1289        ),
+1290        (
+1291            "google/gemma-7b",
+1292            "GemmaModel",
+1293            {
+1294                "file_256": ["01676b4c6e765f737a5e9854a315de3887e939c370cae116d505777729099a68"],  # lumina next sft d
+1295                "layer_b3": [
+1296                    "438d82c867240f194a4e15798eef2886a911c8f57fa2d9f4ffad1d56e7bd1ccf",
+1297                    "1de38e09f5f2c5345de48b8cd4dddcfff3e341cc0059752446e186b3863f0981",
+1298                ],
+1299                "layer_256": [
+1300                    "e4835a72d582b4ae066d6ff0519f2ee9f8b21fb02e8c28d8eaa317f8d1e9ea75",
+1301                    "1657c7180b48672004f4463308dfdd56d92eedeb23d1408ea766985ca208e5aa",
+1302                ],
+1303            },
+1304        ),
+1305        (
+1306            "google/mt5-small",
+1307            "MT5Model",
+1308            {
+1309                "identifiers": [[250112, 2048], "text_encoders.mt5xl.transformer.shared.weight"],
+1310                "file_256": [
+1311                    "0524484ec81425ba9deef6fac1393a78ba9b1c9bfed704a4be5f9c7255975cc1",  # fp16
+1312                    "32f70f1d187e131a5fc3e4f0edc97ce89360d8e2f1d90177a443a05296097acc",  # fp16 enc
+1313                ],
+1314                "layer_b3": [
+1315                    "a1d616c37711ec7b9073d04734af2f5fd02f9035a322eb46efeace922e104c51",
+1316                    # "bc71d4259f4feaa0fb27c1f288765004840f39247cddc98b3ac37329ff1354d0",  # fp16 enc
+1317                ],
+1318                "layer_256": [
+1319                    "bd337daf0c1aa36896013109b406a0580aa3bb8ab9291d89df3015d737358e95",
+1320                    "2e40c48c96fc7df636aad96d3e78ed0ba9f68c3059e21b7fcf917f284c569a61",  # fp16 enc
+1321                ],
+1322            },
+1323        ),
+1324        (
+1325            "Qwen/Qwen3-15B-A2B",
+1326            "Qwen3MoeModel",
+1327            {
+1328                "file_256": [
+1329                    "c56947057481fb5e7cdf766e442da81717b34addc88bbe8f3728fd25bd03cbae",  # qwen3 coder 53 a35
+1330                ],
+1331                "layer_b3": [
+1332                    "d2d1e0875202f5c9c84c781a2105620250733bd01832f67b2c17bc981d1eb508"  # qwen3 coder 53 a35
+1333                ],
+1334                "layer_256": [
+1335                    "408c01da57c4968b7b0e36d98a74e321153e7aeb058fea63ffd140e323526476",  # qwen3 coder 53 a35
+1336                ],
+1337            },
+1338        ),
+1339        (
+1340            "Qwen/Qwen2-VL-7B-Instruct",
+1341            "Qwen2VLTextModel",
+1342            {
+1343                "file_256": [
+1344                    "1f48ac458d6fbd0aec53a116065a7ee3f1d34bddde544e25c16a05c9d5392b78",  # orsta 32
+1345                    "0e85c7111ce849293e97aa09ce1172352ecece023a3ecea7ac8311e326b47f3a",  # orsta 7
+1346                    "d725335e4ea2399be706469e4b8807716a8fa64bd03468252e9f7acf2415fee4",  # qwen img
+1347                    "e10bd9583a77250376d9134cd6b46799029dfa3b4d7989c1050b3ec149cc7cf5",  # qwen flux
+1348                ],
+1349                "layer_b3": [
+1350                    "e4f681bde70a753f30f83495a2aa340d251bf3d818eb5a1cbe58f85fd6ea0d40",  # orsta 32
+1351                    "47b062ce8ddb14845fb1a71d2fd88fd52a82e26561ba3eb05be057915a867775",  # orsta 7
+1352                    "b6386f70b528ffa9e09fdd8db8a7b91a7c462ed97b06963576c6139e25fdcf31",  # qwen img
+1353                    "4cd449df9f9004a7e53005583a7e4cfa6de42912f03647d2ea799d489e9c1406",  # qwen flux
+1354                ],
+1355                "layer_256": [
+1356                    "ed36a4a11c4ebebb10d1e010cb93e2e43fcaf975cd42bb6c9958537593d0d44d",  # orsta 32
+1357                    "f7f6f64e7b6d7826400a2fc0eef942a47c47bd5914e051ad0c8cd9ff5ff7982b",  # orsta 7
+1358                    "f341ed0f792cf0570ceb21d3b64ed14bf9875e9fcb90116851364eeed683a6ca",  # qwen img
+1359                    "ba031d0da78afe24ae63558ad29b8028244a7bd4750a5615dab9079fe32a5fd7",  # qwen flux
+1360                ],
+1361            },
+1362        ),
+1363        (
+1364            "openai/gpt-oss-120b",
+1365            "GptOssModel",
+1366            {
+1367                "file_256": [
+1368                    "68a8dc1f8e2e5996cb702f14332a25ddf3463daeab2df68e21ca09ef181203c3",  # original model
+1369                    "a881aa5f561b26a22b14a8262aa61849ace349ffd73d74769e030ac90a1fcf8a",  # diffusers
+1370                ],
+1371                "layer_b3": [
+1372                    "b52807536902cabbf84f99e4fa2f8713fb4ef77e739f06367ee0d486e3222faa",  # gguf
+1373                    "43c618018db1fd6e915dead610652da261d9058b73bc5355c85c6ac69af4d913",  # "original model"
+1374                    "ab27ce7391b7fbd6ce3c319faa119afdac68f746af6a0ce2c3400a132f36f6ac",  # diffusers
+1375                ],
+1376                "layer_256": [
+1377                    "de5dcad822be5ed6196f0f3f6965739993118d14db97b33a94a269f4f1b7a363",  # "original model"
+1378                    "575f1977ed42d95a050e13dadaafc05a6d94c8aadca8364dca8a62aa4f2b146c",  # diffusers
+1379                ],
+1380            },
+1381        ),
+1382        (
+1383            "microsoft/Phi-4-multimodal-instruct",
+1384            "Phi4MultimodalModel",
+1385            {
+1386                "file_256": [
+1387                    "bc703090b63eda16f639fa4de7ac54635c23105ab1da2f6ec4d3403151d38ee6",  # mini
+1388                ],
+1389                "layer_b3": [
+1390                    "cf4add4ada6082f448788eaf2937f645b5212db88e06ee81475b8be0e99063dc",  # mini
+1391                ],
+1392                "layer_256": [
+1393                    "7ff992b780b2f8993dd6bb9612207943638b2a42badc976ce80893bc205e801b",  # mini
+1394                ],
+1395            },
+1396        ),
+1397        (
+1398            "laion/clap-htsat-fused",
+1399            "ClapModel",
+1400            {
+1401                "file_256": [
+1402                    "c92b5a2bee69ff5dd05820d9e0a5cddbc9c9b9dd19a6cb3214f0cf4f29a4d1b0",  # audio ldm
+1403                    "ae69f555e7f1a2333b8e684c9fa8233f44a47bbadf76d484f941b74f74d2753d",  # music ldm
+1404                ],
+1405                "layer_b3": [
+1406                    "a4d26450ac399d51b9abbe37859615bb02a5cbf63521da4c7cdc549d04a2872c",
+1407                    "ddf310d8eb2d4e3f61e605978675a9d3a748cad9406b9aee8335eae013e77573",  # music ldm
+1408                ],
+1409                "layer_256": [
+1410                    "843ba86000971d6067bfc4f3ed6dd01bd6f6726188aaa15d86b05554f4fe8481",
+1411                    "27529e30442d030a28badf9d62710f4b74e38e9c4424ed169c7e0ac072f5a771",  # musicldm
+1412                ],
+1413            },
+1414        ),
+1415        (
+1416            "google-bert/bert-base-uncased",
+1417            "BertModel",
+1418            {
+1419                "file_256": [
+1420                    "c6c6348af2cb4d5852fe51102ce39605903dbe7925c005cf8995506cc21ea914",  # hunyuandit
+1421                ],
+1422                "layer_b3": [
+1423                    "30d7d2cc3ec9e4ba45844e005d0bbcb5887b6a0976042f73da916237dc5c4c12",
+1424                ],
+1425                "layer_256": [
+1426                    "94fd2508680ff684eff57e4a5a8ca46bf338fc356a9cf6fe8db2b84543dd7971",
+1427                ],
+1428            },
+1429        ),
+1430        (
+1431            "llava-hf/llava-9b",
+1432            "LlavaModel",
+1433            {
+1434                "file_256": [
+1435                    "f5ad57d3eda300a3195bc9c0bb36ab76ebe88831f128e9851e63440aff4a6741",  # hunyuanvideo
+1436                ],
+1437                "layer_b3": [
+1438                    "d7d6ccb9dbba90b64e4cd259b6309e56708b3f4fbd6e9f85e9f0410e549133ef",
+1439                ],
+1440                "layer_256": [
+1441                    "9969c41152aba689413b7f63888ecdc0c0badad2c2960e689ebc4c0e4a696c73",
+1442                ],
+1443            },
+1444        ),
+1445    ]
+1446
+1447    additional_tags = [tag_pipe(*entry) for entry in diffusers_addons]
+1448    additional_tags.extend([tag_base_model(*entry) for entry in transformers_addons])
+1449
+1450    assimilate(
+1451        mir_db,  # format
+1452        additional_tags,
+1453    )
+1454
+1455
+1456def add_mir_diffusion(mir_db: MIRDatabase):
+1457    """Create MIR entries missing from the database"""
+1458
+1459    repo = "microsoft/speecht5_hifigan"
+1460    series, comp = make_mir_tag(repo)
+1461    mir_db.add(
+1462        mir_entry(
+1463            domain="info",
+1464            arch="gan",
+1465            series=series,
+1466            comp=comp,
+1467            file_256=[
+1468                "d9dc6513c30a5b86c2497712690c04fe74b4aa79fdab6d490b34fcb4e24c590c",
+1469            ],
+1470            layer_b3=[
+1471                "85b5acdf29ad04c63f885383340d8e3445ae0055521f82cabb82bd09cfb9a956",
+1472            ],
+1473            layer_256=[
+1474                "bd52b538e7ac05711be9321cfb7619d4056996ce32923c9c91ee02cf69154770",
+1475            ],
+1476        )
+1477    )
+1478    series, comp = make_mir_tag("lodestones/Chroma")
+1479    repo = "lodestones/Chroma1-HD"
+1480    mir_db.add(
+1481        mir_entry(
+1482            domain="info",
+1483            arch="dit",
+1484            series=series,
+1485            comp=make_mir_tag(repo)[0],
+1486            repo=repo,
+1487            pkg={
+1488                "0": {
+1489                    # "diffusers": "ChromaPipeline",
+1490                    "generation": {
+1491                        "num_inference_steps": 40,
+1492                        # "guidance_scale": 3.0,
+1493                        # "num_images_per_prompt": 1,
+1494                    },
+1495                }
+1496            },
+1497            file_256=[
+1498                "d845553f11e6afe8139c41ca73678f9f03eab2e68d2e1c6f03ae19509a4d546",  # sai
+1499                "1b2993a44e63b2250496f69edce643bac2fb79833cf92ba8dd95cbd764d970c7",  # annealed sai
+1500                "2dd46f08516246df1f582047cc09268ce4f747357baff05b13148e71519029fc",  # diffusers
+1501            ],
+1502            # layer_b3=[
+1503            # "8da38c3719e77a38a20356c9f92f5ca0101c17406d7a9817323cf67b74088520",  # diffusers
+1504            # ],
+1505            # layer_256=[
+1506            # "267798815e0855c2253061c6a6ab70edf9590e8ea1ba9b4621eeb0f6615ee37b",
+1507            # ],
+1508        )
+1509    )
+1510    repo = "lodestones/Chroma1-Flash"
+1511    mir_db.add(
+1512        mir_entry(
+1513            domain="info",
+1514            arch="dit",
+1515            series=series,
+1516            comp=make_mir_tag(repo)[0],
+1517            repo=repo,
+1518            pkg={
+1519                "0": {
+1520                    "diffusers": "ChromaPipeline",
+1521                    "generation": {
+1522                        "num_inference_steps": 8,
+1523                        "guidance_scale": 1.0,
+1524                        "num_images_per_prompt": 1,
+1525                    },
+1526                },
+1527            },
+1528            file_256=[
+1529                "2c0c7d908d04418a48b453c293237a9826d54472cf0ba76e28697d1309d1021b",  # sai
+1530                "c88f6794753ba23e8f6bf8c84cf220daa35a6aa16d54ea0c3e0136f52e5da7e1",  # sai delta
+1531                "c759d67ca3ef50a9a1c242e3291c57f406646f226a95f43f66577996494986db",  # diffusers
+1532            ],
+1533            # layer_b3= [""],
+1534            # "layer_256"= [""],
+1535        )
+1536    )
+1537    mir_db.add(
+1538        mir_entry(
+1539            domain="info",
+1540            arch="unet",
+1541            series=sdxl_series,
+1542            comp="pony-diffusion",
+1543            file_256=["67ab2fd8ec439a89b3fedb15cc65f54336af163c7eb5e4f2acc98f090a29b0b3"],
+1544            layer_b3=["bf4c2154daa4ece7292277b210d081f98759e9ed4d5c889564632e3ccc4a1071"],
+1545            layer_256=["465425d4420dcf5aa4b4d5b456db11a1fcc7c8f61b2e4a87e2470297c98bb96e"],
+1546        )
+1547    )
+1548    mir_db.add(
+1549        mir_entry(
+1550            domain="info",
+1551            arch="unet",
+1552            series=sdxl_series,
+1553            comp="pony-diffusion-turbo",
+1554            file_256=[
+1555                "7555ac941f3a767833830ba5cc9a4508a9777cbf97b487b6baf0400ab7000587",  # turbomerge
+1556                "9322f9d91b28abf09e4137bc02ec806af23510221a164e71b81778e61cc3b4b2",  # turbosimple
+1557            ],
+1558            layer_b3=[
+1559                "1e8f23fcd4be0f00eb52368b91c709fffa8a3b8e21772b92b2e0671eed9117d0",
+1560                "5c8b3f34f9d0a58135cf72fbfe9b5d75b5545a10e3d726478543fa7cc510a8bc",
+1561            ],
+1562            layer_256=[
+1563                "7edf51ef09b39c46937a4e4141707c040cd12af0d95299a4d3cd2b7d3fabe035",
+1564                "74e4dbc89d57d61ff7e8af8b0fddcf7466ba233d53ca4ffb7777138991bc3d52",
+1565            ],
+1566        )
+1567    )
+1568    repo = "cagliostrolab/animagine-xl-4.0"
+1569    mir_db.add(
+1570        mir_entry(
+1571            domain="info",
+1572            arch="unet",
+1573            series=sdxl_series,
+1574            comp=make_mir_tag(repo)[0],
+1575            repo=repo,
+1576            file_256=[
+1577                "8ece83aa1bed1fb39a2b81f1660f0ce6889218e493c1f2ed55e9f15f59a7e03f",  # v4
+1578                "6327eca98bfb6538dd7a4edce22484a1bbc57a8cff6b11d075d40da1afb847ac",  # v4 opt
+1579                "1449e5b0b9de87b0f414c5f29cb11ce3b3dc61fa2b320e784c9441720bf7b766",  # v3
+1580                "e3c47aedb06418c6c331443cd89f2b3b3b34b7ed2102a3d4c4408a8d35aad6b0",  # v3.1
+1581            ],
+1582            layer_b3=[
+1583                "268ffbb120670b9c4b25158bd474c787740884b7738b48203aa03c4c3f00028f",
+1584                "18fda1a55cad137d62c81d4328f5ece85d88b126261e06b9e14ab68055d5d484",
+1585                "bae9bc8a5c43145bcf92ee3391618d9eaddd689f626991bae202de9cf5f1e70e",
+1586                "d6bc5ccafa2b97c867b13a1e7a8c2c7ad9c4877055a66c71bb773557bc306447",
+1587            ],
+1588            layer_256=[
+1589                "c21d1c38813e078817122e12866ab39f5aa7f56945dd4a8beee3cae1e0f139e7",
+1590                "b916c162c981155aaf74e93d5314038af6767bb5a129c51ee05a1fb6a206c6ac",
+1591                "ecc6bfc73824a2d7c3b0ca184854a235859f329c83768f017b07a19a535d17b4",
+1592                "97f6ca05de7fbdae7aacb2427a552f924492176c474a23dd252c192e1c0e9d65",
+1593            ],
+1594        )
+1595    )
+1596    repo = "OnomaAIResearch/Illustrious-XL-v2.0"
+1597    mir_db.add(
+1598        mir_entry(
+1599            domain="info",
+1600            arch="unet",
+1601            series=sdxl_series,
+1602            comp=make_mir_tag(repo)[0],
+1603            repo=repo,
+1604            file_256=[
+1605                "c2a1a3eaa13d4c107dc7e00c3fe830cab427aa026362740ea094745b3422a331",  # v2
+1606                "536863e9f0c13b0ce834e2f8a19ada425ee4f722c0ad3d0051ec7e6adaa8156c",  # 1.1
+1607                "3e15ba00387db678ab4a099f75771c4f5ac67fda9e7100a01d263eaf30145aa9",  # 0.1
+1608                "e3d12d0f76d61aa31d2668a2217e5b642592193f2946842c44d7056ea5469cce",  # 0.1 guided
+1609                "735cf3fefcbdc4f7817f53247e38b836ffd27c7641af6d8daa21d245242cb4bd",  # 1.0
+1610            ],
+1611            layer_b3=[
+1612                "93b061baf21d743d592327a61f027d099d8e18da9808a76c7704ad123eba4a29",
+1613                "dc05fed2acbc73cef4c377cfa2a681c5cf6d065b88d8bf70d371bbcce6a223a8",
+1614                "8eb1c30327e5b71b35b9a4513dc5f2cac9f244667393c0eedb10a26aa9991cd8",
+1615                "3dafbe31f6ebaffa3d054e1b37049e1147faa2474ceb6dab7bc3c4cded0c845e",
+1616                "892533778ee14454938f7b50830093f58e12f1e14560a148f71927e4ccff5f5c",
+1617            ],
+1618            layer_256=[
+1619                "397791b3d77affb7bd35c5ded7377493c6bf456920a41388ba95bd0157109803",
+1620                "b23c02b8519c6777a1f271662f4251a59468c4b3e11184a2d722fa8929b4ea48",
+1621                "a373981494f5508c124a1960bdd096bbc96935fbb54b1218f563206d3892c176",
+1622                "b709df257c40d9d981f686f2880bbe64f43b78805b7213768d659a142a593efd",
+1623                "f1e6b4cab0fce608dca6fa851384e8728202449f16270fbd1f0c4c5ec4946c10",
+1624            ],
+1625        )
+1626    )
+1627    repo = "playgroundai/playground-v2.5-1024px-aesthetic"
+1628    mir_db.add(
+1629        mir_entry(
+1630            domain="info",
+1631            arch="unet",
+1632            series=sdxl_series,
+1633            comp=make_mir_tag(repo)[0],
+1634            repo=repo,
+1635            file_256=[
+1636                "11b6d7bce65674659cc6b7ea960658436edfd80e566cb240ebd4bfbc3e2076c8",  # 2.5 diffusers
+1637                "bcaa7dd6780974f000b17b5a6c63e6f867a75c51ffa85c67d6b196882c69b992",  # 2.5 aes sai fp16
+1638                "956dca99114aaa5c3eb526381309d37ee96737e78ed64c8ae613409f47c3f65a",  # 2.5 aes sai
+1639                "933778ce76c1fc0ca918b37e1488411b8a99bbd3279c12f527a3ac995a340864",  # 2.5 fp16 diffusers
+1640                "5c7d38880d0940e6795158b7608ccef89217272b1f2a9331c5b0a2adffcd82c4",  # v2 sai
+1641                "0411e988479884b1a3ecd184123efe38d051d8d0ef24270585a7d1d57499464a",  # v2 sai fp16
+1642            ],
+1643            layer_b3=[
+1644                "d55b22740da2d5b98020ad2390cdc0a7ee08cf9e0d98c11957f16cc20c49815b",  # 2.5 diffusers
+1645                "7e9be9bd9a3aed1ad7207e2f77c98c24c3a75f6adcc9b53514033c6c3365d289",  # 2.5 aes sai fp16
+1646                "5c6dfcc8d01dfb64723f8f5785caa080e2987859c0a050470bfdbe5312be9efc",  # 2.5 aes sai
+1647                "703f775c6e48ed5b0eba6e847414f047bcd4adc677dbc1bf221b3ef05b2ac471",  # 2.5 diffusers fp16
+1648                "72d4ebe4af61f8a7add8fe36b8acd16602894279fb5a744ad50b5b5bac7067b8",  # v2 sai
+1649                "acb757b851db12cdf9d4365a45ee0d6e64afa77ac95583bb82711baf7c4125fd",  # v2 sai fp16
+1650            ],
+1651            layer_256=[
+1652                "adb7be228d4ee6e583c3e5ae4ddb579fef64c3987617ce4d4aff3eb7f8d6a3f7",
+1653                "d4813e9f984aa76cb4ac9bf0972d55442923292d276e97e95cb2f49a57227843",  # 2.5 aes sai fp16
+1654                "fe2e9edf7e3923a80e64c2552139d8bae926cc3b028ca4773573a6ba60e67c20",
+1655                "bc7021473a04a6de3fe0d0fed600875d852ad1ad9d47c445278f66ce9e8ec7a0"  # 2.5 fp16 diffusers
+1656                "fc94481f0c52b21c5ac1fdade8d9c5b210f7239253f86ef21e6198fe393ed60e",  # v2 sai
+1657                "a6f31493ceeb51c88c5239188b9078dc64ba66d3fc5958ad48c119115b06120c",  # v2 sai fp16
+1658            ],
+1659            pkg={
+1660                0: {
+1661                    "diffusers": "DiffusionPipeline",
+1662                    "precision": "ops.precision.float.F16",
+1663                    "generation": {"num_inference_steps": 50, "guidance_scale": 3},
+1664                }
+1665            },
+1666            identifiers=[
+1667                "edm_mean",
+1668                [1, 4, 1, 1],
+1669                2516,
+1670            ],
+1671        )
+1672    )
+1673    repo = "segmind/Segmind-Vega"
+1674    mir_db.add(
+1675        mir_entry(
+1676            domain="info",
+1677            arch="unet",
+1678            series=sdxl_series,
+1679            comp=make_mir_tag(repo)[0],
+1680            repo=repo,
+1681            file_256=[
+1682                "94762e983e5942056be73c5c1d4464b8ffa1ada500b4fef1267550e2447953ce",  # modelspec sai
+1683                "1ab33e37fbb2566c55cd729e4ab79cc2f99cd9d0a578fabc7a2cf4ee47968be1",  # diffusers
+1684                "8cfa375669b1222d6fecf470f41b2abb370c76a90ab9568964c4bb15b34ec8a2",  # diffusers fp16
+1685            ],
+1686            layer_b3=[
+1687                "2f353c5e6ed0a2c05af00d014e18e65f69f1ce8c48f8eefbf8ad71b34f940fbf",
+1688                "cc34bd3135d7cafc3cb6e3f6e7cb6896c98277bad52877a952ddbd2ffe222e01",
+1689                "b90efdc848f5386d5250b6fb233ce380cf6cc299f497cfa1d2feaef22f87c9d1",
+1690            ],
+1691            layer_256=[
+1692                "029b89ee311110c8f945dbdfc52c1d5daeb1e78c353c38aa3141ec68ce28e7cc",
+1693                "5cdb948e5f3873300679073391d48fc648171f02093d7737d078557ff75762bb",
+1694                "f73afbe43cc76571cb86ebcfced618668a2fb2252b0bc6ba88d6e942bae75741",
+1695            ],
+1696        )
+1697    )
+1698    repo = "segmind/SSD-1B"
+1699
+1700    mir_db.add(
+1701        mir_entry(
+1702            domain="info",
+1703            arch="unet",
+1704            series=sdxl_series,
+1705            comp=make_mir_tag(repo)[0],
+1706            repo=repo,
+1707            file_256=[
+1708                "7cb406ec0662e91570a79f3c4fb8f0ea5325bffe6af5d9382edae838698f72bd",  # modelspec sai
+1709                "1895a00bfc769a00b0c0c43a95e433e79e9db8a85402b45a33e8448785bde94d",  # a1111 aio
+1710                "0bf1ce6b065a6b969ab02dc8e8fa21eb20ee189b10935c49ce68c77a7e432c1c",
+1711                "02ed8ebd0ed55aec686fcf20946d7a1659a31f9f8d9c3798cd254ba6b67434ca",  # diffusers
+1712                "40d8ea9159f3e875278dacc7879442d58c45850cf13c62f5e26681061c51829a",  # diffusers fp16
+1713            ],
+1714            layer_b3=[
+1715                "c074dc38e8ec836816b91cbcc2ca17f80d6106de8d196d416ef9a27c8837ee45",  # modelspec sai
+1716                "1d6c0216da57fe98e7ad29e9653566725f5b2a87845fdbdcda257b3be817b5f4",  # a1111 aio
+1717                "c074dc38e8ec836816b91cbcc2ca17f80d6106de8d196d416ef9a27c8837ee45",
+1718                "89f86d9c846495870416b4945b6a46a517f28405e5bab666feb4057f012340be",
+1719                "535b47e9b70da6494878ca6d45af3f2e201b7f17748432911c12232e586855e6",
+1720            ],
+1721            layer_256=[
+1722                "52267d5d327a2ba92c7a14261a9d081df621b8366819b1bb3a47d130523a813c",
+1723                "b365a3631c6c74532f3a571c84c68e088be35496d35be1e932031713ddd2a2f4",
+1724                "52267d5d327a2ba92c7a14261a9d081df621b8366819b1bb3a47d130523a813c",
+1725                "89f86d9c846495870416b4945b6a46a517f28405e5bab666feb4057f012340be",
+1726                "535b47e9b70da6494878ca6d45af3f2e201b7f17748432911c12232e586855e6",
+1727            ],
+1728        )
+1729    )
+1730    repo = "shuttleai/shuttle-3.1-aesthetic"
+1731    mir_db.add(
+1732        mir_entry(
+1733            domain="info",
+1734            arch="dit",
+1735            series=schnell_series,
+1736            comp=make_mir_tag(repo)[0],
+1737            repo=repo,
+1738            pkg={
+1739                2: {
+1740                    "diffusers": "DiffusionPipeline",
+1741                    "generation": {"guidance_scale": 3.5, "num_inference_steps": 4},
+1742                }
+1743            },
+1744            file_256=[
+1745                "176871da1d5d2d511a52ae9b0dd70faa1f5d1b7734b7e33ed6b4bffa52050e0d",
+1746                "4b80d37681eaed07b7f5b3825a392da929d1620933ede7c2749ef3613cc53f42",
+1747            ],
+1748            layer_b3=[
+1749                "ff422d1734abf33366e87bbf44267dc6096c5d499e695287c35558174877412e",
+1750                "5ad8034eac6b82d842311437101c52b5d35826ce34994940d9e667e702a0d45c",
+1751            ],
+1752            layer_256=[
+1753                "e5d95de314cbfc49b79479118a1ac0b90fc95ccd6bb1a5c95803996d6cebf8fe",
+1754                "d299e8ea4a605917ab98a4a7330d4d398b4ae295efbf458eeeceb5ff1bd7959a",
+1755            ],
+1756        )
+1757    )
+1758    repo = "shuttleai/shuttle-3-diffusion"
+1759    mir_db.add(
+1760        mir_entry(
+1761            domain="info",
+1762            arch="dit",
+1763            series=schnell_series,
+1764            comp=make_mir_tag(repo)[0],
+1765            repo=repo,
+1766            pkg={
+1767                2: {
+1768                    "diffusers": "DiffusionPipeline",
+1769                    "generation": {"guidance_scale": 3.5, "num_inference_steps": 4},
+1770                }
+1771            },
+1772            file_256=[
+1773                "a5b04df4072698395387c21e8da0176d03f6557e0c38ff1dd3bf469ebab9d0fd",  # fp8
+1774                "a91b46de2055b3511ee87523b57862648856e8c00100161d5b520543a7302755",  # norm
+1775                "23a77c86189d5934da48bf44bb871cf80ba99177ffd3fd5272cdecb208c8b8be",  # mlx q8
+1776                "d3782d5a8f6e82c6676e8e26d54020934ada589d2aceb17fc5ca604b1bd55da8",  # mlx q4
+1777            ],
+1778            layer_b3=[
+1779                "4dd3174edf6b680ce9daf3de643e33ae2c4f09a4d5968da61ea48885f3a193c0",
+1780                "9fdf191b2c58b2a6e190396e12314530593dca4f2a2bee389ec5175da5e52af8",
+1781                "ad203ad6a00d8b1315337e34069e7c41016ea407469a536de8ad6807042017fd",
+1782            ],
+1783            layer_256=[
+1784                "14d0e1b573023deb5a4feaddf85ebca10ab2abf3452c433e2e3ae93acb216443",
+1785                "7ce8d449b32a9c959431ade729b513ee7a6457f11e1c13e3ef04dd8db3494621",
+1786                "9c3395f67a3d844483b77f0ddd5e2ea64b61732fa9d9da19845bb8ae574c1f8c",
+1787            ],
+1788        )
+1789    )
+1790    repo = "enhanceaiteam/Mystic"
+1791    mir_db.add(
+1792        mir_entry(
+1793            domain="info",
+1794            arch="dit",
+1795            series=dev_series,
+1796            comp=make_mir_tag(repo)[0],
+1797            repo=repo,
+1798            pkg={0: {"generation": {"num_inference_steps": 16, "guidance_scale": 7.5, "width": 768, "height": 1024}}},
+1799            file_256=[
+1800                "179d4000e44295f6dfadc0e4ac210146454724d46371b82657200ff9fb5c68a9",  # mlx 0
+1801                "48ca85274e3b67f07f70dd84b67725e62395c2f7b188394342716f783ea4c6ac",  # mlx q8
+1802            ],
+1803            layer_b3=[
+1804                "91074aaebe1b5f3b2e7755d3c092af7eb240e92a192360690f1033949d3c8a68",  # mlx 0
+1805            ],
+1806            layer_256=[
+1807                "3942e6a52dbb0abaf63b031d9c4eda0df47576b51d4c81361978a3dc27b1309e",  # mlx 0
+1808            ],
+1809        )
+1810    )
+1811    repo = "shuttleai/shuttle-jaguar"
+1812    mir_db.add(
+1813        mir_entry(
+1814            domain="info",
+1815            arch="dit",
+1816            series=schnell_series,
+1817            comp=make_mir_tag(repo)[0],
+1818            repo=repo,
+1819            pkg={
+1820                2: {
+1821                    "diffusers": "DiffusionPipeline",
+1822                    "generation": {"guidance_scale": 3.5, "num_inference_steps": 4},
+1823                }
+1824            },
+1825            file_256=[
+1826                "dcbc4f2470b177eed12c7d7515c0e7342515a849ebd31a50c8d8d43913d7bd32",
+1827                "26a7aa64c0798a3549e1d767932da0a7fb82b49f8edcbdcde804a20d9ed1478f",  # mlx q8
+1828            ],
+1829            layer_b3=[
+1830                "9906c29933d0c33a6ee8d9712f33fa8bd4b35b46a1c7b565ae48832b757dd980",
+1831                "89c453c4bf99220405687eed984dace4492bdae1b6fb08f3d9629145b1a11672",  # mlx q8
+1832            ],
+1833            sha_256=[
+1834                "4eacf27e5659f5dc42f34c407cbe9e1e202290692df754eb68fe913f59fa2941",
+1835            ],
+1836        )
+1837    )
+1838    repo = "freepik/flux.1-lite-8b"
+1839    mir_db.add(
+1840        mir_entry(
+1841            domain="info",
+1842            arch="dit",
+1843            series=dev_series,
+1844            comp=make_mir_tag(repo)[0],
+1845            repo=repo,
+1846            pkg={0: {"generation": {"num_inference_steps": 28}}},
+1847            file_256=[
+1848                "09e970a7b8d1813ea7cacd48f9a944fd223882b137a8f4f3b61d864cdc20bbec",  # mlx q8
+1849                "de90e69945c2f4afcb9b6a057ce48190905c984370fce76b16ba3b97d46e2747",  # mlx q4
+1850            ],
+1851            layer_b3=[
+1852                "9276fa4805efeb45c08cca32c5b51d490e57a2ce5c15ef476a8e468a509c5cdf",
+1853            ],
+1854            layer_256=[
+1855                "e1afe2f9b1ca55b3c659293cf3237f6b5571f5c4e826bad025ff0f7b54dc34ee",
+1856            ],
+1857        )
+1858    )
+1859    repo = "freepik/f-lite-7b"
+1860    mir_db.add(
+1861        mir_entry(
+1862            domain="info",
+1863            arch="dit",
+1864            series=dev_series,
+1865            comp=make_mir_tag(repo)[0],
+1866            repo=repo,
+1867            pkg={0: {"f_lite": "FLitePipeline", "generation": {"num_inference_steps": 28}}},
+1868        )
+1869    )
+1870    repo = "freepik/f-lite-texture"
+1871    mir_db.add(
+1872        mir_entry(
+1873            domain="info",
+1874            arch="dit",
+1875            series=dev_series,
+1876            comp=make_mir_tag(repo)[0],
+1877            repo=repo,
+1878            pkg={0: {"f_lite": "FLitePipeline", "generation": {"num_inference_steps": 28}}},
+1879        )
+1880    )
+1881    repo = "freepik/f-lite"
+1882    mir_db.add(
+1883        mir_entry(
+1884            domain="info",
+1885            arch="dit",
+1886            series=dev_series,
+1887            comp=make_mir_tag(repo)[0],
+1888            repo=repo,
+1889            pkg={0: {"f_lite": "FLitePipeline", "generation": {"num_inference_steps": 28}}},
+1890        )
+1891    )
+1892    repo = "TencentARC/flux-mini"
+1893    mir_db.add(
+1894        mir_entry(
+1895            domain="info",
+1896            arch="dit",
+1897            series=dev_series,
+1898            comp=make_mir_tag(repo)[0],
+1899            repo=repo,
+1900            file_256=["4236455adeaeb4ed444d63b253ec99805022d17e962ed7261ada9c72ce11cfee"],
+1901            layer_b3=["c1a6f83585398fe452d20596a79a522e2986f4c2c01a40e7bfd787af113735d3"],
+1902            layer_256=["e4a0d8cf2034da094518ab058da1d4aea14e00d132c6152a266ec196ffef02d0"],
+1903        ),
+1904    )
+1905    repo = "ostris/Flex.2-preview"
+1906    mir_db.add(
+1907        mir_entry(
+1908            domain="info",
+1909            arch="dit",
+1910            series=dev_series,
+1911            comp=make_mir_tag(repo)[0],
+1912            repo=repo,
+1913            file_256=[
+1914                "0407108e446a4f57efffc5e7518bc374876af970d3c6068dc4074de0d221c615",  # modelspec sai
+1915                "df168ba94d5f96c478b24604a6beedff6189047152190509c73c162ea0d8ec02",  # mlx
+1916            ],
+1917            layer_b3=[
+1918                "7f85cdc186896da6965b57d5edb672f08663075d2b207f0e20e328c4034a8076",  # mlx
+1919            ],
+1920            layer_256=[
+1921                "5063de856be5365807d12b47ef6919b4ac611a72651739b2b4050e113bed7a83"  # mlx,
+1922            ],
+1923        ),
+1924    )
+1925    repo = "ostris/Flex.1-alpha"
+1926    mir_db.add(
+1927        mir_entry(
+1928            domain="info",
+1929            arch="dit",
+1930            series=dev_series,
+1931            comp=make_mir_tag(repo)[0],
+1932            repo=repo,
+1933            file_256=[
+1934                "5d6dce30a266ccbf530c3a3bf253cd5486720a8fb71cdeed556c28304201dc2f",  # modelspec sai
+1935                "7acf8771b80a91eaa21566abe8c7d9d3ba33d8688e6e98446827749aee7ca1ee",  # mlx
+1936            ],
+1937            layer_b3=[
+1938                "cb3d3edafd81651eefd62894b3572deb02c5304f4b5d4f7ab8654f1fb922ecd6",  # mlx
+1939            ],
+1940            layer_256=[
+1941                "a6b9af6efc25fa77cd24046b81ee66fea09a9987d2a8e56ffca9b7a1c9c9c519"  # mlx,
+1942            ],
+1943        ),
+1944    )
+1945    repo = "tensorart/stable-diffusion-3.5-medium-turbo"
+1946    mir_db.add(
+1947        mir_entry(
+1948            domain="info",
+1949            arch="dit",
+1950            series=sd3_series,
+1951            comp=make_mir_tag(repo)[0],
+1952            repo=repo,
+1953            pkg={
+1954                0: {
+1955                    "precision": "ops.precision.bfloat.B16",
+1956                    "generation": {"num_inference_steps": 8, "guidance_scale": 1.5, "height": 1024, "width": 768},
+1957                }
+1958            },
+1959            file_256=[
+1960                "5b0530e8d71b49fa1358f1208047cd789a40bae5b44406c9524b0f0d88f8b246",  # diffusers
+1961                "07119c77c3548a1d9eb30923df4dd55ec74914dc5ec81626804dcbe51ce17a5d",  # sai
+1962                "3c379381344d2a2b3ee3d7a1bc97f7d1e58fa95c6b5187fb48b3ce446f99f17b",  # q4km gguf
+1963                "6b3806cafdb4303ea2638e9e08eb186067b4a46a95ddf344ccdbe56537afaf6e",  # q8km gguf
+1964            ],
+1965            layer_b3=[
+1966                "873821614080a98e1ebfe56673bc96c2ac57379720d4ad2f97e4bca317571d48",  # diffusers
+1967                "7284d2027523482af9ef47405667ca891cc518bfb6ebf1f1d4666cb0accc8cd5",
+1968                "d938ee5738c73f701760ed18acad274b074d2796123aee3f2eee1328b6c36ea4",
+1969                "c4c40056c2a77959083b5a69a1a4b205caa463ccabde057352c5c4e38b2c67b6",
+1970            ],
+1971            layer_256=[
+1972                "3c324055a1ec6eb4ee0242e344bb2b6356afcbd2e215fdd9d160cda691a72fae",
+1973                "7284d2027523482af9ef47405667ca891cc518bfb6ebf1f1d4666cb0accc8cd5",
+1974                "d938ee5738c73f701760ed18acad274b074d2796123aee3f2eee1328b6c36ea4",
+1975                "c4c40056c2a77959083b5a69a1a4b205caa463ccabde057352c5c4e38b2c67b6",
+1976            ],
+1977        ),
+1978    )
+1979    repo = "Wan-AI/Wan2.1-FLF2V-14B-720P-Diffusers"
+1980    series, comp = make_mir_tag(repo)
+1981    mir_db.add(
+1982        mir_entry(
+1983            domain="info",
+1984            arch="dit",
+1985            series=series,
+1986            comp=comp,
+1987            repo=repo,
+1988            file_256=[
+1989                "",
+1990                "",
+1991            ],
+1992            layer_b3=[
+1993                "",
+1994            ],
+1995            layer_256=[""],
+1996        ),
+1997    )
+1998    repo = "OnomaAIResearch/Illustrious-Lumina-v0.03"
+1999    mir_db.add(
+2000        mir_entry(
+2001            domain="info",
+2002            arch="dit",
+2003            series=make_mir_tag("Alpha-VLLM/Lumina-Image-2.0")[0],
+2004            comp=make_mir_tag(repo)[0],
+2005            repo=repo,
+2006            file_256=[
+2007                "dc6cffcfb0ccfca6332ddb5d2fe25bcb5f496f44b481627f48c42626156fa6a8",  # 2b 22100 ema unified fp32
+2008                "2ac549741fa1c6de2d6cd8be06abcdce52d472eeae2439f948e285258b66a214",  # 0.03 ema
+2009            ],
+2010            layer_b3=[
+2011                "a97b4a63e1e7678e8e7154fae55252267bd1f0ba76b03dba622d801644e657ac",
+2012                "aa6c1b2d1971cea3c4ed0963c8d68d4c50db683f8eab9f77f60ea2d04ed6ce5c",
+2013            ],
+2014            layer_256=[
+2015                "39086c199b9ac296dcba53461ba1e113906d91fbc1b12556d92f5cc77ca11f9f",
+2016                "e51ba2ded40f1af5ca6f78c46eed8305fbd87cd6401e9d439837e10d35cc5828",
+2017            ],
+2018        )
+2019    )
+2020    mir_db.add(
+2021        mir_entry(
+2022            domain="ops",
+2023            arch="patch",
+2024            series="hidiffusion",
+2025            comp=sdxl_series,
+2026            pkg={
+2027                0: {
+2028                    "hidiffusion": {"apply_hidiffusion": {"timesteps": "StableDiffusionXLTimesteps"}},
+2029                    "generation": {"height": 2048, "width": 2048, "eta": 1.0, "guidance_scale": 7.5, "num_inference_steps": 10},
+2030                },
+2031            },
+2032        )
+2033    )
+2034    mir_db.add(
+2035        mir_entry(
+2036            domain="ops",
+2037            arch="scheduler",
+2038            series="align-your-steps",
+2039            comp=sdxl_series,
+2040            pkg={
+2041                0: {
+2042                    "diffusers": "schedulers.scheduling_utils.AysSchedules",
+2043                    "generation": {"timesteps": "StableDiffusionXLTimesteps", "num_inference_steps": 10},
+2044                }
+2045            },
+2046        )
+2047    )
+2048    # possible mixed-type architecture?
+2049    # fusion / united / universal
+2050
+2051
+2052def add_mir_llm(mir_db: MIRDatabase):
+2053    base_arch, base_series, base_comp = tag_base_model(repo_path="facebook/chameleon-7b", class_name="ChameleonModel")
+2054    repo = "Alpha-VLLM/Lumina-mGPT-7B-1024"
+2055    series, comp = make_mir_tag(repo)
+2056    mir_db.add(
+2057        mir_entry(
+2058            domain="info",
+2059            arch=base_arch,
+2060            series=base_series,
+2061            comp=series,
+2062            repo=repo,
+2063            pkg={
+2064                0: {
+2065                    "inference_solver": {"FlexARInferenceSolver": {"precision": "bf16", "target_size": 768}},
+2066                    "generation": {"images": [], "qas": [["q1", None]], "max_gen_len": 8192, "temperature": 1.0},
+2067                },
+2068                1: {"inference_solver": "ChameleonXLLMXForConditionalGeneration"},
+2069            },
+2070            identifiers=["model.embed_tokens.weight"],
+2071            file_256=[
+2072                "6b71408a7c574d98f00114ab770ac6addc71471770456e482e7b5ec641c02345",
+2073                "1d5d8d5532bae0f32ba35d10d411e506d61e4378dc9fc338f2b1e6af2aa322ec",  # 768
+2074                "a8fe636bbee30fef06dcd8e806ffc65b2aed0ad08a07fdc62f35717d0f851be5",  # 512 multi
+2075                "6420fa13483576d46263996627ba7add2237a01f46dedd3b7750112c0cc2d95b",  # 512
+2076            ],
+2077            layer_b3=["6cd6b3caaea270feb5aff8e9fec205a27da4f48a1e740e63dc9a08f16e70a656"],
+2078            layer_256=["eaa882db6a69cf8ed0104a15b2cdbbb570a23a06ab8c8f65f4c6c21719c6ba25"],
+2079        ),
+2080    )
+2081    repo = "openai/clip-vit-large-patch14"
+2082    series, comp = make_mir_tag(repo)
+2083    mir_db.add(
+2084        mir_entry(
+2085            domain="info",
+2086            arch="vit",
+2087            series=series,
+2088            comp=comp,
+2089            repo=repo,
+2090            pkg={0: {"transformers": "CLIPTextModel"}},
+2091            identifiers=["text_model.encoder.layers.0.mlp.fc1.weight", "clip-l"],
+2092            file_256=[
+2093                "cb0cba1ead482a850532ebe5ff6b5c8d4456aee32a5228acf0a31e7d9472415e",  # long vit best
+2094                "39e79c916feca4ddf546d9fe923e664714b59ea61074f7228037d17c302f3d17",  # vit l detail improved hit gmp
+2095                "893d67a23f4693ed42cdab4cbad7fe3e727cf59609c40da28a46b5470f9ed082",  # flux/shuttle 3 aes
+2096                "778d02eb9e707c3fbaae0b67b79ea0d1399b52e624fb634f2f19375ae7c047c3",  # playground 2.5
+2097                "660c6f5b1abae9dc498ac2d21e1347d2abdb0cf6c0c0c8576cd796491d9a6cdd",  # playground 2.5 fp16
+2098                "71e183d11db0c6b6282a4d9e0abb74125edc8692393e89ed8ee5571005f35cb1",  # sd3.5 fp16
+2099                "5c3d6454dd2d23414b56aa1b5858a72487a656937847b6fea8d0606d7a42cdbc",  # sdxl diffusers
+2100                "87c1c0b0894c9e9e10b962e597e8d64dd3a3a2d372c389922b335a53c250b2ae",  # L
+2101                "bd289dd57fee86bc8816b55919a2b03f9c3c75af6025e21777325a6730872325",  # jaguar mlx
+2102                "8377b1ca9d88fe06ec483dd7b3cfc62e5e8dbf8ddd252f455e79d659fa0553c5",  # ssd-1b
+2103                "5487ea0eee9c9a9bff8abd097908d4deff3ae1fa87b3b67397f8b9538139d447",  # ssd-1b fp16
+2104                "92b998a9a64549bfa05c019bde114be6681549a0c79caee903fe30c9444d08b9",  # vega
+2105                "1e090d6a828fd92401be5f83e615fd7b4fb1f4a22e9af9040a38f602e839317c",  # vega fp16
+2106                "11807cb2522cfe99240e5ee2bbeb1ccb42cecca2215102ee872567c7773b28b9",  # flux
+2107                "d008943c017f0092921106440254dbbe00b6a285f7883ec8ba160c3faad88334",  # sd1
+2108                "77795e2023adcf39bc29a884661950380bd093cf0750a966d473d1718dc9ef4e",  # sd1 fp16
+2109                "b70c11ad5d7e9abf6109348908f599ea382f8019e1f36910bbc8ebecde936633",  # hidream i1
+2110                "fc42badf529dd83f2f7c3d20fe6bda1e22036162f37c4c668b9e130884e20561",
+2111                "e27bafa0b3029ad637ef3ace24ce1efe85b8d0dbd22e03a2e70bda6fc88963a1",  # onnx
+2112            ],
+2113            layer_b3=[
+2114                "f58a22a381f79985b6d38782f6110a52c2f319b40fdedd3b88b24945dfcbdf64",
+2115                "8faa00b8fd1dbd9286a7237df18caeb8c91af100a6813849b6bae272a01dd7b7",
+2116                "ab5bebc98299c155251a06deccde599ba0128038ee3ce021e8c59a45f58f72c0",
+2117                "c70e9d86a9dcbbbe7c269ef9dfac96ce9c96c46922577338cc1902e5fe936315",
+2118                "f285e9b7b70745df81adc8b558ec74b536b79b6fc02a453ecc61ea9d13f25f1a",
+2119                "7ab17bfa06ab8d65840997ef641f3f593d096860e20141f1eeb0169d131c1c23",
+2120                "2737d3f327e8176dbb549b9c5c4994821430a6c3b07e3bbc925d97511c802636",  # jaguar mlx q8
+2121                "58a826a4a5fe555b4df188a1ebc0d8d9c96cedae3a26ce84c247861dbb93388f",  # sd1
+2122                "1540fd8844898960e18ce8fd153e5f21a8c446bd8c4d6f536a7cf11418f02bf3",  # sd1
+2123                "c4c9caccdbec12b965d93688c521893f75e0bf9a5e0aad70a6a962b669e7b9d5",  # vega
+2124                "e43fae8d5fd1e562607da172369cc0c5ec99b834e42502e682287ff7d12baacc",  # vega fp16
+2125                "c6f79f7416a882891957b815fbdfd6edfaa253c43970b1a25ef14e217599c7bc",  # flux
+2126                "daf5e09f67ad09a909f58a01298fec0132324634cb8fca2a604c3a240c2c453f",  # jaguar mlx q8
+2127                "3f62bfb6bbde05f01435129326166c44aeb113ac0d9f735f31ed3f7dd04f6980",  # hidream i1
+2128                "22f866f3c96a92bc61e9965cf366d706db942ad047ba8cb82109edcd4e68fa40",  # sd3 turbo
+2129                "f3fa9d7a8f15741621c1fe82f8a1bcc5c601c900d947ac09fba7016615a252a5",  # shap-e
+2130            ],
+2131            layer_256=[
+2132                "48daa3d8f939972e69f044533a4312a941971c18c78255f5e555fa26faf664c1",
+2133                "60f5734a74c342be8b0011fc704e718431839790bcfdc7d7004fc39d70f7fec6",
+2134                "6e76e25b4a55dddfa2eecf4b7ab189a8148658a9f6df165c00170f6ce661033c",
+2135                "2d5249df489fec9137cc3a5e9bda499dd9b72a957ddd8e7ad4e99ff3684bad99",
+2136                "3bf085e701713ed3e79775dafea375c3e2a43659ad1ee788b1b393c0aeff9f0e",
+2137                "efb7976800692772e449c81a739339f59394886590ff3f768b0f9ddd87d2a94c",
+2138                "9b0ac8d127c6c457b2eb8c7236f18c4e4ba9e8bbf27130aa8fe854d7c3f7b1e0",
+2139                "24a9ee3d60cdde6c967f08e4b2ec7088fe1bfe308c6896e73caa874860570a5c",
+2140                "5d6d9d0cc7943eb1b8c16862bfd5bee5c3766d0df027ec837e90fac715ac2bd3",
+2141                "68fb122f7d6c3cfbef320341b2af8f5916678e36a69ed36fa8cfcb19e7d5c43d",
+2142                "11807cb2522cfe99240e5ee2bbeb1ccb42cecca2215102ee872567c7773b28b9",
+2143                "50c46cdddbe9f0162278c69b9a1f818519330e3a91b994272e19b5c789670471",  # jaguar mlx q8
+2144                "ffe1c4f55e07c2010ace7b9cf35798bb9f431bc954a32784e5acbdc16acc0364",  # hidream i1
+2145                "146ea48d234e05a934db9d8988e9a9dd86b2ac70f535eaa550ecb0ee23ec135e",  # sd3 turbo
+2146                "d97560cf9704cf71711f6121df2bf55e55a1eda4b574a6ddba074767420bc8c3",
+2147            ],
+2148        )
+2149    )
+2150    repo = "laion/CLIP-ViT-g-14-laion2B-s12B-b42K"
+2151    series, comp = make_mir_tag(repo)
+2152    mir_db.add(
+2153        mir_entry(
+2154            domain="info",
+2155            arch="vit",
+2156            series=series,
+2157            comp=comp,
+2158            repo=repo,
+2159            pkg={0: {"transformers": "CLIPTextModelWithProjection"}},
+2160            identifiers=["31.self_attn.k_proj.weight", "text_model.encoder.layers.22.mlp.fc1.weight", "clip-g"],
+2161            file_256=[
+2162                "ca18e0c67c1ef1e64cac22926266765b60688f692307ecc06283d987c5768134",  # seaart furry g
+2163                "ec310df2af79c318e24d20511b601a591ca8cd4f1fce1d8dff822a356bcdb1f4",  # modelspec sai
+2164                "fa5b2e6f4c2efc2d82e4b8312faec1a5540eabfc6415126c9a05c8436a530ef4",  # playground 2.5
+2165                "b84f413eebecbd049b72874c1df533a516510cb5a2489ae58c7e320209cf0ebe",  # ssd1b
+2166                "d3df577f6e3799c8e1bd9b40e30133710e02e8e25d0ce48cdcc790e7dfe12d6d",  # ssd1b fp16
+2167                "943a2924ee888295a156dd47089d67181d633b782337890af11ef4b15af17ec5",  # vega
+2168                "5b98e4a57a9292eeb819d67e2d2100f66f17db723cde4ecea27a7c3741160d0c",  # vega fp16
+2169                "4d6effa7a5e600cabf7528ed7234146a13ead1b2c151211d706b293a060b112a",  # hidream i1
+2170                "3a6032f63d37ae02bbc74ccd6a27440578cd71701f96532229d0154f55a8d3ff",  # modelspec sai
+2171                "162042ac6556e73f93d4172d4c67532c1cbe4dc7a6a8fa7e44dd2e3d7cbb772b",  # onnx
+2172            ],
+2173            layer_b3=[
+2174                "d754db276f2d89d2808abb7086b3b8eccee43ac521c128d21a071f3a631474a8",
+2175                "2eb93685b34719e1d1e0541d8902b0a592d95848f80657e32816cf3b152a0f31",
+2176                "e253a5cf3a6242c58037abd6b378bf0281f278e441f28dff7ca1bcfcd3cd6bd8",  # ssd1b
+2177                "16d0eec4e55b0aa63cdca4e4d36f78f66a4b1b9605ce3b1089305026f853c3d2",  # ssd1b fp16
+2178                "f606463295ecf3bae8920d3d45bb9d180793418b3d08c3e84d4c4135c7dc2aa5",  # vega
+2179                "7060993a5eb32d94d1ea8aef7a7301e7be73b199c639c63f8f7cfbfcd2abf10e",  # vega fp16
+2180                "b92af95334c657371af6051a91374a41b5455907fa6622bb66a8c112dc511600",  # hidream i1
+2181            ],
+2182            layer_256=[
+2183                "270e998633eb22145100a3889a62ca270d5080654735e5ff8dda09a7c233af8d",
+2184                "df18800c2a9d9318c4323d991a0fb24a6a9afceb41bea203812f60517c301536",
+2185                "4c228b104f6b9b383e0808c9baa1998957f5125d8f90a4d98c1a86e71edd72dc",  # ssd1b
+2186                "f7fc81d8b5ae91ec28a5106ecc0d067be9a94fd3f394c4aa4686ed131ce5a5b3",  # ssd1b fp16
+2187                "61ab42bd5c0fcb9fd3db1d4014cb844ccae8dc17fd69a108cf077a573d092946",  # vega
+2188                "6c64e36cdda3bec7067e94b05619f882f5d31070792acaadac60ddbef580453a",  # vega fp16
+2189                "43c9e64995b485a7f128771c48defce128640df28e65c7f79537d472f43ebe46",  # hidream i1
+2190            ],
+2191        )
+2192    )
+2193    repo = "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
+2194    series, comp = make_mir_tag(repo)
+2195    mir_db.add(
+2196        mir_entry(
+2197            domain="info",
+2198            arch="vit",
+2199            series=series,
+2200            comp=comp,
+2201            repo=repo,
+2202            pkg={
+2203                0: {"transformers": "CLIPModel"},
+2204            },
+2205            file_256=[
+2206                "036e6e2bd49697511f4f8b8cb5ee465f93025f7a69a145eadeb9a881ace9b18d",
+2207                "0084e75319a50ad85ef45377bad5bc38f2f58824459eb690048d51c9f8863be5",  # open clip
+2208                "64a7ef761bfccbadbaa3da77366aac4185a6c58fa5de5f589b42a65bcc21f161",  # wan sai
+2209            ],
+2210            layer_b3=[
+2211                "227f26ed63120b9034f4a0c90b6b37eede721a8260f2c1e8f7ea3ccc0d109e7e",
+2212                "3a38ffd1b60499cf2f451f3065079ff26efb9190a86f23ad1c8d993bbeb9af05",  # open clip
+2213                "ce06cf1fd684269ee96631b2bf9334c6ecde6a84a55760dfa0d9d2a6411f28e4",  # wan sai
+2214            ],
+2215            layer_256=[
+2216                "130a94ed12569e099196a6ca27388181922e20148dee5bcb58c5e309acfc2352",
+2217                "cfdbd3fd2b90b64ba12d395a62dd7c3c3ea3e811f0a54593e91bae6516ca5061",  # open clip
+2218                "9125ce5970c649d6f9368c25493d3aaa6b41e224d4cc427e955115f7b7e53d1c",  # wan sai
+2219            ],
+2220        )
+2221    )
+2222    repo = "zai-org/chatglm3-6b"  # formerly THUDM
+2223    series, comp = make_mir_tag(repo)
+2224    mir_db.add(
+2225        mir_entry(
+2226            domain="info",
+2227            arch="aet",
+2228            series=series,
+2229            comp=comp,
+2230            repo=repo,
+2231            pkg={
+2232                0: {"transformers": "AutoModel"},
+2233            },
+2234            file_256=[
+2235                "0054d03310248928fdabdeef3fdc753170218dc49a1e9eb5f98323e27683f654",  # kolors
+2236                "b1052386eac358a18add3d0f92521c85ab338979da8eeb08a6499555b857f80d",
+2237            ],
+2238            layer_b3=[
+2239                "a45dfba6a9fa8739777c76deb845fc9589b40f88670d3ce4661646a7b7b1d481",  # kolors
+2240            ],
+2241            layer_256=[
+2242                "174924fd7a07f370bb6fcd1ad07a73eecb7de901f15eefb80f420c1042c47d44",  # kolors
+2243            ],
+2244        )
+2245    )
+2246    base_arch, base_series, base_comp = tag_base_model(repo_path="Qwen/Qwen2-7B-beta", class_name="Qwen2Model")
+2247    repo = "ByteDance-Seed/BAGEL-7B-MoT"
+2248    series, comp = make_mir_tag(repo)
+2249    mir_db.add(
+2250        mir_entry(
+2251            domain="info",
+2252            arch=base_arch,
+2253            series=base_series,
+2254            comp=series,
+2255            repo=repo,
+2256            pkg={0: {"Bagel": "app"}},
+2257        )
+2258    )
+2259
+2260
+2261def add_mir_audio(mir_db: MIRDatabase):
+2262    """Create MIR audio modality entries"""
+2263    repo = "facebook/audiogen-medium"
+2264    series, comp = make_mir_tag(repo)
+2265    mir_db.add(
+2266        mir_entry(
+2267            domain="info",
+2268            arch="art",
+2269            series=series,
+2270            comp=comp,
+2271            repo=repo,
+2272            pkg={
+2273                0: {
+2274                    "audiocraft": "models.AudioGen",
+2275                    "generation": {"duration": 5},
+2276                    "stage_2": {
+2277                        "audiocraft": ".data.audioaudio_write",
+2278                        "generation": {"strategy": "loudness", "loudness_compressor": True},
+2279                    },
+2280                }
+2281            },
+2282        )
+2283    )
+2284    repo = "parler-tts/parler-tts-tiny-v1"
+2285    series, comp = make_mir_tag(repo)
+2286    mir_db.add(
+2287        mir_entry(
+2288            domain="info",
+2289            arch="art",
+2290            series=series,
+2291            comp=comp,
+2292            repo=repo,
+2293            pkg={
+2294                0: {
+2295                    "parler_tts": "ParlerTTSForConditionalGeneration",
+2296                    "generation": {"return_tensors": "pt"},
+2297                },
+2298            },
+2299        )
+2300    )
+2301    repo = "Zuellni/snac-24khz-ST"
+2302    series, comp = make_mir_tag(repo)
+2303    (
+2304        mir_db.add(
+2305            mir_entry(
+2306                domain="info",
+2307                arch="gan",
+2308                series=series,
+2309                comp=comp,
+2310                repo=repo,
+2311                pkg={
+2312                    0: {
+2313                        "snac": "SNAC",
+2314                    },
+2315                    "1": {
+2316                        "mlx_audio": "tts.generate.generate_audio",
+2317                    },
+2318                },
+2319                file_256=["e61ae2f638f56ee07a37592cd5a6a9e7d642560ddc78a76ee4a7f96d6922f1be", "973ee1be4032319fd9685ec54eee1b93e79c7bc98c786e67f17c04669714f11d"],
+2320                layer_b3=["18307b00460a64cc4893f9061592ce8d7e15b70fc54065cc8ae0f0155381ec46", "d599b1bb36dee3cee4674b7922fcd69e5ec05b74413f611d21cfdfdf8f9b6119"],
+2321                layer_256=["35ba9aa1feb931010559a178fcac243673d2efdd1396a4b69d406c9853a88300", "5a22c4707ed6c928043f23b59f2d102a579db3a9af41cf6e60d7c3958f182841"],
+2322            )
+2323        ),
+2324    )
+2325    repo = "parler-tts/parler-tts-large-v1"
+2326    series, comp = make_mir_tag(repo)
+2327    mir_db.add(
+2328        mir_entry(
+2329            domain="info",
+2330            arch="art",
+2331            series=series,
+2332            comp=comp,
+2333            repo=repo,
+2334            pkg={
+2335                0: {
+2336                    "parler_tts": "ParlerTTSForConditionalGeneration",
+2337                    "generation": {"return_tensors": "pt"},
+2338                },
+2339            },
+2340        )
+2341    )
+2342    repo = "hexgrad/Kokoro-82M"
+2343    series, comp = make_mir_tag(repo)
+2344    mir_db.add(
+2345        mir_entry(
+2346            domain="info",
+2347            arch="gan",
+2348            series=series,
+2349            comp=comp,
+2350            repo=repo,
+2351            pkg={
+2352                0: {"kokoro": "KPipeline"},
+2353                1: {
+2354                    "mlx_audio": "tts.generate.generate_audio",
+2355                    "generation": {"audio_format": "wav", "join_audio": True, "verbose": False},
+2356                },
+2357            },
+2358            file_256=[
+2359                "5a5cb3d87478f2e74dfca208ee52209ccfce024095e137097fd276026506e45f",
+2360                "496dba118d1a58f5f3db2efc88dbdc216e0483fc89fe6e47ee1f2c53f18ad1e4",
+2361            ],
+2362            layer_b3=[
+2363                "3e9b5017cfe67a7804ac717b18b6add42ffc0bd3353490df2bcc520eaaef79b6",
+2364                "379660a87a64524bab69a267e3d9580f04b5eec4f7e3fbd48c6597d164d9b17d",  # safetensors
+2365                "997f154f5a78879ef3ba1a1556977c40b28b9c21076b8f583f752c57ecc36e93"  # pytorch
+2366                "2dc3dba29452b85ea85266084a6248f9e0efe642d5f75b43e64f25b9f2837f92",
+2367            ],
+2368            layer_256=[
+2369                "dbedf0e2115aa309b92689f86534be4a77b91d7900365e1717879fbb19b849f6",
+2370                "2c68574571b3f9229e015a909788116ea2251142e29c1bd5c687863192124e8b",
+2371            ],
+2372        )
+2373    )
+2374    repo = "freddyaboulton/silero-vad"
+2375    series, comp = make_mir_tag(repo)
+2376    mir_db.add(
+2377        mir_entry(
+2378            domain="info",
+2379            arch="stst",
+2380            series=series,
+2381            comp=comp,
+2382            repo=repo,
+2383            pkg={
+2384                0: {
+2385                    "onnx": "onnx",
+2386                },
+2387                1: {
+2388                    "mlx_audio": "tts.generate.generate_audio",
+2389                    "generation": {"audio_format": "wav", "join_audio": True, "verbose": False},
+2390                },
+2391            },
+2392            file_256=["591f853590d11ddde2f2a54f9e7ccecb2533a8af7716330e8adfa6f3849787a9"],
+2393            layer_b3=[
+2394                "41ca5931452b3ffee588c6c7e5bd327c4e914141604eaf3fd05f4a790ac83bb2",
+2395                "7dc736cd5d840182792bde4edfbf5ddc5aeaf16826a9c72d1ba8166c1e3fab9b",
+2396                "6e2c1bdbad74f56663ffb5710c7cb849a2b91ba331d81acdba47a21f69107434",  # onnx
+2397                "ab5ff443aece9171af5e7603d0b4309d3ecc934e3940ccedefff10f0b54b931e",  # onnx vad
+2398                # "7939427700c3b4d91428a490bde1a6d893f63ee5d79b86f68de9e89c7094d3e7"  # onnx # <- clip-g ?? unet? inaccurate test at layer level
+2399            ],
+2400            layer_256=[
+2401                "2ffef1834d5fe14ad8db58fc78d769d5dc38dda5eddbfc396786f74b326215fd",
+2402                # "94ea015f5f7f65b1d8e80f7d52859535e7761d7ed2752e24d57a8d9d9da96672", # onnx lose reliability with layer search apparently
+2403            ],
+2404        ),
+2405    )
+2406    repo = "facebook/wav2vec2-conformer-rope-large-960h-ft"
+2407    series, comp = make_mir_tag(repo)
+2408    mir_db.add(
+2409        mir_entry(
+2410            domain="info",
+2411            arch="stst",
+2412            series=series,
+2413            comp=comp,
+2414            repo=repo,
+2415            pkg={
+2416                0: {
+2417                    "transformers": "Wav2Vec2ConformerForCTC",
+2418                },
+2419            },
+2420            file_256=["97bb9761fb71ec1225100bc81ccf7d002e0d0ba3d0604c1fd2dbda7d7d491f1d"],
+2421            layer_b3=["6c9c5642aa8dce62bcb3eb577bc519619a2d868005c767c5e65371c583a8a8eb"],
+2422            layer_256=["1afcfda68307a75caa1a1c4456cf97e20c7914e8aba828006e9fe17e8675a79d"],
+2423        ),
+2424    )
+2425    repo = "canopylabs/orpheus-3b-0.1-ft"
+2426    series, comp = make_mir_tag(repo)
+2427    mir_db.add(
+2428        mir_entry(
+2429            domain="info",
+2430            arch="art",
+2431            series=series,
+2432            comp=comp,
+2433            repo=repo,
+2434            pkg={
+2435                0: {
+2436                    "orpheus_tts": "OrpheusModel",
+2437                    "generation": {"max_model_len": 2048},
+2438                },
+2439                1: {
+2440                    "mlx_audio": "tts.generate.generate_audio",
+2441                    "generation": {"audio_format": "wav", "join_audio": True, "verbose": False},
+2442                },
+2443            },
+2444        )
+2445    )
+2446    repo = "OuteAI/OuteTTS-0.3-1B"
+2447    series, comp = make_mir_tag(repo)
+2448    mir_db.add(
+2449        mir_entry(
+2450            domain="info",
+2451            arch="art",
+2452            series=series,
+2453            comp=comp,
+2454            repo=repo,
+2455            pkg={
+2456                0: {"outetts": "InterfaceHF"},
+2457                1: {
+2458                    "mlx_audio": "tts.generate.generate_audio",
+2459                    "generation": {"audio_format": "wav", "join_audio": True, "verbose": False},
+2460                },
+2461            },
+2462        )
+2463    )
+2464
+2465
+2466def add_mir_lora(mir_db: MIRDatabase):
+2467    """Create MIR lora entries"""
+2468    mir_db.add(
+2469        mir_entry(
+2470            domain="info",
+2471            arch="lora",
+2472            series="dmd",
+2473            comp=sdxl_series,
+2474            repo="tianweiy/DMD2",
+2475            pkg={
+2476                0: {
+2477                    "diffusers": {"load_lora_weights": {}},
+2478                    "generation": {"num_inference_steps": 4, "guidance_scale": 0, "timesteps": [999, 749, 499, 249]},
+2479                    "scheduler": {"ops.scheduler.lcm": ""},
+2480                }
+2481            },
+2482            file_256=[
+2483                "b3d9173815a4b595991c3a7a0e0e63ad821080f314a0b2a3cc31ecd7fcf2cbb8",
+2484                "a374289e9446d7f14d2037c4b3770756b7b52c292142a691377c3c755010a1bb",
+2485            ],
+2486        )
+2487    )
+2488    mir_db.add(
+2489        mir_entry(
+2490            domain="info",
+2491            arch="lora",
+2492            series="dpo",
+2493            comp=sdxl_series,
+2494            repo="radames/sdxl-DPO-LoRA",
+2495            pkg={
+2496                0: {
+2497                    "diffusers": {"load_lora_weights": {}},
+2498                    "generation": {"guidance_scale": 7.5, "num_inference_steps": 4},
+2499                    "scheduler": {"ops.scheduler.dpm": {"algorithm_type": "sde-dpmsolver++", "use_karras_sigmas": True, "order": 2}},
+2500                },
+2501            },
+2502            file_256=[
+2503                "666f71a833fc41229ec7e8a264fb7b0fcb8bf47a80e366ae7486c18f38ec9fc0",
+2504                "6b1dcbfb234d7b6000948b5b95ccebc8f903450ce2ba1b50bc3456987c9087ad",
+2505            ],
+2506        )
+2507    )
+2508    mir_db.add(
+2509        mir_entry(
+2510            domain="info",
+2511            arch="lora",
+2512            series="flash",
+2513            comp=sdxl_series,
+2514            repo="jasperai/flash-sdxl",
+2515            pkg={
+2516                0: {
+2517                    "diffusers": {"load_lora_weights": {}},
+2518                    "scheduler": "ops.scheduler.lcm",
+2519                }
+2520            },
+2521            file_256=["afe2ca6e27c4c6087f50ef42772c45d7b0efbc471b76e422492403f9cae724d7"],
+2522        ),
+2523    )
+2524    mir_db.add(
+2525        mir_entry(
+2526            domain="info",
+2527            arch="lora",
+2528            series="flash",
+2529            comp="pixart-alpha",
+2530            repo="jasperai/flash-pixart",
+2531            pkg={
+2532                0: {"diffusers": {"load_lora_weights": {}}},
+2533            },
+2534            file_256=["99ef037fe3c1fb6d6bbefdbb85ad60df434fcc0577d34c768d752d60cf69681b"],
+2535        )
+2536    )
+2537    mir_db.add(
+2538        mir_entry(
+2539            domain="info",
+2540            arch="lora",
+2541            series="flash",
+2542            comp=sd3_series,
+2543            repo="jasperai/flash-sd3",
+2544            pkg={
+2545                0: {"diffusers": {"load_lora_weights": {}}},
+2546            },
+2547            file_256=["85fce13c36e3739aa42930f745eb9fceb6c53d53fb17e2a687e3234c1a58ee15"],
+2548        )
+2549    )
+2550    mir_db.add(
+2551        mir_entry(
+2552            domain="info",
+2553            arch="lora",
+2554            series="flash",
+2555            comp=sd1_series,
+2556            repo="jasperai/flash-sd",
+2557            pkg={
+2558                0: {"diffusers": {"load_lora_weights": {}}, "generation": {"num_inference_steps": 4, "guidance_scale": 0}},
+2559            },
+2560            file_256=["99353444c1a0f40719a1b3037049dbd24800317979a73c312025c05af3574a5f"],
+2561        ),
+2562    )
+2563    mir_db.add(
+2564        mir_entry(
+2565            domain="info",
+2566            arch="lora",
+2567            series="hyper",
+2568            comp=sdxl_series,
+2569            repo="ByteDance/Hyper-SD",
+2570            pkg={0: {"diffusers": {"load_lora_weights": {"fuse": 1.0}}}},
+2571            file_256={
+2572                "0b97f447b5878323a28fbe7c51ba7acebd21f4d77552ba77b04b11c8911825b6": {"num_inference_steps": 12},
+2573                "55b51334c85061afff5eff7c550b61963c8b8607a5868bbe4f26db49374719b1": {"num_inference_steps": 8},
+2574                "c912df184c5116792d2c604d26c6bc2aa916685f4a793755255cda1c43a3c78a": {"num_inference_steps": 1, "guidance_scale": 0.0},
+2575                "69b25c0187ced301c3603c599c0bc509ac99b8ac34db89a2aecc3d5f77a35187": {"num_inference_steps": 2, "guidance_scale": 0.0},
+2576                "12f81a27d00a751a40d68fd15597091896c5a90f3bd632fb6c475607cbdad76e": {"num_inference_steps": 4, "guidance_scale": 0.0},
+2577                "ca689190e8c46038550384b5675488526cfe5a40d35f82b27acb75c100f417c1": {"num_inference_steps": 8, "guidance_scale": 0.0},
+2578            },
+2579        ),
+2580    )
+2581    mir_db.add(
+2582        mir_entry(
+2583            domain="info",
+2584            arch="lora",
+2585            series="hyper",
+2586            comp=dev_series,
+2587            repo="ByteDance/Hyper-SD",
+2588            pkg={0: {"diffusers": {"load_lora_weights": {"fuse": 0.125}}}},
+2589            file_256={
+2590                "6461f67dfc1a967ae60344c3b3f350877149ccab758c273cc37f5e8a87b5842e": {"num_inference_steps": 16, "guidance_scale": 0.0},
+2591                "e0ab0fdf569cd01a382f19bd87681f628879dea7ad51fe5a3799b6c18c7b2d03": {"num_inference_steps": 8, "guidance_scale": 0.0},
+2592            },
+2593        ),
+2594    )
+2595    mir_db.add(
+2596        mir_entry(
+2597            domain="info",
+2598            arch="lora",
+2599            series="hyper",
+2600            comp=sd3_series,
+2601            repo="ByteDance/Hyper-SD",
+2602            pkg={0: {"diffusers": {"load_lora_weights": {"fuse": 0.125}}}},
+2603            file_256={
+2604                "5b4d0b99d58deb811bdbbe521a06f4dbf56a2e9148ff3211c594e0502b656bc9": {"num_inference_steps": 16},
+2605                "0ee4e529abd17b06d4295e3bb91c0d4ddae393afad86b2b43c4f5eeb9e401602": {"num_inference_steps": 4},
+2606                "fc6a3e73e14ed11e21e4820e960d7befcffe7e333850ada9545f239e9aa6027e": {"num_inference_steps": 8},
+2607            },
+2608        ),
+2609    )
+2610    mir_db.add(
+2611        mir_entry(
+2612            domain="info",
+2613            arch="lora",
+2614            series="hyper",
+2615            comp=sd1_series,
+2616            repo="ByteDance/Hyper-SD",
+2617            pkg={0: {"diffusers": {"load_lora_weights": {}}}},
+2618            file_256={
+2619                "64b98437383537cd968fda6f87a05c33160ece9c79ff4757949a1e212ff78361": {"num_inference_steps": 12},
+2620                "f6123d5b950d5250ab6c33600e27f4dcf71b3099ebf888685e01e9e8117ce482": {"num_inference_steps": 8},
+2621                "a04fd9a535c1e56d38f7590ee72a13fd5ca0409853b4fff021e5a9482cf1ca3b": {"num_inference_steps": 1, "guidance_scale": 0.0},
+2622                "2f26dcc1d883feb07557a552315baae2ca2a04ac08556b08a355a244547e8c3a": {"num_inference_steps": 2, "guidance_scale": 0.0},
+2623                "c5dd058616461ed5053e2b14eec4dbe3fa0eea3b13688642f6d6c80ea2ba5958": {"num_inference_steps": 4, "guidance_scale": 0.0},
+2624                "91fc3186236e956d64dbb4357f2e120c69b968b78af7d2db9884a5ca74d3cd13": {"num_inference_steps": 8, "guidance_scale": 0.0},
+2625            },
+2626        )
+2627    )
+2628    mir_db.add(
+2629        mir_entry(
+2630            domain="info",
+2631            arch="lora",
+2632            series="lcm",
+2633            comp=sdxl_series,
+2634            repo="latent-consistency/lcm-lora-sdxl",
+2635            pkg={
+2636                0: {
+2637                    "diffusers": {"load_lora_weights": {"fuse": 1.0}},
+2638                    "scheduler": {"ops.scheduler.lcm": {"timestep_spacing": "trailing"}},
+2639                    "generation": {"num_inference_steps": 8},
+2640                },
+2641            },
+2642            file_256=["a764e6859b6e04047cd761c08ff0cee96413a8e004c9f07707530cd776b19141"],
+2643        )
+2644    )
+2645    mir_db.add(
+2646        mir_entry(
+2647            domain="info",
+2648            arch="lora",
+2649            series="lcm",
+2650            comp=ssd_series,
+2651            repo="latent-consistency/lcm-lora-ssd-1b",
+2652            pkg={0: {"diffusers": {"load_lora_weights": {}}, "generation": {"num_inference_steps": 8}}},
+2653            file_256=["7adaaa69db6f011058a19fd1d5315fdf19ef79fcd513cdab30e173833fd5c59b"],
+2654        ),
+2655    )
+2656    mir_db.add(
+2657        mir_entry(
+2658            domain="info",
+2659            arch="lora",
+2660            series="lcm",
+2661            comp=vega_series,
+2662            repo="segmind/Segmind-VegaRT",
+2663            pkg={0: {"diffusers": {"load_lora_weights": {}}, "gen_kwargs": {"num_inference_steps": 8}}},
+2664            file_256=["9b6e8cd833fa205eaeeed391ca623a6f2546e447470bd1c5dcce3fa8d2f26afb"],
+2665        ),
+2666    )
+2667    mir_db.add(
+2668        mir_entry(
+2669            domain="info",
+2670            arch="lora",
+2671            series="lcm",
+2672            comp=sd1_series,
+2673            repo="latent-consistency/lcm-lora-sdv1-5",
+2674            pkg={0: {"diffusers": {"load_lora_weights": {}}, "generation": {"num_inference_steps": 8}}},
+2675            file_256=["8f90d840e075ff588a58e22c6586e2ae9a6f7922996ee6649a7f01072333afe4"],
+2676        ),
+2677    )
+2678    mir_db.add(
+2679        mir_entry(
+2680            domain="info",
+2681            arch="lora",
+2682            series="lightning",
+2683            comp=sdxl_series,
+2684            repo="ByteDance/SDXL-Lightning",
+2685            pkg={0: {"diffusers": {"load_lora_weights": {}}, "generation": {"num_inference_steps": 4, "guidance_scale": 0}}},
+2686        ),
+2687    )
+2688    mir_db.add(
+2689        mir_entry(
+2690            domain="info",
+2691            arch="lora",
+2692            series="pcm",
+2693            comp=sdxl_series,
+2694            repo="wangfuyun/PCM_Weights",
+2695            pkg={0: {"diffusers": {"load_lora_weights": {}}}},
+2696            file_256={
+2697                "0365f6107250a4fed1b83e8ae6a070065e026a2ba54bff65f55a50284232bbe6": {"num_inference_steps": 4, "guidance_scale": 0.0},
+2698                "04ea827435d5750e63d113dc509174b4f6e8a069ff8f91970c3d25299c10b1f8": {"num_inference_steps": 16},
+2699                "7eb353b2abcaabab6251ba4e17d6cbe2e763feb0674b0f950555552212b44621": {"num_inference_steps": 16},
+2700                "a85cf70ac16ed42011630a5cd6b5927722cb7c40a2107eff85e2670f9a38c893": {"num_inference_steps": 4},  # float16
+2701                "9f7f13bb019925eacd89aeff678e4fd831f7b60245b986855dff6634aee4eba9": {"num_inference_steps": 4},
+2702                "3b9c970a3e4c0e182931e71b3f769c1956f16c6b06db98b4d67236790d4d0b1d": {"num_inference_steps": 8},
+2703                "7f04ba8911b4c25ef2c7cbf74abcb6daa3b4f0e4bc6a03896bdae7601f2f180b": {"num_inference_steps": 8},
+2704                "13fb038025ce9dad93b8ee1b67fc81bac8affb59a77b67d408d286e0b0365a1d": {"num_inference_steps": 16, "guidance_scale": 0.0},
+2705                "3442eff271aa3b60a094fd6f9169d03e49e4051044a974f6fcf690507959191f": {"num_inference_steps": 16, "guidance_scale": 0.0},
+2706                "242cbe4695fe3f2e248faa71cf53f2ccbf248a316973e4b2f38ab9e34f35a5ab": {"num_inference_steps": 2, "guidance_scale": 0.0},
+2707                "e1f600491bb8e0cd94f41144321e44fdb2cb346447f31e71f6e53f1c24cccfbf": {"num_inference_steps": 2, "guidance_scale": 0.0},
+2708                "d0bf40a7f280829195563486bec7253f043a06b1f218602b20901c367641023e": {"num_inference_steps": 4, "guidance_scale": 0.0},
+2709                "212150d7953627fb89df99aad579d6763645a1cb2ef26b19fee8b398d5e5ff4d": {"num_inference_steps": 4, "guidance_scale": 0.0},
+2710                "e80fcf46d15f4d3821d3d9611bdb3022a4a8b647b2536833b168d317a91e4f74": {"num_inference_steps": 8, "guidance_scale": 0.0},
+2711                "56ed9dc9f51f4bb0d6172e13b7947f215c347fc0da341c8951b2c12b9507d09e": {"num_inference_steps": 8, "guidance_scale": 0.0},
+2712            },
+2713        )
+2714    )
+2715    mir_db.add(
+2716        mir_entry(
+2717            domain="info",
+2718            arch="lora",
+2719            series="pcm",
+2720            comp=sd1_series,
+2721            repo="wangfuyun/PCM_Weights",
+2722            pkg={0: {"diffusers": {"load_lora_weights": {}}}},
+2723            file_256={
+2724                "b80b27dd6504f1c3a7637237dda86bc7e26fa5766da30c4fc853c0a1d46bad31": {"num_inference_steps": 4, "guidance_scale": 0.0},
+2725                "8f605ffde3616592deb37ed8c6bacb83fe98963c1fd0883c2a4f93787098aa45": {"num_inference_steps": 16},
+2726                "fa6acb94f11dba3bf4120af5a12e3c88cd2b9572d43ec1a6fb04eede9f32829e": {"num_inference_steps": 4},
+2727                "bff3d4499718b61455b0757b5f8d98fe23e73a768b538c82ecf91c693b69dbcd": {"num_inference_steps": 8},
+2728                "c7ac2fa3df3a5b7080ebe63f259ab13630014f104c93c3c706d77b05cc48506b": {"num_inference_steps": 16, "guidance_scale": 0.0},
+2729                "4c5f27a727d12146de4b1d987cee3343bca89b085d12b03c45297af05ce88ef4": {"num_inference_steps": 2, "guidance_scale": 0.0},
+2730                "29278bc86274fdfc840961e3c250758ff5e2dc4666d940f103e78630d5b879d3": {"num_inference_steps": 4, "guidance_scale": 0.0},
+2731                "41a7f0b966d18f643d16c4401f0b5ef6b9ef7362c20e17128322f17874709107": {"num_inference_steps": 8, "guidance_scale": 0.0},
+2732            },
+2733        )
+2734    )
+2735    mir_db.add(
+2736        mir_entry(
+2737            domain="info",
+2738            arch="lora",
+2739            series="pcm",
+2740            comp=sd3_series,
+2741            repo="wangfuyun/PCM_Weights",
+2742            pkg={0: {"diffusers": {"load_lora_weights": {}}}},
+2743            file_256={
+2744                "8a45878ecc34e53855fe21146cb6ef32682053b7c4eacc013be89fb08c4c19d8": {"num_inference_steps": 2, "guidance_scale": 1.2},
+2745                "9444a5cead551c56c4d1c455ce829ba9f96f01fbcca31294277e0862a6a15b76": {"num_inference_steps": 4, "guidance_scale": 1.2},
+2746                "e365902c208cbc0456ca5e7c41a490f637c15f3f7b98691cbba21f96a8c960b4": {"num_inference_steps": 4, "guidance_scale": 1.2},
+2747                "3550fa018cd0b60d9e36ac94c31b30f27e402d3855ed63e47668bb181b35a0ad": {"num_inference_steps": 4, "guidance_scale": 1.2},
+2748            },
+2749        )
+2750    )
+2751    mir_db.add(
+2752        mir_entry(
+2753            domain="info",
+2754            arch="lora",
+2755            series="slam",
+2756            comp=sdxl_series,
+2757            repo="alimama-creative/slam-lora-sdxl",
+2758            pkg={
+2759                0: {
+2760                    "diffusers": {"load_lora_weights": {}},
+2761                    "scheduler": {"ops.scheduler.lcm": {"timestep_spacing": "trailing"}},
+2762                    "generation": {"num_inference_steps": 4, "guidance_scale": 1},
+2763                }
+2764            },
+2765            file_256=["22569a946b0db645aa3b8eb782c674c8e726a7cc0d655887c21fecf6dfe6ad91"],
+2766        )
+2767    )
+2768    mir_db.add(
+2769        mir_entry(
+2770            domain="info",
+2771            arch="lora",
+2772            series="slam",
+2773            comp=sd1_series,
+2774            repo="alimama-creative/slam-sd1.5",
+2775            pkg={0: {"diffusers": {"load_lora_weights": {}}}},
+2776        )
+2777    )
+2778    mir_db.add(
+2779        mir_entry(
+2780            domain="info",
+2781            arch="lora",
+2782            series="spo",
+2783            comp=sdxl_series,
+2784            repo="SPO-Diffusion-Models/SPO-SDXL_4k-p_10ep_LoRA",
+2785            pkg={0: {"diffusers": {"load_lora_weights": {}}, "generation": {"guidance_scale": 5.0}}},
+2786            file_256=["0b9896f30d29daa5eedcfc9e7ad03304df6efc5114508f6ca9c328c0b4f057df"],
+2787        ),
+2788    )
+2789    mir_db.add(
+2790        mir_entry(
+2791            domain="info",
+2792            arch="lora",
+2793            series="spo",
+2794            comp=sd1_series,
+2795            repo="SPO-Diffusion-Models/SPO-SD-v1-5_4k-p_10ep_LoRA",
+2796            pkg={0: {"diffusers": {"load_lora_weights": {}}, "generation": {"guidance_scale": 7.5}}},
+2797            file_256=["1be130c5be2de0beacadd3bf0bafe3bedd7e7a380729932a1e369fb29efa86f4"],
+2798        ),
+2799    )
+2800    mir_db.add(
+2801        mir_entry(
+2802            domain="info",
+2803            arch="lora",
+2804            series="tcd",
+2805            comp=sdxl_series,
+2806            repo="h1t/TCD-SDXL-LoRA",
+2807            pkg={
+2808                0: {
+2809                    "diffusers": {"load_lora_weights": {}},
+2810                    "generation": {"num_inference_steps": 4, "guidance_scale": 0, "eta": 0.3},
+2811                    "scheduler": {"ops.scheduler.tcd": {}},
+2812                }
+2813            },
+2814            file_256=["2c777bc60abf41d3eb0fe405d23d73c280a020eea5adf97a82a141592c33feba"],
+2815        ),
+2816    )
+2817    mir_db.add(
+2818        mir_entry(
+2819            domain="info",
+2820            arch="lora",
+2821            series="tcd",
+2822            comp=sd1_series,
+2823            repo="h1t/TCD-SD15-LoRA",
+2824            pkg={0: {"diffusers": {"load_lora_weights": {}}}},
+2825            file_256=["eaecb24a1cda4411eab67275b1d991071216ac93693e8fa0c9226c9df0386232"],
+2826            layer_b3=["90158259812a89beb8874216009c799f420334aac49bbf4fa1bf0ebf4bbd256b"],
+2827            layer_256=["e9825b81bca684126ac3cc8867d2ebc655f74268bc26bea4e4b7e58a52ad6c75"],
+2828        )
+2829    )
+2830    mir_db.add(
+2831        mir_entry(
+2832            domain="info",
+2833            arch="lora",
+2834            series="turbo",
+2835            comp=sdxl_series,
+2836            file_256=["a599c42a9f4f7494c7f410dbc0fd432cf0242720509e9d52fa41aac7a88d1b69"],
+2837        )
+2838    )
+2839    mir_db.add(
+2840        mir_entry(
+2841            domain="info",
+2842            arch="lora",
+2843            series="turbo",
+2844            comp=dev_series,
+2845            repo="alimama-creative/FLUX.1-Turbo-Alpha",
+2846            pkg={
+2847                0: {
+2848                    "diffusers": {"load_lora_weights": {"fuse": 0.125}},
+2849                    "generation": {"guidance_scale": 3.5, "num_inference_steps": 8, "max_sequence_length": 512},
+2850                }
+2851            },
+2852            file_256=["77f7523a5e9c3da6cfc730c6b07461129fa52997ea06168e9ed5312228aa0bff"],
+2853        )
+2854    )
+2855    mir_db.add(
+2856        mir_entry(
+2857            domain="info",
+2858            arch="lora",
+2859            series="turbo",
+2860            comp=sd3_series,
+2861            repo="tensorart/stable-diffusion-3.5-medium-turbo",
+2862            pkg={0: {"diffusers": {"load_lora_weights": {"fuse": 1.0}}, "scheduler": {"ops.scheduler.flow-match": {"shift": 5}}}},
+2863            file_256={"bdcbdfa3ec8ed838b77b1020eea3bc7917a2d42573688a034feb921fde8b1858": {"num_inference_steps": "4"}},
+2864        )
+2865    )
+2866    mir_db.add(
+2867        mir_entry(
+2868            domain="info",
+2869            arch="lora",
+2870            series="turbo",
+2871            comp=sd3_series,
+2872            repo="tensorart/stable-diffusion-3.5-large-TurboX",
+2873            pkg={0: {"diffusers": {"load_lora_weights": {"fuse": 1.0}}, "scheduler": {"ops.scheduler.flow-match": {"shift": 5}}}},
+2874            file_256={"fae59d1b749c0d14a8fd4c68cc94eaac92876cee7b91fa75cf8fde3160e09548": {"num_inference_steps": "8"}},
+2875        )
+2876    )
+2877
+2878
+2879def add_mir_vae(mir_db: MIRDatabase):
+2880    """Create MIR VAE missing from the database"""
+2881    mir_db.add(
+2882        mir_entry(
+2883            domain="info",
+2884            arch="vae",
+2885            series="tae",
+2886            comp=sd3_series,
+2887            repo="madebyollin/taesd3",
+2888            pkg={0: {"diffusers": "AutoencoderTiny"}},
+2889            file_256=["6f79c1397cb9ce1dac363722dbe70147aee0ccca75e28338f8482fe515891399"],
+2890        )
+2891    )
+2892    mir_db.add(
+2893        mir_entry(
+2894            domain="info",
+2895            arch="vae",
+2896            series="tae",
+2897            comp=sdxl_series,
+2898            repo="madebyollin/taesdxl",
+2899            pkg={0: {"diffusers": "AutoencoderTiny"}},
+2900            file_256=["ff4824aca94dd6111e0340fa749347fb74101060d9712cb5ef1ca8f1cf17502f"],
+2901        )
+2902    )
+2903    mir_db.add(
+2904        mir_entry(
+2905            domain="info",
+2906            arch="vae",
+2907            series="tae",
+2908            comp=sd1_series,
+2909            repo="madebyollin/taesd",
+2910            pkg={0: {"diffusers": "AutoencoderTiny"}},
+2911            file_256=["db169d69145ec4ff064e49d99c95fa05d3eb04ee453de35824a6d0f325513549"],
+2912        )
+2913    )
+2914    mir_db.add(
+2915        mir_entry(
+2916            domain="info",
+2917            arch="vae",
+2918            series="tae",
+2919            comp=dev_series,
+2920            repo="madebyollin/taef1",
+2921            pkg={0: {"diffusers": "AutoencoderTiny"}},
+2922            file_256=["927f7de7f11bbd3b2d5ce402e608d97a7649e0921a9601995b044e8efc81e449"],
+2923        )
+2924    )
+2925    series, comp = make_mir_tag("Qwen/Qwen-Image")
+2926    mir_db.add(
+2927        mir_entry(
+2928            domain="info",
+2929            arch="vae",
+2930            series="kl",
+2931            comp=series,
+2932            # no repo here, may conflict
+2933            pkg={
+2934                0: {"diffusers": "AutoencoderKLQwenImage"},
+2935            },
+2936            file_256=[
+2937                "0c8bc8b758c649abef9ea407b95408389a3b2f610d0d10fcb054fe171d0a8344",  # diffusers
+2938            ],
+2939            layer_b3=[
+2940                "64af8fb08d2054c81ad2aef94965be8fb1366fcc6136cb9222ae046550af014b",  # diffusers
+2941            ],
+2942            layer_256=[
+2943                "42f255440ef1d379a8a731456bc44312a73a8568716caa6100803990cd5ea7dc",  # diffusers
+2944            ],
+2945        )
+2946    )
+2947    series, comp = make_mir_tag("Wan-AI/Wan2.1-I2V-14B-480P-Diffusers")
+2948    sr_series_text2v, _ = make_mir_tag("Skywork/SkyReels-V2-T2V-14B-720P-Diffusers")
+2949    sr_series_image2v, _ = make_mir_tag("Skywork/SkyReels-V2-I2V-14B-720P-Diffusers")
+2950    mir_db.add(
+2951        mir_entry(
+2952            domain="info",
+2953            arch="vae",
+2954            series="wan",
+2955            comp=series,
+2956            # no repo here, may conflict
+2957            pkg={
+2958                0: {
+2959                    "diffusers": "AutoencoderKLWan",
+2960                    "precision": "ops.precision.float.F32",
+2961                }
+2962            },
+2963            file_256=[
+2964                "d6e524b3fffede1787a74e81b30976dce5400c4439ba64222168e607ed19e793",  # diffusers
+2965                "2fc39d31359a4b0a64f55876d8ff7fa8d780956ae2cb13463b0223e15148976b",  # sai
+2966            ],
+2967            layer_b3=[
+2968                "f867543d636029ebfc05b8075e572be0b313a83b0470e56bcf4bbad07a6db010",  # diffusers
+2969                "6b5b229727a2d4e37993687c62c94ff8519a371ab4103c699ff1f5969ca0b433",  # sai
+2970            ],
+2971            layer_256=[
+2972                "121b3974b39263dcca9d644d1b5c9b9251a911b6a8a8e307fcb21ca778e78ed2",
+2973                "364be43a8959012d798d3f98e17d8b5c4b99ba1e70077008dd19acca3ced395e",
+2974            ],
+2975        )
+2976    )
+2977    mir_db.add(
+2978        mir_entry(
+2979            domain="info",
+2980            arch="vae",
+2981            series="wan",
+2982            comp=sr_series_text2v,
+2983            # no repo here, may conflict
+2984            file_256=[],
+2985            layer_b3=[],
+2986            layer_256=[],
+2987        )
+2988    )
+2989    mir_db.add(
+2990        mir_entry(
+2991            domain="info",
+2992            arch="vae",
+2993            series="wan",
+2994            comp=sr_series_image2v,
+2995            # no repo here, may conflict
+2996            file_256=[],
+2997            layer_b3=[],
+2998            layer_256=[],
+2999        )
+3000    )
+3001    series, comp = make_mir_tag("Lightricks/LTX-Video")
+3002    mir_db.add(
+3003        mir_entry(
+3004            domain="info",
+3005            arch="vae",
+3006            series="kl",
+3007            comp=series,
+3008            # no repo here, may conflict
+3009            pkg={
+3010                0: {"diffusers": "AutoencoderKLLTXVideo"},
+3011            },
+3012            file_256=[],
+3013            layer_b3=[],
+3014            layer_256=[],
+3015        )
+3016    )
+3017    series, comp = make_mir_tag("rhymes-ai/Allegro")
+3018    mir_db.add(
+3019        mir_entry(
+3020            domain="info",
+3021            arch="vae",
+3022            series="kl",
+3023            comp=series,
+3024            # no repo here, may conflict
+3025            pkg={
+3026                0: {"diffusers": "AutoencoderKLAllegro"},
+3027            },
+3028            file_256=[],
+3029            layer_b3=[],
+3030            layer_256=[],
+3031        )
+3032    )
+3033    series, comp = make_mir_tag("zai-org/CogVideoX-5b-I2V")
+3034    series_fun, _ = make_mir_tag("alibaba-pai/CogVideoX-Fun-V1.1-5b-Pose")
+3035    series_wish, _ = make_mir_tag("BestWishYsh/ConsisID-preview")
+3036    mir_db.add(
+3037        mir_entry(
+3038            domain="info",
+3039            arch="vae",
+3040            series="cogvideox",
+3041            comp=series,
+3042            # no repo here, may conflict
+3043            pkg={
+3044                0: {"diffusers": "AutoencoderKLCogVideoX"},
+3045            },
+3046            file_256=["a410e48d988c8224cef392b68db0654485cfd41f345f4a3a81d3e6b765bb995e"],
+3047            layer_b3=["246addb8dc798240638bffee4546a3c5c83572139b4a2a602d68b4c4146226eb"],
+3048            layer_256=["43c7e9cb4364e55fd563817f01484ede8a09ff19a8e69eb61a32a12f93d6f66e"],
+3049        )
+3050    )
+3051    mir_db.add(
+3052        mir_entry(
+3053            domain="info",
+3054            arch="vae",
+3055            series="cogvideox",
+3056            comp=series_fun,
+3057            # no repo here, may conflict
+3058            file_256=[],
+3059            layer_b3=[],
+3060            layer_256=[],
+3061        )
+3062    )
+3063    mir_db.add(
+3064        mir_entry(
+3065            domain="info",
+3066            arch="vae",
+3067            series="cogvideox",
+3068            comp=series_wish,
+3069            # no repo here, may conflict
+3070            file_256=[],
+3071            layer_b3=[],
+3072            layer_256=[],
+3073        )
+3074    )
+3075    series, comp = make_mir_tag("nvidia/Cosmos-1.0-Diffusion-7B-Video2World")
+3076    mir_db.add(
+3077        mir_entry(
+3078            domain="info",
+3079            arch="vae",
+3080            series="kl",
+3081            comp=series,
+3082            # no repo here, may conflict
+3083            pkg={
+3084                0: {"diffusers": "AutoencoderKLCosmos"},
+3085            },
+3086            file_256=[],
+3087            layer_b3=[],
+3088            layer_256=[],
+3089        )
+3090    )
+3091    series, comp = make_mir_tag("alibaba-pai/EasyAnimateV5.1-7b-zh-diffusers")
+3092    mir_db.add(
+3093        mir_entry(
+3094            domain="info",
+3095            arch="vae",
+3096            series="kl",
+3097            comp=series,
+3098            # no repo here, may conflict
+3099            pkg={
+3100                0: {"diffusers": "AutoencoderKLMagvit"},
+3101            },
+3102            file_256=[],
+3103            layer_b3=[],
+3104            layer_256=[],
+3105        )
+3106    )
+3107    series, comp = make_mir_tag("hunyuanvideo-community/HunyuanVideo-I2V")
+3108    mir_db.add(
+3109        mir_entry(
+3110            domain="info",
+3111            arch="vae",
+3112            series="kl",
+3113            comp=series,
+3114            # no repo here, may conflict
+3115            pkg={
+3116                0: {"diffusers": "AutoencoderKLHunyuanVideo"},
+3117            },
+3118            file_256=[
+3119                "95d1fc707c1421ccd88ea542838ab4c5d45a5babb48205bac9ce0985525f9818",  # pt,
+3120                "7c68a6295f9034a88225fbafb1f3258291a08d57a1fdb938233fa57b1b8f4883",
+3121                "fbe5ea338431bc8ba20f7019b474e83379fe5763abfd562adcc04b1c0d35c728",
+3122                "019973c147e0c3462629d8d06bdbdbb83408f3ebd4ea4b4ae21a99c3cdcb54c0",
+3123            ],
+3124            # layer_b3=[],
+3125            # layer_256=[],
+3126        )
+3127    )
+3128    series, comp = make_mir_tag("genmo/mochi-1-preview")
+3129    mir_db.add(
+3130        mir_entry(
+3131            domain="info",
+3132            arch="vae",
+3133            series="kl",
+3134            comp=series,
+3135            # no repo here, may conflict
+3136            pkg={
+3137                0: {"diffusers": "AutoencoderKLMochi"},
+3138            },
+3139            file_256=[],
+3140            layer_b3=[],
+3141            layer_256=[],
+3142        )
+3143    )
+3144    series, comp = make_mir_tag("rhymes-ai/Allegro")
+3145    mir_db.add(
+3146        mir_entry(
+3147            domain="info",
+3148            arch="vae",
+3149            series="kl",
+3150            comp=series,
+3151            # no repo here, may conflict
+3152            pkg={
+3153                0: {
+3154                    "diffusers": "AutoencoderKLAllegro",
+3155                },
+3156            },
+3157            file_256=["47871a698b18f92f15019d361a81cbc8af4676f8eef9a47fd2b95354a39f831a"],
+3158            layer_b3=["93654cbab7541504d2377c66e72943c7fd9947fca2eb1be01bcc8877c322c1e0"],
+3159            layer_256=["bfd496586118165a13243997101fc7cdd4f855b2d8a73ee2b771a4484c4c2f9f"],
+3160        )
+3161    )
+3162    series, comp = make_mir_tag("cvssp/audioldm-s-full-v2")
+3163    mir_db.add(
+3164        mir_entry(
+3165            domain="info",
+3166            arch="vae",
+3167            series="kl",
+3168            comp=series,
+3169            # no repo here, may conflict
+3170            pkg={
+3171                0: {
+3172                    "diffusers": "AutoencoderKL",
+3173                },
+3174            },
+3175            file_256=["42f64f7565b23eabde68c9694e39f18b8bba5f7a14f477e7ed4b51e0ea7de8a5"],
+3176            layer_b3=["00959677dae940b9cfdbe5380c8cbb5a6b4951864cd26f8211d74a3d22b4f3de"],
+3177            layer_256=["54d075953d5253a3abac651de070736c1d5510b857a8ab24c624304f428146b6"],
+3178        )
+3179    )
+3180
+3181    series, comp = make_mir_tag("Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers")
+3182    mir_db.add(
+3183        mir_entry(
+3184            domain="info",
+3185            arch="vae",
+3186            series="dc",
+3187            comp=series,
+3188            # no repo here, may conflict
+3189            pkg={
+3190                0: {"diffusers": "AutoencoderDC"},
+3191            },
+3192            file_256=["15a4b09e56d95b768a0ec9da50b702e21d920333fc9b3480d66bb5c7fad9d87f"],
+3193            layer_b3=["cf4ecc6697d18b0663e4eac58203f1dd6d9fb689cf99adfeadbc0019de0c73d0"],
+3194            layer_256=["abfc39d1a6d71f03dde7bc40fec4a90478a97d17ae1688be9aad00e0512b9bde"],
+3195        )
+3196    )
+3197    series, comp = make_mir_tag("stabilityai/stable-audio-open-1.0")
+3198    mir_db.add(
+3199        mir_entry(
+3200            domain="info",
+3201            arch="vae",
+3202            series="oobleck",
+3203            comp=series,
+3204            # no repo here, may conflict
+3205            pkg={
+3206                0: {"diffusers": "AutoencoderOobleck"},
+3207            },
+3208            # file_256=[],
+3209            # layer_b3=[],
+3210            # layer_256=[],
+3211        )
+3212    )
+3213    series, comp = make_mir_tag("stable-video-diffusion-img2vid-xt")
+3214    mir_db.add(
+3215        mir_entry(
+3216            domain="info",
+3217            arch="vae",
+3218            series="kl",
+3219            comp=series,
+3220            # no repo here, may conflict
+3221            pkg={
+3222                0: {"diffusers": "AutoencoderKLTemporalDecoder"},
+3223            },
+3224            # file_256=[],
+3225            # layer_b3=[],
+3226            # layer_256=[],
+3227        )
+3228    )
+3229    mir_db.add(
+3230        mir_entry(
+3231            domain="info",
+3232            arch="vae",
+3233            series="kl",
+3234            comp=sdxl_series,
+3235            repo="madebyollin/sdxl-vae-fp16-fix",
+3236            pkg={
+3237                0: {"diffusers": "AutoencoderKL"},
+3238            },
+3239            file_256=[
+3240                "235745af8d86bf4a4c1b5b4f529868b37019a10f7c0b2e79ad0abca3a22bc6e1",  # modelspec sai
+3241                "1b909373b28f2137098b0fd9dbc6f97f8410854f31f84ddc9fa04b077b0ace2c",  # diffusers
+3242                "78f6189c8492013e3cac81637a1f657f790a237387f8a9dfd6bfa5fee28eb646",  # ssd1b diffusers
+3243                "6353737672c94b96174cb590f711eac6edf2fcce5b6e91aa9d73c5adc589ee48",  # ssd1b diffusers fp16
+3244                "bcb60880a46b63dea58e9bc591abe15f8350bde47b405f9c38f4be70c6161e68",  # kolors fp16
+3245                "1598f3d24932bcfe6634e8b618ea1e30ab1d57f5aad13a6d2de446d2199f2341",  # vega / lumina next sft d / auraflow
+3246                "703abdcd7c389316b5128faa9b750a530ea1680b453170b27afebac5e4db30c4",  # pixart a
+3247                "98a14dc6fe8d71c83576f135a87c61a16561c9c080abba418d2cc976ee034f88",  # hyd 1.1
+3248            ],
+3249            layer_b3=[
+3250                "bd5b356b509814025a9cf692710b87116d4fcd0e30a8232ed1db133e908d0e74",  # modelspec sai
+3251                "9106380403dee83238af63ff1738396d2fdff9f6d78d0d9c1d0bf770ae4294d0",  # diffusers
+3252                # "245070a60a25ca080cb4951220c3fb1503da43829930d5f6f7a6770b491eafe1",
+3253                # "50e65a628b5fe379798d8956e4a4e1d4b105c84b329f088d577f7f28c22abc49",  # diffusers fp16 matches sd1
+3254            ],
+3255            layer_256=[
+3256                "c9399a4cd39a180a0bb2af96a8297b9330541e090c21e83317cebb2f7cc651da",  # modelspec sai
+3257                "2240ae134a3b983abf45200c198f07e3d8068012fbbd2f658bbaa1fd6a0629c0",  # diffusers
+3258                # "35641f65ad7ea600cb931dcab556f7503279f1d8d99eda170fe7976d48502a2a",  # diffusers fp16 matches sd1 (incorrect)
+3259            ],
+3260        )
+3261    )
+3262    mir_db.add(
+3263        mir_entry(
+3264            domain="info",
+3265            arch="vae",
+3266            series="kl",
+3267            comp=sdxl_series + sdxl_comp,
+3268            pkg={
+3269                0: {"diffusers": "AutoencoderKL"},
+3270            },
+3271            file_256=[
+3272                "235745af8d86bf4a4c1b5b4f529868b37019a10f7c0b2e79ad0abca3a22bc6e1",  # modelspec sai
+3273                "27ed3b02e09638568e99d4398c67bc654dde04e6c0db61fb2d21dba630e7058a",  # diffusers
+3274                "eb6516ab7e1104d5d1a174a4d65c57835ae38061531d0a2192103aecfb790cc1",  # diffusers fp16
+3275                "e6bb9ea85bbf7bf6478a7c6d18b71246f22e95d41bcdd80ed40aa212c33cfeff",  # modelspec sai vae 0.9
+3276            ],
+3277            layer_b3=[
+3278                "bd5b356b509814025a9cf692710b87116d4fcd0e30a8232ed1db133e908d0e74",  # modelspec sai
+3279                # "9106380403dee83238af63ff1738396d2fdff9f6d78d0d9c1d0bf770ae4294d0",  # diffusers
+3280                # "245070a60a25ca080cb4951220c3fb1503da43829930d5f6f7a6770b491eafe1",
+3281                # "50e65a628b5fe379798d8956e4a4e1d4b105c84b329f088d577f7f28c22abc49",  # diffusers fp16 matches sd1
+3282            ],
+3283            layer_256=[
+3284                "c9399a4cd39a180a0bb2af96a8297b9330541e090c21e83317cebb2f7cc651da",  # modelspec sai
+3285                "2240ae134a3b983abf45200c198f07e3d8068012fbbd2f658bbaa1fd6a0629c0",  # diffusers
+3286                # "35641f65ad7ea600cb931dcab556f7503279f1d8d99eda170fe7976d48502a2a",  # diffusers fp16 matches sd1 (incorrect)
+3287            ],
+3288        )
+3289    )
+3290
+3291    repo = "shuttleai/shuttle-jaguar"
+3292    mir_db.add(
+3293        mir_entry(
+3294            domain="info",
+3295            arch="vae",
+3296            series="kl",
+3297            comp=make_mir_tag(repo)[0],
+3298            # no repo here, may conflict
+3299            pkg={
+3300                0: {"diffusers": "AutoencoderKL"},
+3301            },
+3302            file_256=[
+3303                "6fdfa2add4f04d94f36157cbb0197f97966b612e3f8eff4095315aefea74b904",
+3304            ],  # q8,
+3305            layer_b3=[
+3306                "0ebf9b7010accc44e219e355dd24bf1e3128004093c0c1dfc06f88c0a39fdbdd",
+3307                "d0e7ef3c4af06fa08b4c0485a073e2df55f7b1e9e3ba8f7b261688bc562568f0",  # q8
+3308            ],
+3309            layer_256=[
+3310                "9b28f36873ea283905094a64e1ccb7cfc2b0f0aa166201d0ca63807ac37caa7b",  # q8
+3311            ],
+3312        )
+3313    )
+3314    mir_db.add(
+3315        mir_entry(
+3316            domain="info",
+3317            arch="vae",
+3318            series="kl",
+3319            comp=dev_series,
+3320            # no repo here, may conflict
+3321            pkg={
+3322                0: {"diffusers": "AutoencoderKL"},
+3323            },
+3324            file_256=[
+3325                "afc8e28272cd15db3919bacdb6918ce9c1ed22e96cb12c4d5ed0fba823529e38",  # dev
+3326                "f5b59a26851551b67ae1fe58d32e76486e1e812def4696a4bea97f16604d40a3",  # dev diffusers
+3327                "8c717328c8ad41faab2ccfd52ae17332505c6833cf176aad56e7b58f2c4d4c94",  # lumina2
+3328                "8f53304a79335b55e13ec50f63e5157fee4deb2f30d5fae0654e2b2653c109dc",  # sd3 turbo
+3329            ],
+3330            layer_b3=[
+3331                "b6db93ed78c4a10d69e80831c1b8fbc1447f04e9b3d494889ee2056b98d41f17",  # diffusers
+3332                "a8a3ebdec4d7b38d65b7169d3604c19b587330e5e66f69ebf0ded56a24ec6903",  # lumina2
+3333                # "245070a60a25ca080cb4951220c3fb1503da43829930d5f6f7a6770b491eafe1",
+3334            ],
+3335            layer_256=[
+3336                "7950e4f3897c75affaa5f9f3c51c88b4d9a27bfd9b05ad41c3f71d8c1c620b89",
+3337                "79d2bfe93a2ac037cdc59ccb5576e32d00d75d4741fba49fc7e82b9724928216",  # diffusers
+3338                "8f084dc91fd5b481875bc9c86a4ef05e5f176896b7d31c6a5c2ce45c2e174004",  # dev diffusers
+3339                "322e01bd511e20bc2a3c27cd611f81ed85f0046b7c023b5622c2c9a5b8b34f80",  # lumina2
+3340            ],
+3341        )
+3342    )
+3343    mir_db.add(
+3344        mir_entry(
+3345            domain="info",
+3346            arch="vae",
+3347            series="eq",
+3348            comp=sdxl_series,
+3349            repo="KBlueLeaf/EQ-SDXL-VAE",
+3350            pkg={
+3351                0: {"diffusers": "AutoencoderKL"},
+3352            },
+3353        )
+3354    )
+3355    mir_db.add(
+3356        mir_entry(
+3357            domain="info",
+3358            arch="vae",
+3359            series="ms-lc-eq",
+3360            comp=sdxl_series,
+3361            repo="Anzhc/MS-LC-EQ-D-VR_VAE",
+3362            pkg={
+3363                0: {
+3364                    "diffusers": "AutoencoderKL",
+3365                },
+3366            },
+3367        )
+3368    )
+3369    repo = "ucsd-reach/musicldm"
+3370    mir_db.add(
+3371        mir_entry(
+3372            domain="info",
+3373            arch="vae",
+3374            series="kl",
+3375            comp=make_mir_tag(repo)[0],
+3376            # no repo here, may conflict
+3377            file_256=[
+3378                "16e0c6c7c34e459c19500cc15cf538e6331db14969ea15917caa9b0966e44fd4",
+3379            ],  # q8,
+3380            layer_b3=[
+3381                "c5c32b3fb3e73799838836ccce27d883254254daecd10f86ba8ddc55214014e0",
+3382            ],
+3383            layer_256=[
+3384                "1610c0ce39d1379091eb9ab2a4d14a8567e0f1a5dc6cca40fc0fa6f8e4e97c0f",
+3385            ],
+3386        )
+3387    )
+3388
+3389    mir_db.add(
+3390        mir_entry(
+3391            domain="info",
+3392            arch="vae",
+3393            series="kl",
+3394            comp=sd1_series,
+3395            pkg={
+3396                0: {"diffusers": "AutoencoderKL"},
+3397            },
+3398            file_256=[
+3399                "0b204ad0cae549e0a7e298d803d57e36363760dec71c63109c1da3e1147ec520",  # ckpt ema original ema pruned
+3400                "95f26a5ab04779d5467d1fcecaf93160ffa523afe399b835b3e1bb77ff2d937a",  # safetensors ema original ema pruned
+3401                "32db726da04f06c1b6b14c0043ce115cc87a501482945c5add89a40d838fcb46",  # safetensors ema diffusers
+3402                "c6a580b13a5bc05a5e16e4dbb80608ff2ec251a162311590c1f34c013d7f3dab",  # ckpt mse original ema pruned
+3403                "735e4c3a447a3255760d7f86845f09f937809baa529c17370d83e4c3758f3c75",  # safetensors mse original ema pruned
+3404                "a1d993488569e928462932c8c38a0760b874d166399b14414135bd9c42df5815",  # safetensors mse diffusers
+3405                "a2b5134f4dbc140d9c11f11cba3233099e00af40f262f136c691fb7d38d2194c",  # safetensors diffusers
+3406                "4fbcf0ebe55a0984f5a5e00d8c4521d52359af7229bb4d81890039d2aa16dd7c",  # safetensors fp16 diffusers
+3407            ],
+3408            layer_b3=[
+3409                "82e2dc440a23d78bb91df8c9fce069a8512da51f8f54ea29e3431f545808171e",  # safetensors original
+3410                "2230487833925a104bee96e7ecfebaa4c3c43cc426c7a5b863f2584313dd4833",  # safetensors diffusers
+3411            ],
+3412            layer_256=[
+3413                "e43f3a227b5ecb43a6272fa92ed6011d2e9abcadadd1032dfa7ea7f875f9d5bd",  # safetensors original
+3414                "2494154245becf98891be884f943276aa3f54e9b3f0ea1042903fc15fba488f3",  # safetensors diffusers
+3415            ],
+3416        )
+3417    )
+
+ + +
+
+ +
+ + def + assimilate( mir_db: mir.maid.MIRDatabase, data_tuple: List[Tuple[Dict[str, <built-in function any>]]]) -> None: + + + +
+ + 
114def assimilate(mir_db: MIRDatabase, data_tuple: List[Tuple[Dict[str, any]]]) -> None:
+115    """Merge new data into a pre-generated MIR database, updating while preserving existing data structures.\n
+116    :param mir_db: The MIRDatabase instance
+117    :param data_tuple: A list of tuples, each containing:\n
+118            - arch (str): The architecture name
+119            - series (str): The series name
+120            - `new_data`: New data to be merged into the database.
+121    :raises TypeError: If any field in `new_data` is not a dictionary.
+122    """
+123
+124    def update_nested_dict(target, source):
+125        for key, value in source.items():
+126            if isinstance(value, dict) and key in target:
+127                if isinstance(target, dict):
+128                    update_nested_dict(target[key], value)
+129            else:
+130                if isinstance(source, dict):
+131                    # dbuq(target)
+132                    target.setdefault(key, value)
+133                else:
+134                    target = {key: value}
+135
+136    dbuq(f"{data_tuple}, {len(data_tuple)}")
+137    for arch, series, new_data in data_tuple:
+138        mir_data = mir_db.database[f"{arch}.{series}"]
+139        for comp, field_data in new_data.items():
+140            if not isinstance(field_data, dict):
+141                raise TypeError(f"{field_data} <-- Cannot combine with database: Not `dict()`")
+142
+143            # dbuq(f"{arch}.{series} : {comp}")
+144            update_nested_dict(mir_data.setdefault(comp, {}), field_data)
+145
+146            if series == sdxl_series:
+147                for field, field_data in field_data.items():
+148                    if isinstance(field_data, dict):
+149                        for definition, sub_def_data in field_data.items():
+150                            # dbug(definition)
+151                            if isinstance(sub_def_data, dict):
+152                                mir_data[comp][field].setdefault(definition, {})
+153                                update_nested_dict(mir_data[comp][field][definition], sub_def_data)
+
+ + +

Merge new data into a pre-generated MIR database, updating while preserving existing data structures.

+ +
Parameters
+ +
    +
  • mir_db: The MIRDatabase instance
    • +

  • data_tuple: A list of tuples, each containing:

    + +
    - arch (str): The architecture name
+- series (str): The series name
+- `new_data`: New data to be merged into the database.
+
    • +
+ +
Raises
+ +
    +
  • TypeError: If any field in new_data is not a dictionary.
    • +
+
+ + +
+
+ +
+ + def + hf_pkg_to_mir(mir_db: mir.maid.MIRDatabase): + + + +
+ + 
156def hf_pkg_to_mir(mir_db: MIRDatabase):
+157    """Generate MIR HF Hub model database"""
+158    mir_data = diffusers_index() | transformers_index()
+159    for series, comp_name in mir_data.items():
+160        id_segment = series.split(".")
+161        for compatibility in comp_name:
+162            # dbug(id_segment)
+163            try:
+164                mir_db.add(
+165                    mir_entry(
+166                        domain=id_segment[0],
+167                        arch=id_segment[1],
+168                        series=id_segment[2],
+169                        comp=compatibility,
+170                        **mir_data[series][compatibility],
+171                    ),
+172                )
+173            except IndexError:  # as error_log:
+174                nfo(f"Failed to create series: {series}  compatibility: {comp_name}  ")
+175                # dbug(error_log)
+
+ + +

Generate MIR HF Hub model database

+
+ + +
+
+ +
+ + def + add_mir_dtype(mir_db: mir.maid.MIRDatabase): + + + +
+ + 
178def add_mir_dtype(mir_db: MIRDatabase):
+179    """Create mir info database"""
+180
+181    available_dtypes: List[str] = [dtype for dtype in torch.__dict__.values() if isinstance(dtype, torch.dtype)]
+182    series_name = "_"
+183    for precision in available_dtypes:
+184        dep_name, class_name = str(precision).split(".")
+185        if "_" in class_name:
+186            comp_name = class_name[0].upper() + "8_" + class_name.split("_")[1].upper()
+187            if comp_name.endswith("FN"):
+188                comp_name = comp_name[:-2]
+189        else:
+190            comp_name = class_name[0].upper() + str(slice_number(class_name))
+191        variant_name = class_name.replace("bfloat", "bf").replace("float", "fp")
+192        dbuq(variant_name)
+193        patterns = [r"complex", r"bits", r"quint", r"uint", r"int", r"bfloat", r"float", r"bool"]
+194        for precision_name in patterns:
+195            compiled = re.compile(precision_name)
+196            dtype = re.search(compiled, class_name)
+197            if dtype:
+198                series_name = dtype.group()
+199                break
+200
+201        mir_db.add(
+202            mir_entry(
+203                domain="ops",
+204                arch="precision",
+205                series=series_name,
+206                comp=comp_name,
+207                pkg={0: {dep_name.lower(): {class_name.lower(): {"variant": variant_name}}}},
+208            )
+209        )
+
+ + +

Create mir info database

+
+ + +
+
+ +
+ + def + add_mir_schedulers(mir_db: mir.maid.MIRDatabase): + + + +
+ + 
212def add_mir_schedulers(mir_db: MIRDatabase):
+213    """Create mir info database"""
+214
+215    for class_name in _import_structure["schedulers"]:
+216        if class_name != "SchedulerMixin":
+217            series_name, comp_name = make_scheduler_tag(class_name)
+218            class_obj = import_module("diffusers.schedulers")
+219            class_path = getattr(class_obj, class_name).__module__
+220            mir_db.add(
+221                mir_entry(
+222                    domain="ops",
+223                    arch="scheduler",
+224                    series=series_name,
+225                    comp=comp_name.lower(),
+226                    pkg={
+227                        0: {
+228                            "diffusers": class_name,
+229                            "module_path": class_path,
+230                        },
+231                    },
+232                )
+233            )
+234
+235    class_name = "KarrasDiffusionSchedulers"
+236    series_name, comp_name = make_scheduler_tag(class_name)
+237    class_obj = import_module("diffusers.schedulers.scheduling_utils")
+238    class_path = getattr(class_obj, class_name).__module__
+239    mir_db.add(
+240        mir_entry(
+241            domain="ops",
+242            arch="scheduler",
+243            series=series_name,
+244            comp=comp_name,
+245            pkg={
+246                0: {
+247                    "diffusers": class_name,
+248                    "module_path": class_path,
+249                },
+250            },
+251        ),
+252    )
+
+ + +

Create mir info database

+
+ + +
+
+ +
+ + def + mir_update( mir_db: mir.maid.MIRDatabase, task_list: list = None, pipe_list: list = None): + + + +
+ + 
 259def mir_update(mir_db: MIRDatabase, task_list: list = None, pipe_list: list = None):
+ 260    """Create mir unet info database"""
+ 261
+ 262    diffusers_addons = [
+ 263        (
+ 264            "stabilityai/stable-diffusion-xl-base-1.0",
+ 265            "StableDiffusionXLPipeline",
+ 266            {
+ 267                "pkg": {
+ 268                    0: {
+ 269                        "precision": "ops.precision.float.F16",
+ 270                        "generation": {
+ 271                            "denoising_end": 0.8,
+ 272                            "num_inference_steps": 40,
+ 273                            "output_type": "latent",
+ 274                            "safety_checker": False,
+ 275                            "width": 1024,
+ 276                            "height": 1024,
+ 277                        },
+ 278                    },
+ 279                    1: {"diffusers": "DiffusionPipeline"},
+ 280                },
+ 281                "file_256": [
+ 282                    "357650fbfb3c7b4d94c1f5fd7664da819ad1ff5a839430484b4ec422d03f710a",  # diffusers
+ 283                    "83e012a805b84c7ca28e5646747c90a243c65c8ba4f070e2d7ddc9d74661e139",  # fp16 diffusers
+ 284                    "31e35c80fc4829d14f90153f4c74cd59c90b779f6afe05a74cd6120b893f7e5b",  # modelspec sai
+ 285                    "6f001c090fb13c0d0f8b0a5916da814712a94400b99471fabe77c1c4a51ecaaf",  # onnx
+ 286                ],
+ 287                "layer_256": [
+ 288                    "62a5ab1b5fdfa4fedb32323841298c6effe1af25be94a8583350b0a7641503ef",  # any modelspec sai
+ 289                    "34dff8d98898baa0f10e71943e56b588cc114253b0d2f1051f3ce7a8a45fee0b",  # diffusers
+ 290                    "56b1ccd89b0d6ab658048aa34d659788b6ed663f13ef566f4b11bccef590b9da",  # diffusers fp16
+ 291                ],
+ 292                "layer_b3": [
+ 293                    "8be44fa13c1efa60f8bcadaa57f1d718473f9660f03c4f0e65dc037960d8cba1",  # any modelspec sai
+ 294                    "c9ab95ed1851418b65ef99651c1eb6bbdd2e3b0715e0e435d6d1e56ce310fac3",  # diffusers
+ 295                    "adfa260098d87616d748e3cf9c10bb2c90ff8890a84abbb2853d4aa69664070b",  # diffusers fp16
+ 296                ],
+ 297                "identifiers": ["logit_scale", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.k_proj.weight", "add_embedding.linear_2.bias"],
+ 298            },
+ 299        ),
+ 300        (
+ 301            "stabilityai/stable-diffusion-xl-refiner-1.0",
+ 302            "StableDiffusionXLImg2ImgPipeline",
+ 303            {
+ 304                "pkg": {
+ 305                    1: {
+ 306                        "diffusers": "DiffusionPipeline",
+ 307                        "generation": {"num_inference_steps": 40, "denoising_end": 0.8},
+ 308                    }
+ 309                },
+ 310                "identifiers": ["conditioner.embedders.0.model.transformer.resblocks.9.mlp.c_proj.bias"],
+ 311                "file_256": [
+ 312                    "54f9cd2f2daf3aeec0b2708fa3dbc0e84e4f8ddd1ddead42e5bc60c6572c989f",  # diffusers
+ 313                    "7440042bbdc8a24813002c09b6b69b64dc90fded4472613437b7f55f9b7d9c5f",  # modelspec sai
+ 314                    "3ea0376dcf065eaefd27806394a90e310001b1a71d4f1cf1f655e86c0e566ffe",  # fp16 diffusers
+ 315                ],
+ 316                "layer_b3": [
+ 317                    "6281355dbb37e5769c9460ae0ac75506d89932e2f97b09d9ade32ecf191e75ba",
+ 318                    "afb0639aae2eb65577c12d4a30cf7c9b3620ae63ba64a8fa632b58608c8a7a2e",
+ 319                    "669046014b69d98ab0f6fbb59547644436e0275f8b638f467ce2a873c3313683",
+ 320                ],
+ 321                "layer_256": [
+ 322                    "bb9eadbfabb52c0d8645783525a3fa70b59e9d7d09d5290d742a303262e793a2",
+ 323                    "c5adb56fe51343af2c3d493eb9f41515c204bd91eb9f40b983d45f70a1fa3b6d",
+ 324                    "1f838e39ed6e916258aee6990b72c09b34aa8eb3b5342234a497b8852b3df1c6",
+ 325                ],
+ 326            },
+ 327        ),
+ 328        (
+ 329            "lodestones/Chroma",
+ 330            "ChromaPipeline",
+ 331            {
+ 332                "pkg": {
+ 333                    1: {
+ 334                        "generation": {"neg_text": "", "num_steps": "28", "latent_size": [64, 64]},
+ 335                    }
+ 336                },
+ 337                "file_256": [
+ 338                    "53adcb3b6b6005758d40e2d8058b044ed4892bc8616efb7a62cc2dd384be07de",  # v1
+ 339                    "2c41e8a9831f3be1eaff2c2ed590abb62e4534e814f7ec58a5fd74ff71dc2036",  # v46,
+ 340                    "0a7b2d9699dbd22b3744ee2692900cabcfb731a43dac13729c33807f2bb7c9f6",  # v37 detail
+ 341                    "6ddc9e2bbe3376ab5ee9f10b2d947f127b6bf6f879f06f316a2208bb0da357b8",  # mlx chroma / v36 detail
+ 342                ],
+ 343                "layer_b3": [
+ 344                    "15e227ced8a89c41abaa9cc44f84dfffdf5ead0c626035e5a2dde2bbb0935479",
+ 345                ],
+ 346                "layer_256": ["a4daa6ff6f45ca70c738adb8c19bc3b6f228df931e6bf2a3394463e4dd7ec882"],
+ 347            },
+ 348        ),
+ 349        (
+ 350            "fal/AuraFlow",
+ 351            "AuraFlowPipeline",
+ 352            {
+ 353                "identifiers": [[8192, 3072], "mlpX.c_fc2.weight", "joint_transformer_blocks.2.ff_context.linear_2.weight"],
+ 354                "file_256": [
+ 355                    "ce3e475246258b94ee9dcb8b83292cb34edfffc2bbde46c74604d9c6cd7c585c",
+ 356                    "526be97cf581c89ad87c6b19c1f7c2378851137698f7ec436596d061a382d37b",  # sai
+ 357                    "6a40b011f287452dbca80face78e667055904c5ad97eb2097ade3200259b2203",  # diffusers fp16
+ 358                    "05e5493018333d947bb5940083dbc2f071093027ff414bc5b1b1229e4836e5cb",  # diffusers
+ 359                ],
+ 360                "layer_b3": [
+ 361                    "cc6d383576c35a9709798d2e2b9e3eb31ba8c608040cf3712bc37871cfd14e21",
+ 362                    "ddd54c44fa28fbddecf7cfae91cfa04917fd2f2fa94fc78c528cef2356a4ec3a",  # sai
+ 363                    "90c694e7d1e20e6da49b571e9954338d384775419790be315304103227b1051b",
+ 364                    "9e85aec1bdb616f52f88c80ddc7ab1eae8c16c0b5fbfcdb61a71ac02c325003d",
+ 365                ],
+ 366                "layer_256": [
+ 367                    "3c13e6a965d03a49227d8b1606ba6a343a23772d8768407cc78d4ddb9102bc80",
+ 368                    "b356cc84a23bc93bda4cc0fce1d0ba1b8e3d5a521e659ffc72e9e4a2d2c7f204",
+ 369                    "270df7317fe01abf06333acbbd4f15f8fc7a7c56053219f42efb598454a3af24",
+ 370                    "7ab6aa4514dd09f3cf589587d51a81734193ce45dd51bda9db0bd62fe48ef7d5",
+ 371                ],
+ 372            },
+ 373        ),
+ 374        (
+ 375            "Tencent-Hunyuan/HunyuanDiT-v1.2-Diffusers",
+ 376            "HunyuanDiTPipeline",
+ 377            {
+ 378                "identifiers": ["extra_embedder", "model.blocks", "skip_norm.weight"],
+ 379                "file_256": [
+ 380                    "4fb84f84079cda457d171b3c6b15d1be95b5a3e5d9825703951a99ddf92d1787",  # normal
+ 381                    "e01db5e129e8ca1117e9cf473fc5a2b096949f03ab90048aeabbc328de7ec800",  # distilled
+ 382                    "8af691cadb78047d55721259355d708e87ddbba1b7845df9377d9a5ae917b45d",  # 1.2
+ 383                ],
+ 384                "layer_b3": [
+ 385                    "aead6b61b17ebc77c4c186a4b82c193f11ec267b20d909726422ee9852e2e0b2",
+ 386                    "885a056b94f6f9844c0660be489844d63bb74cc13316f441d10968fff3dd3120",  # distilled
+ 387                    "390d951cbdda6e2cffb690031b60f02921624651534c2effaaa7d68ab476c700",
+ 388                ],
+ 389                "layer_256": [
+ 390                    "d4842ce2b7f927203326b25ff4d6738ec9a8b95327f06791c387e4a351ed6ed0",
+ 391                    "5af943f96f5dc9fecb1e92fe2b1fa17c94dd6947690201f4a5ee1a4a2721a68e",  # distilled
+ 392                    "4a1f2b8234fa4336e263842e042d42e8d64d8a4d3941d9c0c78366b50303950c",  # 1.2
+ 393                ],
+ 394            },
+ 395        ),
+ 396        (
+ 397            "Alpha-VLLM/Lumina-Next-SFT-diffusers",
+ 398            "LuminaPipeline",
+ 399            {
+ 400                "pkg": {
+ 401                    0: {
+ 402                        "precision": " ops.precision.bfloat.B16",
+ 403                    },
+ 404                },
+ 405                "identifiers": ["time_caption", "feed_forward"],
+ 406                "file_256": [
+ 407                    "371153b7c7b7a64899d4016970c7cc472039f9c9b21ebe073adf0b8525cdf1bd",
+ 408                ],
+ 409                "layer_b3": [
+ 410                    "fa134efd6e9672e7de2965e4895fc58879bd0a6c4fdf9165c278f2748254675f",
+ 411                    "4d960ec35c53f72f065b94b836bcd923ea6074d38ad49881061f315d62e3c839",
+ 412                ],
+ 413                "layer_256": [
+ 414                    "3938a85568d9df186923edf04391d79e89e6199123bc175afb520e0948d1ae05",
+ 415                    "c0ca51fdea051fcd042bf4b56d32e1e8bb9525a921f2e197f370f101e90527f0",
+ 416                ],
+ 417            },
+ 418        ),
+ 419        (
+ 420            "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS",
+ 421            "PixArtSigmaPipeline",
+ 422            {
+ 423                "identifiers": ["adaln_single", "scale_shift_table"],
+ 424                "file_256": [
+ 425                    "c34b520ef473329b945c2a21083cdf1337c5a468d23b3215b65576789bfd0305",
+ 426                    "2fa4dee9229c02b03163f57bdb8e80c7a5ee364b7161796abe9c05e8dd13f239",
+ 427                ],
+ 428                "layer_b3": [
+ 429                    "a199930ff537994872da77391955f0dd52eddd22ab9105388f0c5852f1b8021f",
+ 430                    "ee6f980c32e98da6885f3e97d3f88d9158031e362cd3a49b20d1e23924b251e3",
+ 431                ],
+ 432                "layer_256": [
+ 433                    "e0afd203aff5a1d192e325d0f59361373273d85d138b51768c3f10a75c154dc0",
+ 434                    "987f3c2ff5d399191e5fd7dd7b1f1f285c197dc8124ad77f05cde7f2fb677a3c",
+ 435                ],
+ 436            },
+ 437        ),
+ 438        (
+ 439            "PixArt-alpha/PixArt-XL-2-1024-MS",
+ 440            "PixArtAlphaPipeline",
+ 441            {
+ 442                "identifiers": ["aspect_ratio", "y_embedding", "emb.resolution", "caption_projection"],
+ 443                "file_256": ["809a92d52a4a228f381a4b4f4b76051294b73285fb0cbb02f0ad24f9372217a8"],
+ 444                "layer_b3": ["c5be83545ce9dbc564bcc9fd8fe4157d131347ccfc8f62adc877ec205b20acee"],
+ 445                "layer_256": ["117225c0e91423746114b23d3e409708ad55c90ff52b21fa7a1c5105d2e935a5"],
+ 446            },
+ 447        ),
+ 448        (
+ 449            "stabilityai/stable-diffusion-3.5-medium",
+ 450            "StableDiffusion3Pipeline",
+ 451            {
+ 452                "pkg": {
+ 453                    0: {"precision": "ops.precision.float.F16"},
+ 454                },
+ 455                "identifiers": [
+ 456                    "model.diffusion_model.joint_blocks.",
+ 457                    "transformer_blocks.21.norm1_context.linear.weight",
+ 458                    "transformer_blocks.31.norm1_context.linear.weight",
+ 459                    "blocks.11.ff.net.2.weight",
+ 460                ],
+ 461                "file_256": [
+ 462                    "ffef7a279d9134626e6ce0d494fba84fc1c7e720b3c7df2d19a09dc3796d8f93",  # large
+ 463                    "11fe06e22364b823dfeedc275912336b932b32a293a0b2f35ffac071990cc4de",  # medium
+ 464                ],
+ 465                "layer_b3": [
+ 466                    "e411016545785046810b29cc3999f40bc6392be134a1318386c6f1c48f98726a",
+ 467                    "a81e07ee67bc627e8b3c5e292ec1ca239009517a2106e8249d670ced0a88f746",  # med
+ 468                ],
+ 469                "layer_256": [
+ 470                    "13c982a6dc82d21c9f459e837d8c6f6d4696fd6e7e7b5783bdd2250b1f4fec61",
+ 471                    "6ee79050373337bf63ac20916596df778bb22022bb38af986128a7459eda1463",  # med
+ 472                ],
+ 473            },
+ 474        ),
+ 475        (
+ 476            "Efficient-Large-Model/Sana-1600M-1024px-BF16-diffusers",
+ 477            "SanaPipeline",
+ 478            {
+ 479                "pkg": {
+ 480                    0: {
+ 481                        "generation": {
+ 482                            "height": 1024,
+ 483                            "width": 1024,
+ 484                            "guidance_scale": 4.5,
+ 485                            "num_inference_steps": 20,
+ 486                        },
+ 487                        "precision": "ops.precision.bfloat.B16",
+ 488                    },
+ 489                },
+ 490                "file_256": [
+ 491                    "b0b50c33be8758713459aa3c760feef6315d4bea31521fb5b8c3e8fdd9841ffe",
+ 492                ],
+ 493                "layer_b3": [
+ 494                    "461e3d83dfa7e075ef21e2138ef153922ecfadde3db464b03dff92819f3e86dd",
+ 495                ],
+ 496                "layer_256": [
+ 497                    "b928bbcc2ce99d55d21c189e2b1c57498bc313ef5b1457036e356107d567fc4e",
+ 498                ],
+ 499            },
+ 500        ),
+ 501        (
+ 502            "stable-diffusion-v1-5/stable-diffusion-v1-5",
+ 503            "StableDiffusionPipeline",
+ 504            {
+ 505                "identifiers": ["up_blocks.3.attentions.0.transformer_blocks.0.norm3.weight"],
+ 506                "file_256": [
+ 507                    "6ce0161689b3853acaa03779ec93eafe75a02f4ced659bee03f50797806fa2fa",  # pruned ema only original safetensors
+ 508                    "1a189f0be69d6106a48548e7626207dddd7042a418dbf372cefd05e0cdba61b6",  # pruned original safetensors
+ 509                    "e1441589a6f3c5a53f5f54d0975a18a7feb7cdf0b0dee276dfc3331ae376a053",  # ema pruned original ckpt
+ 510                    "cc6cb27103417325ff94f52b7a5d2dde45a7515b25c255d8e396c90014281516",  # pruned ema original ckpt
+ 511                    "19da7aaa4b880e59d56843f1fcb4dd9b599c28a1d9d9af7c1143057c8ffae9f1",  # diffusers safetensors
+ 512                    "cd1b6db09a81cb1d39fbd245a89c1e3db9da9fe8eba5e8f9098ea6c4994221d3",  # diffusers non ema safetensors
+ 513                    "c83908253f9a64d08c25fc90874c9c8aef9a329ce1ca5fb909d73b0c83d1ea21",  # diffusers fp16
+ 514                ],
+ 515                "layer_b3": [
+ 516                    "909c6ff3192ab2767e789a6125865bc23163db467ab78b1c633bad46a4293fad",
+ 517                    "b52807536902cabbf84f99e4fa2f8713fb4ef77e739f06367ee0d486e3222faa",  # ckpt
+ 518                    "d31382d71a1044b636d80d861a2b4dbca51826bed34d34b5c14608b7679ccefd",  # safetensors ema pruned
+ 519                    "5fd8b28013b7e5a64c7c235f0a93d93e48bc19a0e5dde7b646a87b429219643a",  # safetensors pruned
+ 520                    "731f552f29edcb4f86112cc94d296377f3533a9633ccf83e202d9e1785d94a00",  # diffusers
+ 521                    "2d2f97574a161cf01a6f6d476b141c7be06f940d94b695ffc12c4e74eca2de1c",  # diffusers fp16
+ 522                ],
+ 523                "layer_256": [
+ 524                    "ece771354ad470a82d56eda413ae3dd6c00d2de28ab3c56a88201d08d4424b4b",
+ 525                    "65b084dada803461ab9ca9be9b892d211870a121dd6c555a111eea470b951c54",  # st
+ 526                    "dc937b59892604f5a86ac96936cd7ff09e25f18ae6b758e8014a24c7fa039e91",  # ckpt
+ 527                    "92565dec90f7c8412dc872e820f66cd0c56263bbbc392439645b6fee270f41bb",  # st fp16
+ 528                ],
+ 529            },
+ 530        ),
+ 531        (
+ 532            "Kwai-Kolors/Kolors-diffusers",
+ 533            "KolorsPipeline",
+ 534            {
+ 535                "pkg": {
+ 536                    0: {
+ 537                        "precision": "ops.precision.float.F16",
+ 538                        "generation": {
+ 539                            "negative_prompt": "",
+ 540                            "guidance_scale": 5.0,
+ 541                            "num_inference_steps": 50,
+ 542                            "width": 1024,
+ 543                            "height": 1024,
+ 544                        },
+ 545                    },
+ 546                    1: {"diffusers": "DiffusionPipeline"},
+ 547                },
+ 548                "file_256": [
+ 549                    "425ff1dcbe3a70ac13d3afdd69bd4e3176b0c3260722527c80b210f11d2d966c",  # fp16,
+ 550                ],
+ 551                "layer_b3": [
+ 552                    "6eb15506fa38b4cbb26391ab1b6c9ead05f86c711e46583bfbe8fc4421571414",  # fp16
+ 553                ],
+ 554                "layer_256": [
+ 555                    "04e3c17170b8a200481f6941b370fdc5056a00fe5a16956de01790f8a93c0dcd",  # fp16
+ 556                ],
+ 557                "identifiers": [".DenseReluDense.wi.weight", "encoder_hid_proj.weight"],
+ 558            },
+ 559        ),
+ 560        (
+ 561            "stabilityai/stable-cascade-prior",
+ 562            "StableCascadePriorPipeline",
+ 563            {
+ 564                "pkg": {
+ 565                    0: {
+ 566                        "precision": "ops.precision.bfloat.B16",
+ 567                        "generation": {
+ 568                            "negative_prompt": "",
+ 569                            "num_images_per_prompt": 1,
+ 570                            "num_inference_steps": 20,
+ 571                            "guidance_scale": 4.0,
+ 572                            "width": 1024,
+ 573                            "height": 1024,
+ 574                        },
+ 575                    }
+ 576                },
+ 577                "file_256": [
+ 578                    "673b3173b037fb5f65b14fde37267390641a36726683de75dcf9df76fce2b866",  # lite bf16
+ 579                    "45c1eb5ce9b69efac891ad459b15c215cd90a986adbbfaf3effd3a89578cbcaf",  # pretrained
+ 580                    "088ddf1e444abf399007b2da2bac87791df165c69f477994f6b3c745a20904b0",  # stage c modelspec sai
+ 581                    "39cec96c7212607f9e526db719bf1df507166d09f4748676c13b0d31cd4adb07",  # stage c
+ 582                    "31ffe2f1a3e2351d658fc7d3002a4eca22466a680f7fb3715b1e3768476f9633",  # stage c lite
+ 583                    "dfe24009fc881011f350d08d9d13be13a1a3b3cbfed667435efe0fd419aca099",  # bf16
+ 584                ],
+ 585                "layer_b3": [
+ 586                    "c55c83fa435ed128457f605bf1312e54727996d1c94413fc5ab5b49e9933857c",
+ 587                    "6fb07ed9fc6ee636e50783802754b3a37bbecfc67037813b616223aeaf6fe877",
+ 588                    "2ea194240e105c8962923e2baca88cb6a0c826794afc2ef82474301694711d68",
+ 589                    "3412c8a184805621e4595d57268ced0b5c3c1974cd221bf67b2c908eec4fd61c",
+ 590                    "53abfb013cfb0e41d0bc7b96bb83e42a4d4c67cb7325f9acf645b02d90efd8fe",
+ 591                    "34556558f680c183adc2accd493cb9888a98ba853226bbecb07d95eb2055ff4f",
+ 592                ],
+ 593                "layer_256": [
+ 594                    "4f5e0a738b963d3d4f8413387a0966ac1ce51f0f985bcbcc124fa221a2fff467",
+ 595                    "8aa77e732a398b7d0dcd9a35d5682c2b5ab090ae90e915c7c91878abff0284d8",
+ 596                    "4bbd46ded0916de3108f0da7145a80f5c7acea26ed35b0aaa29af12008352453",
+ 597                    "415d1f3ecd06416708c1b83ab21e50b39c9d88d19dc33e60b977b7b7061880b9",
+ 598                    "f678c32815c238e14091f690c8a83c3375c8f7738dc7abff79ff086ed9b59204",
+ 599                    "17c8da803df7b9bbc8b1d7cc0c44916fea5b5ac0891330c4fdf0326fcd4496cb",
+ 600                ],
+ 601                "identifiers": ["down_blocks.0.2.kv_mapper", "previewer", "backbone"],
+ 602            },
+ 603        ),
+ 604        (
+ 605            "black-forest-labs/FLUX.1-dev",
+ 606            "FluxPipeline",
+ 607            {
+ 608                "pkg": {
+ 609                    0: {
+ 610                        "precision": "ops.precision.bfloat.B16",
+ 611                        "generation": {
+ 612                            "height": 1024,
+ 613                            "width": 1024,
+ 614                            "guidance_scale": 3.5,
+ 615                            "num_inference_steps": 50,
+ 616                            "max_sequence_length": 512,
+ 617                        },
+ 618                    },
+ 619                    1: {
+ 620                        "mflux": "flux.flux.Flux1",
+ 621                        "generation": {
+ 622                            "height": 1024,
+ 623                            "width": 1024,
+ 624                            "gudance": 3.5,
+ 625                            "num_inference_steps": 25,
+ 626                        },
+ 627                    },
+ 628                },
+ 629                "file_256": [
+ 630                    "f6315581b7cddd450b9aba72b4e9ccf8b6580dc1a6b9538aff43ee26a1a3b6c2",  # krea sai
+ 631                    "1b2170ac37156d4cf91909eb6834bb8adac84bc1fce8098a29cfb03738df84ad",  # krea diffusers
+ 632                    "4610115bb0c89560703c892c59ac2742fa821e60ef5871b33493ba544683abd7",  # modelspec sai
+ 633                    "d86a3038eacaa720682cb9b1da3c49fecf8a3ded605af4def6061eaa18903eb8",  # diffusers
+ 634                    "b7d840eef01c27dfd72ae9143c261355a51bab3b2662263a6cb0059d55347c3d",  # qwen2
+ 635                ],
+ 636                "layer_b3": [
+ 637                    "261559c8eaccae558f72621804a9ee188d338e45e2c622a58db709ac190198ba",
+ 638                    "87f5d565c66e40eb02eb96498243ad81afcbf86192db99a4fc8fff215470320e",  # modelspec sai
+ 639                    "e61d10a394902dadca9367467b2245070f651f4553ec4a96192fbba64e820acb",  # diffusers
+ 640                ],
+ 641                "layer_256": [
+ 642                    "3db58cf834d2f81abb1e035131956da4c90451074c681d0db10810e55e60c2c4",
+ 643                    "ddf1a34a06b355ce2bcd0f9beb0713450d9bcdc61a03a6bc37716361735e96f1",  # diffusers
+ 644                    "ad8763121f98e28bc4a3d5a8b494c1e8f385f14abe92fc0ca5e4ab3191f3a881",  # modelspec sai
+ 645                ],
+ 646                "identifiers": [
+ 647                    "double_blocks.12.txt_mod.lin.weight",
+ 648                    "add_q_proj.weight",
+ 649                    "single_transformer_blocks.9.norm.linear.weight",
+ 650                ],
+ 651            },
+ 652        ),
+ 653        (
+ 654            "black-forest-labs/FLUX.1-schnell",
+ 655            "FluxPipeline",
+ 656            {
+ 657                "pkg": {
+ 658                    0: {
+ 659                        "precision": "ops.precision.bfloat.B16",
+ 660                        "generation": {
+ 661                            "height": 1024,
+ 662                            "width": 1024,
+ 663                            "guidance_scale": 0.0,
+ 664                            "num_inference_steps": 4,
+ 665                            "max_sequence_length": 256,
+ 666                        },
+ 667                    },
+ 668                    1: {
+ 669                        "mflux": "flux.flux.Flux1",
+ 670                        "generation": {
+ 671                            "height": 1024,
+ 672                            "width": 1024,
+ 673                            "num_inference_steps": 4,
+ 674                        },
+ 675                    },
+ 676                },
+ 677                "identifiers": [
+ 678                    "double_blocks.12.txt_mod.lin.weight",
+ 679                    "add_q_proj.weight",
+ 680                    "single_transformer_blocks.9.norm.linear.weight",
+ 681                ],
+ 682                "file_256": [
+ 683                    "9403429e0052277ac2a87ad800adece5481eecefd9ed334e1f348723621d2a0a",  # sai modelspec
+ 684                    "9b633dbe87316385c5b1c262bd4b5a01e3d955170661d63dcec8a01e89c0d820",  # diffusers
+ 685                ],
+ 686                "layer_b3": [
+ 687                    "c65ba812ce3ce056eb1585673f62fb896afe6ec049faaf00a97bc35c9a398c44",
+ 688                    "03049273329fc7db2da10de6d3eb27cb03f190e379c0556cc97b3f0f29001d0c",  # sai modelspec
+ 689                    "483c4be8ef031c56bc8450d1a3cfbe54445ed317bcd801be5abe89f1d3c48790",  # diffusers
+ 690                ],
+ 691                "layer_256": [
+ 692                    "79c07e339865fe9e22c80f723d728c778130acd07a330339c68218b92bb7b3b8",
+ 693                    "ef5c9cd1ebe6e3be5e8b1347eca0a6f0b138986c71220a7f1c2c14f29d01beed",  # sai modelspec
+ 694                    "27bc71eca2d2ff7459165acc12010230911db7709a4f6a5c255befedfa6b1649",  # diffusers
+ 695                ],
+ 696            },
+ 697        ),
+ 698        (
+ 699            "stabilityai/stable-cascade",
+ 700            "StableCascadeDecoderPipeline",
+ 701            {
+ 702                "pkg": {  # prior=prior_unet
+ 703                    0: {
+ 704                        "generation": {  # image_embeddings=prior_output.image_embeddings,
+ 705                            "negative_prompt": "",
+ 706                            "guidance_scale": 0.0,
+ 707                            "output_type": "pil",
+ 708                            "num_inference_steps": 10,
+ 709                        },
+ 710                        "precision": "ops.precision.bfloat.B16",
+ 711                    },
+ 712                },
+ 713                "file_256": [
+ 714                    "fe92687deefcfb33bb3ec181254b55fe4e434c5084ce9d38815eaa32487ad376",  # lite bf16
+ 715                    "2c8d58b267678aecfa6705a0a0375c88613065a8a8d32ad3a4c3867f5461cb3a",  # bf16
+ 716                    "6c218dc948575e3b14b03dffe2014d7870ac505005770ce3abdc28e920a03c05",  # b modelspec sai
+ 717                    "a6c3d534a9be308e95d2c3224af94a854bebd9b503f620f1ae3c8e6ba4a341bf",  # lite
+ 718                    "7b431ea7d0f10e72b3eaece353bf6bf2f6bc717b6f4207411be186b40dec1f43",  # b
+ 719                ],
+ 720                "layer_b3": [
+ 721                    "9506d989de0226018de214f7ced4670eb5aad4a0c399a9229488ceccdf9a3ceb",
+ 722                    "6c09dcb83e0cd7ad735eb763c5e3721c579d796853f0b9d31ba74fb13cad4f94",
+ 723                    "e07025965cee925e31f1d617ea8baa575e7db910d40cc0482fd83df317c0812b",
+ 724                    "d9a42e4226fb2778aaeaf0d6bda173a4ff95aa574c6d9e27e41542aa469e40a3",
+ 725                    "8dcd87dc7a9b877e8e2a00abac44c4da9eadf2b8df4ae68f27415bb791381a96",
+ 726                ],
+ 727                "layer_256": [
+ 728                    "630ec0f3adf97145316c034139836f9df952060d0237ac4e478c55d9a3a50bc8",
+ 729                    "80904f707c192ddd06be2cebeb2ebbec3eb0e9c99076d50824d391ef3ac67bf2",
+ 730                    "8ccedbe1e8cc4093f05b5f8d90e6103e688ae1ac71e0d6261fb17c42ff7c25e4",
+ 731                    "3524e7fa9ca6f7ef695bc2d3410934eabd5272946a05c8cacd7f329e0bd9f1dd",
+ 732                    "40499a8f45ae28558ed2fe4fc549a4cb469bd237434b331ccc0b1910310ed733",
+ 733                ],
+ 734                "identifiers": ["0.2.channelwise", "clip_mapper.bias", ".12.self_attn.k_proj.weight"],
+ 735            },
+ 736        ),
+ 737        (
+ 738            "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers",
+ 739            "WanImageToVideoPipeline",
+ 740            {
+ 741                "file_256": [
+ 742                    "b4602c35fa0519750a42c03e3f296c02d542291e344c4d702522cddbd1711f13",  # 480 diffusers
+ 743                    "6d7a34b63b70eb608324e546d979167a5e787ac6bca3528e63f54a11572d66aa",  # 720 fp8 scaled sai
+ 744                    "b2051cd29d6b2f0c924fa7a3e78a4772f0134d7b059f21590dcce416f4f6cbe8",  # 720 fp8 sai
+ 745                    "7664fe075b3c82dcecf89012ad3429eee41ee9f10d476f60bc2d2ae3c4ca986c",  # 720 fp16 sai
+ 746                    "8ef7ea5bf9eea636b9b3ebd84c40671b4a18ae2704cb4c8595cb5b25c1d8e8b9",  # 720 bf16 sai
+ 747                    "b2de21b99b2e72cb0ff15253b07e926f26e7cf1b7e229efc32f94ad1f1ed9395",  # 480 fp8e4m scaled sai
+ 748                    "0ca75338e7a47ca7cacddb7e626647e65829c497387f718ecb6ea0bae456944a",  # 480 fp8 scaled
+ 749                    "c058a4ac5363c35d1ab4dd3bdec788c23b267fa42a0d7c68aba599f2f74600c9",  # 480 bf16 sai
+ 750                    "27988f6b510eb8d5fdd7485671b54897f8683f2bba7a772c5671be21d3491253",  # 480 fp16 sai
+ 751                ],
+ 752                "layer_b3": [
+ 753                    "4b6c3354c9ee5694e00a78f5658fdf14129f159c3b78a57f82fb18e0f265a83d",
+ 754                    "c36c783559a40d22504f6c4bfb4f5aae760f3f46bbb3a595be79880935122175",  # fp8 scaled
+ 755                    "ac62f7d5583fd2e85b738fafaf233e2cde6e2857e04351135bb9ded45f9082ce",  # fp8
+ 756                    "215e89e855b5e9456af9aa68bc67567dc2269002aaa6b01d849ffec425fc628d",  # fp16
+ 757                    "324b8b6c2d512547a2c31bafa12e20acf313fd3aad587b293334f9f629edeec6",  # bf16
+ 758                ],
+ 759                "layer_256": [
+ 760                    "137881dad8c00063bc8bf05f93067736e419173cd171acc22f77b730db688a19",
+ 761                    "8c5952fd3d333d3a4b719bf7d8ce6b12d1d2e78caaa7e42d713788cfdcadd244",  # fp8 scaled
+ 762                    "86c58bc4864c97f394ea6bccb2ecedc4aab7166f5b9bfeb313edfdcb2918164a",
+ 763                    "cac45f7d8f1a0628cb0738bd308689e439b1cc6206e5f887d60d5b37d30138f2",
+ 764                    "60e4f71a0961b1346b6f6b5ebe4c8cc93219239c5e13b4c0f1e19e9b8e1324d5",
+ 765                ],
+ 766            },
+ 767        ),
+ 768        (
+ 769            "Qwen/Qwen-Image",
+ 770            "QwenImagePipeline",
+ 771            {
+ 772                "file_256": [
+ 773                    "9f33a59093af3abcc2836d4cf4b7bd122c238ca70a26c70f34fdde64646b3bcd",
+ 774                ],
+ 775                "layer_b3": [
+ 776                    "c87eedda853c12844a8deb3592a90bbcbd4dff2f7a850c28755e4aa171432150",  # diffusers
+ 777                ],
+ 778                "layer_256": [
+ 779                    "fda2472d8ef6587a4c979021a2390eeb7c8fc2bcf565330ab8dc6b22f5348ec9",  # diffusers
+ 780                ],
+ 781            },
+ 782        ),
+ 783        (
+ 784            "Wan-AI/Wan2.1-VACE-1.3B-diffusers",
+ 785            "WanVACEPipeline",
+ 786            {
+ 787                "file_256": [
+ 788                    "bd8bbb8834a274525ab65cbb063f21aa58973a054bfd1638bfe395504c9d9b99",  # diffusers 14
+ 789                    "192804a4e10b5bb0a13f5c224bc4ec9707b3b8cc0def8eea005dbce7c9d6752a",  # diffusers 1.3
+ 790                    "f202a5c59b8a91ada1862c46a038214f1f7f216c61ec8350d25f69b919da4307",  # 14 fp16 sai
+ 791                    "654693bf2a93a27cd67c3bcee238bc1d0cbb0dd9a74928ed7155fb21a2a1900a",  # 1.3 preview fp16 sai
+ 792                    "640ccc0577e6a5d4bb15cd91b11b699ef914fc55f126c5a1c544e152130784f2",  # 1.3 fp16 sai
+ 793                ],
+ 794                "layer_b3": [
+ 795                    "5357d78799a61cd2d72a8a2824c919d63f718eb3fba624af63689e9c657db032",  # diffusers 14
+ 796                    "7ae67b7ccf79d1c3f4531ae138e1eb63d52dd97a66b3fcbe1d68fded8df4d5b1",  # diffusers 1.3
+ 797                    "ee63ecdfb3da6901853a59ec950f3e7c3f6595ac46347a03881a4a9c71425377",  # 14 fp16 sai
+ 798                    "82762df3539021d3c0342e0da04137ddbe95ef37ea933cd0a68c09c2c650f2ac",  # 1.3 fp16 sai
+ 799                ],
+ 800                "layer_256": [
+ 801                    "2684413479030170fb3f08c1069c02957ffc386a59168d23b55d579d5c675269",  # diffusers 14
+ 802                    "d527680fa735e5f30ef8852aabf8a49f02a094bc4718f0787c5b85710a13c026",  # diffusers 1.3
+ 803                    "9677492a107b3ed827c7285db3393f5321d451cc6d922a4d0488d2a67e939446",  # 14 fp16 sai
+ 804                    "aaef66a4f65ecf852888d160b2122753fe4c6d642b5d41db29e4ce9e6855b5a0",  # 1.3 fp16 sai
+ 805                ],
+ 806            },
+ 807        ),
+ 808        (
+ 809            "Wan-AI/Wan2.1-T2V-14B-Diffusers",
+ 810            "WanPipeline",
+ 811            {
+ 812                "pkg": {
+ 813                    0: {
+ 814                        "precision": "ops.precision.bfloat.B16",
+ 815                        "generation": {
+ 816                            "height": 480,
+ 817                            "width": 832,
+ 818                            "num_frames": 81,
+ 819                            "guidance_scale": 5.0,
+ 820                        },
+ 821                    },
+ 822                },
+ 823                "file_256": [
+ 824                    "299e6304544f2783896372fa919e755a8bb9ab8caf898ce08a678dae391e1179",  # diffusers
+ 825                    "a9278e6e9c82d174e6c67b3c97d8b97fef30af51dcf59160f2fc241f6819f5dc",  # diffusers 2
+ 826                    "be531024cd9018cb5b48c40cfbb6a6191645b1c792eb8bf4f8c1c6e10f924dc5",  # fp16 sai 1.3
+ 827                    "6f999b0d6cb9a72b3d98ac386ed96f57f8cecae13994a69232514ea4974ad5fd",  # bf16 sai 1.3
+ 828                    "2e39adde59c5e0e90edbb35873126b0d67928b5c11c501e384e976d6dc597cce",  # fp8 scaled sai
+ 829                    "2ee88ab18d7ed7691c5b7f8bdc3d0a9815e6efe75499287564830fd209d3cdfb",  # fp8 sai
+ 830                    "46c27d3693bf2475990a912e08bf67fc6e6cd5396eab87b5e8dd1fcd3651364a",  # fp16 sai
+ 831                    "193535c6450045f718df5f011de6d94d49bd9b13f37ca0412500f050dbbb01a8",  # bf16 sai
+ 832                ],
+ 833                "layer_b3": [
+ 834                    "32266d1c79b518adb9d21837e6a427f6ae55b68cfdd673a7dadb38820fddeb48",  # diff
+ 835                    "3b6989856f4f05368524c1852d8660b73c84cfbe44460af017d7139c2a4641b8",  # fp16 sai 1.3
+ 836                    "f4d6cee3c112db93b3c9137ad102ec0e79ec7ab68b9bbc59004fbc268ccd5ddb",  # bf16 sai
+ 837                    "e627144f41055619eb5407699c46e69ac0d87cf8873721e3e48c9e842656abf8",  # fp8 scaled sai
+ 838                    "6c00f3fadedacb841c4b9b4321b94a11ef85a08c9dd9253e5f9ba95856715579",  # fp8 sai
+ 839                    "a0c339253c714b05877c8fbab649ed631cf021930978f3696a46f685a07c9092",  # fp16 sai
+ 840                    "6435da89a870fd0e88680d31de75b9a40c408a4768eff384ce9b9e99481e8e66",
+ 841                ],
+ 842                "layer_256": [
+ 843                    "52493c23c5fc1d087a283bc4eabb151421b7ae09affa12a5bb059d62656c5766",
+ 844                    "058dedb3d2683a9a5b671c6302690e22722c93f6ed92281d5fa74ab190e632a1",
+ 845                    "5fbed4b95e7196d3626003ea9e0fbbffd074b4297ca406e01b5b6c5d881a6080",
+ 846                    "3a2335c8e7a4359c071b50333b5c00eef6f42a1d5206915e2ee99464a8c5eae7",
+ 847                    "0542780670dd75d4cd9deda123d2e150730646c0a1a8d34582460991498a77a6",
+ 848                    "e925b8222774905c8fbf10af77811fde7870e563eedcde2c94bd5c727e952d49",
+ 849                    "3d915854976284347efa7aa0a117c0fc3b415c4208e1a6c94beb4ccb9720743d",
+ 850                ],
+ 851            },
+ 852        ),
+ 853        (
+ 854            "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
+ 855            "WanVideoToVideoPipeline",
+ 856            {
+ 857                "pkg": {
+ 858                    0: {
+ 859                        "diffusers": "WanPipeline",
+ 860                        "precision": "ops.precision.bfloat.B16",
+ 861                        "generation": {"height": 480, "width": 832, "num_frames": 81, "guidance_scale": 5.0},
+ 862                    },
+ 863                }
+ 864            },
+ 865        ),
+ 866        (
+ 867            "nvidia/cosmos-predict2-text2image",
+ 868            "Cosmos2TextToImagePipeline",
+ 869            {
+ 870                "file_256": [
+ 871                    "7fbd20dae97cc26a55c7aff3024bc84e554cff8f69966c725a24c8238c5431ec",  # gguf
+ 872                    "6d211f1c14cd793156da3a840dd5462ae072046fcd6f1dc64c613a5343bfe896",
+ 873                    "95a2b32ad31a271eb64d35985c7ea46f1448528af70932eb1f35d57f90c27be2",
+ 874                    "344e67faf333b7849fa94290c9028bdd5e40eb19700754c833cda0423bc10ad0",
+ 875                    "ce15ef565cbb9ef414a6f7a396c455d82d5f762d2174493da87fe009c5fee75b",
+ 876                    "94aa9f2b59330b88e97b6b439e2f206a51c86e6b154fb66d43ed149bfac23cf8",
+ 877                    "636de5388da249130d51752991a1792b90af31cbf43f021ae07f75756ee2d79a",
+ 878                    "472c5e4cf5056a1a59085addb5a86d801de39bf5e000d253f206a7f63c710029",
+ 879                    "663266ace67c22529c3b6bfa0e8bd69f0ba6e683f5f02b8e3da50881057ba142",
+ 880                    "21a674b314c1364d0dbb3712f5ed702996a7b7403c452835cac22709e01c2f77",
+ 881                    "3bf2df806c6472e039efc9e8d3181163d7faa7b385e61519b7d17d5e9c993a49",
+ 882                    "1de35e1603c4c30bc80b132ccea15fc0503369caf68290708f17e679e98cd41f",
+ 883                    "0738e559bbd71f7351ccba34b2b47362a3f829b92f3dbcffeaf1e44b0d52f42c",
+ 884                ],
+ 885                "layer_b3": [
+ 886                    "5a18ba14c41c6601dcc1195ca180ac7744357eb15ace39272788bda1a7151e9b",  # gguf
+ 887                    "67cc3eaf7987c89cd7ccff13de6bc03e3eec59d260d44486e2367cd946ce6f20",
+ 888                    "3c6fefa107742488d2e6856714198a762f2fd35c67edd50d4657eaf4b59c7ca3",
+ 889                    "4e1f90ee1e8959d334c9b1ea2cc5e58d0b8340e271c35f81c8a5ec26e16d9d76",
+ 890                    "f8171071e828524fcc2806126ad100a2198e450c82c0864c8fe8b358c5cbbfbd",
+ 891                    "8126101a0207ecfbd741394fd59f306bcb4c492b2a921e0921c426ca7bd38985",
+ 892                    "c942c5a85ff7cb602d8ca894f5d180c2224e91f0b62c3a21f6a425f9e0e8554b",
+ 893                    "c8c500de74da879a547875fe1046f62ab18bdfd09c09eb3da723cbc2319cb4e3",
+ 894                    "c0ac3f67501004e9e9a55d1658402ad97e42bf8a266edf81f6f3bb835ee476b9",
+ 895                    "84f5926eb4e11d826815682b076ed7d3bba4c86520859be80aa1ef92c72b26a4",
+ 896                    "1d4375aab5548708559b0fde150754a2163cd211eb20a5471e17afaeeb26e082",
+ 897                    "68bd8982f59c60d69c301d16dfb5a60f5d43d66c0b60138d48a22f5ded598e7b",
+ 898                    "c3e9a10cad7aebf979072092008be6e2815d03d28cbf316c15e8daf22116bd7d",
+ 899                ],
+ 900                "layer_256": [
+ 901                    "38f2a75eab667c0cc85f3946a23ca6dc2278438c25a9f93aaaa9f79c3808e180",  # gguf
+ 902                    "ee8434a5e9bc6fa07199de2d0c69fb87f7922c31792bafd13f527c9d92fecb0c",
+ 903                    "2f8382657babb4d0ae4f8e425ae33b21ad71deb6ba457fd6734f05208d52e06a",
+ 904                    "34b181a8291b571857cdbf67ac0081fea594a2f223bf20bd2fc8b0c889e9602d",
+ 905                    "d198c412b972e381acfb812304fa98ed0d97a2f072ddc195cd9a1eb83b1d8146",
+ 906                    "79580a13aff9859e67b0a9f4f8893236cdcfa58c3d43770641aaac8daee55a94",
+ 907                    "cfd48c7ad71c913fa8768167ed0c2ee8c207311b22b1e5a8761369b5a780e8d6",
+ 908                    "da91362ad85d4d2e80a2cb7a55e4ae0e52c9eef8b437a95894ce5ab75d36568c",
+ 909                    "15f84001f5205b6dd8c6f1334cb51c46f6171c7795fb2a557ea16b874f0c71e5",
+ 910                    "5d29179ad15a15d2561defcdda66f1d1e4d065c1e0738f9cba4db5b68b93d2ea",
+ 911                    "7ec489d1e461f5fb2af627b68034ca57f19c516aeccbc5d188b3bd27e3353a15",
+ 912                    "c8dc42fe7b411d746ebdf86286b91cd6893c5f028076b8fe4103f7ea8e1d8833",
+ 913                    "86df7c095aee01588e961438f322b85ca0100a9e440b8a2b6c724e00f748d8b5",
+ 914                ],
+ 915            },
+ 916        ),
+ 917        (
+ 918            "rhymes-ai/Allegro",
+ 919            "AllegroPipeline",
+ 920            {
+ 921                "pkg": {
+ 922                    0: {
+ 923                        "precision": "ops.precision.bfloat.B16",
+ 924                        "generation": {
+ 925                            "guidance_scale": 7.5,
+ 926                            "max_sequence_length": 512,
+ 927                            "num_inference_steps": 100,
+ 928                        },
+ 929                    },
+ 930                },
+ 931                "file_256": ["6927dcc812841c1da549bf11c97ddf30532aee0e708a6642fa64cf8e0dfcdef7"],
+ 932                "layer_b3": ["8b20714a6af89ea4bf4ada1f805c5b9d529ef136c229e9b75392242d62d80c3e"],
+ 933                "layer_256": ["9e44e6c919dc71c24a193641e6265cd9983a2a773b9bbaf527c10ac4837b29fd"],
+ 934            },
+ 935        ),
+ 936        (
+ 937            "audioldm-s-v2",
+ 938            "AudioLDMPipeline",
+ 939            {
+ 940                "file_256": ["fc30d5b5a3bb8d08672736efb1fff10755ba7024dace39b2dcb579a105aa2a5a"],
+ 941                "layer_b3": ["82fbcc553c1ad770d28fd1866b935249c5ebfbf75f3166ae823e1bc6ef39a95a"],
+ 942                "layer_256": ["d076446a58a36bf436e37444679d62bcf2f45689d4aa3d799b3fe801c71ed2c8"],
+ 943            },
+ 944        ),
+ 945        (
+ 946            "zai-org/CogVideoX-2b",
+ 947            "CogVideoXPipeline",
+ 948            {
+ 949                "pkg": {
+ 950                    0: {
+ 951                        "precision": "ops.precision.float.F16",
+ 952                        "generation": {"num_videos_per_prompt": 1, "num_inference_steps": 50, "num_frames": 49, "guidance_scale": 6},
+ 953                    }
+ 954                },
+ 955                "file_256": ["8fbb6a5e67c70885a8ed8e33df144ac61253e45977be5035fa18cfdf77d386c7"],
+ 956                "layer_b3": ["1db3439649b5362448455fb2ed6ebde0c3b973655a206832731149757ad165bb"],
+ 957                "layer_256": ["edd6bd51f1236f528ff8d32dc754f0b86cfac901b800642ea497358156dc00bd"],
+ 958            },
+ 959        ),
+ 960        (
+ 961            "HiDream-ai/HiDream-I1-Full",
+ 962            "StableDiffusion3Pipeline",
+ 963            {
+ 964                "file_256": ["3cb3f6d77a3fce19b90fa7f66da0cbe997b0785a38a788b559290d3062f6fd26"],
+ 965                "layer_b3": ["612eb9b2676a3e7b28b10aae045a97a95de2a399fe3801c8f6369589c3a832a6"],
+ 966                "layer_256": ["78fbfb7fddb9ccbdf91f22b0c3d304cbf0cc7305dbccb216982233849ec727df"],
+ 967            },
+ 968        ),
+ 969        (
+ 970            "cvssp/audioldm2",
+ 971            "AudioLDM2Pipeline",
+ 972            {
+ 973                "pkg": {
+ 974                    0: {
+ 975                        "precision": "ops.precision.float.F16",
+ 976                        "generation": {"num_inference_steps": 200, "audio_length_in_s": 10.0},
+ 977                    },
+ 978                },
+ 979                "file_256": ["359a5ffb89a844beb2fcfac584aae2cd7cd6e87c3ab1ec4e892ef45d91db77c2"],
+ 980                "layer_b3": ["eac241273f9f30982fc04aa88b4dc1c38b533430956a55b9ed4d3e5c717ec962"],
+ 981                "layer_256": ["ab109d01b43788063802f00c6ecab024c830ea58d668f5c2df9e3ae5b87d86cb"],
+ 982            },
+ 983        ),
+ 984        (
+ 985            "tencent-hunyuan/hunyuandiT-v1.2-diffusers",
+ 986            "HunyuanDiTPipeline",
+ 987            {
+ 988                "pkg": {
+ 989                    0: {
+ 990                        "precision": "ops.precision.float.F16",
+ 991                    }
+ 992                },
+ 993                "file_256": ["7d31ac8fa389ff39dd0a81430010e52c43b59f15adc00c83625a47881e16830e"],
+ 994                "layer_b3": ["bccd37ecc9f85d132b46d0bb67b4facb49fc6c091428a4feba9ab9a93140f5fe"],
+ 995                "layer_256": ["ed25d241d58ca298d28abd5919e70341ad194e77dce4859436b52ea4d8fcb616"],
+ 996            },
+ 997        ),
+ 998        (
+ 999            "Alpha-VLLM/Lumina-Image-2.0",
+1000            "Lumina2Pipeline",
+1001            {
+1002                "pkg": {},
+1003                "file_256": [
+1004                    "132b4d213fdd3cfc14333746fc3eb8bbe6358cd73c3bc95ac4ccec230b97dca3",
+1005                    "a7c09ebae62996a8289782161338a3cdba58c11d2d849c50b2d6502e152b0d6d",  # pth single file
+1006                ],
+1007                "layer_b3": [
+1008                    "198bde52f09736f1fc650dcdbd0e6b0f6a5ce186582554c1d9ee8ab16ac0feb2",
+1009                    "b52807536902cabbf84f99e4fa2f8713fb4ef77e739f06367ee0d486e3222faa",
+1010                ],
+1011                "layer_256": [
+1012                    "982893c99860aac8198c2e435cf85f782fce8f10732daf1f2881a26864400a4e",
+1013                    "dc937b59892604f5a86ac96936cd7ff09e25f18ae6b758e8014a24c7fa039e91",
+1014                ],
+1015            },
+1016        ),
+1017        (
+1018            "ucsd-reach/musicldm",
+1019            "MusicLDMPipeline",
+1020            {
+1021                "pkg": {
+1022                    0: {
+1023                        "generation": {
+1024                            "num_inference_steps": 200,
+1025                            "audio_length_in_s": 10.0,
+1026                        },
+1027                    }
+1028                },
+1029                "file_256": [
+1030                    "853d0ef1d61cbf5d682872322ea8b761ba3d2f85bfbccd58363bd6b2f837268f",  #
+1031                ],
+1032                "layer_b3": [
+1033                    "82fbcc553c1ad770d28fd1866b935249c5ebfbf75f3166ae823e1bc6ef39a95a"  #
+1034                ],
+1035                "layer_256": [
+1036                    "d076446a58a36bf436e37444679d62bcf2f45689d4aa3d799b3fe801c71ed2c8",  #
+1037                ],
+1038            },
+1039        ),
+1040        (
+1041            "openai/shap-e",
+1042            "ShapEPipeline",
+1043            {
+1044                "pkg": {
+1045                    0: {
+1046                        "precision": "ops.precision.float.F16",
+1047                        "generation": {"num_inference_steps": 64, "size": 256, "guidance_scale": 15},
+1048                    }
+1049                },
+1050            },
+1051        ),
+1052        (
+1053            "hunyuanvideo-community/HunyuanVideo",
+1054            "HunyuanVideoPipeline",
+1055            {
+1056                "file_256": [
+1057                    "bdb957b35585ea74ae42ca92865a68fa1bf1ebc6c5b7e686a889e5c977dc24c7",  #
+1058                ],
+1059                "layer_b3": [
+1060                    "d31c56b4c9444d4c2f1b10120fe964e0956f6b8c7e7c1e4cc5a1f37406fc49f5"  #
+1061                ],
+1062                "layer_256": [
+1063                    "fe741fdfd163bcb1e0ed81d80f79ac3576dbf6e6740674efadfeff782a48bed4",  #
+1064                ],
+1065            },
+1066        ),
+1067        (
+1068            "zai-org/CogView3-Plus-3B",
+1069            "CogView3PlusPipeline",
+1070            {
+1071                "pkg": {
+1072                    0: {
+1073                        "precision": "ops.precision.float.F16",
+1074                        "generation": {
+1075                            "guidance_scale": 7.0,
+1076                            "num_images_per_prompt": 1,
+1077                            "num_inference_steps": 50,
+1078                            "width": 1024,
+1079                            "height": 1024,
+1080                        },
+1081                    },
+1082                },
+1083            },
+1084        ),
+1085        (
+1086            "stabilityai/stable-audio-open-1.0",
+1087            "StableAudioPipeline",
+1088            {
+1089                "pkg": {
+1090                    0: {
+1091                        "precision": "ops.precision.float.F16",
+1092                        "generation": {
+1093                            "num_inference_steps": 200,
+1094                            "audio_end_in_s": 10,
+1095                            "num_waveforms_per_prompt": 3,
+1096                        },
+1097                    }
+1098                }
+1099            },
+1100        ),
+1101    ]
+1102
+1103    transformers_addons = [
+1104        (
+1105            "google-t5/t5-small",
+1106            "T5Model",
+1107            {
+1108                "identifiers": [
+1109                    [4096],
+1110                    "encoder.embed_tokens.weight",
+1111                    "text_encoders.t5xxl.transformer.shared.weight",
+1112                    "t5xxl",
+1113                    "encoder.block.0.layer.1.DenseReluDense.wi.weight",  # small\
+1114                ],
+1115                "file_256": [
+1116                    "ec87bffd1923e8b2774a6d240c922a41f6143081d52cf83b8fe39e9d838c893e",  # shuttle/flux diffusers# flux dev
+1117                    "565cb2487351282e8e4dbeb88e63f4ad28217ce0439f5a8e6525a924807d2d9b",  # bf16 modelspec sai
+1118                    "6e480b09fae049a72d2a8c5fbccb8d3e92febeb233bbe9dfe7256958a9167635",  # fp16 modelspec sai
+1119                    "4f2751ceeb2a96edd693e539dc5d6bba0b8d3814f49a9b3798403a0cec4b2e3d",  # fp16 diffusers  cogvideox
+1120                    "83690f3cc37cecb5e907f41ab0f7abb0855ef24a0a8aab9259f2888ce85a34e2",  # flux diffusers
+1121                    "7d330da4816157540d6bb7838bf63a0f02f573fc48ca4d8de34bb0cbfd514f09",  # fp8_e4m3fn
+1122                    "8490f7a22615c20651a63dbe7b4241929826a4de20292dc8e63bfc3c61e3654f",  # qfp8_e4m34n
+1123                    "d8720addef2596fef86b1b22e4b62875c9118779ba8723759a75dfcbc649ffd5",  # mystic mlx
+1124                    "7d0eac95abe8daae454bcd3d166b8bfc6a35fe68278f97479d62dbb6850f38c0",  # mlx flex2
+1125                    "ceabd6f71c7112cfaa4dfca8711dda97b79fb9b25983f1c95532de226045f1f8",  # mlx jaguar q8
+1126                    "49e139f50824fef40908ef4307c851e7adaa8b91bed44054c4829600dbedfdda",  # mlx shuttle 3 q4
+1127                    "211ade1d474f5dc83190aec8be5c4baf52643777790d64de0cbd84f63613e5e9",  # mlx flex1 q8
+1128                    "7894547154ba3fd6e364e66e2951ee82b4c3fc1ae0f95df6a4f9d1c5a4e98f17",  # DeepFloyd/t5-v1_1-xxl sft
+1129                    "eb529f693f4b17773a24e787fcba29486d5e1700dadcc20bb91e4c8b00212d08",  # pixart a
+1130                    "d80116f6fc39801e4eef425a584e7a7a41cbe5119797bef2dad67299909fe2ae",  # Q6K
+1131                    "31ebe18e901bfb6e5709a20ec1c95fce29bce2b9545073231e0f909a53239f5c",  # Q3 KS
+1132                    "6be2b0b7e2de7cf2919340c88cb802a103a997ce46c53131cec91958c1db1af4",  # Q4 KM
+1133                    "b51cbb10b1a7aac6dd1c3b62f0ed908bfd06e0b42d2f3577d43e061361f51dae",  # q5 k m gguf
+1134                    "9ec60f6028534b7fe5af439fcb535d75a68592a9ca3fcdeb175ef89e3ee99825",  # q8 0
+1135                    "8f5ab879234384235d56732f0cda07bf8801f30a49645248c5bfdeeb1665f64b",  # q3 kl
+1136                    "86427a1f4dba48940e45bf78d6db5bf0d48fce8b4656f5aba27955f06af9628e",  # q5ks
+1137                    "88b696cfae098f03bb078cc5944ef03aec1e91ec020a6b016b723a0f0532558c",  # q4ks
+1138                    "1dc600961d3c5ed081f6700485cdc7ed9cfb4631f2dc385b7ac6bd3c80846d0d",  # f16 gguf
+1139                    "f28631189911f8d7931e8fe642a4cb2a3c51f50da7cabbfa06b89bafc19c00d0",  # q3km
+1140                    "de9dfdd19d7ba6859993cadec5100665dc7a4fb71e1c6c8970959cbdaf4366e3",  # f32gguf
+1141                    "7a68b2c8c080696a10109612a649bc69330991ecfea65930ccfdfbdb011f2686",  # allegro
+1142                    "2c0c539ab8e8fba3877cc94bc483e427f74c525f817a809b028ebc8d96d75a94",  # hyd 1.1
+1143                ],
+1144                "layer_b3": [
+1145                    "ca94e03b7b1fdcb0d6ff5205eac56f145d2dff8a9c489faf80935bfec8387f18",  # bf16
+1146                    "c0e2b054bedd782909191b05748a88c28d1538fa91789fec63f036ba01dcc001",  # fp16 sd35
+1147                    "672de9b79d14001de7d1109ffc52e4d0cccc3bfee6f45648fa347703b58e2b99",  # fp16 sd35 diffusers
+1148                    "abdb187a996c51cb0469630c124b14eeb0bb8f5f635aca6c71dea264f8bd61ae",  # shuttle 3 aesthetic diffusers
+1149                    "8926f862b7763fd9688af317eba7809aa71a478484be0c738c269de368ace4a7",  # diffusers
+1150                    "e616b754cf55e55b3f9f17ab7e1fff95f0607c81782822fc1223ae22fb1e9f36",  # fp8 e4m3fn
+1151                    "b79e5f1878a62cd726bb4f9fc1415cacb071d278440e9026290c7b36cb41e1d4",  # fp8 e4m3fn sd35
+1152                    "77619d5278d9f547ddac17d4d99df56cb6a3a9e660ae31b2f896a4297907e62e",  # mlx t5 jaguar
+1153                    "c87c9d3cc7becc46ee34821299cf8551a6df5541582a45469a031bccdc4bd340",  # mlx shuttle t5 q8
+1154                    "7e6c32c01c89fc5d1610c410135aa9708e77a7444510e5e479fa677ff2b53643",  # mlx jaguar q8
+1155                    "a49c2bc301733967ddff113790e301773dc5dd71368b657af4141458de593ced",  # mlx flex2 preview
+1156                    "c2ea94030ea362e03d73d448fa5353ace0a449dc38c51a4a49fb148444ebb8ef",  # mlx shuttle3 diff q4
+1157                    "4a90463350f08ef41479da1d561ab41b8f8b792f1603a092226a838156aebfb0",  # mlx flex1 alpha q8
+1158                    "f86cd0324eebbffb81b15ad47dc8b63fedfa51dc222e44e1a958a7becce2bcb0",  # df safetensors
+1159                    "48c54c61c5f14e42761c6177539b2da3a22222516dab053952ca8d8e92f93d65",  # pixart a
+1160                    "311332d9738773669128814d944b1e860a8e3176b37abf43370bc06b43b454d0",  # flux
+1161                    "3f4e51dec6d542759cdea49b3bec14c090a4908f953fa3e182e2ea43b5b05402",  #  q5 k m gguf
+1162                    "beb25461e168359108add77263ea5cc121b7584cc4aa304ffc4e134783bb1d88",  # ggufs
+1163                    "43313f90a359c8c1c787a7a833b1ab9f7a38204ba36d0ba587c658d0d9bf0852",
+1164                    "fa9e97cdad26f55fedab83a3f114e0338c9cca3ea2bf8f1b168a6dfc5919bf8e",
+1165                    "93108d67f8829a7e1e8f3773e9ce53c67f365889c2acfd69816ac80fd43f8e08",
+1166                    "fc65a6cc55e89394d7bc0fa4ee952d63ce3bdc143b84b5aa4bb3edf7722a6b83",
+1167                    "8163bc781a7e013dfeb806bbb828a36913cf119363ea5fcd9071d87a0c227cda",
+1168                    "ad2ba63e1134bad1b15ee339313bc130708b2995e8b4b76fb44d727f28c26ad9",
+1169                    "4a844772638ffed2f61d45eaac984094b92540fa1391a4098608fc73a6cd4fd8",
+1170                    "76c31e1fd35da7de7cee97c1e7c5ccde640e6fac3e17a62e115ecf484c7196c3",
+1171                    "a4d672e22b5bdd8f8b0885cec4a173d0466bb1dcbfbf8400cedcc41c2494f16c",  # ggufs
+1172                    "d1860c3f01dc9f260d98b50d3d2bbc8dc2d3eefaa93778a8de9d7adfb897fc6e",  # allegro
+1173                    "b8719092fc58487406211f52dc55bf40b573ccfd29933a989c33a36b694f6f0a",  # cogvideox
+1174                    "795e272409bc4fa55f402485acf86b607256f91aa965295c5bb771c61f8e9e74",  # hyd 1.1
+1175                ],
+1176                "layer_256": [
+1177                    "bb20f7805209379aea4d6548f17e551cf27d0f8426ca169e4df8234f718ed5ef",
+1178                    "431580c2d86f9a9ed3500f776a4c997223e5644aed211f965354869ccfa4d76e",
+1179                    "2ccd548c4ffe34168c60779ebd497b9b410981a2fda813c8723a24a805c94ea0",
+1180                    "a608fc4e1cc9762e46187a1ce66e98e8ba4bc3a604cbfd96174bd876baea0fa1",
+1181                    "dc9e74cdf535e0b7a17e1335d0d8b38a00f94facf0cb01363baee09945a25278",
+1182                    "f07409710a69b2247aa4723a9b40d2225d5e5bfba7b60c51f0ea901fc2ef5ad9",
+1183                    "ed28f8b6cc472f352fc840b5a9f841ff17d76ae6918f0676464dca20529aa92b",
+1184                    "97c1a08f87c59b4c55ad4672841977cfce43ca7730bcd11d8c178a9330de1855",
+1185                    "968972839b859a9c4457f190fad2e17e8585ce27d9ef318df4f5b4e902143944",
+1186                    "4dbdeadc957c898c327197a3d8770188535672e9208beb29bbf48dfdf51c8955",
+1187                    "669172c2b5e8b97774d9dd0227ede40c4d25cae3adae97d9f281d03531e7e137",
+1188                    "39fff130b9ee240102c28a78ee1c4a643e9f800b734ff133f3ab2ad1357bd2f6",
+1189                    "6e047ed8cb7007034ff15840dd53c92096f0e7ed5befa07808de8afa35d35874",  # safetensors
+1190                    "adbd0baa059074501b7686db2b0c01715f3a317275c2657c5dfbfd6ee92389b7",
+1191                    "eb63790fb32b5660de34fa42c2e608df58f7aa3680b4984f0ee9008fe613729c",
+1192                    "f125c20a33b0ff2dbd4e8ad9acebc34383cb2ef98668169ef79a8c06655ced35",
+1193                    "e64e0ac83a785ef584a0e86b347fae8f9e2bd84324a49396ca8a9fe7532a947b",  # GGUF
+1194                    "70001b3ac1b66522142bb86e4c3e87e20c2bbd07276c763878e0838ef6184aad",
+1195                    "f46fd1e2b5fef3b9f7ae80d183cc77f7be181117a72a0bb933bdef0bc6cd679e",
+1196                    "83676d73726d101325a47c7f8a60cedf10bab99ea79a6bedad7761220cb4a625",
+1197                    "a621a907586e5e270e7c7873b167364d8a935ff347d8240fa9bab319678da690",
+1198                    "f0af1a089f40d8611db5c59469314f1547e2df23c6eff24860359b37ea9bd966",
+1199                    "72478320b8dbfd9aeaea010dcf0896e3116fa5ab940f3b472882d9f9d2d7333f",
+1200                    "9c1a88e36334a48d8482fec54b14ea1d5fd31f0dbb65d13cc616e63dc7c42be5",
+1201                    "d0689f727e8ac4fef3ec4b1f29e8a3bd12e1116559eeefb2a1a457cd4e676d1e",
+1202                    "fea158a4afcfaa6e95e04799bae0287de0c4fcb188f3b41768a46ce48c71c9df",
+1203                    "2e5bc4e73312b5aec4c1a55631cb4ed69cf34ccaa6d1f28f7045f137a579b439",  # cogvideox
+1204                    "015fdecbc3b5369dbcb2302e4b79985437ac4496d1b9ad63316423a222fb0803",  # hyd 1.1
+1205                ],
+1206            },
+1207        ),
+1208        (
+1209            "google/umt5-small",
+1210            "UMT5Model",
+1211            {
+1212                "identifiers": ["encoder.block.1.layer.0.SelfAttention.relative_attention_bias.weight"],
+1213                "file_256": [
+1214                    "a8e861969c7433e707cc5a74065d795d36cca07ec96eb6763eb4083df7248f58",  # wan t2i diffusers
+1215                    "decf9b70814ed5e9965bfca9fbd0483462e2bf743790663025b7742f8c014c72",  # fp16
+1216                    "0a07449cf1141c0ec86e653c00465f6f0d79c6e58a2c60c8bcf4203d0e4ec4f6",  # auraflow
+1217                    "c0ef3a140898e228a3520c9adec60743d2e8e5b3d229651bb37f1a3921919f99",  # wan
+1218                    "7b8850f1961e1cf8a77cca4c964a358d303f490833c6c087d0cff4b2f99db2af",  # wan i2ixxl sai fp16
+1219                    "c3355d30191f1f066b26d93fba017ae9809dce6c627dda5f6a66eaa651204f68",  # wan i2i xxl sai fp8_e4m3fn scaled sai
+1220                    "fa1d36fd54f171ae60fea915c23bd77986b330bbed9729f0d2f8ecbe9168bc48",  # gguf
+1221                    "4a3176f32fd70c0a335b4419fcbf8c86cc875e23498c0fc06f5b4aa0930889e0",
+1222                    "adbc782b9145a27e15d63dfa25057efca0ac75e2db7d372c901ddaa130ca2def",
+1223                    "b7e2ca4c493c9d51fa951005e8ceba2f4b6b6877cfb4c36a8955c6cd68a1dba7",
+1224                    "2521d4de0bf9e1cc6549866463ceae85e4ec3239bc6063f7488810be39033bbc",
+1225                    "9209b4c77b34ad8cf3f06b04c6eaa27e7beeebb348a31f85e3b38a1d719b09ed",
+1226                    "8bc12d80bc0413573fa58a93626117440b4528f640dd9cb310732e05fa9e6c3e",
+1227                    "f64f8d6dc4d8a24276df69d0ccea789aae686f7417950a41e6568c30cb478a5c",
+1228                    "17cf97a5bbbc60a646d6105b832b6f657ce904a8a1ad970e4b59df0c67584a40",
+1229                    "eaea358bb438c5d211721a4feecc162000e3636e9cb96f51e216f1f44ebd12ce",
+1230                ],
+1231                "layer_b3": [
+1232                    "cd92b29c9099a640e3f5d4a76e64b3467f87f6c056119e0defdff94d311ad6de",  # wan t2i diff
+1233                    "1c943dbcb8b328a7c6c852921ddaefbd84c9df8c83bc51fe303c1f06cb734102",  # fp16
+1234                    "1639a6467af0db1e15828d33b878e568cba1335947eeadd481170bcdc9ba8e33",
+1235                    "72a0329740dee29a2c099eec3c320b3945590a74293356014c30249fe69652e5",  # wan
+1236                    "0374cba03c607ffe8ab8f04994d82f82e80901dc7578f1a9a6cb2637608be5d5",
+1237                    "d75a407f873e1cfa1a0a36214b53b14bfebe9253ea263465151c07f0d57f3f29",
+1238                    "621153502b985c143d304318c91dc3d10296d24268c81e3538fc336fdc84c915",  # gguf
+1239                    "43bb052945d38a68bec27c3d26162e88e306e6074d027d3b4b2b8ae2b1851691",
+1240                    "98f50ea5d55e61c1478df47e567e48bdd036d240b9129e64d53a826406900adc",
+1241                    "9400313b8eae31699473daa5f840d25a4ef660f68de9a7894f1a28f214f23384",
+1242                    "9f13826b8e4ddde24d80de6a947a7868e26cea25dda52790ee6ed695ff72b9bb",
+1243                    "475773ab108a537ff904b84e7f3a80129ba4983deb7170b6b52c922ece6069ce",
+1244                    "5ef27b3c1eddb08cfe41b452cf9529d86dff811645d40c165bae324486d19e96",
+1245                    "e170559d8551cfe651344594e54c0a9a90c0068b00f3866f6e9a3737e20925cb",
+1246                    "e8dc7442a20bcdc7b6e5dd0265939d88896eab5ddd33ee16f1f09537e65914b8",
+1247                    "4d3d5049857d01741780daf01e96617092973305637b435f4895499a26bbaede",
+1248                    "7a2adadc2372feda23b2169337276adda6d1fdef82ba69f0d3321c4c6ba8c604",
+1249                    "0a7c61a85bb3f51f75924de48ef3f5e87cbf8901f600cbfcae97f5e2919c4148",
+1250                ],
+1251                "layer_256": [
+1252                    "467916d35f3053dce1d40d998fcaf6aa03feda75aa578d964dd61461e23641a3",  # wan i2i diff
+1253                    "58deeef888d4ded4ffababfbf8da27227a4a6ff8adfa42016e12c0180f713816",  # fp16
+1254                    "178ebd3fa3418d33a2e45a80d8b9d3662ff4a8e75f3de3f0332f82c505d8152a",
+1255                    "8700dcb651465fe6c925b7ad6068b58b32951832fff0ed19819510f8d0713ee5",  # wan
+1256                    "954f2129ba166e746c71433f717b572d8869ec14b32b7f214d1701d3b1120047",
+1257                    "32f5fc1daea014b6488b96c2a1330e0aad87e074844fa3e2e3f20b9e58440395",
+1258                    "9245abaf6df8a4b5fcc828ecbcd7b21a1b19bf5f3c4388fb5c8eabc140276dce",
+1259                    "172d0fbbd379ae014a7008e148813818494e9e645db802fd000d443369df9d17",  # gguf
+1260                    "2fa68a26b0386aaf9123d2b4067dafc8631ee724602197dd353f3ea5a61dac8a",
+1261                    "16f0054014e6d07b86b0526d5bcfed7d2aa3aebe3e44e6758933d90cbd3da46e",
+1262                    "fd62047f5d27ff43210c117dc0f253c101e694a5331d6b684688606c92c65ccf",
+1263                    "ddc4f38db9f132fb1b736c1d693b5c039a2d6fe83bdf4f1c1e7a2745b5d79124",
+1264                    "9e9ab11b3ea059b84ae2bcc5be76ab3f730a486d92a16f1fd2a959bdc2ede08f",
+1265                    "bfb178b1ce27f00e122d2328c662fdef6cc239c07efc749aa61ae2d395441b02",
+1266                    "50addf6a911b90194a75b0212429d1af55eb2f9d24715479b9ccc4a40adc299b",
+1267                    "2e46e9f1b714d72160d3b3b775a845b3049a01396fab935f1278d9e8de2ef0c6",
+1268                    "db8d2b49d9042e39d6531b33ec3bebb9cdf42b9e6ad56163f08da2a7da2a53cd",
+1269                    "2d81d19ad5440422b85e0b17c71914269f6c25c9b1fa321c0dd6119ddb41d62d",
+1270                ],
+1271            },
+1272        ),
+1273        (
+1274            "google/gemma2-9b",
+1275            "Gemma2Model",
+1276            {
+1277                "file_256": [
+1278                    "e909230aabafad02d097c7dc02f2ae062b4e6b0593477c1f07679d277e09ce71",  # sana bf16
+1279                    "d61628bc793240439e608c5ae744f55ec8770f684abb63602648a24cb6da60bc",  # lumina 2
+1280                ],
+1281                "layer_b3": [
+1282                    "55a3c812ac0832d154867f5927365bcc776926e48e65f7f35a81fc11f4bb81da",
+1283                    "543572889beb25cad83a43ce70cdd255d2c82951d6595e8c97ff62fd05871c99",
+1284                ],
+1285                "layer_256": [
+1286                    "a0d820c39578cf888f398579d9a00d69b31c81e049795ba70008dad8fe5b3a33",
+1287                    "abc83b04a04467579ea1952a7efbdd252b8641ac0e2a6a9be2a5a73e371111d6",
+1288                ],
+1289            },
+1290        ),
+1291        (
+1292            "google/gemma-7b",
+1293            "GemmaModel",
+1294            {
+1295                "file_256": ["01676b4c6e765f737a5e9854a315de3887e939c370cae116d505777729099a68"],  # lumina next sft d
+1296                "layer_b3": [
+1297                    "438d82c867240f194a4e15798eef2886a911c8f57fa2d9f4ffad1d56e7bd1ccf",
+1298                    "1de38e09f5f2c5345de48b8cd4dddcfff3e341cc0059752446e186b3863f0981",
+1299                ],
+1300                "layer_256": [
+1301                    "e4835a72d582b4ae066d6ff0519f2ee9f8b21fb02e8c28d8eaa317f8d1e9ea75",
+1302                    "1657c7180b48672004f4463308dfdd56d92eedeb23d1408ea766985ca208e5aa",
+1303                ],
+1304            },
+1305        ),
+1306        (
+1307            "google/mt5-small",
+1308            "MT5Model",
+1309            {
+1310                "identifiers": [[250112, 2048], "text_encoders.mt5xl.transformer.shared.weight"],
+1311                "file_256": [
+1312                    "0524484ec81425ba9deef6fac1393a78ba9b1c9bfed704a4be5f9c7255975cc1",  # fp16
+1313                    "32f70f1d187e131a5fc3e4f0edc97ce89360d8e2f1d90177a443a05296097acc",  # fp16 enc
+1314                ],
+1315                "layer_b3": [
+1316                    "a1d616c37711ec7b9073d04734af2f5fd02f9035a322eb46efeace922e104c51",
+1317                    # "bc71d4259f4feaa0fb27c1f288765004840f39247cddc98b3ac37329ff1354d0",  # fp16 enc
+1318                ],
+1319                "layer_256": [
+1320                    "bd337daf0c1aa36896013109b406a0580aa3bb8ab9291d89df3015d737358e95",
+1321                    "2e40c48c96fc7df636aad96d3e78ed0ba9f68c3059e21b7fcf917f284c569a61",  # fp16 enc
+1322                ],
+1323            },
+1324        ),
+1325        (
+1326            "Qwen/Qwen3-15B-A2B",
+1327            "Qwen3MoeModel",
+1328            {
+1329                "file_256": [
+1330                    "c56947057481fb5e7cdf766e442da81717b34addc88bbe8f3728fd25bd03cbae",  # qwen3 coder 53 a35
+1331                ],
+1332                "layer_b3": [
+1333                    "d2d1e0875202f5c9c84c781a2105620250733bd01832f67b2c17bc981d1eb508"  # qwen3 coder 53 a35
+1334                ],
+1335                "layer_256": [
+1336                    "408c01da57c4968b7b0e36d98a74e321153e7aeb058fea63ffd140e323526476",  # qwen3 coder 53 a35
+1337                ],
+1338            },
+1339        ),
+1340        (
+1341            "Qwen/Qwen2-VL-7B-Instruct",
+1342            "Qwen2VLTextModel",
+1343            {
+1344                "file_256": [
+1345                    "1f48ac458d6fbd0aec53a116065a7ee3f1d34bddde544e25c16a05c9d5392b78",  # orsta 32
+1346                    "0e85c7111ce849293e97aa09ce1172352ecece023a3ecea7ac8311e326b47f3a",  # orsta 7
+1347                    "d725335e4ea2399be706469e4b8807716a8fa64bd03468252e9f7acf2415fee4",  # qwen img
+1348                    "e10bd9583a77250376d9134cd6b46799029dfa3b4d7989c1050b3ec149cc7cf5",  # qwen flux
+1349                ],
+1350                "layer_b3": [
+1351                    "e4f681bde70a753f30f83495a2aa340d251bf3d818eb5a1cbe58f85fd6ea0d40",  # orsta 32
+1352                    "47b062ce8ddb14845fb1a71d2fd88fd52a82e26561ba3eb05be057915a867775",  # orsta 7
+1353                    "b6386f70b528ffa9e09fdd8db8a7b91a7c462ed97b06963576c6139e25fdcf31",  # qwen img
+1354                    "4cd449df9f9004a7e53005583a7e4cfa6de42912f03647d2ea799d489e9c1406",  # qwen flux
+1355                ],
+1356                "layer_256": [
+1357                    "ed36a4a11c4ebebb10d1e010cb93e2e43fcaf975cd42bb6c9958537593d0d44d",  # orsta 32
+1358                    "f7f6f64e7b6d7826400a2fc0eef942a47c47bd5914e051ad0c8cd9ff5ff7982b",  # orsta 7
+1359                    "f341ed0f792cf0570ceb21d3b64ed14bf9875e9fcb90116851364eeed683a6ca",  # qwen img
+1360                    "ba031d0da78afe24ae63558ad29b8028244a7bd4750a5615dab9079fe32a5fd7",  # qwen flux
+1361                ],
+1362            },
+1363        ),
+1364        (
+1365            "openai/gpt-oss-120b",
+1366            "GptOssModel",
+1367            {
+1368                "file_256": [
+1369                    "68a8dc1f8e2e5996cb702f14332a25ddf3463daeab2df68e21ca09ef181203c3",  # original model
+1370                    "a881aa5f561b26a22b14a8262aa61849ace349ffd73d74769e030ac90a1fcf8a",  # diffusers
+1371                ],
+1372                "layer_b3": [
+1373                    "b52807536902cabbf84f99e4fa2f8713fb4ef77e739f06367ee0d486e3222faa",  # gguf
+1374                    "43c618018db1fd6e915dead610652da261d9058b73bc5355c85c6ac69af4d913",  # "original model"
+1375                    "ab27ce7391b7fbd6ce3c319faa119afdac68f746af6a0ce2c3400a132f36f6ac",  # diffusers
+1376                ],
+1377                "layer_256": [
+1378                    "de5dcad822be5ed6196f0f3f6965739993118d14db97b33a94a269f4f1b7a363",  # "original model"
+1379                    "575f1977ed42d95a050e13dadaafc05a6d94c8aadca8364dca8a62aa4f2b146c",  # diffusers
+1380                ],
+1381            },
+1382        ),
+1383        (
+1384            "microsoft/Phi-4-multimodal-instruct",
+1385            "Phi4MultimodalModel",
+1386            {
+1387                "file_256": [
+1388                    "bc703090b63eda16f639fa4de7ac54635c23105ab1da2f6ec4d3403151d38ee6",  # mini
+1389                ],
+1390                "layer_b3": [
+1391                    "cf4add4ada6082f448788eaf2937f645b5212db88e06ee81475b8be0e99063dc",  # mini
+1392                ],
+1393                "layer_256": [
+1394                    "7ff992b780b2f8993dd6bb9612207943638b2a42badc976ce80893bc205e801b",  # mini
+1395                ],
+1396            },
+1397        ),
+1398        (
+1399            "laion/clap-htsat-fused",
+1400            "ClapModel",
+1401            {
+1402                "file_256": [
+1403                    "c92b5a2bee69ff5dd05820d9e0a5cddbc9c9b9dd19a6cb3214f0cf4f29a4d1b0",  # audio ldm
+1404                    "ae69f555e7f1a2333b8e684c9fa8233f44a47bbadf76d484f941b74f74d2753d",  # music ldm
+1405                ],
+1406                "layer_b3": [
+1407                    "a4d26450ac399d51b9abbe37859615bb02a5cbf63521da4c7cdc549d04a2872c",
+1408                    "ddf310d8eb2d4e3f61e605978675a9d3a748cad9406b9aee8335eae013e77573",  # music ldm
+1409                ],
+1410                "layer_256": [
+1411                    "843ba86000971d6067bfc4f3ed6dd01bd6f6726188aaa15d86b05554f4fe8481",
+1412                    "27529e30442d030a28badf9d62710f4b74e38e9c4424ed169c7e0ac072f5a771",  # musicldm
+1413                ],
+1414            },
+1415        ),
+1416        (
+1417            "google-bert/bert-base-uncased",
+1418            "BertModel",
+1419            {
+1420                "file_256": [
+1421                    "c6c6348af2cb4d5852fe51102ce39605903dbe7925c005cf8995506cc21ea914",  # hunyuandit
+1422                ],
+1423                "layer_b3": [
+1424                    "30d7d2cc3ec9e4ba45844e005d0bbcb5887b6a0976042f73da916237dc5c4c12",
+1425                ],
+1426                "layer_256": [
+1427                    "94fd2508680ff684eff57e4a5a8ca46bf338fc356a9cf6fe8db2b84543dd7971",
+1428                ],
+1429            },
+1430        ),
+1431        (
+1432            "llava-hf/llava-9b",
+1433            "LlavaModel",
+1434            {
+1435                "file_256": [
+1436                    "f5ad57d3eda300a3195bc9c0bb36ab76ebe88831f128e9851e63440aff4a6741",  # hunyuanvideo
+1437                ],
+1438                "layer_b3": [
+1439                    "d7d6ccb9dbba90b64e4cd259b6309e56708b3f4fbd6e9f85e9f0410e549133ef",
+1440                ],
+1441                "layer_256": [
+1442                    "9969c41152aba689413b7f63888ecdc0c0badad2c2960e689ebc4c0e4a696c73",
+1443                ],
+1444            },
+1445        ),
+1446    ]
+1447
+1448    additional_tags = [tag_pipe(*entry) for entry in diffusers_addons]
+1449    additional_tags.extend([tag_base_model(*entry) for entry in transformers_addons])
+1450
+1451    assimilate(
+1452        mir_db,  # format
+1453        additional_tags,
+1454    )
+
+ + +

Create mir unet info database

+
+ + +
+
+ +
+ + def + add_mir_diffusion(mir_db: mir.maid.MIRDatabase): + + + +
+ + 
1457def add_mir_diffusion(mir_db: MIRDatabase):
+1458    """Create MIR entries missing from the database"""
+1459
+1460    repo = "microsoft/speecht5_hifigan"
+1461    series, comp = make_mir_tag(repo)
+1462    mir_db.add(
+1463        mir_entry(
+1464            domain="info",
+1465            arch="gan",
+1466            series=series,
+1467            comp=comp,
+1468            file_256=[
+1469                "d9dc6513c30a5b86c2497712690c04fe74b4aa79fdab6d490b34fcb4e24c590c",
+1470            ],
+1471            layer_b3=[
+1472                "85b5acdf29ad04c63f885383340d8e3445ae0055521f82cabb82bd09cfb9a956",
+1473            ],
+1474            layer_256=[
+1475                "bd52b538e7ac05711be9321cfb7619d4056996ce32923c9c91ee02cf69154770",
+1476            ],
+1477        )
+1478    )
+1479    series, comp = make_mir_tag("lodestones/Chroma")
+1480    repo = "lodestones/Chroma1-HD"
+1481    mir_db.add(
+1482        mir_entry(
+1483            domain="info",
+1484            arch="dit",
+1485            series=series,
+1486            comp=make_mir_tag(repo)[0],
+1487            repo=repo,
+1488            pkg={
+1489                "0": {
+1490                    # "diffusers": "ChromaPipeline",
+1491                    "generation": {
+1492                        "num_inference_steps": 40,
+1493                        # "guidance_scale": 3.0,
+1494                        # "num_images_per_prompt": 1,
+1495                    },
+1496                }
+1497            },
+1498            file_256=[
+1499                "d845553f11e6afe8139c41ca73678f9f03eab2e68d2e1c6f03ae19509a4d546",  # sai
+1500                "1b2993a44e63b2250496f69edce643bac2fb79833cf92ba8dd95cbd764d970c7",  # annealed sai
+1501                "2dd46f08516246df1f582047cc09268ce4f747357baff05b13148e71519029fc",  # diffusers
+1502            ],
+1503            # layer_b3=[
+1504            # "8da38c3719e77a38a20356c9f92f5ca0101c17406d7a9817323cf67b74088520",  # diffusers
+1505            # ],
+1506            # layer_256=[
+1507            # "267798815e0855c2253061c6a6ab70edf9590e8ea1ba9b4621eeb0f6615ee37b",
+1508            # ],
+1509        )
+1510    )
+1511    repo = "lodestones/Chroma1-Flash"
+1512    mir_db.add(
+1513        mir_entry(
+1514            domain="info",
+1515            arch="dit",
+1516            series=series,
+1517            comp=make_mir_tag(repo)[0],
+1518            repo=repo,
+1519            pkg={
+1520                "0": {
+1521                    "diffusers": "ChromaPipeline",
+1522                    "generation": {
+1523                        "num_inference_steps": 8,
+1524                        "guidance_scale": 1.0,
+1525                        "num_images_per_prompt": 1,
+1526                    },
+1527                },
+1528            },
+1529            file_256=[
+1530                "2c0c7d908d04418a48b453c293237a9826d54472cf0ba76e28697d1309d1021b",  # sai
+1531                "c88f6794753ba23e8f6bf8c84cf220daa35a6aa16d54ea0c3e0136f52e5da7e1",  # sai delta
+1532                "c759d67ca3ef50a9a1c242e3291c57f406646f226a95f43f66577996494986db",  # diffusers
+1533            ],
+1534            # layer_b3= [""],
+1535            # "layer_256"= [""],
+1536        )
+1537    )
+1538    mir_db.add(
+1539        mir_entry(
+1540            domain="info",
+1541            arch="unet",
+1542            series=sdxl_series,
+1543            comp="pony-diffusion",
+1544            file_256=["67ab2fd8ec439a89b3fedb15cc65f54336af163c7eb5e4f2acc98f090a29b0b3"],
+1545            layer_b3=["bf4c2154daa4ece7292277b210d081f98759e9ed4d5c889564632e3ccc4a1071"],
+1546            layer_256=["465425d4420dcf5aa4b4d5b456db11a1fcc7c8f61b2e4a87e2470297c98bb96e"],
+1547        )
+1548    )
+1549    mir_db.add(
+1550        mir_entry(
+1551            domain="info",
+1552            arch="unet",
+1553            series=sdxl_series,
+1554            comp="pony-diffusion-turbo",
+1555            file_256=[
+1556                "7555ac941f3a767833830ba5cc9a4508a9777cbf97b487b6baf0400ab7000587",  # turbomerge
+1557                "9322f9d91b28abf09e4137bc02ec806af23510221a164e71b81778e61cc3b4b2",  # turbosimple
+1558            ],
+1559            layer_b3=[
+1560                "1e8f23fcd4be0f00eb52368b91c709fffa8a3b8e21772b92b2e0671eed9117d0",
+1561                "5c8b3f34f9d0a58135cf72fbfe9b5d75b5545a10e3d726478543fa7cc510a8bc",
+1562            ],
+1563            layer_256=[
+1564                "7edf51ef09b39c46937a4e4141707c040cd12af0d95299a4d3cd2b7d3fabe035",
+1565                "74e4dbc89d57d61ff7e8af8b0fddcf7466ba233d53ca4ffb7777138991bc3d52",
+1566            ],
+1567        )
+1568    )
+1569    repo = "cagliostrolab/animagine-xl-4.0"
+1570    mir_db.add(
+1571        mir_entry(
+1572            domain="info",
+1573            arch="unet",
+1574            series=sdxl_series,
+1575            comp=make_mir_tag(repo)[0],
+1576            repo=repo,
+1577            file_256=[
+1578                "8ece83aa1bed1fb39a2b81f1660f0ce6889218e493c1f2ed55e9f15f59a7e03f",  # v4
+1579                "6327eca98bfb6538dd7a4edce22484a1bbc57a8cff6b11d075d40da1afb847ac",  # v4 opt
+1580                "1449e5b0b9de87b0f414c5f29cb11ce3b3dc61fa2b320e784c9441720bf7b766",  # v3
+1581                "e3c47aedb06418c6c331443cd89f2b3b3b34b7ed2102a3d4c4408a8d35aad6b0",  # v3.1
+1582            ],
+1583            layer_b3=[
+1584                "268ffbb120670b9c4b25158bd474c787740884b7738b48203aa03c4c3f00028f",
+1585                "18fda1a55cad137d62c81d4328f5ece85d88b126261e06b9e14ab68055d5d484",
+1586                "bae9bc8a5c43145bcf92ee3391618d9eaddd689f626991bae202de9cf5f1e70e",
+1587                "d6bc5ccafa2b97c867b13a1e7a8c2c7ad9c4877055a66c71bb773557bc306447",
+1588            ],
+1589            layer_256=[
+1590                "c21d1c38813e078817122e12866ab39f5aa7f56945dd4a8beee3cae1e0f139e7",
+1591                "b916c162c981155aaf74e93d5314038af6767bb5a129c51ee05a1fb6a206c6ac",
+1592                "ecc6bfc73824a2d7c3b0ca184854a235859f329c83768f017b07a19a535d17b4",
+1593                "97f6ca05de7fbdae7aacb2427a552f924492176c474a23dd252c192e1c0e9d65",
+1594            ],
+1595        )
+1596    )
+1597    repo = "OnomaAIResearch/Illustrious-XL-v2.0"
+1598    mir_db.add(
+1599        mir_entry(
+1600            domain="info",
+1601            arch="unet",
+1602            series=sdxl_series,
+1603            comp=make_mir_tag(repo)[0],
+1604            repo=repo,
+1605            file_256=[
+1606                "c2a1a3eaa13d4c107dc7e00c3fe830cab427aa026362740ea094745b3422a331",  # v2
+1607                "536863e9f0c13b0ce834e2f8a19ada425ee4f722c0ad3d0051ec7e6adaa8156c",  # 1.1
+1608                "3e15ba00387db678ab4a099f75771c4f5ac67fda9e7100a01d263eaf30145aa9",  # 0.1
+1609                "e3d12d0f76d61aa31d2668a2217e5b642592193f2946842c44d7056ea5469cce",  # 0.1 guided
+1610                "735cf3fefcbdc4f7817f53247e38b836ffd27c7641af6d8daa21d245242cb4bd",  # 1.0
+1611            ],
+1612            layer_b3=[
+1613                "93b061baf21d743d592327a61f027d099d8e18da9808a76c7704ad123eba4a29",
+1614                "dc05fed2acbc73cef4c377cfa2a681c5cf6d065b88d8bf70d371bbcce6a223a8",
+1615                "8eb1c30327e5b71b35b9a4513dc5f2cac9f244667393c0eedb10a26aa9991cd8",
+1616                "3dafbe31f6ebaffa3d054e1b37049e1147faa2474ceb6dab7bc3c4cded0c845e",
+1617                "892533778ee14454938f7b50830093f58e12f1e14560a148f71927e4ccff5f5c",
+1618            ],
+1619            layer_256=[
+1620                "397791b3d77affb7bd35c5ded7377493c6bf456920a41388ba95bd0157109803",
+1621                "b23c02b8519c6777a1f271662f4251a59468c4b3e11184a2d722fa8929b4ea48",
+1622                "a373981494f5508c124a1960bdd096bbc96935fbb54b1218f563206d3892c176",
+1623                "b709df257c40d9d981f686f2880bbe64f43b78805b7213768d659a142a593efd",
+1624                "f1e6b4cab0fce608dca6fa851384e8728202449f16270fbd1f0c4c5ec4946c10",
+1625            ],
+1626        )
+1627    )
+1628    repo = "playgroundai/playground-v2.5-1024px-aesthetic"
+1629    mir_db.add(
+1630        mir_entry(
+1631            domain="info",
+1632            arch="unet",
+1633            series=sdxl_series,
+1634            comp=make_mir_tag(repo)[0],
+1635            repo=repo,
+1636            file_256=[
+1637                "11b6d7bce65674659cc6b7ea960658436edfd80e566cb240ebd4bfbc3e2076c8",  # 2.5 diffusers
+1638                "bcaa7dd6780974f000b17b5a6c63e6f867a75c51ffa85c67d6b196882c69b992",  # 2.5 aes sai fp16
+1639                "956dca99114aaa5c3eb526381309d37ee96737e78ed64c8ae613409f47c3f65a",  # 2.5 aes sai
+1640                "933778ce76c1fc0ca918b37e1488411b8a99bbd3279c12f527a3ac995a340864",  # 2.5 fp16 diffusers
+1641                "5c7d38880d0940e6795158b7608ccef89217272b1f2a9331c5b0a2adffcd82c4",  # v2 sai
+1642                "0411e988479884b1a3ecd184123efe38d051d8d0ef24270585a7d1d57499464a",  # v2 sai fp16
+1643            ],
+1644            layer_b3=[
+1645                "d55b22740da2d5b98020ad2390cdc0a7ee08cf9e0d98c11957f16cc20c49815b",  # 2.5 diffusers
+1646                "7e9be9bd9a3aed1ad7207e2f77c98c24c3a75f6adcc9b53514033c6c3365d289",  # 2.5 aes sai fp16
+1647                "5c6dfcc8d01dfb64723f8f5785caa080e2987859c0a050470bfdbe5312be9efc",  # 2.5 aes sai
+1648                "703f775c6e48ed5b0eba6e847414f047bcd4adc677dbc1bf221b3ef05b2ac471",  # 2.5 diffusers fp16
+1649                "72d4ebe4af61f8a7add8fe36b8acd16602894279fb5a744ad50b5b5bac7067b8",  # v2 sai
+1650                "acb757b851db12cdf9d4365a45ee0d6e64afa77ac95583bb82711baf7c4125fd",  # v2 sai fp16
+1651            ],
+1652            layer_256=[
+1653                "adb7be228d4ee6e583c3e5ae4ddb579fef64c3987617ce4d4aff3eb7f8d6a3f7",
+1654                "d4813e9f984aa76cb4ac9bf0972d55442923292d276e97e95cb2f49a57227843",  # 2.5 aes sai fp16
+1655                "fe2e9edf7e3923a80e64c2552139d8bae926cc3b028ca4773573a6ba60e67c20",
+1656                "bc7021473a04a6de3fe0d0fed600875d852ad1ad9d47c445278f66ce9e8ec7a0"  # 2.5 fp16 diffusers
+1657                "fc94481f0c52b21c5ac1fdade8d9c5b210f7239253f86ef21e6198fe393ed60e",  # v2 sai
+1658                "a6f31493ceeb51c88c5239188b9078dc64ba66d3fc5958ad48c119115b06120c",  # v2 sai fp16
+1659            ],
+1660            pkg={
+1661                0: {
+1662                    "diffusers": "DiffusionPipeline",
+1663                    "precision": "ops.precision.float.F16",
+1664                    "generation": {"num_inference_steps": 50, "guidance_scale": 3},
+1665                }
+1666            },
+1667            identifiers=[
+1668                "edm_mean",
+1669                [1, 4, 1, 1],
+1670                2516,
+1671            ],
+1672        )
+1673    )
+1674    repo = "segmind/Segmind-Vega"
+1675    mir_db.add(
+1676        mir_entry(
+1677            domain="info",
+1678            arch="unet",
+1679            series=sdxl_series,
+1680            comp=make_mir_tag(repo)[0],
+1681            repo=repo,
+1682            file_256=[
+1683                "94762e983e5942056be73c5c1d4464b8ffa1ada500b4fef1267550e2447953ce",  # modelspec sai
+1684                "1ab33e37fbb2566c55cd729e4ab79cc2f99cd9d0a578fabc7a2cf4ee47968be1",  # diffusers
+1685                "8cfa375669b1222d6fecf470f41b2abb370c76a90ab9568964c4bb15b34ec8a2",  # diffusers fp16
+1686            ],
+1687            layer_b3=[
+1688                "2f353c5e6ed0a2c05af00d014e18e65f69f1ce8c48f8eefbf8ad71b34f940fbf",
+1689                "cc34bd3135d7cafc3cb6e3f6e7cb6896c98277bad52877a952ddbd2ffe222e01",
+1690                "b90efdc848f5386d5250b6fb233ce380cf6cc299f497cfa1d2feaef22f87c9d1",
+1691            ],
+1692            layer_256=[
+1693                "029b89ee311110c8f945dbdfc52c1d5daeb1e78c353c38aa3141ec68ce28e7cc",
+1694                "5cdb948e5f3873300679073391d48fc648171f02093d7737d078557ff75762bb",
+1695                "f73afbe43cc76571cb86ebcfced618668a2fb2252b0bc6ba88d6e942bae75741",
+1696            ],
+1697        )
+1698    )
+1699    repo = "segmind/SSD-1B"
+1700
+1701    mir_db.add(
+1702        mir_entry(
+1703            domain="info",
+1704            arch="unet",
+1705            series=sdxl_series,
+1706            comp=make_mir_tag(repo)[0],
+1707            repo=repo,
+1708            file_256=[
+1709                "7cb406ec0662e91570a79f3c4fb8f0ea5325bffe6af5d9382edae838698f72bd",  # modelspec sai
+1710                "1895a00bfc769a00b0c0c43a95e433e79e9db8a85402b45a33e8448785bde94d",  # a1111 aio
+1711                "0bf1ce6b065a6b969ab02dc8e8fa21eb20ee189b10935c49ce68c77a7e432c1c",
+1712                "02ed8ebd0ed55aec686fcf20946d7a1659a31f9f8d9c3798cd254ba6b67434ca",  # diffusers
+1713                "40d8ea9159f3e875278dacc7879442d58c45850cf13c62f5e26681061c51829a",  # diffusers fp16
+1714            ],
+1715            layer_b3=[
+1716                "c074dc38e8ec836816b91cbcc2ca17f80d6106de8d196d416ef9a27c8837ee45",  # modelspec sai
+1717                "1d6c0216da57fe98e7ad29e9653566725f5b2a87845fdbdcda257b3be817b5f4",  # a1111 aio
+1718                "c074dc38e8ec836816b91cbcc2ca17f80d6106de8d196d416ef9a27c8837ee45",
+1719                "89f86d9c846495870416b4945b6a46a517f28405e5bab666feb4057f012340be",
+1720                "535b47e9b70da6494878ca6d45af3f2e201b7f17748432911c12232e586855e6",
+1721            ],
+1722            layer_256=[
+1723                "52267d5d327a2ba92c7a14261a9d081df621b8366819b1bb3a47d130523a813c",
+1724                "b365a3631c6c74532f3a571c84c68e088be35496d35be1e932031713ddd2a2f4",
+1725                "52267d5d327a2ba92c7a14261a9d081df621b8366819b1bb3a47d130523a813c",
+1726                "89f86d9c846495870416b4945b6a46a517f28405e5bab666feb4057f012340be",
+1727                "535b47e9b70da6494878ca6d45af3f2e201b7f17748432911c12232e586855e6",
+1728            ],
+1729        )
+1730    )
+1731    repo = "shuttleai/shuttle-3.1-aesthetic"
+1732    mir_db.add(
+1733        mir_entry(
+1734            domain="info",
+1735            arch="dit",
+1736            series=schnell_series,
+1737            comp=make_mir_tag(repo)[0],
+1738            repo=repo,
+1739            pkg={
+1740                2: {
+1741                    "diffusers": "DiffusionPipeline",
+1742                    "generation": {"guidance_scale": 3.5, "num_inference_steps": 4},
+1743                }
+1744            },
+1745            file_256=[
+1746                "176871da1d5d2d511a52ae9b0dd70faa1f5d1b7734b7e33ed6b4bffa52050e0d",
+1747                "4b80d37681eaed07b7f5b3825a392da929d1620933ede7c2749ef3613cc53f42",
+1748            ],
+1749            layer_b3=[
+1750                "ff422d1734abf33366e87bbf44267dc6096c5d499e695287c35558174877412e",
+1751                "5ad8034eac6b82d842311437101c52b5d35826ce34994940d9e667e702a0d45c",
+1752            ],
+1753            layer_256=[
+1754                "e5d95de314cbfc49b79479118a1ac0b90fc95ccd6bb1a5c95803996d6cebf8fe",
+1755                "d299e8ea4a605917ab98a4a7330d4d398b4ae295efbf458eeeceb5ff1bd7959a",
+1756            ],
+1757        )
+1758    )
+1759    repo = "shuttleai/shuttle-3-diffusion"
+1760    mir_db.add(
+1761        mir_entry(
+1762            domain="info",
+1763            arch="dit",
+1764            series=schnell_series,
+1765            comp=make_mir_tag(repo)[0],
+1766            repo=repo,
+1767            pkg={
+1768                2: {
+1769                    "diffusers": "DiffusionPipeline",
+1770                    "generation": {"guidance_scale": 3.5, "num_inference_steps": 4},
+1771                }
+1772            },
+1773            file_256=[
+1774                "a5b04df4072698395387c21e8da0176d03f6557e0c38ff1dd3bf469ebab9d0fd",  # fp8
+1775                "a91b46de2055b3511ee87523b57862648856e8c00100161d5b520543a7302755",  # norm
+1776                "23a77c86189d5934da48bf44bb871cf80ba99177ffd3fd5272cdecb208c8b8be",  # mlx q8
+1777                "d3782d5a8f6e82c6676e8e26d54020934ada589d2aceb17fc5ca604b1bd55da8",  # mlx q4
+1778            ],
+1779            layer_b3=[
+1780                "4dd3174edf6b680ce9daf3de643e33ae2c4f09a4d5968da61ea48885f3a193c0",
+1781                "9fdf191b2c58b2a6e190396e12314530593dca4f2a2bee389ec5175da5e52af8",
+1782                "ad203ad6a00d8b1315337e34069e7c41016ea407469a536de8ad6807042017fd",
+1783            ],
+1784            layer_256=[
+1785                "14d0e1b573023deb5a4feaddf85ebca10ab2abf3452c433e2e3ae93acb216443",
+1786                "7ce8d449b32a9c959431ade729b513ee7a6457f11e1c13e3ef04dd8db3494621",
+1787                "9c3395f67a3d844483b77f0ddd5e2ea64b61732fa9d9da19845bb8ae574c1f8c",
+1788            ],
+1789        )
+1790    )
+1791    repo = "enhanceaiteam/Mystic"
+1792    mir_db.add(
+1793        mir_entry(
+1794            domain="info",
+1795            arch="dit",
+1796            series=dev_series,
+1797            comp=make_mir_tag(repo)[0],
+1798            repo=repo,
+1799            pkg={0: {"generation": {"num_inference_steps": 16, "guidance_scale": 7.5, "width": 768, "height": 1024}}},
+1800            file_256=[
+1801                "179d4000e44295f6dfadc0e4ac210146454724d46371b82657200ff9fb5c68a9",  # mlx 0
+1802                "48ca85274e3b67f07f70dd84b67725e62395c2f7b188394342716f783ea4c6ac",  # mlx q8
+1803            ],
+1804            layer_b3=[
+1805                "91074aaebe1b5f3b2e7755d3c092af7eb240e92a192360690f1033949d3c8a68",  # mlx 0
+1806            ],
+1807            layer_256=[
+1808                "3942e6a52dbb0abaf63b031d9c4eda0df47576b51d4c81361978a3dc27b1309e",  # mlx 0
+1809            ],
+1810        )
+1811    )
+1812    repo = "shuttleai/shuttle-jaguar"
+1813    mir_db.add(
+1814        mir_entry(
+1815            domain="info",
+1816            arch="dit",
+1817            series=schnell_series,
+1818            comp=make_mir_tag(repo)[0],
+1819            repo=repo,
+1820            pkg={
+1821                2: {
+1822                    "diffusers": "DiffusionPipeline",
+1823                    "generation": {"guidance_scale": 3.5, "num_inference_steps": 4},
+1824                }
+1825            },
+1826            file_256=[
+1827                "dcbc4f2470b177eed12c7d7515c0e7342515a849ebd31a50c8d8d43913d7bd32",
+1828                "26a7aa64c0798a3549e1d767932da0a7fb82b49f8edcbdcde804a20d9ed1478f",  # mlx q8
+1829            ],
+1830            layer_b3=[
+1831                "9906c29933d0c33a6ee8d9712f33fa8bd4b35b46a1c7b565ae48832b757dd980",
+1832                "89c453c4bf99220405687eed984dace4492bdae1b6fb08f3d9629145b1a11672",  # mlx q8
+1833            ],
+1834            sha_256=[
+1835                "4eacf27e5659f5dc42f34c407cbe9e1e202290692df754eb68fe913f59fa2941",
+1836            ],
+1837        )
+1838    )
+1839    repo = "freepik/flux.1-lite-8b"
+1840    mir_db.add(
+1841        mir_entry(
+1842            domain="info",
+1843            arch="dit",
+1844            series=dev_series,
+1845            comp=make_mir_tag(repo)[0],
+1846            repo=repo,
+1847            pkg={0: {"generation": {"num_inference_steps": 28}}},
+1848            file_256=[
+1849                "09e970a7b8d1813ea7cacd48f9a944fd223882b137a8f4f3b61d864cdc20bbec",  # mlx q8
+1850                "de90e69945c2f4afcb9b6a057ce48190905c984370fce76b16ba3b97d46e2747",  # mlx q4
+1851            ],
+1852            layer_b3=[
+1853                "9276fa4805efeb45c08cca32c5b51d490e57a2ce5c15ef476a8e468a509c5cdf",
+1854            ],
+1855            layer_256=[
+1856                "e1afe2f9b1ca55b3c659293cf3237f6b5571f5c4e826bad025ff0f7b54dc34ee",
+1857            ],
+1858        )
+1859    )
+1860    repo = "freepik/f-lite-7b"
+1861    mir_db.add(
+1862        mir_entry(
+1863            domain="info",
+1864            arch="dit",
+1865            series=dev_series,
+1866            comp=make_mir_tag(repo)[0],
+1867            repo=repo,
+1868            pkg={0: {"f_lite": "FLitePipeline", "generation": {"num_inference_steps": 28}}},
+1869        )
+1870    )
+1871    repo = "freepik/f-lite-texture"
+1872    mir_db.add(
+1873        mir_entry(
+1874            domain="info",
+1875            arch="dit",
+1876            series=dev_series,
+1877            comp=make_mir_tag(repo)[0],
+1878            repo=repo,
+1879            pkg={0: {"f_lite": "FLitePipeline", "generation": {"num_inference_steps": 28}}},
+1880        )
+1881    )
+1882    repo = "freepik/f-lite"
+1883    mir_db.add(
+1884        mir_entry(
+1885            domain="info",
+1886            arch="dit",
+1887            series=dev_series,
+1888            comp=make_mir_tag(repo)[0],
+1889            repo=repo,
+1890            pkg={0: {"f_lite": "FLitePipeline", "generation": {"num_inference_steps": 28}}},
+1891        )
+1892    )
+1893    repo = "TencentARC/flux-mini"
+1894    mir_db.add(
+1895        mir_entry(
+1896            domain="info",
+1897            arch="dit",
+1898            series=dev_series,
+1899            comp=make_mir_tag(repo)[0],
+1900            repo=repo,
+1901            file_256=["4236455adeaeb4ed444d63b253ec99805022d17e962ed7261ada9c72ce11cfee"],
+1902            layer_b3=["c1a6f83585398fe452d20596a79a522e2986f4c2c01a40e7bfd787af113735d3"],
+1903            layer_256=["e4a0d8cf2034da094518ab058da1d4aea14e00d132c6152a266ec196ffef02d0"],
+1904        ),
+1905    )
+1906    repo = "ostris/Flex.2-preview"
+1907    mir_db.add(
+1908        mir_entry(
+1909            domain="info",
+1910            arch="dit",
+1911            series=dev_series,
+1912            comp=make_mir_tag(repo)[0],
+1913            repo=repo,
+1914            file_256=[
+1915                "0407108e446a4f57efffc5e7518bc374876af970d3c6068dc4074de0d221c615",  # modelspec sai
+1916                "df168ba94d5f96c478b24604a6beedff6189047152190509c73c162ea0d8ec02",  # mlx
+1917            ],
+1918            layer_b3=[
+1919                "7f85cdc186896da6965b57d5edb672f08663075d2b207f0e20e328c4034a8076",  # mlx
+1920            ],
+1921            layer_256=[
+1922                "5063de856be5365807d12b47ef6919b4ac611a72651739b2b4050e113bed7a83"  # mlx,
+1923            ],
+1924        ),
+1925    )
+1926    repo = "ostris/Flex.1-alpha"
+1927    mir_db.add(
+1928        mir_entry(
+1929            domain="info",
+1930            arch="dit",
+1931            series=dev_series,
+1932            comp=make_mir_tag(repo)[0],
+1933            repo=repo,
+1934            file_256=[
+1935                "5d6dce30a266ccbf530c3a3bf253cd5486720a8fb71cdeed556c28304201dc2f",  # modelspec sai
+1936                "7acf8771b80a91eaa21566abe8c7d9d3ba33d8688e6e98446827749aee7ca1ee",  # mlx
+1937            ],
+1938            layer_b3=[
+1939                "cb3d3edafd81651eefd62894b3572deb02c5304f4b5d4f7ab8654f1fb922ecd6",  # mlx
+1940            ],
+1941            layer_256=[
+1942                "a6b9af6efc25fa77cd24046b81ee66fea09a9987d2a8e56ffca9b7a1c9c9c519"  # mlx,
+1943            ],
+1944        ),
+1945    )
+1946    repo = "tensorart/stable-diffusion-3.5-medium-turbo"
+1947    mir_db.add(
+1948        mir_entry(
+1949            domain="info",
+1950            arch="dit",
+1951            series=sd3_series,
+1952            comp=make_mir_tag(repo)[0],
+1953            repo=repo,
+1954            pkg={
+1955                0: {
+1956                    "precision": "ops.precision.bfloat.B16",
+1957                    "generation": {"num_inference_steps": 8, "guidance_scale": 1.5, "height": 1024, "width": 768},
+1958                }
+1959            },
+1960            file_256=[
+1961                "5b0530e8d71b49fa1358f1208047cd789a40bae5b44406c9524b0f0d88f8b246",  # diffusers
+1962                "07119c77c3548a1d9eb30923df4dd55ec74914dc5ec81626804dcbe51ce17a5d",  # sai
+1963                "3c379381344d2a2b3ee3d7a1bc97f7d1e58fa95c6b5187fb48b3ce446f99f17b",  # q4km gguf
+1964                "6b3806cafdb4303ea2638e9e08eb186067b4a46a95ddf344ccdbe56537afaf6e",  # q8km gguf
+1965            ],
+1966            layer_b3=[
+1967                "873821614080a98e1ebfe56673bc96c2ac57379720d4ad2f97e4bca317571d48",  # diffusers
+1968                "7284d2027523482af9ef47405667ca891cc518bfb6ebf1f1d4666cb0accc8cd5",
+1969                "d938ee5738c73f701760ed18acad274b074d2796123aee3f2eee1328b6c36ea4",
+1970                "c4c40056c2a77959083b5a69a1a4b205caa463ccabde057352c5c4e38b2c67b6",
+1971            ],
+1972            layer_256=[
+1973                "3c324055a1ec6eb4ee0242e344bb2b6356afcbd2e215fdd9d160cda691a72fae",
+1974                "7284d2027523482af9ef47405667ca891cc518bfb6ebf1f1d4666cb0accc8cd5",
+1975                "d938ee5738c73f701760ed18acad274b074d2796123aee3f2eee1328b6c36ea4",
+1976                "c4c40056c2a77959083b5a69a1a4b205caa463ccabde057352c5c4e38b2c67b6",
+1977            ],
+1978        ),
+1979    )
+1980    repo = "Wan-AI/Wan2.1-FLF2V-14B-720P-Diffusers"
+1981    series, comp = make_mir_tag(repo)
+1982    mir_db.add(
+1983        mir_entry(
+1984            domain="info",
+1985            arch="dit",
+1986            series=series,
+1987            comp=comp,
+1988            repo=repo,
+1989            file_256=[
+1990                "",
+1991                "",
+1992            ],
+1993            layer_b3=[
+1994                "",
+1995            ],
+1996            layer_256=[""],
+1997        ),
+1998    )
+1999    repo = "OnomaAIResearch/Illustrious-Lumina-v0.03"
+2000    mir_db.add(
+2001        mir_entry(
+2002            domain="info",
+2003            arch="dit",
+2004            series=make_mir_tag("Alpha-VLLM/Lumina-Image-2.0")[0],
+2005            comp=make_mir_tag(repo)[0],
+2006            repo=repo,
+2007            file_256=[
+2008                "dc6cffcfb0ccfca6332ddb5d2fe25bcb5f496f44b481627f48c42626156fa6a8",  # 2b 22100 ema unified fp32
+2009                "2ac549741fa1c6de2d6cd8be06abcdce52d472eeae2439f948e285258b66a214",  # 0.03 ema
+2010            ],
+2011            layer_b3=[
+2012                "a97b4a63e1e7678e8e7154fae55252267bd1f0ba76b03dba622d801644e657ac",
+2013                "aa6c1b2d1971cea3c4ed0963c8d68d4c50db683f8eab9f77f60ea2d04ed6ce5c",
+2014            ],
+2015            layer_256=[
+2016                "39086c199b9ac296dcba53461ba1e113906d91fbc1b12556d92f5cc77ca11f9f",
+2017                "e51ba2ded40f1af5ca6f78c46eed8305fbd87cd6401e9d439837e10d35cc5828",
+2018            ],
+2019        )
+2020    )
+2021    mir_db.add(
+2022        mir_entry(
+2023            domain="ops",
+2024            arch="patch",
+2025            series="hidiffusion",
+2026            comp=sdxl_series,
+2027            pkg={
+2028                0: {
+2029                    "hidiffusion": {"apply_hidiffusion": {"timesteps": "StableDiffusionXLTimesteps"}},
+2030                    "generation": {"height": 2048, "width": 2048, "eta": 1.0, "guidance_scale": 7.5, "num_inference_steps": 10},
+2031                },
+2032            },
+2033        )
+2034    )
+2035    mir_db.add(
+2036        mir_entry(
+2037            domain="ops",
+2038            arch="scheduler",
+2039            series="align-your-steps",
+2040            comp=sdxl_series,
+2041            pkg={
+2042                0: {
+2043                    "diffusers": "schedulers.scheduling_utils.AysSchedules",
+2044                    "generation": {"timesteps": "StableDiffusionXLTimesteps", "num_inference_steps": 10},
+2045                }
+2046            },
+2047        )
+2048    )
+2049    # possible mixed-type architecture?
+2050    # fusion / united / universal
+
+ + +

Create MIR entries missing from the database

+
+ + +
+
+ +
+ + def + add_mir_llm(mir_db: mir.maid.MIRDatabase): + + + +
+ + 
2053def add_mir_llm(mir_db: MIRDatabase):
+2054    base_arch, base_series, base_comp = tag_base_model(repo_path="facebook/chameleon-7b", class_name="ChameleonModel")
+2055    repo = "Alpha-VLLM/Lumina-mGPT-7B-1024"
+2056    series, comp = make_mir_tag(repo)
+2057    mir_db.add(
+2058        mir_entry(
+2059            domain="info",
+2060            arch=base_arch,
+2061            series=base_series,
+2062            comp=series,
+2063            repo=repo,
+2064            pkg={
+2065                0: {
+2066                    "inference_solver": {"FlexARInferenceSolver": {"precision": "bf16", "target_size": 768}},
+2067                    "generation": {"images": [], "qas": [["q1", None]], "max_gen_len": 8192, "temperature": 1.0},
+2068                },
+2069                1: {"inference_solver": "ChameleonXLLMXForConditionalGeneration"},
+2070            },
+2071            identifiers=["model.embed_tokens.weight"],
+2072            file_256=[
+2073                "6b71408a7c574d98f00114ab770ac6addc71471770456e482e7b5ec641c02345",
+2074                "1d5d8d5532bae0f32ba35d10d411e506d61e4378dc9fc338f2b1e6af2aa322ec",  # 768
+2075                "a8fe636bbee30fef06dcd8e806ffc65b2aed0ad08a07fdc62f35717d0f851be5",  # 512 multi
+2076                "6420fa13483576d46263996627ba7add2237a01f46dedd3b7750112c0cc2d95b",  # 512
+2077            ],
+2078            layer_b3=["6cd6b3caaea270feb5aff8e9fec205a27da4f48a1e740e63dc9a08f16e70a656"],
+2079            layer_256=["eaa882db6a69cf8ed0104a15b2cdbbb570a23a06ab8c8f65f4c6c21719c6ba25"],
+2080        ),
+2081    )
+2082    repo = "openai/clip-vit-large-patch14"
+2083    series, comp = make_mir_tag(repo)
+2084    mir_db.add(
+2085        mir_entry(
+2086            domain="info",
+2087            arch="vit",
+2088            series=series,
+2089            comp=comp,
+2090            repo=repo,
+2091            pkg={0: {"transformers": "CLIPTextModel"}},
+2092            identifiers=["text_model.encoder.layers.0.mlp.fc1.weight", "clip-l"],
+2093            file_256=[
+2094                "cb0cba1ead482a850532ebe5ff6b5c8d4456aee32a5228acf0a31e7d9472415e",  # long vit best
+2095                "39e79c916feca4ddf546d9fe923e664714b59ea61074f7228037d17c302f3d17",  # vit l detail improved hit gmp
+2096                "893d67a23f4693ed42cdab4cbad7fe3e727cf59609c40da28a46b5470f9ed082",  # flux/shuttle 3 aes
+2097                "778d02eb9e707c3fbaae0b67b79ea0d1399b52e624fb634f2f19375ae7c047c3",  # playground 2.5
+2098                "660c6f5b1abae9dc498ac2d21e1347d2abdb0cf6c0c0c8576cd796491d9a6cdd",  # playground 2.5 fp16
+2099                "71e183d11db0c6b6282a4d9e0abb74125edc8692393e89ed8ee5571005f35cb1",  # sd3.5 fp16
+2100                "5c3d6454dd2d23414b56aa1b5858a72487a656937847b6fea8d0606d7a42cdbc",  # sdxl diffusers
+2101                "87c1c0b0894c9e9e10b962e597e8d64dd3a3a2d372c389922b335a53c250b2ae",  # L
+2102                "bd289dd57fee86bc8816b55919a2b03f9c3c75af6025e21777325a6730872325",  # jaguar mlx
+2103                "8377b1ca9d88fe06ec483dd7b3cfc62e5e8dbf8ddd252f455e79d659fa0553c5",  # ssd-1b
+2104                "5487ea0eee9c9a9bff8abd097908d4deff3ae1fa87b3b67397f8b9538139d447",  # ssd-1b fp16
+2105                "92b998a9a64549bfa05c019bde114be6681549a0c79caee903fe30c9444d08b9",  # vega
+2106                "1e090d6a828fd92401be5f83e615fd7b4fb1f4a22e9af9040a38f602e839317c",  # vega fp16
+2107                "11807cb2522cfe99240e5ee2bbeb1ccb42cecca2215102ee872567c7773b28b9",  # flux
+2108                "d008943c017f0092921106440254dbbe00b6a285f7883ec8ba160c3faad88334",  # sd1
+2109                "77795e2023adcf39bc29a884661950380bd093cf0750a966d473d1718dc9ef4e",  # sd1 fp16
+2110                "b70c11ad5d7e9abf6109348908f599ea382f8019e1f36910bbc8ebecde936633",  # hidream i1
+2111                "fc42badf529dd83f2f7c3d20fe6bda1e22036162f37c4c668b9e130884e20561",
+2112                "e27bafa0b3029ad637ef3ace24ce1efe85b8d0dbd22e03a2e70bda6fc88963a1",  # onnx
+2113            ],
+2114            layer_b3=[
+2115                "f58a22a381f79985b6d38782f6110a52c2f319b40fdedd3b88b24945dfcbdf64",
+2116                "8faa00b8fd1dbd9286a7237df18caeb8c91af100a6813849b6bae272a01dd7b7",
+2117                "ab5bebc98299c155251a06deccde599ba0128038ee3ce021e8c59a45f58f72c0",
+2118                "c70e9d86a9dcbbbe7c269ef9dfac96ce9c96c46922577338cc1902e5fe936315",
+2119                "f285e9b7b70745df81adc8b558ec74b536b79b6fc02a453ecc61ea9d13f25f1a",
+2120                "7ab17bfa06ab8d65840997ef641f3f593d096860e20141f1eeb0169d131c1c23",
+2121                "2737d3f327e8176dbb549b9c5c4994821430a6c3b07e3bbc925d97511c802636",  # jaguar mlx q8
+2122                "58a826a4a5fe555b4df188a1ebc0d8d9c96cedae3a26ce84c247861dbb93388f",  # sd1
+2123                "1540fd8844898960e18ce8fd153e5f21a8c446bd8c4d6f536a7cf11418f02bf3",  # sd1
+2124                "c4c9caccdbec12b965d93688c521893f75e0bf9a5e0aad70a6a962b669e7b9d5",  # vega
+2125                "e43fae8d5fd1e562607da172369cc0c5ec99b834e42502e682287ff7d12baacc",  # vega fp16
+2126                "c6f79f7416a882891957b815fbdfd6edfaa253c43970b1a25ef14e217599c7bc",  # flux
+2127                "daf5e09f67ad09a909f58a01298fec0132324634cb8fca2a604c3a240c2c453f",  # jaguar mlx q8
+2128                "3f62bfb6bbde05f01435129326166c44aeb113ac0d9f735f31ed3f7dd04f6980",  # hidream i1
+2129                "22f866f3c96a92bc61e9965cf366d706db942ad047ba8cb82109edcd4e68fa40",  # sd3 turbo
+2130                "f3fa9d7a8f15741621c1fe82f8a1bcc5c601c900d947ac09fba7016615a252a5",  # shap-e
+2131            ],
+2132            layer_256=[
+2133                "48daa3d8f939972e69f044533a4312a941971c18c78255f5e555fa26faf664c1",
+2134                "60f5734a74c342be8b0011fc704e718431839790bcfdc7d7004fc39d70f7fec6",
+2135                "6e76e25b4a55dddfa2eecf4b7ab189a8148658a9f6df165c00170f6ce661033c",
+2136                "2d5249df489fec9137cc3a5e9bda499dd9b72a957ddd8e7ad4e99ff3684bad99",
+2137                "3bf085e701713ed3e79775dafea375c3e2a43659ad1ee788b1b393c0aeff9f0e",
+2138                "efb7976800692772e449c81a739339f59394886590ff3f768b0f9ddd87d2a94c",
+2139                "9b0ac8d127c6c457b2eb8c7236f18c4e4ba9e8bbf27130aa8fe854d7c3f7b1e0",
+2140                "24a9ee3d60cdde6c967f08e4b2ec7088fe1bfe308c6896e73caa874860570a5c",
+2141                "5d6d9d0cc7943eb1b8c16862bfd5bee5c3766d0df027ec837e90fac715ac2bd3",
+2142                "68fb122f7d6c3cfbef320341b2af8f5916678e36a69ed36fa8cfcb19e7d5c43d",
+2143                "11807cb2522cfe99240e5ee2bbeb1ccb42cecca2215102ee872567c7773b28b9",
+2144                "50c46cdddbe9f0162278c69b9a1f818519330e3a91b994272e19b5c789670471",  # jaguar mlx q8
+2145                "ffe1c4f55e07c2010ace7b9cf35798bb9f431bc954a32784e5acbdc16acc0364",  # hidream i1
+2146                "146ea48d234e05a934db9d8988e9a9dd86b2ac70f535eaa550ecb0ee23ec135e",  # sd3 turbo
+2147                "d97560cf9704cf71711f6121df2bf55e55a1eda4b574a6ddba074767420bc8c3",
+2148            ],
+2149        )
+2150    )
+2151    repo = "laion/CLIP-ViT-g-14-laion2B-s12B-b42K"
+2152    series, comp = make_mir_tag(repo)
+2153    mir_db.add(
+2154        mir_entry(
+2155            domain="info",
+2156            arch="vit",
+2157            series=series,
+2158            comp=comp,
+2159            repo=repo,
+2160            pkg={0: {"transformers": "CLIPTextModelWithProjection"}},
+2161            identifiers=["31.self_attn.k_proj.weight", "text_model.encoder.layers.22.mlp.fc1.weight", "clip-g"],
+2162            file_256=[
+2163                "ca18e0c67c1ef1e64cac22926266765b60688f692307ecc06283d987c5768134",  # seaart furry g
+2164                "ec310df2af79c318e24d20511b601a591ca8cd4f1fce1d8dff822a356bcdb1f4",  # modelspec sai
+2165                "fa5b2e6f4c2efc2d82e4b8312faec1a5540eabfc6415126c9a05c8436a530ef4",  # playground 2.5
+2166                "b84f413eebecbd049b72874c1df533a516510cb5a2489ae58c7e320209cf0ebe",  # ssd1b
+2167                "d3df577f6e3799c8e1bd9b40e30133710e02e8e25d0ce48cdcc790e7dfe12d6d",  # ssd1b fp16
+2168                "943a2924ee888295a156dd47089d67181d633b782337890af11ef4b15af17ec5",  # vega
+2169                "5b98e4a57a9292eeb819d67e2d2100f66f17db723cde4ecea27a7c3741160d0c",  # vega fp16
+2170                "4d6effa7a5e600cabf7528ed7234146a13ead1b2c151211d706b293a060b112a",  # hidream i1
+2171                "3a6032f63d37ae02bbc74ccd6a27440578cd71701f96532229d0154f55a8d3ff",  # modelspec sai
+2172                "162042ac6556e73f93d4172d4c67532c1cbe4dc7a6a8fa7e44dd2e3d7cbb772b",  # onnx
+2173            ],
+2174            layer_b3=[
+2175                "d754db276f2d89d2808abb7086b3b8eccee43ac521c128d21a071f3a631474a8",
+2176                "2eb93685b34719e1d1e0541d8902b0a592d95848f80657e32816cf3b152a0f31",
+2177                "e253a5cf3a6242c58037abd6b378bf0281f278e441f28dff7ca1bcfcd3cd6bd8",  # ssd1b
+2178                "16d0eec4e55b0aa63cdca4e4d36f78f66a4b1b9605ce3b1089305026f853c3d2",  # ssd1b fp16
+2179                "f606463295ecf3bae8920d3d45bb9d180793418b3d08c3e84d4c4135c7dc2aa5",  # vega
+2180                "7060993a5eb32d94d1ea8aef7a7301e7be73b199c639c63f8f7cfbfcd2abf10e",  # vega fp16
+2181                "b92af95334c657371af6051a91374a41b5455907fa6622bb66a8c112dc511600",  # hidream i1
+2182            ],
+2183            layer_256=[
+2184                "270e998633eb22145100a3889a62ca270d5080654735e5ff8dda09a7c233af8d",
+2185                "df18800c2a9d9318c4323d991a0fb24a6a9afceb41bea203812f60517c301536",
+2186                "4c228b104f6b9b383e0808c9baa1998957f5125d8f90a4d98c1a86e71edd72dc",  # ssd1b
+2187                "f7fc81d8b5ae91ec28a5106ecc0d067be9a94fd3f394c4aa4686ed131ce5a5b3",  # ssd1b fp16
+2188                "61ab42bd5c0fcb9fd3db1d4014cb844ccae8dc17fd69a108cf077a573d092946",  # vega
+2189                "6c64e36cdda3bec7067e94b05619f882f5d31070792acaadac60ddbef580453a",  # vega fp16
+2190                "43c9e64995b485a7f128771c48defce128640df28e65c7f79537d472f43ebe46",  # hidream i1
+2191            ],
+2192        )
+2193    )
+2194    repo = "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
+2195    series, comp = make_mir_tag(repo)
+2196    mir_db.add(
+2197        mir_entry(
+2198            domain="info",
+2199            arch="vit",
+2200            series=series,
+2201            comp=comp,
+2202            repo=repo,
+2203            pkg={
+2204                0: {"transformers": "CLIPModel"},
+2205            },
+2206            file_256=[
+2207                "036e6e2bd49697511f4f8b8cb5ee465f93025f7a69a145eadeb9a881ace9b18d",
+2208                "0084e75319a50ad85ef45377bad5bc38f2f58824459eb690048d51c9f8863be5",  # open clip
+2209                "64a7ef761bfccbadbaa3da77366aac4185a6c58fa5de5f589b42a65bcc21f161",  # wan sai
+2210            ],
+2211            layer_b3=[
+2212                "227f26ed63120b9034f4a0c90b6b37eede721a8260f2c1e8f7ea3ccc0d109e7e",
+2213                "3a38ffd1b60499cf2f451f3065079ff26efb9190a86f23ad1c8d993bbeb9af05",  # open clip
+2214                "ce06cf1fd684269ee96631b2bf9334c6ecde6a84a55760dfa0d9d2a6411f28e4",  # wan sai
+2215            ],
+2216            layer_256=[
+2217                "130a94ed12569e099196a6ca27388181922e20148dee5bcb58c5e309acfc2352",
+2218                "cfdbd3fd2b90b64ba12d395a62dd7c3c3ea3e811f0a54593e91bae6516ca5061",  # open clip
+2219                "9125ce5970c649d6f9368c25493d3aaa6b41e224d4cc427e955115f7b7e53d1c",  # wan sai
+2220            ],
+2221        )
+2222    )
+2223    repo = "zai-org/chatglm3-6b"  # formerly THUDM
+2224    series, comp = make_mir_tag(repo)
+2225    mir_db.add(
+2226        mir_entry(
+2227            domain="info",
+2228            arch="aet",
+2229            series=series,
+2230            comp=comp,
+2231            repo=repo,
+2232            pkg={
+2233                0: {"transformers": "AutoModel"},
+2234            },
+2235            file_256=[
+2236                "0054d03310248928fdabdeef3fdc753170218dc49a1e9eb5f98323e27683f654",  # kolors
+2237                "b1052386eac358a18add3d0f92521c85ab338979da8eeb08a6499555b857f80d",
+2238            ],
+2239            layer_b3=[
+2240                "a45dfba6a9fa8739777c76deb845fc9589b40f88670d3ce4661646a7b7b1d481",  # kolors
+2241            ],
+2242            layer_256=[
+2243                "174924fd7a07f370bb6fcd1ad07a73eecb7de901f15eefb80f420c1042c47d44",  # kolors
+2244            ],
+2245        )
+2246    )
+2247    base_arch, base_series, base_comp = tag_base_model(repo_path="Qwen/Qwen2-7B-beta", class_name="Qwen2Model")
+2248    repo = "ByteDance-Seed/BAGEL-7B-MoT"
+2249    series, comp = make_mir_tag(repo)
+2250    mir_db.add(
+2251        mir_entry(
+2252            domain="info",
+2253            arch=base_arch,
+2254            series=base_series,
+2255            comp=series,
+2256            repo=repo,
+2257            pkg={0: {"Bagel": "app"}},
+2258        )
+2259    )
+
+ + + + +
+
+ +
+ + def + add_mir_audio(mir_db: mir.maid.MIRDatabase): + + + +
+ + 
2262def add_mir_audio(mir_db: MIRDatabase):
+2263    """Create MIR audio modality entries"""
+2264    repo = "facebook/audiogen-medium"
+2265    series, comp = make_mir_tag(repo)
+2266    mir_db.add(
+2267        mir_entry(
+2268            domain="info",
+2269            arch="art",
+2270            series=series,
+2271            comp=comp,
+2272            repo=repo,
+2273            pkg={
+2274                0: {
+2275                    "audiocraft": "models.AudioGen",
+2276                    "generation": {"duration": 5},
+2277                    "stage_2": {
+2278                        "audiocraft": ".data.audioaudio_write",
+2279                        "generation": {"strategy": "loudness", "loudness_compressor": True},
+2280                    },
+2281                }
+2282            },
+2283        )
+2284    )
+2285    repo = "parler-tts/parler-tts-tiny-v1"
+2286    series, comp = make_mir_tag(repo)
+2287    mir_db.add(
+2288        mir_entry(
+2289            domain="info",
+2290            arch="art",
+2291            series=series,
+2292            comp=comp,
+2293            repo=repo,
+2294            pkg={
+2295                0: {
+2296                    "parler_tts": "ParlerTTSForConditionalGeneration",
+2297                    "generation": {"return_tensors": "pt"},
+2298                },
+2299            },
+2300        )
+2301    )
+2302    repo = "Zuellni/snac-24khz-ST"
+2303    series, comp = make_mir_tag(repo)
+2304    (
+2305        mir_db.add(
+2306            mir_entry(
+2307                domain="info",
+2308                arch="gan",
+2309                series=series,
+2310                comp=comp,
+2311                repo=repo,
+2312                pkg={
+2313                    0: {
+2314                        "snac": "SNAC",
+2315                    },
+2316                    "1": {
+2317                        "mlx_audio": "tts.generate.generate_audio",
+2318                    },
+2319                },
+2320                file_256=["e61ae2f638f56ee07a37592cd5a6a9e7d642560ddc78a76ee4a7f96d6922f1be", "973ee1be4032319fd9685ec54eee1b93e79c7bc98c786e67f17c04669714f11d"],
+2321                layer_b3=["18307b00460a64cc4893f9061592ce8d7e15b70fc54065cc8ae0f0155381ec46", "d599b1bb36dee3cee4674b7922fcd69e5ec05b74413f611d21cfdfdf8f9b6119"],
+2322                layer_256=["35ba9aa1feb931010559a178fcac243673d2efdd1396a4b69d406c9853a88300", "5a22c4707ed6c928043f23b59f2d102a579db3a9af41cf6e60d7c3958f182841"],
+2323            )
+2324        ),
+2325    )
+2326    repo = "parler-tts/parler-tts-large-v1"
+2327    series, comp = make_mir_tag(repo)
+2328    mir_db.add(
+2329        mir_entry(
+2330            domain="info",
+2331            arch="art",
+2332            series=series,
+2333            comp=comp,
+2334            repo=repo,
+2335            pkg={
+2336                0: {
+2337                    "parler_tts": "ParlerTTSForConditionalGeneration",
+2338                    "generation": {"return_tensors": "pt"},
+2339                },
+2340            },
+2341        )
+2342    )
+2343    repo = "hexgrad/Kokoro-82M"
+2344    series, comp = make_mir_tag(repo)
+2345    mir_db.add(
+2346        mir_entry(
+2347            domain="info",
+2348            arch="gan",
+2349            series=series,
+2350            comp=comp,
+2351            repo=repo,
+2352            pkg={
+2353                0: {"kokoro": "KPipeline"},
+2354                1: {
+2355                    "mlx_audio": "tts.generate.generate_audio",
+2356                    "generation": {"audio_format": "wav", "join_audio": True, "verbose": False},
+2357                },
+2358            },
+2359            file_256=[
+2360                "5a5cb3d87478f2e74dfca208ee52209ccfce024095e137097fd276026506e45f",
+2361                "496dba118d1a58f5f3db2efc88dbdc216e0483fc89fe6e47ee1f2c53f18ad1e4",
+2362            ],
+2363            layer_b3=[
+2364                "3e9b5017cfe67a7804ac717b18b6add42ffc0bd3353490df2bcc520eaaef79b6",
+2365                "379660a87a64524bab69a267e3d9580f04b5eec4f7e3fbd48c6597d164d9b17d",  # safetensors
+2366                "997f154f5a78879ef3ba1a1556977c40b28b9c21076b8f583f752c57ecc36e93"  # pytorch
+2367                "2dc3dba29452b85ea85266084a6248f9e0efe642d5f75b43e64f25b9f2837f92",
+2368            ],
+2369            layer_256=[
+2370                "dbedf0e2115aa309b92689f86534be4a77b91d7900365e1717879fbb19b849f6",
+2371                "2c68574571b3f9229e015a909788116ea2251142e29c1bd5c687863192124e8b",
+2372            ],
+2373        )
+2374    )
+2375    repo = "freddyaboulton/silero-vad"
+2376    series, comp = make_mir_tag(repo)
+2377    mir_db.add(
+2378        mir_entry(
+2379            domain="info",
+2380            arch="stst",
+2381            series=series,
+2382            comp=comp,
+2383            repo=repo,
+2384            pkg={
+2385                0: {
+2386                    "onnx": "onnx",
+2387                },
+2388                1: {
+2389                    "mlx_audio": "tts.generate.generate_audio",
+2390                    "generation": {"audio_format": "wav", "join_audio": True, "verbose": False},
+2391                },
+2392            },
+2393            file_256=["591f853590d11ddde2f2a54f9e7ccecb2533a8af7716330e8adfa6f3849787a9"],
+2394            layer_b3=[
+2395                "41ca5931452b3ffee588c6c7e5bd327c4e914141604eaf3fd05f4a790ac83bb2",
+2396                "7dc736cd5d840182792bde4edfbf5ddc5aeaf16826a9c72d1ba8166c1e3fab9b",
+2397                "6e2c1bdbad74f56663ffb5710c7cb849a2b91ba331d81acdba47a21f69107434",  # onnx
+2398                "ab5ff443aece9171af5e7603d0b4309d3ecc934e3940ccedefff10f0b54b931e",  # onnx vad
+2399                # "7939427700c3b4d91428a490bde1a6d893f63ee5d79b86f68de9e89c7094d3e7"  # onnx # <- clip-g ?? unet? inaccurate test at layer level
+2400            ],
+2401            layer_256=[
+2402                "2ffef1834d5fe14ad8db58fc78d769d5dc38dda5eddbfc396786f74b326215fd",
+2403                # "94ea015f5f7f65b1d8e80f7d52859535e7761d7ed2752e24d57a8d9d9da96672", # onnx lose reliability with layer search apparently
+2404            ],
+2405        ),
+2406    )
+2407    repo = "facebook/wav2vec2-conformer-rope-large-960h-ft"
+2408    series, comp = make_mir_tag(repo)
+2409    mir_db.add(
+2410        mir_entry(
+2411            domain="info",
+2412            arch="stst",
+2413            series=series,
+2414            comp=comp,
+2415            repo=repo,
+2416            pkg={
+2417                0: {
+2418                    "transformers": "Wav2Vec2ConformerForCTC",
+2419                },
+2420            },
+2421            file_256=["97bb9761fb71ec1225100bc81ccf7d002e0d0ba3d0604c1fd2dbda7d7d491f1d"],
+2422            layer_b3=["6c9c5642aa8dce62bcb3eb577bc519619a2d868005c767c5e65371c583a8a8eb"],
+2423            layer_256=["1afcfda68307a75caa1a1c4456cf97e20c7914e8aba828006e9fe17e8675a79d"],
+2424        ),
+2425    )
+2426    repo = "canopylabs/orpheus-3b-0.1-ft"
+2427    series, comp = make_mir_tag(repo)
+2428    mir_db.add(
+2429        mir_entry(
+2430            domain="info",
+2431            arch="art",
+2432            series=series,
+2433            comp=comp,
+2434            repo=repo,
+2435            pkg={
+2436                0: {
+2437                    "orpheus_tts": "OrpheusModel",
+2438                    "generation": {"max_model_len": 2048},
+2439                },
+2440                1: {
+2441                    "mlx_audio": "tts.generate.generate_audio",
+2442                    "generation": {"audio_format": "wav", "join_audio": True, "verbose": False},
+2443                },
+2444            },
+2445        )
+2446    )
+2447    repo = "OuteAI/OuteTTS-0.3-1B"
+2448    series, comp = make_mir_tag(repo)
+2449    mir_db.add(
+2450        mir_entry(
+2451            domain="info",
+2452            arch="art",
+2453            series=series,
+2454            comp=comp,
+2455            repo=repo,
+2456            pkg={
+2457                0: {"outetts": "InterfaceHF"},
+2458                1: {
+2459                    "mlx_audio": "tts.generate.generate_audio",
+2460                    "generation": {"audio_format": "wav", "join_audio": True, "verbose": False},
+2461                },
+2462            },
+2463        )
+2464    )
+
+ + +

Create MIR audio modality entries

+
+ + +
+
+ +
+ + def + add_mir_lora(mir_db: mir.maid.MIRDatabase): + + + +
+ + 
2467def add_mir_lora(mir_db: MIRDatabase):
+2468    """Create MIR lora entries"""
+2469    mir_db.add(
+2470        mir_entry(
+2471            domain="info",
+2472            arch="lora",
+2473            series="dmd",
+2474            comp=sdxl_series,
+2475            repo="tianweiy/DMD2",
+2476            pkg={
+2477                0: {
+2478                    "diffusers": {"load_lora_weights": {}},
+2479                    "generation": {"num_inference_steps": 4, "guidance_scale": 0, "timesteps": [999, 749, 499, 249]},
+2480                    "scheduler": {"ops.scheduler.lcm": ""},
+2481                }
+2482            },
+2483            file_256=[
+2484                "b3d9173815a4b595991c3a7a0e0e63ad821080f314a0b2a3cc31ecd7fcf2cbb8",
+2485                "a374289e9446d7f14d2037c4b3770756b7b52c292142a691377c3c755010a1bb",
+2486            ],
+2487        )
+2488    )
+2489    mir_db.add(
+2490        mir_entry(
+2491            domain="info",
+2492            arch="lora",
+2493            series="dpo",
+2494            comp=sdxl_series,
+2495            repo="radames/sdxl-DPO-LoRA",
+2496            pkg={
+2497                0: {
+2498                    "diffusers": {"load_lora_weights": {}},
+2499                    "generation": {"guidance_scale": 7.5, "num_inference_steps": 4},
+2500                    "scheduler": {"ops.scheduler.dpm": {"algorithm_type": "sde-dpmsolver++", "use_karras_sigmas": True, "order": 2}},
+2501                },
+2502            },
+2503            file_256=[
+2504                "666f71a833fc41229ec7e8a264fb7b0fcb8bf47a80e366ae7486c18f38ec9fc0",
+2505                "6b1dcbfb234d7b6000948b5b95ccebc8f903450ce2ba1b50bc3456987c9087ad",
+2506            ],
+2507        )
+2508    )
+2509    mir_db.add(
+2510        mir_entry(
+2511            domain="info",
+2512            arch="lora",
+2513            series="flash",
+2514            comp=sdxl_series,
+2515            repo="jasperai/flash-sdxl",
+2516            pkg={
+2517                0: {
+2518                    "diffusers": {"load_lora_weights": {}},
+2519                    "scheduler": "ops.scheduler.lcm",
+2520                }
+2521            },
+2522            file_256=["afe2ca6e27c4c6087f50ef42772c45d7b0efbc471b76e422492403f9cae724d7"],
+2523        ),
+2524    )
+2525    mir_db.add(
+2526        mir_entry(
+2527            domain="info",
+2528            arch="lora",
+2529            series="flash",
+2530            comp="pixart-alpha",
+2531            repo="jasperai/flash-pixart",
+2532            pkg={
+2533                0: {"diffusers": {"load_lora_weights": {}}},
+2534            },
+2535            file_256=["99ef037fe3c1fb6d6bbefdbb85ad60df434fcc0577d34c768d752d60cf69681b"],
+2536        )
+2537    )
+2538    mir_db.add(
+2539        mir_entry(
+2540            domain="info",
+2541            arch="lora",
+2542            series="flash",
+2543            comp=sd3_series,
+2544            repo="jasperai/flash-sd3",
+2545            pkg={
+2546                0: {"diffusers": {"load_lora_weights": {}}},
+2547            },
+2548            file_256=["85fce13c36e3739aa42930f745eb9fceb6c53d53fb17e2a687e3234c1a58ee15"],
+2549        )
+2550    )
+2551    mir_db.add(
+2552        mir_entry(
+2553            domain="info",
+2554            arch="lora",
+2555            series="flash",
+2556            comp=sd1_series,
+2557            repo="jasperai/flash-sd",
+2558            pkg={
+2559                0: {"diffusers": {"load_lora_weights": {}}, "generation": {"num_inference_steps": 4, "guidance_scale": 0}},
+2560            },
+2561            file_256=["99353444c1a0f40719a1b3037049dbd24800317979a73c312025c05af3574a5f"],
+2562        ),
+2563    )
+2564    mir_db.add(
+2565        mir_entry(
+2566            domain="info",
+2567            arch="lora",
+2568            series="hyper",
+2569            comp=sdxl_series,
+2570            repo="ByteDance/Hyper-SD",
+2571            pkg={0: {"diffusers": {"load_lora_weights": {"fuse": 1.0}}}},
+2572            file_256={
+2573                "0b97f447b5878323a28fbe7c51ba7acebd21f4d77552ba77b04b11c8911825b6": {"num_inference_steps": 12},
+2574                "55b51334c85061afff5eff7c550b61963c8b8607a5868bbe4f26db49374719b1": {"num_inference_steps": 8},
+2575                "c912df184c5116792d2c604d26c6bc2aa916685f4a793755255cda1c43a3c78a": {"num_inference_steps": 1, "guidance_scale": 0.0},
+2576                "69b25c0187ced301c3603c599c0bc509ac99b8ac34db89a2aecc3d5f77a35187": {"num_inference_steps": 2, "guidance_scale": 0.0},
+2577                "12f81a27d00a751a40d68fd15597091896c5a90f3bd632fb6c475607cbdad76e": {"num_inference_steps": 4, "guidance_scale": 0.0},
+2578                "ca689190e8c46038550384b5675488526cfe5a40d35f82b27acb75c100f417c1": {"num_inference_steps": 8, "guidance_scale": 0.0},
+2579            },
+2580        ),
+2581    )
+2582    mir_db.add(
+2583        mir_entry(
+2584            domain="info",
+2585            arch="lora",
+2586            series="hyper",
+2587            comp=dev_series,
+2588            repo="ByteDance/Hyper-SD",
+2589            pkg={0: {"diffusers": {"load_lora_weights": {"fuse": 0.125}}}},
+2590            file_256={
+2591                "6461f67dfc1a967ae60344c3b3f350877149ccab758c273cc37f5e8a87b5842e": {"num_inference_steps": 16, "guidance_scale": 0.0},
+2592                "e0ab0fdf569cd01a382f19bd87681f628879dea7ad51fe5a3799b6c18c7b2d03": {"num_inference_steps": 8, "guidance_scale": 0.0},
+2593            },
+2594        ),
+2595    )
+2596    mir_db.add(
+2597        mir_entry(
+2598            domain="info",
+2599            arch="lora",
+2600            series="hyper",
+2601            comp=sd3_series,
+2602            repo="ByteDance/Hyper-SD",
+2603            pkg={0: {"diffusers": {"load_lora_weights": {"fuse": 0.125}}}},
+2604            file_256={
+2605                "5b4d0b99d58deb811bdbbe521a06f4dbf56a2e9148ff3211c594e0502b656bc9": {"num_inference_steps": 16},
+2606                "0ee4e529abd17b06d4295e3bb91c0d4ddae393afad86b2b43c4f5eeb9e401602": {"num_inference_steps": 4},
+2607                "fc6a3e73e14ed11e21e4820e960d7befcffe7e333850ada9545f239e9aa6027e": {"num_inference_steps": 8},
+2608            },
+2609        ),
+2610    )
+2611    mir_db.add(
+2612        mir_entry(
+2613            domain="info",
+2614            arch="lora",
+2615            series="hyper",
+2616            comp=sd1_series,
+2617            repo="ByteDance/Hyper-SD",
+2618            pkg={0: {"diffusers": {"load_lora_weights": {}}}},
+2619            file_256={
+2620                "64b98437383537cd968fda6f87a05c33160ece9c79ff4757949a1e212ff78361": {"num_inference_steps": 12},
+2621                "f6123d5b950d5250ab6c33600e27f4dcf71b3099ebf888685e01e9e8117ce482": {"num_inference_steps": 8},
+2622                "a04fd9a535c1e56d38f7590ee72a13fd5ca0409853b4fff021e5a9482cf1ca3b": {"num_inference_steps": 1, "guidance_scale": 0.0},
+2623                "2f26dcc1d883feb07557a552315baae2ca2a04ac08556b08a355a244547e8c3a": {"num_inference_steps": 2, "guidance_scale": 0.0},
+2624                "c5dd058616461ed5053e2b14eec4dbe3fa0eea3b13688642f6d6c80ea2ba5958": {"num_inference_steps": 4, "guidance_scale": 0.0},
+2625                "91fc3186236e956d64dbb4357f2e120c69b968b78af7d2db9884a5ca74d3cd13": {"num_inference_steps": 8, "guidance_scale": 0.0},
+2626            },
+2627        )
+2628    )
+2629    mir_db.add(
+2630        mir_entry(
+2631            domain="info",
+2632            arch="lora",
+2633            series="lcm",
+2634            comp=sdxl_series,
+2635            repo="latent-consistency/lcm-lora-sdxl",
+2636            pkg={
+2637                0: {
+2638                    "diffusers": {"load_lora_weights": {"fuse": 1.0}},
+2639                    "scheduler": {"ops.scheduler.lcm": {"timestep_spacing": "trailing"}},
+2640                    "generation": {"num_inference_steps": 8},
+2641                },
+2642            },
+2643            file_256=["a764e6859b6e04047cd761c08ff0cee96413a8e004c9f07707530cd776b19141"],
+2644        )
+2645    )
+2646    mir_db.add(
+2647        mir_entry(
+2648            domain="info",
+2649            arch="lora",
+2650            series="lcm",
+2651            comp=ssd_series,
+2652            repo="latent-consistency/lcm-lora-ssd-1b",
+2653            pkg={0: {"diffusers": {"load_lora_weights": {}}, "generation": {"num_inference_steps": 8}}},
+2654            file_256=["7adaaa69db6f011058a19fd1d5315fdf19ef79fcd513cdab30e173833fd5c59b"],
+2655        ),
+2656    )
+2657    mir_db.add(
+2658        mir_entry(
+2659            domain="info",
+2660            arch="lora",
+2661            series="lcm",
+2662            comp=vega_series,
+2663            repo="segmind/Segmind-VegaRT",
+2664            pkg={0: {"diffusers": {"load_lora_weights": {}}, "gen_kwargs": {"num_inference_steps": 8}}},
+2665            file_256=["9b6e8cd833fa205eaeeed391ca623a6f2546e447470bd1c5dcce3fa8d2f26afb"],
+2666        ),
+2667    )
+2668    mir_db.add(
+2669        mir_entry(
+2670            domain="info",
+2671            arch="lora",
+2672            series="lcm",
+2673            comp=sd1_series,
+2674            repo="latent-consistency/lcm-lora-sdv1-5",
+2675            pkg={0: {"diffusers": {"load_lora_weights": {}}, "generation": {"num_inference_steps": 8}}},
+2676            file_256=["8f90d840e075ff588a58e22c6586e2ae9a6f7922996ee6649a7f01072333afe4"],
+2677        ),
+2678    )
+2679    mir_db.add(
+2680        mir_entry(
+2681            domain="info",
+2682            arch="lora",
+2683            series="lightning",
+2684            comp=sdxl_series,
+2685            repo="ByteDance/SDXL-Lightning",
+2686            pkg={0: {"diffusers": {"load_lora_weights": {}}, "generation": {"num_inference_steps": 4, "guidance_scale": 0}}},
+2687        ),
+2688    )
+2689    mir_db.add(
+2690        mir_entry(
+2691            domain="info",
+2692            arch="lora",
+2693            series="pcm",
+2694            comp=sdxl_series,
+2695            repo="wangfuyun/PCM_Weights",
+2696            pkg={0: {"diffusers": {"load_lora_weights": {}}}},
+2697            file_256={
+2698                "0365f6107250a4fed1b83e8ae6a070065e026a2ba54bff65f55a50284232bbe6": {"num_inference_steps": 4, "guidance_scale": 0.0},
+2699                "04ea827435d5750e63d113dc509174b4f6e8a069ff8f91970c3d25299c10b1f8": {"num_inference_steps": 16},
+2700                "7eb353b2abcaabab6251ba4e17d6cbe2e763feb0674b0f950555552212b44621": {"num_inference_steps": 16},
+2701                "a85cf70ac16ed42011630a5cd6b5927722cb7c40a2107eff85e2670f9a38c893": {"num_inference_steps": 4},  # float16
+2702                "9f7f13bb019925eacd89aeff678e4fd831f7b60245b986855dff6634aee4eba9": {"num_inference_steps": 4},
+2703                "3b9c970a3e4c0e182931e71b3f769c1956f16c6b06db98b4d67236790d4d0b1d": {"num_inference_steps": 8},
+2704                "7f04ba8911b4c25ef2c7cbf74abcb6daa3b4f0e4bc6a03896bdae7601f2f180b": {"num_inference_steps": 8},
+2705                "13fb038025ce9dad93b8ee1b67fc81bac8affb59a77b67d408d286e0b0365a1d": {"num_inference_steps": 16, "guidance_scale": 0.0},
+2706                "3442eff271aa3b60a094fd6f9169d03e49e4051044a974f6fcf690507959191f": {"num_inference_steps": 16, "guidance_scale": 0.0},
+2707                "242cbe4695fe3f2e248faa71cf53f2ccbf248a316973e4b2f38ab9e34f35a5ab": {"num_inference_steps": 2, "guidance_scale": 0.0},
+2708                "e1f600491bb8e0cd94f41144321e44fdb2cb346447f31e71f6e53f1c24cccfbf": {"num_inference_steps": 2, "guidance_scale": 0.0},
+2709                "d0bf40a7f280829195563486bec7253f043a06b1f218602b20901c367641023e": {"num_inference_steps": 4, "guidance_scale": 0.0},
+2710                "212150d7953627fb89df99aad579d6763645a1cb2ef26b19fee8b398d5e5ff4d": {"num_inference_steps": 4, "guidance_scale": 0.0},
+2711                "e80fcf46d15f4d3821d3d9611bdb3022a4a8b647b2536833b168d317a91e4f74": {"num_inference_steps": 8, "guidance_scale": 0.0},
+2712                "56ed9dc9f51f4bb0d6172e13b7947f215c347fc0da341c8951b2c12b9507d09e": {"num_inference_steps": 8, "guidance_scale": 0.0},
+2713            },
+2714        )
+2715    )
+2716    mir_db.add(
+2717        mir_entry(
+2718            domain="info",
+2719            arch="lora",
+2720            series="pcm",
+2721            comp=sd1_series,
+2722            repo="wangfuyun/PCM_Weights",
+2723            pkg={0: {"diffusers": {"load_lora_weights": {}}}},
+2724            file_256={
+2725                "b80b27dd6504f1c3a7637237dda86bc7e26fa5766da30c4fc853c0a1d46bad31": {"num_inference_steps": 4, "guidance_scale": 0.0},
+2726                "8f605ffde3616592deb37ed8c6bacb83fe98963c1fd0883c2a4f93787098aa45": {"num_inference_steps": 16},
+2727                "fa6acb94f11dba3bf4120af5a12e3c88cd2b9572d43ec1a6fb04eede9f32829e": {"num_inference_steps": 4},
+2728                "bff3d4499718b61455b0757b5f8d98fe23e73a768b538c82ecf91c693b69dbcd": {"num_inference_steps": 8},
+2729                "c7ac2fa3df3a5b7080ebe63f259ab13630014f104c93c3c706d77b05cc48506b": {"num_inference_steps": 16, "guidance_scale": 0.0},
+2730                "4c5f27a727d12146de4b1d987cee3343bca89b085d12b03c45297af05ce88ef4": {"num_inference_steps": 2, "guidance_scale": 0.0},
+2731                "29278bc86274fdfc840961e3c250758ff5e2dc4666d940f103e78630d5b879d3": {"num_inference_steps": 4, "guidance_scale": 0.0},
+2732                "41a7f0b966d18f643d16c4401f0b5ef6b9ef7362c20e17128322f17874709107": {"num_inference_steps": 8, "guidance_scale": 0.0},
+2733            },
+2734        )
+2735    )
+2736    mir_db.add(
+2737        mir_entry(
+2738            domain="info",
+2739            arch="lora",
+2740            series="pcm",
+2741            comp=sd3_series,
+2742            repo="wangfuyun/PCM_Weights",
+2743            pkg={0: {"diffusers": {"load_lora_weights": {}}}},
+2744            file_256={
+2745                "8a45878ecc34e53855fe21146cb6ef32682053b7c4eacc013be89fb08c4c19d8": {"num_inference_steps": 2, "guidance_scale": 1.2},
+2746                "9444a5cead551c56c4d1c455ce829ba9f96f01fbcca31294277e0862a6a15b76": {"num_inference_steps": 4, "guidance_scale": 1.2},
+2747                "e365902c208cbc0456ca5e7c41a490f637c15f3f7b98691cbba21f96a8c960b4": {"num_inference_steps": 4, "guidance_scale": 1.2},
+2748                "3550fa018cd0b60d9e36ac94c31b30f27e402d3855ed63e47668bb181b35a0ad": {"num_inference_steps": 4, "guidance_scale": 1.2},
+2749            },
+2750        )
+2751    )
+2752    mir_db.add(
+2753        mir_entry(
+2754            domain="info",
+2755            arch="lora",
+2756            series="slam",
+2757            comp=sdxl_series,
+2758            repo="alimama-creative/slam-lora-sdxl",
+2759            pkg={
+2760                0: {
+2761                    "diffusers": {"load_lora_weights": {}},
+2762                    "scheduler": {"ops.scheduler.lcm": {"timestep_spacing": "trailing"}},
+2763                    "generation": {"num_inference_steps": 4, "guidance_scale": 1},
+2764                }
+2765            },
+2766            file_256=["22569a946b0db645aa3b8eb782c674c8e726a7cc0d655887c21fecf6dfe6ad91"],
+2767        )
+2768    )
+2769    mir_db.add(
+2770        mir_entry(
+2771            domain="info",
+2772            arch="lora",
+2773            series="slam",
+2774            comp=sd1_series,
+2775            repo="alimama-creative/slam-sd1.5",
+2776            pkg={0: {"diffusers": {"load_lora_weights": {}}}},
+2777        )
+2778    )
+2779    mir_db.add(
+2780        mir_entry(
+2781            domain="info",
+2782            arch="lora",
+2783            series="spo",
+2784            comp=sdxl_series,
+2785            repo="SPO-Diffusion-Models/SPO-SDXL_4k-p_10ep_LoRA",
+2786            pkg={0: {"diffusers": {"load_lora_weights": {}}, "generation": {"guidance_scale": 5.0}}},
+2787            file_256=["0b9896f30d29daa5eedcfc9e7ad03304df6efc5114508f6ca9c328c0b4f057df"],
+2788        ),
+2789    )
+2790    mir_db.add(
+2791        mir_entry(
+2792            domain="info",
+2793            arch="lora",
+2794            series="spo",
+2795            comp=sd1_series,
+2796            repo="SPO-Diffusion-Models/SPO-SD-v1-5_4k-p_10ep_LoRA",
+2797            pkg={0: {"diffusers": {"load_lora_weights": {}}, "generation": {"guidance_scale": 7.5}}},
+2798            file_256=["1be130c5be2de0beacadd3bf0bafe3bedd7e7a380729932a1e369fb29efa86f4"],
+2799        ),
+2800    )
+2801    mir_db.add(
+2802        mir_entry(
+2803            domain="info",
+2804            arch="lora",
+2805            series="tcd",
+2806            comp=sdxl_series,
+2807            repo="h1t/TCD-SDXL-LoRA",
+2808            pkg={
+2809                0: {
+2810                    "diffusers": {"load_lora_weights": {}},
+2811                    "generation": {"num_inference_steps": 4, "guidance_scale": 0, "eta": 0.3},
+2812                    "scheduler": {"ops.scheduler.tcd": {}},
+2813                }
+2814            },
+2815            file_256=["2c777bc60abf41d3eb0fe405d23d73c280a020eea5adf97a82a141592c33feba"],
+2816        ),
+2817    )
+2818    mir_db.add(
+2819        mir_entry(
+2820            domain="info",
+2821            arch="lora",
+2822            series="tcd",
+2823            comp=sd1_series,
+2824            repo="h1t/TCD-SD15-LoRA",
+2825            pkg={0: {"diffusers": {"load_lora_weights": {}}}},
+2826            file_256=["eaecb24a1cda4411eab67275b1d991071216ac93693e8fa0c9226c9df0386232"],
+2827            layer_b3=["90158259812a89beb8874216009c799f420334aac49bbf4fa1bf0ebf4bbd256b"],
+2828            layer_256=["e9825b81bca684126ac3cc8867d2ebc655f74268bc26bea4e4b7e58a52ad6c75"],
+2829        )
+2830    )
+2831    mir_db.add(
+2832        mir_entry(
+2833            domain="info",
+2834            arch="lora",
+2835            series="turbo",
+2836            comp=sdxl_series,
+2837            file_256=["a599c42a9f4f7494c7f410dbc0fd432cf0242720509e9d52fa41aac7a88d1b69"],
+2838        )
+2839    )
+2840    mir_db.add(
+2841        mir_entry(
+2842            domain="info",
+2843            arch="lora",
+2844            series="turbo",
+2845            comp=dev_series,
+2846            repo="alimama-creative/FLUX.1-Turbo-Alpha",
+2847            pkg={
+2848                0: {
+2849                    "diffusers": {"load_lora_weights": {"fuse": 0.125}},
+2850                    "generation": {"guidance_scale": 3.5, "num_inference_steps": 8, "max_sequence_length": 512},
+2851                }
+2852            },
+2853            file_256=["77f7523a5e9c3da6cfc730c6b07461129fa52997ea06168e9ed5312228aa0bff"],
+2854        )
+2855    )
+2856    mir_db.add(
+2857        mir_entry(
+2858            domain="info",
+2859            arch="lora",
+2860            series="turbo",
+2861            comp=sd3_series,
+2862            repo="tensorart/stable-diffusion-3.5-medium-turbo",
+2863            pkg={0: {"diffusers": {"load_lora_weights": {"fuse": 1.0}}, "scheduler": {"ops.scheduler.flow-match": {"shift": 5}}}},
+2864            file_256={"bdcbdfa3ec8ed838b77b1020eea3bc7917a2d42573688a034feb921fde8b1858": {"num_inference_steps": "4"}},
+2865        )
+2866    )
+2867    mir_db.add(
+2868        mir_entry(
+2869            domain="info",
+2870            arch="lora",
+2871            series="turbo",
+2872            comp=sd3_series,
+2873            repo="tensorart/stable-diffusion-3.5-large-TurboX",
+2874            pkg={0: {"diffusers": {"load_lora_weights": {"fuse": 1.0}}, "scheduler": {"ops.scheduler.flow-match": {"shift": 5}}}},
+2875            file_256={"fae59d1b749c0d14a8fd4c68cc94eaac92876cee7b91fa75cf8fde3160e09548": {"num_inference_steps": "8"}},
+2876        )
+2877    )
+
+ + +

Create MIR lora entries

+
+ + +
+
+ +
+ + def + add_mir_vae(mir_db: mir.maid.MIRDatabase): + + + +
+ + 
2880def add_mir_vae(mir_db: MIRDatabase):
+2881    """Create MIR VAE missing from the database"""
+2882    mir_db.add(
+2883        mir_entry(
+2884            domain="info",
+2885            arch="vae",
+2886            series="tae",
+2887            comp=sd3_series,
+2888            repo="madebyollin/taesd3",
+2889            pkg={0: {"diffusers": "AutoencoderTiny"}},
+2890            file_256=["6f79c1397cb9ce1dac363722dbe70147aee0ccca75e28338f8482fe515891399"],
+2891        )
+2892    )
+2893    mir_db.add(
+2894        mir_entry(
+2895            domain="info",
+2896            arch="vae",
+2897            series="tae",
+2898            comp=sdxl_series,
+2899            repo="madebyollin/taesdxl",
+2900            pkg={0: {"diffusers": "AutoencoderTiny"}},
+2901            file_256=["ff4824aca94dd6111e0340fa749347fb74101060d9712cb5ef1ca8f1cf17502f"],
+2902        )
+2903    )
+2904    mir_db.add(
+2905        mir_entry(
+2906            domain="info",
+2907            arch="vae",
+2908            series="tae",
+2909            comp=sd1_series,
+2910            repo="madebyollin/taesd",
+2911            pkg={0: {"diffusers": "AutoencoderTiny"}},
+2912            file_256=["db169d69145ec4ff064e49d99c95fa05d3eb04ee453de35824a6d0f325513549"],
+2913        )
+2914    )
+2915    mir_db.add(
+2916        mir_entry(
+2917            domain="info",
+2918            arch="vae",
+2919            series="tae",
+2920            comp=dev_series,
+2921            repo="madebyollin/taef1",
+2922            pkg={0: {"diffusers": "AutoencoderTiny"}},
+2923            file_256=["927f7de7f11bbd3b2d5ce402e608d97a7649e0921a9601995b044e8efc81e449"],
+2924        )
+2925    )
+2926    series, comp = make_mir_tag("Qwen/Qwen-Image")
+2927    mir_db.add(
+2928        mir_entry(
+2929            domain="info",
+2930            arch="vae",
+2931            series="kl",
+2932            comp=series,
+2933            # no repo here, may conflict
+2934            pkg={
+2935                0: {"diffusers": "AutoencoderKLQwenImage"},
+2936            },
+2937            file_256=[
+2938                "0c8bc8b758c649abef9ea407b95408389a3b2f610d0d10fcb054fe171d0a8344",  # diffusers
+2939            ],
+2940            layer_b3=[
+2941                "64af8fb08d2054c81ad2aef94965be8fb1366fcc6136cb9222ae046550af014b",  # diffusers
+2942            ],
+2943            layer_256=[
+2944                "42f255440ef1d379a8a731456bc44312a73a8568716caa6100803990cd5ea7dc",  # diffusers
+2945            ],
+2946        )
+2947    )
+2948    series, comp = make_mir_tag("Wan-AI/Wan2.1-I2V-14B-480P-Diffusers")
+2949    sr_series_text2v, _ = make_mir_tag("Skywork/SkyReels-V2-T2V-14B-720P-Diffusers")
+2950    sr_series_image2v, _ = make_mir_tag("Skywork/SkyReels-V2-I2V-14B-720P-Diffusers")
+2951    mir_db.add(
+2952        mir_entry(
+2953            domain="info",
+2954            arch="vae",
+2955            series="wan",
+2956            comp=series,
+2957            # no repo here, may conflict
+2958            pkg={
+2959                0: {
+2960                    "diffusers": "AutoencoderKLWan",
+2961                    "precision": "ops.precision.float.F32",
+2962                }
+2963            },
+2964            file_256=[
+2965                "d6e524b3fffede1787a74e81b30976dce5400c4439ba64222168e607ed19e793",  # diffusers
+2966                "2fc39d31359a4b0a64f55876d8ff7fa8d780956ae2cb13463b0223e15148976b",  # sai
+2967            ],
+2968            layer_b3=[
+2969                "f867543d636029ebfc05b8075e572be0b313a83b0470e56bcf4bbad07a6db010",  # diffusers
+2970                "6b5b229727a2d4e37993687c62c94ff8519a371ab4103c699ff1f5969ca0b433",  # sai
+2971            ],
+2972            layer_256=[
+2973                "121b3974b39263dcca9d644d1b5c9b9251a911b6a8a8e307fcb21ca778e78ed2",
+2974                "364be43a8959012d798d3f98e17d8b5c4b99ba1e70077008dd19acca3ced395e",
+2975            ],
+2976        )
+2977    )
+2978    mir_db.add(
+2979        mir_entry(
+2980            domain="info",
+2981            arch="vae",
+2982            series="wan",
+2983            comp=sr_series_text2v,
+2984            # no repo here, may conflict
+2985            file_256=[],
+2986            layer_b3=[],
+2987            layer_256=[],
+2988        )
+2989    )
+2990    mir_db.add(
+2991        mir_entry(
+2992            domain="info",
+2993            arch="vae",
+2994            series="wan",
+2995            comp=sr_series_image2v,
+2996            # no repo here, may conflict
+2997            file_256=[],
+2998            layer_b3=[],
+2999            layer_256=[],
+3000        )
+3001    )
+3002    series, comp = make_mir_tag("Lightricks/LTX-Video")
+3003    mir_db.add(
+3004        mir_entry(
+3005            domain="info",
+3006            arch="vae",
+3007            series="kl",
+3008            comp=series,
+3009            # no repo here, may conflict
+3010            pkg={
+3011                0: {"diffusers": "AutoencoderKLLTXVideo"},
+3012            },
+3013            file_256=[],
+3014            layer_b3=[],
+3015            layer_256=[],
+3016        )
+3017    )
+3018    series, comp = make_mir_tag("rhymes-ai/Allegro")
+3019    mir_db.add(
+3020        mir_entry(
+3021            domain="info",
+3022            arch="vae",
+3023            series="kl",
+3024            comp=series,
+3025            # no repo here, may conflict
+3026            pkg={
+3027                0: {"diffusers": "AutoencoderKLAllegro"},
+3028            },
+3029            file_256=[],
+3030            layer_b3=[],
+3031            layer_256=[],
+3032        )
+3033    )
+3034    series, comp = make_mir_tag("zai-org/CogVideoX-5b-I2V")
+3035    series_fun, _ = make_mir_tag("alibaba-pai/CogVideoX-Fun-V1.1-5b-Pose")
+3036    series_wish, _ = make_mir_tag("BestWishYsh/ConsisID-preview")
+3037    mir_db.add(
+3038        mir_entry(
+3039            domain="info",
+3040            arch="vae",
+3041            series="cogvideox",
+3042            comp=series,
+3043            # no repo here, may conflict
+3044            pkg={
+3045                0: {"diffusers": "AutoencoderKLCogVideoX"},
+3046            },
+3047            file_256=["a410e48d988c8224cef392b68db0654485cfd41f345f4a3a81d3e6b765bb995e"],
+3048            layer_b3=["246addb8dc798240638bffee4546a3c5c83572139b4a2a602d68b4c4146226eb"],
+3049            layer_256=["43c7e9cb4364e55fd563817f01484ede8a09ff19a8e69eb61a32a12f93d6f66e"],
+3050        )
+3051    )
+3052    mir_db.add(
+3053        mir_entry(
+3054            domain="info",
+3055            arch="vae",
+3056            series="cogvideox",
+3057            comp=series_fun,
+3058            # no repo here, may conflict
+3059            file_256=[],
+3060            layer_b3=[],
+3061            layer_256=[],
+3062        )
+3063    )
+3064    mir_db.add(
+3065        mir_entry(
+3066            domain="info",
+3067            arch="vae",
+3068            series="cogvideox",
+3069            comp=series_wish,
+3070            # no repo here, may conflict
+3071            file_256=[],
+3072            layer_b3=[],
+3073            layer_256=[],
+3074        )
+3075    )
+3076    series, comp = make_mir_tag("nvidia/Cosmos-1.0-Diffusion-7B-Video2World")
+3077    mir_db.add(
+3078        mir_entry(
+3079            domain="info",
+3080            arch="vae",
+3081            series="kl",
+3082            comp=series,
+3083            # no repo here, may conflict
+3084            pkg={
+3085                0: {"diffusers": "AutoencoderKLCosmos"},
+3086            },
+3087            file_256=[],
+3088            layer_b3=[],
+3089            layer_256=[],
+3090        )
+3091    )
+3092    series, comp = make_mir_tag("alibaba-pai/EasyAnimateV5.1-7b-zh-diffusers")
+3093    mir_db.add(
+3094        mir_entry(
+3095            domain="info",
+3096            arch="vae",
+3097            series="kl",
+3098            comp=series,
+3099            # no repo here, may conflict
+3100            pkg={
+3101                0: {"diffusers": "AutoencoderKLMagvit"},
+3102            },
+3103            file_256=[],
+3104            layer_b3=[],
+3105            layer_256=[],
+3106        )
+3107    )
+3108    series, comp = make_mir_tag("hunyuanvideo-community/HunyuanVideo-I2V")
+3109    mir_db.add(
+3110        mir_entry(
+3111            domain="info",
+3112            arch="vae",
+3113            series="kl",
+3114            comp=series,
+3115            # no repo here, may conflict
+3116            pkg={
+3117                0: {"diffusers": "AutoencoderKLHunyuanVideo"},
+3118            },
+3119            file_256=[
+3120                "95d1fc707c1421ccd88ea542838ab4c5d45a5babb48205bac9ce0985525f9818",  # pt,
+3121                "7c68a6295f9034a88225fbafb1f3258291a08d57a1fdb938233fa57b1b8f4883",
+3122                "fbe5ea338431bc8ba20f7019b474e83379fe5763abfd562adcc04b1c0d35c728",
+3123                "019973c147e0c3462629d8d06bdbdbb83408f3ebd4ea4b4ae21a99c3cdcb54c0",
+3124            ],
+3125            # layer_b3=[],
+3126            # layer_256=[],
+3127        )
+3128    )
+3129    series, comp = make_mir_tag("genmo/mochi-1-preview")
+3130    mir_db.add(
+3131        mir_entry(
+3132            domain="info",
+3133            arch="vae",
+3134            series="kl",
+3135            comp=series,
+3136            # no repo here, may conflict
+3137            pkg={
+3138                0: {"diffusers": "AutoencoderKLMochi"},
+3139            },
+3140            file_256=[],
+3141            layer_b3=[],
+3142            layer_256=[],
+3143        )
+3144    )
+3145    series, comp = make_mir_tag("rhymes-ai/Allegro")
+3146    mir_db.add(
+3147        mir_entry(
+3148            domain="info",
+3149            arch="vae",
+3150            series="kl",
+3151            comp=series,
+3152            # no repo here, may conflict
+3153            pkg={
+3154                0: {
+3155                    "diffusers": "AutoencoderKLAllegro",
+3156                },
+3157            },
+3158            file_256=["47871a698b18f92f15019d361a81cbc8af4676f8eef9a47fd2b95354a39f831a"],
+3159            layer_b3=["93654cbab7541504d2377c66e72943c7fd9947fca2eb1be01bcc8877c322c1e0"],
+3160            layer_256=["bfd496586118165a13243997101fc7cdd4f855b2d8a73ee2b771a4484c4c2f9f"],
+3161        )
+3162    )
+3163    series, comp = make_mir_tag("cvssp/audioldm-s-full-v2")
+3164    mir_db.add(
+3165        mir_entry(
+3166            domain="info",
+3167            arch="vae",
+3168            series="kl",
+3169            comp=series,
+3170            # no repo here, may conflict
+3171            pkg={
+3172                0: {
+3173                    "diffusers": "AutoencoderKL",
+3174                },
+3175            },
+3176            file_256=["42f64f7565b23eabde68c9694e39f18b8bba5f7a14f477e7ed4b51e0ea7de8a5"],
+3177            layer_b3=["00959677dae940b9cfdbe5380c8cbb5a6b4951864cd26f8211d74a3d22b4f3de"],
+3178            layer_256=["54d075953d5253a3abac651de070736c1d5510b857a8ab24c624304f428146b6"],
+3179        )
+3180    )
+3181
+3182    series, comp = make_mir_tag("Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers")
+3183    mir_db.add(
+3184        mir_entry(
+3185            domain="info",
+3186            arch="vae",
+3187            series="dc",
+3188            comp=series,
+3189            # no repo here, may conflict
+3190            pkg={
+3191                0: {"diffusers": "AutoencoderDC"},
+3192            },
+3193            file_256=["15a4b09e56d95b768a0ec9da50b702e21d920333fc9b3480d66bb5c7fad9d87f"],
+3194            layer_b3=["cf4ecc6697d18b0663e4eac58203f1dd6d9fb689cf99adfeadbc0019de0c73d0"],
+3195            layer_256=["abfc39d1a6d71f03dde7bc40fec4a90478a97d17ae1688be9aad00e0512b9bde"],
+3196        )
+3197    )
+3198    series, comp = make_mir_tag("stabilityai/stable-audio-open-1.0")
+3199    mir_db.add(
+3200        mir_entry(
+3201            domain="info",
+3202            arch="vae",
+3203            series="oobleck",
+3204            comp=series,
+3205            # no repo here, may conflict
+3206            pkg={
+3207                0: {"diffusers": "AutoencoderOobleck"},
+3208            },
+3209            # file_256=[],
+3210            # layer_b3=[],
+3211            # layer_256=[],
+3212        )
+3213    )
+3214    series, comp = make_mir_tag("stable-video-diffusion-img2vid-xt")
+3215    mir_db.add(
+3216        mir_entry(
+3217            domain="info",
+3218            arch="vae",
+3219            series="kl",
+3220            comp=series,
+3221            # no repo here, may conflict
+3222            pkg={
+3223                0: {"diffusers": "AutoencoderKLTemporalDecoder"},
+3224            },
+3225            # file_256=[],
+3226            # layer_b3=[],
+3227            # layer_256=[],
+3228        )
+3229    )
+3230    mir_db.add(
+3231        mir_entry(
+3232            domain="info",
+3233            arch="vae",
+3234            series="kl",
+3235            comp=sdxl_series,
+3236            repo="madebyollin/sdxl-vae-fp16-fix",
+3237            pkg={
+3238                0: {"diffusers": "AutoencoderKL"},
+3239            },
+3240            file_256=[
+3241                "235745af8d86bf4a4c1b5b4f529868b37019a10f7c0b2e79ad0abca3a22bc6e1",  # modelspec sai
+3242                "1b909373b28f2137098b0fd9dbc6f97f8410854f31f84ddc9fa04b077b0ace2c",  # diffusers
+3243                "78f6189c8492013e3cac81637a1f657f790a237387f8a9dfd6bfa5fee28eb646",  # ssd1b diffusers
+3244                "6353737672c94b96174cb590f711eac6edf2fcce5b6e91aa9d73c5adc589ee48",  # ssd1b diffusers fp16
+3245                "bcb60880a46b63dea58e9bc591abe15f8350bde47b405f9c38f4be70c6161e68",  # kolors fp16
+3246                "1598f3d24932bcfe6634e8b618ea1e30ab1d57f5aad13a6d2de446d2199f2341",  # vega / lumina next sft d / auraflow
+3247                "703abdcd7c389316b5128faa9b750a530ea1680b453170b27afebac5e4db30c4",  # pixart a
+3248                "98a14dc6fe8d71c83576f135a87c61a16561c9c080abba418d2cc976ee034f88",  # hyd 1.1
+3249            ],
+3250            layer_b3=[
+3251                "bd5b356b509814025a9cf692710b87116d4fcd0e30a8232ed1db133e908d0e74",  # modelspec sai
+3252                "9106380403dee83238af63ff1738396d2fdff9f6d78d0d9c1d0bf770ae4294d0",  # diffusers
+3253                # "245070a60a25ca080cb4951220c3fb1503da43829930d5f6f7a6770b491eafe1",
+3254                # "50e65a628b5fe379798d8956e4a4e1d4b105c84b329f088d577f7f28c22abc49",  # diffusers fp16 matches sd1
+3255            ],
+3256            layer_256=[
+3257                "c9399a4cd39a180a0bb2af96a8297b9330541e090c21e83317cebb2f7cc651da",  # modelspec sai
+3258                "2240ae134a3b983abf45200c198f07e3d8068012fbbd2f658bbaa1fd6a0629c0",  # diffusers
+3259                # "35641f65ad7ea600cb931dcab556f7503279f1d8d99eda170fe7976d48502a2a",  # diffusers fp16 matches sd1 (incorrect)
+3260            ],
+3261        )
+3262    )
+3263    mir_db.add(
+3264        mir_entry(
+3265            domain="info",
+3266            arch="vae",
+3267            series="kl",
+3268            comp=sdxl_series + sdxl_comp,
+3269            pkg={
+3270                0: {"diffusers": "AutoencoderKL"},
+3271            },
+3272            file_256=[
+3273                "235745af8d86bf4a4c1b5b4f529868b37019a10f7c0b2e79ad0abca3a22bc6e1",  # modelspec sai
+3274                "27ed3b02e09638568e99d4398c67bc654dde04e6c0db61fb2d21dba630e7058a",  # diffusers
+3275                "eb6516ab7e1104d5d1a174a4d65c57835ae38061531d0a2192103aecfb790cc1",  # diffusers fp16
+3276                "e6bb9ea85bbf7bf6478a7c6d18b71246f22e95d41bcdd80ed40aa212c33cfeff",  # modelspec sai vae 0.9
+3277            ],
+3278            layer_b3=[
+3279                "bd5b356b509814025a9cf692710b87116d4fcd0e30a8232ed1db133e908d0e74",  # modelspec sai
+3280                # "9106380403dee83238af63ff1738396d2fdff9f6d78d0d9c1d0bf770ae4294d0",  # diffusers
+3281                # "245070a60a25ca080cb4951220c3fb1503da43829930d5f6f7a6770b491eafe1",
+3282                # "50e65a628b5fe379798d8956e4a4e1d4b105c84b329f088d577f7f28c22abc49",  # diffusers fp16 matches sd1
+3283            ],
+3284            layer_256=[
+3285                "c9399a4cd39a180a0bb2af96a8297b9330541e090c21e83317cebb2f7cc651da",  # modelspec sai
+3286                "2240ae134a3b983abf45200c198f07e3d8068012fbbd2f658bbaa1fd6a0629c0",  # diffusers
+3287                # "35641f65ad7ea600cb931dcab556f7503279f1d8d99eda170fe7976d48502a2a",  # diffusers fp16 matches sd1 (incorrect)
+3288            ],
+3289        )
+3290    )
+3291
+3292    repo = "shuttleai/shuttle-jaguar"
+3293    mir_db.add(
+3294        mir_entry(
+3295            domain="info",
+3296            arch="vae",
+3297            series="kl",
+3298            comp=make_mir_tag(repo)[0],
+3299            # no repo here, may conflict
+3300            pkg={
+3301                0: {"diffusers": "AutoencoderKL"},
+3302            },
+3303            file_256=[
+3304                "6fdfa2add4f04d94f36157cbb0197f97966b612e3f8eff4095315aefea74b904",
+3305            ],  # q8,
+3306            layer_b3=[
+3307                "0ebf9b7010accc44e219e355dd24bf1e3128004093c0c1dfc06f88c0a39fdbdd",
+3308                "d0e7ef3c4af06fa08b4c0485a073e2df55f7b1e9e3ba8f7b261688bc562568f0",  # q8
+3309            ],
+3310            layer_256=[
+3311                "9b28f36873ea283905094a64e1ccb7cfc2b0f0aa166201d0ca63807ac37caa7b",  # q8
+3312            ],
+3313        )
+3314    )
+3315    mir_db.add(
+3316        mir_entry(
+3317            domain="info",
+3318            arch="vae",
+3319            series="kl",
+3320            comp=dev_series,
+3321            # no repo here, may conflict
+3322            pkg={
+3323                0: {"diffusers": "AutoencoderKL"},
+3324            },
+3325            file_256=[
+3326                "afc8e28272cd15db3919bacdb6918ce9c1ed22e96cb12c4d5ed0fba823529e38",  # dev
+3327                "f5b59a26851551b67ae1fe58d32e76486e1e812def4696a4bea97f16604d40a3",  # dev diffusers
+3328                "8c717328c8ad41faab2ccfd52ae17332505c6833cf176aad56e7b58f2c4d4c94",  # lumina2
+3329                "8f53304a79335b55e13ec50f63e5157fee4deb2f30d5fae0654e2b2653c109dc",  # sd3 turbo
+3330            ],
+3331            layer_b3=[
+3332                "b6db93ed78c4a10d69e80831c1b8fbc1447f04e9b3d494889ee2056b98d41f17",  # diffusers
+3333                "a8a3ebdec4d7b38d65b7169d3604c19b587330e5e66f69ebf0ded56a24ec6903",  # lumina2
+3334                # "245070a60a25ca080cb4951220c3fb1503da43829930d5f6f7a6770b491eafe1",
+3335            ],
+3336            layer_256=[
+3337                "7950e4f3897c75affaa5f9f3c51c88b4d9a27bfd9b05ad41c3f71d8c1c620b89",
+3338                "79d2bfe93a2ac037cdc59ccb5576e32d00d75d4741fba49fc7e82b9724928216",  # diffusers
+3339                "8f084dc91fd5b481875bc9c86a4ef05e5f176896b7d31c6a5c2ce45c2e174004",  # dev diffusers
+3340                "322e01bd511e20bc2a3c27cd611f81ed85f0046b7c023b5622c2c9a5b8b34f80",  # lumina2
+3341            ],
+3342        )
+3343    )
+3344    mir_db.add(
+3345        mir_entry(
+3346            domain="info",
+3347            arch="vae",
+3348            series="eq",
+3349            comp=sdxl_series,
+3350            repo="KBlueLeaf/EQ-SDXL-VAE",
+3351            pkg={
+3352                0: {"diffusers": "AutoencoderKL"},
+3353            },
+3354        )
+3355    )
+3356    mir_db.add(
+3357        mir_entry(
+3358            domain="info",
+3359            arch="vae",
+3360            series="ms-lc-eq",
+3361            comp=sdxl_series,
+3362            repo="Anzhc/MS-LC-EQ-D-VR_VAE",
+3363            pkg={
+3364                0: {
+3365                    "diffusers": "AutoencoderKL",
+3366                },
+3367            },
+3368        )
+3369    )
+3370    repo = "ucsd-reach/musicldm"
+3371    mir_db.add(
+3372        mir_entry(
+3373            domain="info",
+3374            arch="vae",
+3375            series="kl",
+3376            comp=make_mir_tag(repo)[0],
+3377            # no repo here, may conflict
+3378            file_256=[
+3379                "16e0c6c7c34e459c19500cc15cf538e6331db14969ea15917caa9b0966e44fd4",
+3380            ],  # q8,
+3381            layer_b3=[
+3382                "c5c32b3fb3e73799838836ccce27d883254254daecd10f86ba8ddc55214014e0",
+3383            ],
+3384            layer_256=[
+3385                "1610c0ce39d1379091eb9ab2a4d14a8567e0f1a5dc6cca40fc0fa6f8e4e97c0f",
+3386            ],
+3387        )
+3388    )
+3389
+3390    mir_db.add(
+3391        mir_entry(
+3392            domain="info",
+3393            arch="vae",
+3394            series="kl",
+3395            comp=sd1_series,
+3396            pkg={
+3397                0: {"diffusers": "AutoencoderKL"},
+3398            },
+3399            file_256=[
+3400                "0b204ad0cae549e0a7e298d803d57e36363760dec71c63109c1da3e1147ec520",  # ckpt ema original ema pruned
+3401                "95f26a5ab04779d5467d1fcecaf93160ffa523afe399b835b3e1bb77ff2d937a",  # safetensors ema original ema pruned
+3402                "32db726da04f06c1b6b14c0043ce115cc87a501482945c5add89a40d838fcb46",  # safetensors ema diffusers
+3403                "c6a580b13a5bc05a5e16e4dbb80608ff2ec251a162311590c1f34c013d7f3dab",  # ckpt mse original ema pruned
+3404                "735e4c3a447a3255760d7f86845f09f937809baa529c17370d83e4c3758f3c75",  # safetensors mse original ema pruned
+3405                "a1d993488569e928462932c8c38a0760b874d166399b14414135bd9c42df5815",  # safetensors mse diffusers
+3406                "a2b5134f4dbc140d9c11f11cba3233099e00af40f262f136c691fb7d38d2194c",  # safetensors diffusers
+3407                "4fbcf0ebe55a0984f5a5e00d8c4521d52359af7229bb4d81890039d2aa16dd7c",  # safetensors fp16 diffusers
+3408            ],
+3409            layer_b3=[
+3410                "82e2dc440a23d78bb91df8c9fce069a8512da51f8f54ea29e3431f545808171e",  # safetensors original
+3411                "2230487833925a104bee96e7ecfebaa4c3c43cc426c7a5b863f2584313dd4833",  # safetensors diffusers
+3412            ],
+3413            layer_256=[
+3414                "e43f3a227b5ecb43a6272fa92ed6011d2e9abcadadd1032dfa7ea7f875f9d5bd",  # safetensors original
+3415                "2494154245becf98891be884f943276aa3f54e9b3f0ea1042903fc15fba488f3",  # safetensors diffusers
+3416            ],
+3417        )
+3418    )
+
+ + +

Create MIR VAE missing from the database

+
+ + +
+
+ + \ No newline at end of file diff --git a/docs/mir/config.html b/docs/mir/config.html new file mode 100644 index 0000000..eb02fc1 --- /dev/null +++ b/docs/mir/config.html @@ -0,0 +1,245 @@ + + + + + + + mir.config API documentation + + + + + + + + + +
+
+

+mir.config

+ + + + + + + 
1
+
+ + +
+
+ + \ No newline at end of file diff --git a/docs/mir/config/constants.html b/docs/mir/config/constants.html new file mode 100644 index 0000000..d80c8c8 --- /dev/null +++ b/docs/mir/config/constants.html @@ -0,0 +1,786 @@ + + + + + + + mir.config.constants API documentation + + + + + + + + + +
+
+

+mir.config.constants

+ + + + + + + 
  1# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0
+  2# <!-- // /*  d a r k s h a p e s */ -->
+  3from typing import List, Optional, Union
+  4from mir.config.json_io import read_json_file
+  5import os
+  6from transformers.models.auto.modeling_auto import (
+  7    MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
+  8    MODEL_FOR_BACKBONE_MAPPING_NAMES,
+  9    MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
+ 10    MODEL_FOR_CTC_MAPPING_NAMES,
+ 11    MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
+ 12    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
+ 13    MODEL_FOR_IMAGE_MAPPING_NAMES,
+ 14    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES,
+ 15    MODEL_FOR_MASKED_LM_MAPPING_NAMES,
+ 16    MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
+ 17    MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES,
+ 18    MODEL_FOR_PRETRAINING_MAPPING_NAMES,
+ 19    MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
+ 20    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
+ 21    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
+ 22    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
+ 23    MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES,
+ 24    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
+ 25    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
+ 26    MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES,
+ 27    MODEL_MAPPING_NAMES,
+ 28)
+ 29from transformers.configuration_utils import PretrainedConfig
+ 30
+ 31
+ 32def generate_supported_model_class_names(
+ 33    model_name: type[PretrainedConfig],
+ 34    supported_tasks: Optional[Union[str, list[str]]] = None,
+ 35) -> list[str]:
+ 36    task_mapping = {
+ 37        "default": MODEL_MAPPING_NAMES,
+ 38        "pretraining": MODEL_FOR_PRETRAINING_MAPPING_NAMES,
+ 39        "next-sentence-prediction": MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES,
+ 40        "masked-lm": MODEL_FOR_MASKED_LM_MAPPING_NAMES,
+ 41        "causal-lm": MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
+ 42        "seq2seq-lm": MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
+ 43        "speech-seq2seq": MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES,
+ 44        "multiple-choice": MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
+ 45        "document-question-answering": MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
+ 46        "question-answering": MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
+ 47        "sequence-classification": MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
+ 48        "token-classification": MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
+ 49        "masked-image-modeling": MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES,
+ 50        "image-classification": MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
+ 51        "zero-shot-image-classification": MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES,
+ 52        "ctc": MODEL_FOR_CTC_MAPPING_NAMES,
+ 53        "audio-classification": MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
+ 54        "semantic-segmentation": MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
+ 55        "backbone": MODEL_FOR_BACKBONE_MAPPING_NAMES,
+ 56        "image-feature-extraction": MODEL_FOR_IMAGE_MAPPING_NAMES,
+ 57        "video-classification": MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
+ 58    }
+ 59
+ 60    if supported_tasks is None:
+ 61        supported_tasks = task_mapping.keys()
+ 62    if isinstance(supported_tasks, str):
+ 63        supported_tasks = [supported_tasks]
+ 64
+ 65    model_class_names = []
+ 66    for task in supported_tasks:
+ 67        class_name = task_mapping[task].get(model_name, None)
+ 68        if class_name:
+ 69            model_class_names.append(class_name)
+ 70
+ 71    return model_class_names
+ 72
+ 73
+ 74class DocParseData:
+ 75    pipe_class: str
+ 76    pipe_repo: str
+ 77    staged_class: Optional[str] = None
+ 78    staged_repo: Optional[str] = None
+ 79
+ 80    def __init__(self, pipe_class, pipe_repo, staged_class=None, staged_repo=None):
+ 81        self.pipe_class: str = pipe_class
+ 82        self.pipe_repo: str = pipe_repo
+ 83        self.staged_class: str = staged_class
+ 84        self.staged_repo: str = staged_repo
+ 85
+ 86
+ 87class DocStringParserConstants:
+ 88    """Constants used by DocStringParser for parsing docstrings."""
+ 89
+ 90    pipe_prefixes: List[str] = [
+ 91        ">>> motion_adapter = ",
+ 92        ">>> adapter = ",  # if this moves, also change motion_adapter check
+ 93        ">>> controlnet = ",
+ 94        ">>> pipe_prior = ",
+ 95        ">>> pipe = ",
+ 96        ">>> pipeline = ",
+ 97        ">>> blip_diffusion_pipe = ",
+ 98        ">>> prior_pipe = ",
+ 99        ">>> gen_pipe = ",
+100    ]
+101    repo_variables: List[str] = [
+102        "controlnet_model",
+103        "controlnet_id",
+104        "base_model",
+105        "model_id_or_path",
+106        "model_ckpt",
+107        "model_id",
+108        "repo_base",
+109        "repo",
+110        "motion_adapter_id",
+111    ]
+112    call_types: List[str] = [".from_pretrained(", ".from_single_file("]
+113    staged_call_types: List[str] = [
+114        ".from_pretrain(",
+115    ]
+116
+117
+118package_map = {
+119    "diffusers": ("_import_structure", "diffusers.pipelines"),
+120    "transformers": ("MODEL_MAPPING_NAMES", "transformers.models.auto.modeling_auto"),
+121}
+122root_path = os.path.dirname(os.path.dirname(__file__))
+123versions = read_json_file(os.path.join(root_path, "spec", "versions.json"))
+124template = read_json_file(os.path.join(root_path, "spec", "template.json"))
+125
+126MIR_PATH_NAMED = os.path.join(root_path, "mir.json")
+127
+128BREAKING_SUFFIX = r".*(?:-)(prior)$|.*(?:-)(diffusers)$|.*[_-](\d{3,4}px|-T2V$|-I2V$)"
+129PARAMETERS_SUFFIX = r"(\d{1,4}[KkMmBb]|[._-]\d+[\._-]\d+[Bb][._-]).*?$"
+130SEARCH_SUFFIX = r"\d+[._-]?\d+[BbMmKk](it)?|[._-]\d+[BbMmKk](it)?"
+
+ + +
+
+ +
+ + def + generate_supported_model_class_names( model_name: type[transformers.configuration_utils.PreTrainedConfig], supported_tasks: Union[str, list[str], NoneType] = None) -> list[str]: + + + +
+ + 
33def generate_supported_model_class_names(
+34    model_name: type[PretrainedConfig],
+35    supported_tasks: Optional[Union[str, list[str]]] = None,
+36) -> list[str]:
+37    task_mapping = {
+38        "default": MODEL_MAPPING_NAMES,
+39        "pretraining": MODEL_FOR_PRETRAINING_MAPPING_NAMES,
+40        "next-sentence-prediction": MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES,
+41        "masked-lm": MODEL_FOR_MASKED_LM_MAPPING_NAMES,
+42        "causal-lm": MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
+43        "seq2seq-lm": MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
+44        "speech-seq2seq": MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES,
+45        "multiple-choice": MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
+46        "document-question-answering": MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
+47        "question-answering": MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
+48        "sequence-classification": MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
+49        "token-classification": MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
+50        "masked-image-modeling": MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES,
+51        "image-classification": MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
+52        "zero-shot-image-classification": MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES,
+53        "ctc": MODEL_FOR_CTC_MAPPING_NAMES,
+54        "audio-classification": MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
+55        "semantic-segmentation": MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
+56        "backbone": MODEL_FOR_BACKBONE_MAPPING_NAMES,
+57        "image-feature-extraction": MODEL_FOR_IMAGE_MAPPING_NAMES,
+58        "video-classification": MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
+59    }
+60
+61    if supported_tasks is None:
+62        supported_tasks = task_mapping.keys()
+63    if isinstance(supported_tasks, str):
+64        supported_tasks = [supported_tasks]
+65
+66    model_class_names = []
+67    for task in supported_tasks:
+68        class_name = task_mapping[task].get(model_name, None)
+69        if class_name:
+70            model_class_names.append(class_name)
+71
+72    return model_class_names
+
+ + + + +
+
+ +
+ + class + DocParseData: + + + +
+ + 
75class DocParseData:
+76    pipe_class: str
+77    pipe_repo: str
+78    staged_class: Optional[str] = None
+79    staged_repo: Optional[str] = None
+80
+81    def __init__(self, pipe_class, pipe_repo, staged_class=None, staged_repo=None):
+82        self.pipe_class: str = pipe_class
+83        self.pipe_repo: str = pipe_repo
+84        self.staged_class: str = staged_class
+85        self.staged_repo: str = staged_repo
+
+ + + + +
+ +
+ + DocParseData(pipe_class, pipe_repo, staged_class=None, staged_repo=None) + + + +
+ + 
81    def __init__(self, pipe_class, pipe_repo, staged_class=None, staged_repo=None):
+82        self.pipe_class: str = pipe_class
+83        self.pipe_repo: str = pipe_repo
+84        self.staged_class: str = staged_class
+85        self.staged_repo: str = staged_repo
+
+ + + + +
+
+
+ pipe_class: str + + +
+ + + + +
+
+
+ pipe_repo: str + + +
+ + + + +
+
+
+ staged_class: Optional[str] = +None + + +
+ + + + +
+
+
+ staged_repo: Optional[str] = +None + + +
+ + + + +
+
+
+ +
+ + class + DocStringParserConstants: + + + +
+ + 
 88class DocStringParserConstants:
+ 89    """Constants used by DocStringParser for parsing docstrings."""
+ 90
+ 91    pipe_prefixes: List[str] = [
+ 92        ">>> motion_adapter = ",
+ 93        ">>> adapter = ",  # if this moves, also change motion_adapter check
+ 94        ">>> controlnet = ",
+ 95        ">>> pipe_prior = ",
+ 96        ">>> pipe = ",
+ 97        ">>> pipeline = ",
+ 98        ">>> blip_diffusion_pipe = ",
+ 99        ">>> prior_pipe = ",
+100        ">>> gen_pipe = ",
+101    ]
+102    repo_variables: List[str] = [
+103        "controlnet_model",
+104        "controlnet_id",
+105        "base_model",
+106        "model_id_or_path",
+107        "model_ckpt",
+108        "model_id",
+109        "repo_base",
+110        "repo",
+111        "motion_adapter_id",
+112    ]
+113    call_types: List[str] = [".from_pretrained(", ".from_single_file("]
+114    staged_call_types: List[str] = [
+115        ".from_pretrain(",
+116    ]
+
+ + +

Constants used by DocStringParser for parsing docstrings.

+
+ + +
+
+ pipe_prefixes: List[str] = + + ['>>> motion_adapter = ', '>>> adapter = ', '>>> controlnet = ', '>>> pipe_prior = ', '>>> pipe = ', '>>> pipeline = ', '>>> blip_diffusion_pipe = ', '>>> prior_pipe = ', '>>> gen_pipe = '] + + +
+ + + + +
+
+
+ repo_variables: List[str] = + + ['controlnet_model', 'controlnet_id', 'base_model', 'model_id_or_path', 'model_ckpt', 'model_id', 'repo_base', 'repo', 'motion_adapter_id'] + + +
+ + + + +
+
+
+ call_types: List[str] = +['.from_pretrained(', '.from_single_file('] + + +
+ + + + +
+
+
+ staged_call_types: List[str] = +['.from_pretrain('] + + +
+ + + + +
+
+
+
+ package_map = + + {'diffusers': ('_import_structure', 'diffusers.pipelines'), 'transformers': ('MODEL_MAPPING_NAMES', 'transformers.models.auto.modeling_auto')} + + +
+ + + + +
+
+
+ root_path = +'/Users/unauthorized/Documents/GitHub/cursor/darkshapes/MIR/mir' + + +
+ + + + +
+
+
+ versions = + + {'semantic': ['-?\\d+[bBmMkK]', '-?v\\d+', '(?<=\\d)[.-](?=\\d)', '-prior$', '-diffusers$', '-large$', '-medium$'], 'suffixes': ['-\\d{1,2}[bBmMkK]', '-\\d[1-9][bBmMkK]', '-v\\d{1,2}', '-\\d{3,}$', '-\\d{4,}.*', '-\\d{4,}[px].*'], 'ignore': ['-xt$', '-box$', '-preview$', '-base.*', '-Tiny$', '-full$', '-mini.*', '-multimodal.*', '-instruct.*']} + + +
+ + + + +
+
+
+ template = + + {'domain': {'dev': '', 'info': '', 'model': '', 'ops': ''}, 'arch': {'unclassified': {'brnn': '', 'rcnn': '', 'gru': '', 'rbm': '', 'tae': '', 'vae': '', 'embedding': '', 'scheduler': '', 'resnet': ''}, 'transformer': {'lstm': ['sequence_kernel'], 'detr': ['use_timm_backbone', '_resnet_', 'disable_custom_kernels', 'dice_loss_coefficient', 'decoder_method'], 'cnn': ['bbox_cost'], 'rnn': ['lru_width', 'rescale_every'], 'gan': ['codebook_dim', 'kernel_size', 'kernel_predictor_conv_size'], 'ssm': ['mamba_expand', 'parallel_attn', 'state_size', 'expand'], 'vit': ['use_swiglu_ffn', 'projection_dim', 'vlm_config', 'crop_size', 'out_indices', 'logit_scale_init_value', 'image_size', 'vision_config', 'hidden_sizes', 'image_token_id', 'patch_size', 'embed_dim', 'frequency_stride', 'auxiliary_channels', 'out_features', 'vision_feature_select_strategy'], 'dpt': ['fusion_hidden_size'], 'moe': ['num_experts_per_tok'], 'aet': ['classifier_proj_size', 'position_embedding_type', 'separate_cls', 'keypoint_detector_config', 'local_attention', 'act_dropout', 'max_source_positions'], 'stst': ['is_encoder_decoder', 'encoder_attention_heads', 'encoder_layers', 'encoder_hidden_size', 'encoder_config', 'audio_token_index', 'codebook_dim', 'router_ignore_padding_tokens', 'd_ff', 'd_kv', 'audio_config'], 'art': ['ffn_dim', 'num_codebooks', 'vq_config', 'attn_config', 'n_head', 'n_heads', 'n_layer', 'rms_norm_eps', 'rope_theta', 'head_dim', 'layernorm_embedding', 'hidden_dropout_prob', 'rotary_pct', 'embed_dropout', 'nb_priors', 'embd_pdrop', 'n_positions', 'aux_loss_coef', 'residual_dropout', 'no_rope_layers', 'decoder_start_token_id', 'decoder_layers', 'tie_codebooks_embeddings']}, 'diffuser': {'lora': ['motion_adapter'], 'controlnet': ['controlnet'], 'unet': ['unet', 'prior', 'decoder'], 'dit': ['transformer']}}} + + +
+ + + + +
+
+
+ MIR_PATH_NAMED = +'/Users/unauthorized/Documents/GitHub/cursor/darkshapes/MIR/mir/mir.json' + + +
+ + + + +
+
+
+ BREAKING_SUFFIX = +'.*(?:-)(prior)$|.*(?:-)(diffusers)$|.*[_-](\\d{3,4}px|-T2V$|-I2V$)' + + +
+ + + + +
+
+
+ PARAMETERS_SUFFIX = +'(\\d{1,4}[KkMmBb]|[._-]\\d+[\\._-]\\d+[Bb][._-]).*?$' + + +
+ + + + +
+
+
+ SEARCH_SUFFIX = +'\\d+[._-]?\\d+[BbMmKk](it)?|[._-]\\d+[BbMmKk](it)?' + + +
+ + + + +
+
+ + \ No newline at end of file diff --git a/docs/mir/config/conversion.html b/docs/mir/config/conversion.html new file mode 100644 index 0000000..4e9ab93 --- /dev/null +++ b/docs/mir/config/conversion.html @@ -0,0 +1,719 @@ + + + + + + + mir.config.conversion API documentation + + + + + + + + + +
+
+

+mir.config.conversion

+ + + + + + + 
  1# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0
+  2# <!-- // /*  d a r k s h a p e s */ -->
+  3
+  4from typing import Callable, Optional, Union, Type, List, Iterator, Tuple, Dict
+  5from mir.config.logging import dbuq, nfo
+  6
+  7
+  8def import_submodules(module_name: str, pkg_name_or_abs_path: str) -> Optional[Callable]:
+  9    """Convert two strings into a callable function or property\n
+ 10    :param module: The name of the module to import
+ 11    :param library_path: Base package for the module
+ 12    :return: The callable attribute or property
+ 13    """
+ 14    from importlib import import_module
+ 15
+ 16    module = module_name.strip()
+ 17    library = pkg_name_or_abs_path.strip()
+ 18    base_library = import_module(library, module)
+ 19    try:
+ 20        module = getattr(base_library, module)
+ 21        return module
+ 22    except AttributeError:  # as error_log:
+ 23        # dbuq(error_log)
+ 24        return base_library
+ 25
+ 26
+ 27def code_name_to_class_name(
+ 28    code_name: Optional[Union[str, Type]] = None,
+ 29    pkg_name: Optional[str] = "transformers",
+ 30) -> Union[List[str], str]:
+ 31    """Fetch class names from code names from Diffusers or Transformers\n
+ 32    :param class_name: To return only one class, defaults to None
+ 33    :param pkg_name: optional field for library, defaults to "transformers"
+ 34    :return: A list of all code names, or the one corresponding to the provided class"""
+ 35    from mir.config.constants import package_map
+ 36
+ 37    pkg_name = pkg_name.lower()
+ 38    MAPPING_NAMES = import_submodules(*package_map[pkg_name])
+ 39    if code_name:
+ 40        return MAPPING_NAMES.get(code_name)
+ 41    return list(MAPPING_NAMES.keys())
+ 42
+ 43
+ 44def pkg_path_to_docstring(pkg_name: str, folder_path: bool) -> Iterator[Tuple[str, str, str]]:
+ 45    """Processes package folder paths to yield example doc strings if available.\n
+ 46    :param pkg_name: The name of the package under diffusers.pipelines.
+ 47    :param file_specific: A flag indicating whether processing is specific to certain files.
+ 48    :yield: A tuple containing (pkg_name, file_name, EXAMPLE_DOC_STRING) if found.
+ 49    """
+ 50    import os
+ 51    from importlib import import_module
+ 52
+ 53    file_names = list(getattr(folder_path, "_import_structure").keys())
+ 54    module_path = os.path.dirname(import_module("diffusers.pipelines").__file__)
+ 55    for file_name in file_names:
+ 56        if file_name == "pipeline_stable_diffusion_xl_inpaint":
+ 57            continue
+ 58        try:
+ 59            pkg_path = f"diffusers.pipelines.{str(pkg_name)}.{file_name}"
+ 60            dbuq(pkg_path)
+ 61            path_exists = os.path.exists(os.path.join(module_path, pkg_name, file_name + ".py"))
+ 62            if path_exists:
+ 63                pipe_file = import_submodules(file_name, pkg_path)
+ 64        except ModuleNotFoundError:
+ 65            if pkg_name != "skyreels_v2":
+ 66                nfo(f"Module Not Found for {pkg_name}")
+ 67            pipe_file = None
+ 68
+ 69        try:
+ 70            if pipe_file and hasattr(pipe_file, "EXAMPLE_DOC_STRING"):
+ 71                yield (pkg_name, file_name, pipe_file.EXAMPLE_DOC_STRING)
+ 72            else:
+ 73                if path_exists:
+ 74                    pipe_file = import_module(pkg_path)
+ 75        except (ModuleNotFoundError, AttributeError):
+ 76            if pkg_name != "skyreels_v2":
+ 77                nfo(f"Doc String Not Found for {pipe_file} {pkg_name}")
+ 78
+ 79
+ 80def file_name_to_docstring(pkg_name: str, file_specific: bool) -> Iterator[Tuple[str, str, str]]:
+ 81    """Processes package using file name to yield example doc strings if available.\n
+ 82    :param pkg_name: The name of the package under diffusers.pipelines.
+ 83    :param file_specific: A flag indicating whether processing is specific to certain files.
+ 84    :yield: A tuple containing (pkg_name, file_name, EXAMPLE_DOC_STRING) if found.
+ 85    """
+ 86    from importlib import import_module
+ 87
+ 88    file_name = f"pipeline_{file_specific}"
+ 89    try:
+ 90        pkg_path = f"diffusers.pipelines.{str(pkg_name)}"
+ 91        pipe_file = import_submodules(file_name, pkg_path)
+ 92    except ModuleNotFoundError:
+ 93        if pkg_name != "skyreels_v2":
+ 94            nfo(f"Module Not Found for {pkg_name}")
+ 95        pipe_file = None
+ 96    try:
+ 97        if pipe_file and hasattr(pipe_file, "EXAMPLE_DOC_STRING"):
+ 98            yield (pkg_name, file_name, pipe_file.EXAMPLE_DOC_STRING)
+ 99        else:
+100            pipe_file = import_module(pkg_path)
+101
+102    except AttributeError:
+103        if pkg_name != "skyreels_v2":
+104            nfo(f"Doc String Not Found for {pipe_file} {pkg_name}")
+105
+106
+107def class_to_mir_tag(mir_db: Dict[str, str], code_name: str) -> Optional[str]:
+108    """Converts a class identifier to its corresponding MIR tag.\n
+109    :param mir_db: A dictionary mapping series-compatibility pairs to their respective data.
+110    :param code_name: The Transformers class identifier to convert.
+111    :return: An optional list containing the series and compatibility if found, otherwise None."""
+112    from transformers.models.auto.modeling_auto import MODEL_MAPPING_NAMES
+113    from mir.config.constants import template
+114
+115    template_data = template["arch"]["transformer"]
+116
+117    for series, compatibility_data in mir_db.database.items():
+118        if any([template for template in template_data if template in series.split(".")[1]]):
+119            for compatibility, field_data in compatibility_data.items():
+120                if code_name == series.split(".")[2]:
+121                    return [series, compatibility]
+122
+123                class_name = MODEL_MAPPING_NAMES.get(code_name, False)
+124                if not class_name:  # second pass without separators
+125                    recoded_mapping = {code.replace("-", "").replace("_", ""): model for code, model in MODEL_MAPPING_NAMES.items()}
+126                    class_name = recoded_mapping.get(code_name, False)
+127                    if not class_name:
+128                        return None
+129                pkg_data = field_data.get("pkg")
+130                if pkg_data:
+131                    for _, pkg_type_data in pkg_data.items():
+132                        maybe_class = pkg_type_data.get("transformers")
+133                        if maybe_class == class_name:
+134                            return [series, compatibility]
+135    return None
+136
+137
+138def slice_number(text: str) -> Union[int, float, str]:
+139    """Separate a numeral value appended to a string\n
+140    :return: Converted value as int or float, or unmodified string
+141    """
+142    for index, char in enumerate(text):  # Traverse forwards
+143        if char.isdigit():
+144            numbers = text[index:]
+145            if "." in numbers:
+146                return float(numbers)
+147            try:
+148                return int(numbers)
+149            except ValueError:
+150                return numbers
+151    return text
+
+ + +
+
+ +
+ + def + import_submodules(module_name: str, pkg_name_or_abs_path: str) -> Optional[Callable]: + + + +
+ + 
 9def import_submodules(module_name: str, pkg_name_or_abs_path: str) -> Optional[Callable]:
+10    """Convert two strings into a callable function or property\n
+11    :param module: The name of the module to import
+12    :param library_path: Base package for the module
+13    :return: The callable attribute or property
+14    """
+15    from importlib import import_module
+16
+17    module = module_name.strip()
+18    library = pkg_name_or_abs_path.strip()
+19    base_library = import_module(library, module)
+20    try:
+21        module = getattr(base_library, module)
+22        return module
+23    except AttributeError:  # as error_log:
+24        # dbuq(error_log)
+25        return base_library
+
+ + +

Convert two strings into a callable function or property

+ +
Parameters
+ +
    +
  • module: The name of the module to import
    • +
  • library_path: Base package for the module
    • +
+ +
Returns
+ +
+

The callable attribute or property

+
+
+ + +
+
+ +
+ + def + code_name_to_class_name( code_name: Union[str, Type, NoneType] = None, pkg_name: Optional[str] = 'transformers') -> Union[List[str], str]: + + + +
+ + 
28def code_name_to_class_name(
+29    code_name: Optional[Union[str, Type]] = None,
+30    pkg_name: Optional[str] = "transformers",
+31) -> Union[List[str], str]:
+32    """Fetch class names from code names from Diffusers or Transformers\n
+33    :param class_name: To return only one class, defaults to None
+34    :param pkg_name: optional field for library, defaults to "transformers"
+35    :return: A list of all code names, or the one corresponding to the provided class"""
+36    from mir.config.constants import package_map
+37
+38    pkg_name = pkg_name.lower()
+39    MAPPING_NAMES = import_submodules(*package_map[pkg_name])
+40    if code_name:
+41        return MAPPING_NAMES.get(code_name)
+42    return list(MAPPING_NAMES.keys())
+
+ + +

Fetch class names from code names from Diffusers or Transformers

+ +
Parameters
+ +
    +
  • class_name: To return only one class, defaults to None
    • +
  • pkg_name: optional field for library, defaults to "transformers"
    • +
+ +
Returns
+ +
+

A list of all code names, or the one corresponding to the provided class

+
+
+ + +
+
+ +
+ + def + pkg_path_to_docstring(pkg_name: str, folder_path: bool) -> Iterator[Tuple[str, str, str]]: + + + +
+ + 
45def pkg_path_to_docstring(pkg_name: str, folder_path: bool) -> Iterator[Tuple[str, str, str]]:
+46    """Processes package folder paths to yield example doc strings if available.\n
+47    :param pkg_name: The name of the package under diffusers.pipelines.
+48    :param file_specific: A flag indicating whether processing is specific to certain files.
+49    :yield: A tuple containing (pkg_name, file_name, EXAMPLE_DOC_STRING) if found.
+50    """
+51    import os
+52    from importlib import import_module
+53
+54    file_names = list(getattr(folder_path, "_import_structure").keys())
+55    module_path = os.path.dirname(import_module("diffusers.pipelines").__file__)
+56    for file_name in file_names:
+57        if file_name == "pipeline_stable_diffusion_xl_inpaint":
+58            continue
+59        try:
+60            pkg_path = f"diffusers.pipelines.{str(pkg_name)}.{file_name}"
+61            dbuq(pkg_path)
+62            path_exists = os.path.exists(os.path.join(module_path, pkg_name, file_name + ".py"))
+63            if path_exists:
+64                pipe_file = import_submodules(file_name, pkg_path)
+65        except ModuleNotFoundError:
+66            if pkg_name != "skyreels_v2":
+67                nfo(f"Module Not Found for {pkg_name}")
+68            pipe_file = None
+69
+70        try:
+71            if pipe_file and hasattr(pipe_file, "EXAMPLE_DOC_STRING"):
+72                yield (pkg_name, file_name, pipe_file.EXAMPLE_DOC_STRING)
+73            else:
+74                if path_exists:
+75                    pipe_file = import_module(pkg_path)
+76        except (ModuleNotFoundError, AttributeError):
+77            if pkg_name != "skyreels_v2":
+78                nfo(f"Doc String Not Found for {pipe_file} {pkg_name}")
+
+ + +

Processes package folder paths to yield example doc strings if available.

+ +
Parameters
+ +
    +
  • pkg_name: The name of the package under diffusers.pipelines.
    • +
  • file_specific: A flag indicating whether processing is specific to certain files. +:yield: A tuple containing (pkg_name, file_name, EXAMPLE_DOC_STRING) if found.
    • +
+
+ + +
+
+ +
+ + def + file_name_to_docstring(pkg_name: str, file_specific: bool) -> Iterator[Tuple[str, str, str]]: + + + +
+ + 
 81def file_name_to_docstring(pkg_name: str, file_specific: bool) -> Iterator[Tuple[str, str, str]]:
+ 82    """Processes package using file name to yield example doc strings if available.\n
+ 83    :param pkg_name: The name of the package under diffusers.pipelines.
+ 84    :param file_specific: A flag indicating whether processing is specific to certain files.
+ 85    :yield: A tuple containing (pkg_name, file_name, EXAMPLE_DOC_STRING) if found.
+ 86    """
+ 87    from importlib import import_module
+ 88
+ 89    file_name = f"pipeline_{file_specific}"
+ 90    try:
+ 91        pkg_path = f"diffusers.pipelines.{str(pkg_name)}"
+ 92        pipe_file = import_submodules(file_name, pkg_path)
+ 93    except ModuleNotFoundError:
+ 94        if pkg_name != "skyreels_v2":
+ 95            nfo(f"Module Not Found for {pkg_name}")
+ 96        pipe_file = None
+ 97    try:
+ 98        if pipe_file and hasattr(pipe_file, "EXAMPLE_DOC_STRING"):
+ 99            yield (pkg_name, file_name, pipe_file.EXAMPLE_DOC_STRING)
+100        else:
+101            pipe_file = import_module(pkg_path)
+102
+103    except AttributeError:
+104        if pkg_name != "skyreels_v2":
+105            nfo(f"Doc String Not Found for {pipe_file} {pkg_name}")
+
+ + +

Processes package using file name to yield example doc strings if available.

+ +
Parameters
+ +
    +
  • pkg_name: The name of the package under diffusers.pipelines.
    • +
  • file_specific: A flag indicating whether processing is specific to certain files. +:yield: A tuple containing (pkg_name, file_name, EXAMPLE_DOC_STRING) if found.
    • +
+
+ + +
+
+ +
+ + def + class_to_mir_tag(mir_db: Dict[str, str], code_name: str) -> Optional[str]: + + + +
+ + 
108def class_to_mir_tag(mir_db: Dict[str, str], code_name: str) -> Optional[str]:
+109    """Converts a class identifier to its corresponding MIR tag.\n
+110    :param mir_db: A dictionary mapping series-compatibility pairs to their respective data.
+111    :param code_name: The Transformers class identifier to convert.
+112    :return: An optional list containing the series and compatibility if found, otherwise None."""
+113    from transformers.models.auto.modeling_auto import MODEL_MAPPING_NAMES
+114    from mir.config.constants import template
+115
+116    template_data = template["arch"]["transformer"]
+117
+118    for series, compatibility_data in mir_db.database.items():
+119        if any([template for template in template_data if template in series.split(".")[1]]):
+120            for compatibility, field_data in compatibility_data.items():
+121                if code_name == series.split(".")[2]:
+122                    return [series, compatibility]
+123
+124                class_name = MODEL_MAPPING_NAMES.get(code_name, False)
+125                if not class_name:  # second pass without separators
+126                    recoded_mapping = {code.replace("-", "").replace("_", ""): model for code, model in MODEL_MAPPING_NAMES.items()}
+127                    class_name = recoded_mapping.get(code_name, False)
+128                    if not class_name:
+129                        return None
+130                pkg_data = field_data.get("pkg")
+131                if pkg_data:
+132                    for _, pkg_type_data in pkg_data.items():
+133                        maybe_class = pkg_type_data.get("transformers")
+134                        if maybe_class == class_name:
+135                            return [series, compatibility]
+136    return None
+
+ + +

Converts a class identifier to its corresponding MIR tag.

+ +
Parameters
+ +
    +
  • mir_db: A dictionary mapping series-compatibility pairs to their respective data.
    • +
  • code_name: The Transformers class identifier to convert.
    • +
+ +
Returns
+ +
+

An optional list containing the series and compatibility if found, otherwise None.

+
+
+ + +
+
+ +
+ + def + slice_number(text: str) -> Union[int, float, str]: + + + +
+ + 
139def slice_number(text: str) -> Union[int, float, str]:
+140    """Separate a numeral value appended to a string\n
+141    :return: Converted value as int or float, or unmodified string
+142    """
+143    for index, char in enumerate(text):  # Traverse forwards
+144        if char.isdigit():
+145            numbers = text[index:]
+146            if "." in numbers:
+147                return float(numbers)
+148            try:
+149                return int(numbers)
+150            except ValueError:
+151                return numbers
+152    return text
+
+ + +

Separate a numeral value appended to a string

+ +
Returns
+ +
+

Converted value as int or float, or unmodified string

+
+
+ + +
+
+ + \ No newline at end of file diff --git a/docs/mir/config/json_io.html b/docs/mir/config/json_io.html new file mode 100644 index 0000000..509bd8a --- /dev/null +++ b/docs/mir/config/json_io.html @@ -0,0 +1,381 @@ + + + + + + + mir.config.json_io API documentation + + + + + + + + + +
+
+

+mir.config.json_io

+ + + + + + + 
 1# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0
+ 2# <!-- // /*  d a r k s h a p e s */ -->
+ 3
+ 4# pylint: disable=import-outside-toplevel
+ 5
+ 6from typing import Any
+ 7
+ 8
+ 9def write_json_file(folder_path_named: str, file_name: str, data: Any, mode: str = "tw") -> None:
+10    """Save a file to disk as UTF8 JSON\n
+11    :param folder_path_named: The location to save
+12    :param file_name: A file name to save with
+13    :param data: The content to save
+14    :param mode: Type of open to use (default truncate-write)
+15    :returns: Dictionary of json data
+16    """
+17    import json
+18    import os
+19
+20    from mir import dbug
+21
+22    if ".json" not in file_name:
+23        file_name += ".json"
+24    document = os.path.join(folder_path_named, os.path.basename(file_name))
+25    if os.path.exists(document):
+26        try:
+27            os.remove(document)
+28        except FileNotFoundError as error_log:
+29            dbug(f"'File was detected but not found to remove: {document}.'{error_log}", exc_info=True)
+30
+31    with open(document, mode, encoding="UTF-8") as i:
+32        json.dump(data, i, ensure_ascii=False, indent=4, sort_keys=False)
+33
+34
+35def read_json_file(file_path_absolute: str, mode="tr") -> dict:
+36    """Open json file as UTF8 JSON
+37    :param file_path_absolute: Location of the file
+38    :param mode: Type of read to use
+39    :returns: Dictionary of json data
+40    """
+41    import json
+42
+43    with open(file_path_absolute, mode, encoding="UTF-8") as f:
+44        return json.load(f)
+
+ + +
+
+ +
+ + def + write_json_file( folder_path_named: str, file_name: str, data: Any, mode: str = 'tw') -> None: + + + +
+ + 
10def write_json_file(folder_path_named: str, file_name: str, data: Any, mode: str = "tw") -> None:
+11    """Save a file to disk as UTF8 JSON\n
+12    :param folder_path_named: The location to save
+13    :param file_name: A file name to save with
+14    :param data: The content to save
+15    :param mode: Type of open to use (default truncate-write)
+16    :returns: Dictionary of json data
+17    """
+18    import json
+19    import os
+20
+21    from mir import dbug
+22
+23    if ".json" not in file_name:
+24        file_name += ".json"
+25    document = os.path.join(folder_path_named, os.path.basename(file_name))
+26    if os.path.exists(document):
+27        try:
+28            os.remove(document)
+29        except FileNotFoundError as error_log:
+30            dbug(f"'File was detected but not found to remove: {document}.'{error_log}", exc_info=True)
+31
+32    with open(document, mode, encoding="UTF-8") as i:
+33        json.dump(data, i, ensure_ascii=False, indent=4, sort_keys=False)
+
+ + +

Save a file to disk as UTF8 JSON

+ +
Parameters
+ +
    +
  • folder_path_named: The location to save
    • +
  • file_name: A file name to save with
    • +
  • data: The content to save
    • +
  • mode: Type of open to use (default truncate-write) +:returns: Dictionary of json data
    • +
+
+ + +
+
+ +
+ + def + read_json_file(file_path_absolute: str, mode='tr') -> dict: + + + +
+ + 
36def read_json_file(file_path_absolute: str, mode="tr") -> dict:
+37    """Open json file as UTF8 JSON
+38    :param file_path_absolute: Location of the file
+39    :param mode: Type of read to use
+40    :returns: Dictionary of json data
+41    """
+42    import json
+43
+44    with open(file_path_absolute, mode, encoding="UTF-8") as f:
+45        return json.load(f)
+
+ + +

Open json file as UTF8 JSON

+ +
Parameters
+ +
    +
  • file_path_absolute: Location of the file
    • +
  • mode: Type of read to use +:returns: Dictionary of json data
    • +
+
+ + +
+
+ + \ No newline at end of file diff --git a/docs/mir/doc_parser.html b/docs/mir/doc_parser.html new file mode 100644 index 0000000..772dbcd --- /dev/null +++ b/docs/mir/doc_parser.html @@ -0,0 +1,925 @@ + + + + + + + mir.doc_parser API documentation + + + + + + + + + +
+
+

+mir.doc_parser

+ + + + + + + 
  1# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0
+  2# <!-- // /*  d a r k s h a p e s */ -->
+  3
+  4from typing import List, Optional, Tuple
+  5
+  6from pydantic import BaseModel, field_validator
+  7from mir.config.logging import dbuq, nfo
+  8from mir.config.constants import DocParseData, DocStringParserConstants
+  9
+ 10
+ 11def parse_docs(doc_string: str) -> Tuple[Optional[str], Optional[str], Optional[str], Optional[str]]:
+ 12    parser = DocStringParser(doc_string=doc_string)
+ 13    result = parser.parse()
+ 14    if result is not None:
+ 15        return result
+ 16
+ 17
+ 18class DocStringValidator:
+ 19    """Handles validation of docstring data and extracted values."""
+ 20
+ 21    @staticmethod
+ 22    def normalize_doc_string(doc_string: str) -> str:
+ 23        """Normalize docstring by joining lines with spaces.\n
+ 24        :param doc_string: Raw docstring text
+ 25        :returns: Normalized docstring with newlines replaced by spaces
+ 26        """
+ 27        return " ".join(doc_string.splitlines())
+ 28
+ 29    @staticmethod
+ 30    def is_valid_repo_path(repo_path: Optional[str]) -> bool:
+ 31        """Validate that a repository path is valid.\n
+ 32        :param repo_path: Repository path to validate
+ 33        :returns: True if path is valid (not empty and contains '/'), False otherwise
+ 34        """
+ 35        return repo_path is not None and repo_path.strip() != "" and "/" in repo_path
+ 36
+ 37    @staticmethod
+ 38    def validate_repo_path(repo_path: Optional[str], segment: str) -> Optional[str]:
+ 39        """Validate and return repo path, or None if invalid.\n
+ 40        :param repo_path: Repository path to validate
+ 41        :param segment: Segment text for error reporting
+ 42        :returns: Validated repo path or None if invalid
+ 43        """
+ 44        if not DocStringValidator.is_valid_repo_path(repo_path):
+ 45            nfo(f"Warning: Unable to resolve repo path for {segment}")
+ 46            return None
+ 47        return repo_path
+ 48
+ 49    @staticmethod
+ 50    def validate_pipe_class(pipe_class: Optional[str]) -> bool:
+ 51        """Validate that a pipe class name is present.\n
+ 52        :param pipe_class: Pipe class name to validate
+ 53        :returns: True if class name is valid, False otherwise
+ 54        """
+ 55        return pipe_class is not None and pipe_class.strip() != ""
+ 56
+ 57
+ 58class DocStringParser(BaseModel):
+ 59    doc_string: str
+ 60
+ 61    @field_validator("doc_string")
+ 62    def normalize_doc(cls, docs: str) -> str:
+ 63        return DocStringValidator.normalize_doc_string(docs)
+ 64
+ 65    def doc_match(self, prefix_set: List[str] = None):
+ 66        if prefix_set is None:
+ 67            prefix_set = DocStringParserConstants.pipe_prefixes
+ 68        candidate = None
+ 69        staged = None
+ 70        for prefix in prefix_set:
+ 71            candidate = self.doc_string.partition(prefix)[2]
+ 72            prior_candidate = self.doc_string.partition(prefix)[0]
+ 73            if candidate:
+ 74                staged = candidate if any(call_type in candidate for call_type in DocStringParserConstants.staged_call_types) else None
+ 75                break
+ 76
+ 77        return candidate, prior_candidate, staged
+ 78
+ 79    def parse(self) -> DocParseData:
+ 80        candidate, prior_candidate, staged = self.doc_match(DocStringParserConstants.pipe_prefixes)
+ 81        if candidate:
+ 82            pipe_class, pipe_repo = self._extract_class_and_repo(
+ 83                segment=candidate,
+ 84                call_types=DocStringParserConstants.call_types,
+ 85                prior_text=prior_candidate,
+ 86            )
+ 87            motion_adapter = "motion_adapter" in candidate or "adapter" in candidate
+ 88            if motion_adapter and pipe_repo:
+ 89                staged, prior_candidate, _ = self.doc_match(DocStringParserConstants.pipe_prefixes[2:])  # skip the adapter statements
+ 90            staged_class, staged_repo = (
+ 91                self._extract_class_and_repo(
+ 92                    segment=staged,
+ 93                    call_types=DocStringParserConstants.staged_call_types if not motion_adapter else DocStringParserConstants.call_types,
+ 94                    prior_text=prior_candidate,
+ 95                    prior_class=pipe_class,
+ 96                )
+ 97                if staged
+ 98                else (None, None)
+ 99            )
+100            if motion_adapter and pipe_class:
+101                pipe_class = staged_class
+102                staged_repo = None
+103                staged_class = None
+104
+105            if DocStringValidator.validate_pipe_class(pipe_class):
+106                dbuq(f"class :{pipe_class}, repo : {pipe_repo}, staged_class: {staged_class}, staged_repo:{staged_repo} \n")
+107                return DocParseData(pipe_class=pipe_class, pipe_repo=pipe_repo, staged_class=staged_class, staged_repo=staged_repo)
+108
+109    def _extract_class_and_repo(
+110        self,
+111        segment: str,
+112        call_types: List[str],
+113        prior_text: str,
+114        prior_class: Optional[str] = None,
+115    ) -> Tuple[Optional[str], Optional[str]]:
+116        pipe_class = None
+117        pipe_repo = None
+118        for call_type in call_types:
+119            if call_type in segment:
+120                pipe_class = segment.partition(call_type)[0].strip().split("= ")[-1]
+121                if prior_class == pipe_class:
+122                    pipe_class = prior_text.partition(call_type)[0].strip().split("= ")[-1]
+123                    repo_segment = segment.partition(call_type)[2].partition(")")[0]
+124                else:
+125                    repo_segment = segment.partition(call_type)[2].partition(")")[0]
+126                pipe_repo = repo_segment.replace("...", "").partition('",')[0].strip('" ')
+127                if not DocStringValidator.is_valid_repo_path(pipe_repo):
+128                    for reference in DocStringParserConstants.repo_variables:
+129                        if reference in segment:
+130                            pipe_repo = self._resolve_variable(reference, prior_text)
+131                            break  # Not empty!! 確保解析後的路徑不為空!!
+132                pipe_repo = DocStringValidator.validate_repo_path(pipe_repo, segment)
+133                return pipe_class, pipe_repo
+134
+135        return pipe_class, pipe_repo
+136
+137    def _resolve_variable(self, reference: str, prior_text: str) -> Optional[str]:
+138        """Try to find the variable from other lines / 嘗試從其他行中查找（例如多行定義）"""
+139        var_name = reference
+140        search = f"{var_name} ="
+141
+142        for line in prior_text.splitlines():
+143            if search in line:
+144                repo_block = line.partition(search)[2].strip().strip('"').strip("'")
+145                index = repo_block.find('"')
+146                repo_id = repo_block[:index] if index != -1 else repo_block
+147                if repo_id:  # Keep trying if empty"
+148                    return repo_id
+149
+150        for line in prior_text.splitlines():
+151            if var_name in line:
+152                start_index = line.find(var_name)
+153                end_index = line.find("=", start_index)
+154                if end_index != -1:
+155                    repo_block = line[end_index + 1 :].strip().strip('"').strip("'")
+156                    index = repo_block.find('"')
+157                    repo_id = repo_block[:index] if index != -1 else repo_block
+158                    if repo_id:
+159                        return repo_id
+160
+161        nfo(f"Warning: {search} not found in docstring.")
+162        return None
+
+ + +
+
+ +
+ + def + parse_docs( doc_string: str) -> Tuple[Optional[str], Optional[str], Optional[str], Optional[str]]: + + + +
+ + 
12def parse_docs(doc_string: str) -> Tuple[Optional[str], Optional[str], Optional[str], Optional[str]]:
+13    parser = DocStringParser(doc_string=doc_string)
+14    result = parser.parse()
+15    if result is not None:
+16        return result
+
+ + + + +
+
+ +
+ + class + DocStringValidator: + + + +
+ + 
19class DocStringValidator:
+20    """Handles validation of docstring data and extracted values."""
+21
+22    @staticmethod
+23    def normalize_doc_string(doc_string: str) -> str:
+24        """Normalize docstring by joining lines with spaces.\n
+25        :param doc_string: Raw docstring text
+26        :returns: Normalized docstring with newlines replaced by spaces
+27        """
+28        return " ".join(doc_string.splitlines())
+29
+30    @staticmethod
+31    def is_valid_repo_path(repo_path: Optional[str]) -> bool:
+32        """Validate that a repository path is valid.\n
+33        :param repo_path: Repository path to validate
+34        :returns: True if path is valid (not empty and contains '/'), False otherwise
+35        """
+36        return repo_path is not None and repo_path.strip() != "" and "/" in repo_path
+37
+38    @staticmethod
+39    def validate_repo_path(repo_path: Optional[str], segment: str) -> Optional[str]:
+40        """Validate and return repo path, or None if invalid.\n
+41        :param repo_path: Repository path to validate
+42        :param segment: Segment text for error reporting
+43        :returns: Validated repo path or None if invalid
+44        """
+45        if not DocStringValidator.is_valid_repo_path(repo_path):
+46            nfo(f"Warning: Unable to resolve repo path for {segment}")
+47            return None
+48        return repo_path
+49
+50    @staticmethod
+51    def validate_pipe_class(pipe_class: Optional[str]) -> bool:
+52        """Validate that a pipe class name is present.\n
+53        :param pipe_class: Pipe class name to validate
+54        :returns: True if class name is valid, False otherwise
+55        """
+56        return pipe_class is not None and pipe_class.strip() != ""
+
+ + +

Handles validation of docstring data and extracted values.

+
+ + +
+ +
+
@staticmethod
+ + def + normalize_doc_string(doc_string: str) -> str: + + + +
+ + 
22    @staticmethod
+23    def normalize_doc_string(doc_string: str) -> str:
+24        """Normalize docstring by joining lines with spaces.\n
+25        :param doc_string: Raw docstring text
+26        :returns: Normalized docstring with newlines replaced by spaces
+27        """
+28        return " ".join(doc_string.splitlines())
+
+ + +

Normalize docstring by joining lines with spaces.

+ +
Parameters
+ +
    +
  • doc_string: Raw docstring text +:returns: Normalized docstring with newlines replaced by spaces
    • +
+
+ + +
+
+ +
+
@staticmethod
+ + def + is_valid_repo_path(repo_path: Optional[str]) -> bool: + + + +
+ + 
30    @staticmethod
+31    def is_valid_repo_path(repo_path: Optional[str]) -> bool:
+32        """Validate that a repository path is valid.\n
+33        :param repo_path: Repository path to validate
+34        :returns: True if path is valid (not empty and contains '/'), False otherwise
+35        """
+36        return repo_path is not None and repo_path.strip() != "" and "/" in repo_path
+
+ + +

Validate that a repository path is valid.

+ +
Parameters
+ +
    +
  • repo_path: Repository path to validate +:returns: True if path is valid (not empty and contains '/'), False otherwise
    • +
+
+ + +
+
+ +
+
@staticmethod
+ + def + validate_repo_path(repo_path: Optional[str], segment: str) -> Optional[str]: + + + +
+ + 
38    @staticmethod
+39    def validate_repo_path(repo_path: Optional[str], segment: str) -> Optional[str]:
+40        """Validate and return repo path, or None if invalid.\n
+41        :param repo_path: Repository path to validate
+42        :param segment: Segment text for error reporting
+43        :returns: Validated repo path or None if invalid
+44        """
+45        if not DocStringValidator.is_valid_repo_path(repo_path):
+46            nfo(f"Warning: Unable to resolve repo path for {segment}")
+47            return None
+48        return repo_path
+
+ + +

Validate and return repo path, or None if invalid.

+ +
Parameters
+ +
    +
  • repo_path: Repository path to validate
    • +
  • segment: Segment text for error reporting +:returns: Validated repo path or None if invalid
    • +
+
+ + +
+
+ +
+
@staticmethod
+ + def + validate_pipe_class(pipe_class: Optional[str]) -> bool: + + + +
+ + 
50    @staticmethod
+51    def validate_pipe_class(pipe_class: Optional[str]) -> bool:
+52        """Validate that a pipe class name is present.\n
+53        :param pipe_class: Pipe class name to validate
+54        :returns: True if class name is valid, False otherwise
+55        """
+56        return pipe_class is not None and pipe_class.strip() != ""
+
+ + +

Validate that a pipe class name is present.

+ +
Parameters
+ +
    +
  • pipe_class: Pipe class name to validate +:returns: True if class name is valid, False otherwise
    • +
+
+ + +
+
+
+ +
+ + class + DocStringParser(pydantic.main.BaseModel): + + + +
+ + 
 59class DocStringParser(BaseModel):
+ 60    doc_string: str
+ 61
+ 62    @field_validator("doc_string")
+ 63    def normalize_doc(cls, docs: str) -> str:
+ 64        return DocStringValidator.normalize_doc_string(docs)
+ 65
+ 66    def doc_match(self, prefix_set: List[str] = None):
+ 67        if prefix_set is None:
+ 68            prefix_set = DocStringParserConstants.pipe_prefixes
+ 69        candidate = None
+ 70        staged = None
+ 71        for prefix in prefix_set:
+ 72            candidate = self.doc_string.partition(prefix)[2]
+ 73            prior_candidate = self.doc_string.partition(prefix)[0]
+ 74            if candidate:
+ 75                staged = candidate if any(call_type in candidate for call_type in DocStringParserConstants.staged_call_types) else None
+ 76                break
+ 77
+ 78        return candidate, prior_candidate, staged
+ 79
+ 80    def parse(self) -> DocParseData:
+ 81        candidate, prior_candidate, staged = self.doc_match(DocStringParserConstants.pipe_prefixes)
+ 82        if candidate:
+ 83            pipe_class, pipe_repo = self._extract_class_and_repo(
+ 84                segment=candidate,
+ 85                call_types=DocStringParserConstants.call_types,
+ 86                prior_text=prior_candidate,
+ 87            )
+ 88            motion_adapter = "motion_adapter" in candidate or "adapter" in candidate
+ 89            if motion_adapter and pipe_repo:
+ 90                staged, prior_candidate, _ = self.doc_match(DocStringParserConstants.pipe_prefixes[2:])  # skip the adapter statements
+ 91            staged_class, staged_repo = (
+ 92                self._extract_class_and_repo(
+ 93                    segment=staged,
+ 94                    call_types=DocStringParserConstants.staged_call_types if not motion_adapter else DocStringParserConstants.call_types,
+ 95                    prior_text=prior_candidate,
+ 96                    prior_class=pipe_class,
+ 97                )
+ 98                if staged
+ 99                else (None, None)
+100            )
+101            if motion_adapter and pipe_class:
+102                pipe_class = staged_class
+103                staged_repo = None
+104                staged_class = None
+105
+106            if DocStringValidator.validate_pipe_class(pipe_class):
+107                dbuq(f"class :{pipe_class}, repo : {pipe_repo}, staged_class: {staged_class}, staged_repo:{staged_repo} \n")
+108                return DocParseData(pipe_class=pipe_class, pipe_repo=pipe_repo, staged_class=staged_class, staged_repo=staged_repo)
+109
+110    def _extract_class_and_repo(
+111        self,
+112        segment: str,
+113        call_types: List[str],
+114        prior_text: str,
+115        prior_class: Optional[str] = None,
+116    ) -> Tuple[Optional[str], Optional[str]]:
+117        pipe_class = None
+118        pipe_repo = None
+119        for call_type in call_types:
+120            if call_type in segment:
+121                pipe_class = segment.partition(call_type)[0].strip().split("= ")[-1]
+122                if prior_class == pipe_class:
+123                    pipe_class = prior_text.partition(call_type)[0].strip().split("= ")[-1]
+124                    repo_segment = segment.partition(call_type)[2].partition(")")[0]
+125                else:
+126                    repo_segment = segment.partition(call_type)[2].partition(")")[0]
+127                pipe_repo = repo_segment.replace("...", "").partition('",')[0].strip('" ')
+128                if not DocStringValidator.is_valid_repo_path(pipe_repo):
+129                    for reference in DocStringParserConstants.repo_variables:
+130                        if reference in segment:
+131                            pipe_repo = self._resolve_variable(reference, prior_text)
+132                            break  # Not empty!! 確保解析後的路徑不為空!!
+133                pipe_repo = DocStringValidator.validate_repo_path(pipe_repo, segment)
+134                return pipe_class, pipe_repo
+135
+136        return pipe_class, pipe_repo
+137
+138    def _resolve_variable(self, reference: str, prior_text: str) -> Optional[str]:
+139        """Try to find the variable from other lines / 嘗試從其他行中查找（例如多行定義）"""
+140        var_name = reference
+141        search = f"{var_name} ="
+142
+143        for line in prior_text.splitlines():
+144            if search in line:
+145                repo_block = line.partition(search)[2].strip().strip('"').strip("'")
+146                index = repo_block.find('"')
+147                repo_id = repo_block[:index] if index != -1 else repo_block
+148                if repo_id:  # Keep trying if empty"
+149                    return repo_id
+150
+151        for line in prior_text.splitlines():
+152            if var_name in line:
+153                start_index = line.find(var_name)
+154                end_index = line.find("=", start_index)
+155                if end_index != -1:
+156                    repo_block = line[end_index + 1 :].strip().strip('"').strip("'")
+157                    index = repo_block.find('"')
+158                    repo_id = repo_block[:index] if index != -1 else repo_block
+159                    if repo_id:
+160                        return repo_id
+161
+162        nfo(f"Warning: {search} not found in docstring.")
+163        return None
+
+ + +

!!! abstract "Usage Documentation" + Models

+ +

A base class for creating Pydantic models.

+ +

Attributes: + __class_vars__: The names of the class variables defined on the model. + __private_attributes__: Metadata about the private attributes of the model. + __signature__: The synthesized __init__ [Signature][inspect.Signature] of the model.

+ +
__pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
+__pydantic_core_schema__: The core schema of the model.
+__pydantic_custom_init__: Whether the model has a custom `__init__` function.
+__pydantic_decorators__: Metadata containing the decorators defined on the model.
+    This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.
+__pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to
+    __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
+__pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
+__pydantic_post_init__: The name of the post-init method for the model, if defined.
+__pydantic_root_model__: Whether the model is a [`RootModel`][pydantic.root_model.RootModel].
+__pydantic_serializer__: The `pydantic-core` `SchemaSerializer` used to dump instances of the model.
+__pydantic_validator__: The `pydantic-core` `SchemaValidator` used to validate instances of the model.
+
+__pydantic_fields__: A dictionary of field names and their corresponding [`FieldInfo`][pydantic.fields.FieldInfo] objects.
+__pydantic_computed_fields__: A dictionary of computed field names and their corresponding [`ComputedFieldInfo`][pydantic.fields.ComputedFieldInfo] objects.
+
+__pydantic_extra__: A dictionary containing extra values, if [`extra`][pydantic.config.ConfigDict.extra]
+    is set to `'allow'`.
+__pydantic_fields_set__: The names of fields explicitly set during instantiation.
+__pydantic_private__: Values of private attributes set on the model instance.
+
+
+ + +
+
+ doc_string: str = +PydanticUndefined + + +
+ + + + +
+
+ +
+
@field_validator('doc_string')
+ + def + normalize_doc(cls, docs: str) -> str: + + + +
+ + 
62    @field_validator("doc_string")
+63    def normalize_doc(cls, docs: str) -> str:
+64        return DocStringValidator.normalize_doc_string(docs)
+
+ + + + +
+
+ +
+ + def + doc_match(self, prefix_set: List[str] = None): + + + +
+ + 
66    def doc_match(self, prefix_set: List[str] = None):
+67        if prefix_set is None:
+68            prefix_set = DocStringParserConstants.pipe_prefixes
+69        candidate = None
+70        staged = None
+71        for prefix in prefix_set:
+72            candidate = self.doc_string.partition(prefix)[2]
+73            prior_candidate = self.doc_string.partition(prefix)[0]
+74            if candidate:
+75                staged = candidate if any(call_type in candidate for call_type in DocStringParserConstants.staged_call_types) else None
+76                break
+77
+78        return candidate, prior_candidate, staged
+
+ + + + +
+
+ +
+ + def + parse(self) -> mir.config.constants.DocParseData: + + + +
+ + 
 80    def parse(self) -> DocParseData:
+ 81        candidate, prior_candidate, staged = self.doc_match(DocStringParserConstants.pipe_prefixes)
+ 82        if candidate:
+ 83            pipe_class, pipe_repo = self._extract_class_and_repo(
+ 84                segment=candidate,
+ 85                call_types=DocStringParserConstants.call_types,
+ 86                prior_text=prior_candidate,
+ 87            )
+ 88            motion_adapter = "motion_adapter" in candidate or "adapter" in candidate
+ 89            if motion_adapter and pipe_repo:
+ 90                staged, prior_candidate, _ = self.doc_match(DocStringParserConstants.pipe_prefixes[2:])  # skip the adapter statements
+ 91            staged_class, staged_repo = (
+ 92                self._extract_class_and_repo(
+ 93                    segment=staged,
+ 94                    call_types=DocStringParserConstants.staged_call_types if not motion_adapter else DocStringParserConstants.call_types,
+ 95                    prior_text=prior_candidate,
+ 96                    prior_class=pipe_class,
+ 97                )
+ 98                if staged
+ 99                else (None, None)
+100            )
+101            if motion_adapter and pipe_class:
+102                pipe_class = staged_class
+103                staged_repo = None
+104                staged_class = None
+105
+106            if DocStringValidator.validate_pipe_class(pipe_class):
+107                dbuq(f"class :{pipe_class}, repo : {pipe_repo}, staged_class: {staged_class}, staged_repo:{staged_repo} \n")
+108                return DocParseData(pipe_class=pipe_class, pipe_repo=pipe_repo, staged_class=staged_class, staged_repo=staged_repo)
+
+ + + + +
+
+
+ + \ No newline at end of file diff --git a/docs/mir/indexers.html b/docs/mir/indexers.html new file mode 100644 index 0000000..48893a9 --- /dev/null +++ b/docs/mir/indexers.html @@ -0,0 +1,1183 @@ + + + + + + + mir.indexers API documentation + + + + + + + + + +
+
+

+mir.indexers

+ +

類發現和拆卸

+
+ + + + + + 
  1# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0
+  2# <!-- // /*  d a r k s h a p e s */ -->
+  3
+  4"""類發現和拆卸"""
+  5# pylint:disable=no-name-in-module
+  6
+  7import sys
+  8from typing import Any, Callable, Dict, List, Optional
+  9from mir.doc_parser import parse_docs
+ 10from mir.inspect.metadata import gather_transformers_metadata
+ 11from mir.tag import make_mir_tag
+ 12from mir.inspect.classes import resolve_class_names, extract_init_params
+ 13from mir.config.logging import nfo
+ 14from mir.config.conversion import import_submodules
+ 15
+ 16if "pytest" in sys.modules:
+ 17    import diffusers  # noqa # pyright:ignore[reportMissingImports] # pylint:disable=unused-import
+ 18
+ 19
+ 20def check_migrations(repo_path: str):
+ 21    """Replaces old organization names in repository paths with new ones.\n
+ 22    :param repo_path: Original repository path containing old organization names
+ 23    :return: Updated repository path with new organization names"""
+ 24    org_migration: dict[str, str] = {
+ 25        "/helium-2b": "/helium-1-2b",
+ 26        "allenai/Olmo2-7B-1124-hf": "allenai/Olmo-2-1124-7B",
+ 27        "apple/mobilevitv2-1.0": "apple/mobilevitv2-1.0-imagenet1k-256",
+ 28        "caidas/swin2SR-classical-sr-x2-64": "caidas/swin2SR-classical-sr-x2-64",
+ 29        "facebook/hiera-base-224": "facebook/hiera-base-224-hf",
+ 30        "facebook/sam_hq-vit-huge": "syscv-community/sam-hq-vit-huge",
+ 31        "facebook/vit_msn_base": "facebook/vit-msn-base",
+ 32        "facebook/wav2vec2-bert-rel-pos-large": "facebook/w2v-bert-2.0",
+ 33        "google/gemma-3-4b": "google/gemma-3-4b-it",
+ 34        "google/gemma2-7b": "google/gemma-2-9b",
+ 35        "google/gemma3_text-7b": "google/gemma-3-12b-it",
+ 36        "IDEA-Research/dab_detr-base": "IDEA-Research/dab-detr-resnet-50",
+ 37        "LGAI-EXAONE/EXAONE-4.0-Instruct": "LGAI-EXAONE/EXAONE-4.0-32B",
+ 38        "meta/chameleon-7B'": "facebook/chameleon-7b",
+ 39        "mixtralai/Mixtral-8x7B": "mistralai/Mixtral-8x7B-v0.1",
+ 40        "paligemma-hf/paligemma-2b": "google/paligemma2-3b-mix-224",
+ 41        "pixtral-hf/pixtral-9b": "mistralai/Pixtral-12B-Base-2409",
+ 42        "Qwen/Qwen2-7B-beta": "Qwen/Qwen2-7B",
+ 43        "Qwen/Qwen3-15B-A2B": "Qwen/Qwen3-30B-A3B",
+ 44        "s-JoL/Open-Llama-V1": "openlm-research/open_llama_3b",
+ 45        "Salesforce/instruct-blip-flan-t5": "Salesforce/instructblip-flan-t5-xl",
+ 46        "state-spaces/mamba2-2.8b": "AntonV/mamba2-2.7b-hf",
+ 47        "ibm-fms/FalconH1-9.8b-2.2T-hf": "tiiuae/Falcon-H1-34B-Instruct",
+ 48        "nvidia/nemotron-3-8b-base-4k-hf": "mgoin/nemotron-3-8b-chat-4k-sft-hf",
+ 49        "THUDM/": "zai-org/",
+ 50        "THUDM/GLM-4-100B-A10B": "zai-org/GLM-4.5-Air",
+ 51        "zai-org/GLM-4-100B-A10B": "zai-org/GLM-4.5-Air",
+ 52    }
+ 53    for old_name, new_name in org_migration.items():
+ 54        if old_name in repo_path:
+ 55            repo_path = repo_path.replace(old_name, new_name)
+ 56    # print(repo_path)
+ 57    return repo_path
+ 58
+ 59
+ 60def flag_config(transformers: bool = False, data: dict = None, **kwargs):
+ 61    """Set type of MIR prefix depending on model type\n
+ 62    :param transformers: Use transformers data instead of diffusers data, defaults to False
+ 63    :raises ValueError: Model type not detected
+ 64    :return: MIR prefix based on model configuration"""
+ 65    from mir.config.json_io import read_json_file
+ 66
+ 67    data = read_json_file("mir/spec/template.json")
+ 68
+ 69    if transformers:
+ 70        flags = data["arch"]["transformer"]  # pylint:disable=unsubscriptable-object
+ 71    else:
+ 72        flags = data["arch"]["diffuser"]  # pylint:disable=unsubscriptable-object
+ 73    for mir_prefix, key_match in flags.items():
+ 74        if any(kwargs.get(param) for param in key_match):
+ 75            return mir_prefix
+ 76    return None
+ 77    # nfo(f"Unrecognized model type with {kwargs}\n" )
+ 78
+ 79
+ 80def create_pipe_entry(repo_path: str, class_name: str, model_class_obj: Optional[Callable] = None) -> tuple[str, Dict[str, Dict[Any, Any]]]:
+ 81    """Create a pipeline article and generate corresponding information according to the provided repo path and pipeline category\n
+ 82    :param repo_path (str): Repository path.
+ 83    :param model_class_obj (str): The model class function
+ 84    :raises TypeError: If 'repo_path' or 'class_name' are not set.
+ 85    :return: Tuple: The data structure containing mir_series and mir_comp is used for subsequent processing.
+ 86    """
+ 87    import diffusers  # pyright: ignore[reportMissingImports] # pylint:disable=redefined-outer-name
+ 88
+ 89    control_net = ["Control", "Controlnet"]  #
+ 90    mir_prefix = "info"
+ 91    if hasattr(diffusers, class_name):
+ 92        model_class_obj = getattr(diffusers, class_name)
+ 93        sub_segments = extract_init_params(model_class_obj, "diffusers")
+ 94        decoder = "decoder" in sub_segments
+ 95        if repo_path in ["kandinsky-community/kandinsky-3"]:
+ 96            mir_prefix = "info.unet"
+ 97        if repo_path in ["openai/shap-e"]:
+ 98            mir_prefix = "info.unet"
+ 99            class_name = "ShapEPipeline"
+100        elif class_name == "MotionAdapter":
+101            mir_prefix = "info.lora"
+102        elif class_name == "WanPipeline":
+103            mir_prefix = "info.dit"
+104        elif class_name == "CogVideoXVideoToVideoPipeline":
+105            class_name = "CogVideoXPipeline"
+106        elif any(maybe for maybe in control_net if maybe.lower() in class_name.lower()):
+107            mir_prefix = "info.controlnet"
+108        else:
+109            mir_prefix = flag_config(**sub_segments)
+110            if mir_prefix is None and class_name not in ["AutoPipelineForImage2Image", "DiffusionPipeline"]:
+111                nfo(f"Failed to detect type for {class_name} {list(sub_segments)}\n")
+112            else:
+113                mir_prefix = "info." + mir_prefix
+114        if class_name == "StableDiffusion3InpaintPipeline" or repo_path in ["stabilityai/stable-diffusion-3-medium-diffusers"]:
+115            class_name = "StableDiffusion3Pipeline"
+116            repo_path = "stabilityai/stable-diffusion-3.5-medium"
+117        if class_name == "HunyuanVideoFramepackPipeline" or repo_path in ["hunyuanvideo-community/HunyuanVideo"]:
+118            class_name = "HunyuanVideoPipeline"
+119        mir_series, mir_comp = list(make_mir_tag(repo_path, decoder))
+120        mir_series = mir_prefix + "." + mir_series
+121        repo_path = check_migrations(repo_path)
+122        # modalities = add_mode_types(mir_tag=[mir_series, mir_comp])
+123        prefixed_data = {
+124            "repo": repo_path,
+125            "pkg": {0: {"diffusers": class_name}},
+126            # "mode": modalities.get("mode"),
+127        }
+128        return mir_series, {mir_comp: prefixed_data}
+129
+130
+131def diffusers_index() -> Dict[str, Dict[str, Dict[str, Any]]]:
+132    """Generate diffusion model data for MIR index\n
+133    :return: Dictionary ready to be applied to MIR data fields
+134    """
+135    special_repos = {
+136        "black-forest-labs/FLUX.1-schnell": "black-forest-labs/FLUX.1-dev",
+137        # "stabilityai/stable-diffusion-3-medium-diffusers": "stabilityai/stable-diffusion-3.5-medium",
+138    }
+139    special_classes = {
+140        # "StableDiffusion3Pipeline": "stabilityai/stable-diffusion-3.5-medium",  # NOT sd3
+141        "HunyuanDiTPipeline": "tencent-hunyuan/hunyuandiT-v1.2-diffusers",  #  NOT hyd .ckpt
+142        "ChromaPipeline": "lodestones/Chroma",
+143    }
+144    extracted_docs = list(gather_transformers_metadata())
+145    pipe_data = {}  # pipeline_stable_diffusion_xl_inpaint
+146    for code_name, file_name, docs in extracted_docs:
+147        parse_result = parse_docs(docs)
+148
+149        if parse_result:
+150            pipe_class = parse_result.pipe_class
+151            pipe_repo = parse_result.pipe_repo
+152            staged_class = parse_result.staged_class
+153            staged_repo = parse_result.staged_repo
+154            for class_name, swap_repo in special_classes.items():
+155                if pipe_class == class_name:
+156                    pipe_repo = swap_repo
+157                    break
+158            model_class_obj = import_submodules(pipe_class, f"diffusers.pipelines.{code_name}.{file_name}")
+159            extract_init_params(model_class_obj)
+160            try:
+161                series, comp_data = create_pipe_entry(pipe_repo, pipe_class)
+162            except TypeError:
+163                pass  # Attempt 1
+164            if pipe_data.get(series):
+165                if "img2img" in pipe_class.lower():
+166                    continue
+167            pipe_data.setdefault(series, {}).update(comp_data)
+168            special_conditions = special_repos | special_classes
+169            if staged_class or pipe_repo in list(special_conditions):
+170                test = special_conditions.get(pipe_repo)
+171                if test:
+172                    staged_repo = test
+173                    staged_class = pipe_class
+174                try:
+175                    series, comp_data = create_pipe_entry(staged_repo if staged_repo else pipe_repo, staged_class if staged_class else pipe_class)
+176                except TypeError as error_log:
+177                    print(series, comp_data)
+178                    print(error_log)
+179                    continue  # Attempt 2,
+180                pipe_data.setdefault(series, {}).update(comp_data)
+181    return dict(pipe_data)
+182
+183
+184def transformers_index():
+185    """Generate LLM model data for MIR index\n
+186    :return: Dictionary ready to be applied to MIR data fields"""
+187
+188    import re
+189
+190    import transformers
+191    from transformers.models.auto.tokenization_auto import TOKENIZER_MAPPING_NAMES
+192
+193    from mir.inspect.metadata import gather_transformers_metadata
+194
+195    corrections: dict[dict[str, str | dict[str, list[str]]]] = {  # models with incorrect repos or config
+196        "BarkModel": {
+197            "repo_path": "suno/bark",
+198            "sub_segments": {"n_head": [""]},
+199        },
+200        "GraniteSpeechForConditionalGeneration": {
+201            "repo_path": "ibm-granite/granite-speech-3.3-8b",
+202            "sub_segments": {"encoder_layers": [""], "decoder_layers": [""]},
+203        },
+204        "GptOssModel": {
+205            "repo_path": "openai/gpt-oss-120b",
+206        },
+207        "GraniteModel": {
+208            "repo_path": "ibm-granite/granite-3.3-2b-base",
+209            "sub_segments": {"rope_theta": [""]},
+210        },
+211        "DPRQuestionEncoder": {
+212            "repo_path": "facebook/dpr-question_encoder-single-nq-base",
+213            "sub_segments": {"local_attention": [""], "classifier_proj_size": [""]},
+214        },
+215        "CohereModel": {
+216            "repo_path": "CohereForAI/c4ai-command-r-v01",
+217            "sub_segments": {"attn_config": [""], "num_codebooks": [""]},
+218        },
+219        "Cohere2Model": {
+220            "repo_path": "CohereLabs/c4ai-command-r7b-12-2024",
+221            "sub_segments": {"attn_config": [""], "num_codebooks": [""]},
+222        },
+223        "GraniteMoeHybridModel": {
+224            "repo_path": "ibm-research/PowerMoE-3b",
+225        },
+226        "BertForMaskedLM": {
+227            "repo_path": "google-bert/bert-base-uncased",
+228        },
+229        "DistilBertModel": {
+230            "repo_path": "distilbert-base-uncased",
+231        },
+232        "GraniteMoeModel": {
+233            "repo_path": "ibm-research/PowerMoE-3b",
+234        },
+235        "AriaModel": {
+236            "repo_path": "rhymes-ai/Aria-Chat",
+237            "sub_segments": {"vision_config": [""], "text_config": [""]},
+238        },
+239        "TimmWrapperModel": {
+240            "repo_path": "timm/resnet18.a1_in1k",
+241            "sub_segments": {"_resnet_": [""]},
+242        },
+243        "FunnelModel": {
+244            "repo_path": "funnel-transformer/small",
+245            "sub_segments": {"separate_cls": [""]},
+246        },
+247    }
+248
+249    mir_data = {}
+250    # transformers_data = stock_llm_data()
+251    transformers_data: Dict[Callable, List[str]] = gather_transformers_metadata()
+252    for model_class_obj, model_data in transformers_data.items():
+253        class_name = model_class_obj.__name__
+254        if class_name in list(corrections):  # conditional correction from mappings above: `extract_init_params` doesn't return anything in these cases
+255            repo_path = corrections[class_name]["repo_path"]
+256            sub_segments = corrections[class_name].get("sub_segments", extract_init_params(model_data["config"][-1], "transformers"))
+257        else:
+258            repo_path = ""
+259            if model_data.get("config"):
+260                doc_attempt = [getattr(transformers, model_data["config"][-1]), model_class_obj.forward]
+261            for pattern in doc_attempt:
+262                doc_string = pattern.__doc__
+263                matches = re.findall(r"\[([^\]]+)\]", doc_string)
+264                if matches:
+265                    try:
+266                        repo_path = next(iter(snip.strip('"').strip() for snip in matches if "/" in snip))
+267                    except StopIteration as error_log:
+268                        nfo(f"ERROR >>{matches} : LOG >> {error_log}")
+269                        pass
+270                    break
+271            sub_segments: Dict[str, List[str]] = extract_init_params(model_data["config"][-1], "transformers")
+272        if sub_segments and list(sub_segments) != ["kwargs"] and list(sub_segments) != ["use_cache", "kwargs"] and repo_path is not None:
+273            mir_prefix = flag_config(transformers=True, **sub_segments)
+274            if mir_prefix is None:
+275                nfo(f"Failed to detect type for {class_name} {list(sub_segments)}\n")
+276                continue
+277            else:
+278                mir_prefix = "info." + mir_prefix
+279            code_name = resolve_class_names(class_name)
+280            if code_name != "funnel":
+281                mir_suffix, mir_comp = list(make_mir_tag(repo_path))
+282            else:
+283                mir_suffix, mir_comp = ["funnel", "*"]
+284            mir_series = mir_prefix + "." + mir_suffix
+285            # modalities = add_mode_types(mir_tag=[mir_series, mir_comp])
+286            repo_path = check_migrations(repo_path)
+287            tk_pkg = {}
+288            tokenizer_classes = TOKENIZER_MAPPING_NAMES.get(code_name)
+289            # mode = modalities.get("mode")
+290            if tokenizer_classes:
+291                index = 0
+292                for tokenizer in tokenizer_classes:
+293                    if tokenizer:
+294                        tokenizer_class = import_submodules(tokenizer, "transformers")
+295                        tk_pkg.setdefault(index, {"transformers": f"{tokenizer_class.__module__}.{tokenizer_class.__name__}"})
+296                        index += 1
+297                if tk_pkg:
+298                    mir_data.get("info.encoder.tokenizer", mir_data.setdefault("info.encoder.tokenizer", {})).update(
+299                        {
+300                            mir_suffix: {
+301                                "pkg": tk_pkg,
+302                            }
+303                        },
+304                    )
+305            mir_data.setdefault(
+306                mir_series,
+307                {
+308                    mir_comp: {
+309                        "repo": repo_path,
+310                        "pkg": {
+311                            0: {"transformers": class_name},
+312                        },
+313                        # "mode": mode,
+314                    },
+315                },
+316            )
+317    return mir_data
+318
+319
+320def mlx_repo_capture(base_repo: str = "mlx-community"):
+321    import os
+322    import re
+323
+324    try:
+325        import mlx_audio  # type: ignore
+326    except ImportError:
+327        return {}
+328    result = {}
+329    result_2 = {}
+330    folder_path_named: str = os.path.dirname(mlx_audio.__file__)
+331    for root, dir, file_names in os.walk(folder_path_named):
+332        for file in file_names:
+333            if file.endswith((".py", ".html", ".md", ".ts")):
+334                with open(os.path.join(root, file), "r") as open_file:
+335                    content = open_file.read()
+336                    if "mlx-community/" in content:
+337                        matches = re.findall(base_repo + r'/(.*?)"', content)
+338                        for match in matches:
+339                            result[match] = f"{base_repo}/{match}"
+340                            previous_data = content[content.index(match) - 75 : content.index(match)].replace(base_repo, "")
+341                            class_match = re.findall(r"(\w+)\.from_pretrained", previous_data, re.MULTILINE)
+342                            if class_match:
+343                                result_2[match] = {f"{base_repo}/{match}": [*class_match]}
+344                            else:
+345                                if os.path.basename(root) in ["tts", "sts"]:
+346                                    folder_name = match.partition("-")[0]
+347                                    file_path = os.path.join(root, "models", folder_name, folder_name + ".py")
+348                                    if os.path.exists(file_path):
+349                                        with open(file_path, "r") as model_file:
+350                                            read_data = model_file.read()  # type: ignore  # noqa
+351                                            class_match = re.findall(r"(\w+)\.from_pretrained", previous_data, re.MULTILINE)
+352
+353    return result_2
+354
+355
+356# def mlx_repo_capture(base_repo: str = "mlx-community"):
+357#     import os
+358#     import re
+359#     import mlx_audio
+360
+361#     result = {}
+362#     result_2 = {}
+363#     folder_path_named: str = os.path.dirname(mlx_audio.__file__)
+364#     for root, _, file_names in os.walk(folder_path_named):
+365#         for file in file_names:
+366#             if file.endswith((".py", ".html", ".md", ".ts")):
+367#                 with open(os.path.join(root, file), "r") as open_file:
+368#                     content = open_file.read()
+369#                     if "mlx-community/" in content:
+370#                         matches = re.findall(base_repo + r'/(.*?)"', content)
+371#                         for match in matches:
+372#                             print(file)
+373#                             result[match] = f"{base_repo}/{match}"
+374#                             previous_data = content[content.index(match) - 75 : content.index(match)].replace(base_repo, "")
+375#                             matches = re.findall(r"(\w+)\.from_pretrained", previous_data, re.MULTILINE)
+376#                             if matches:
+377#                                 result_2[match] = {f"{base_repo}/{match}": [*matches]}
+378#                             else:
+379#                                 result_2[match] = {f"{base_repo}/{match}": None}
+380#     return result_2
+381
+382
+383# def mlx_audio_scrape(base_repo: str = "mlx-community"):
+384#     import os
+385#     import re
+386#     import mlx_audio
+387
+388#     result = {}
+389#     result_2 = {}
+390#     folder_path_named: str = os.path.dirname(mlx_audio.__file__)
+391#     for root, _, file_names in os.walk(folder_path_named):
+392#         for file in file_names:
+393#             if file.endswith((".py",)):
+394#                 with open(os.path.join(root, file), "r") as open_file:
+395#                     content = open_file.read()
+396#                     if "mlx-community/" in content:
+397#                         matches = re.findall(base_repo + r'/(.*?)"', content)
+398#                         for match in matches:
+399#                             result[match] = f"{base_repo}/{match}"
+400#                             previous_data = content[content.index(match) - 75 : content.index(match)].replace(base_repo, "")
+401#                             matches = re.findall(r"(\w+)\.from_pretrained", previous_data, re.MULTILINE)
+402#                             if len(matches) > 1:
+403#                                 result_2[match] = {f"{base_repo}/{match}": [*matches]}
+404#                             else:
+405#                                 if "nn.Module" in content:
+406#                                     previous_data = content[content.rindex("nn.Module") - 50 : content.rindex("nn.Module")]
+407#                                     matches = re.search(r"(\w+)\.", previous_data, re.MULTILINE)
+408#                                     result_2[match] = {f"{base_repo}/{match}": [*matches]}
+409#     return result_2
+410
+411
+412# @MODE_DATA.decorator
+413# def add_mode_types(mir_tag: list[str], data: dict | None = None) -> dict[str, list[str] | str]:
+414#     """_summary_\n
+415#     :param mir_tag: _description_
+416#     :param data: _description_, defaults to None
+417#     :return: _description_"""
+418#     fused_tag = ".".join(mir_tag)
+419
+420#     mir_details = {
+421#         "mode": data.get(fused_tag, {}).get("pipeline_tag"),
+422#         "pkg_type": data.get(fused_tag, {}).get("library_type"),
+423#         "tags": data.get(fused_tag, {}).get("tags"),
+424#     }
+425#     return mir_details
+
+ + +
+
+ +
+ + def + check_migrations(repo_path: str): + + + +
+ + 
21def check_migrations(repo_path: str):
+22    """Replaces old organization names in repository paths with new ones.\n
+23    :param repo_path: Original repository path containing old organization names
+24    :return: Updated repository path with new organization names"""
+25    org_migration: dict[str, str] = {
+26        "/helium-2b": "/helium-1-2b",
+27        "allenai/Olmo2-7B-1124-hf": "allenai/Olmo-2-1124-7B",
+28        "apple/mobilevitv2-1.0": "apple/mobilevitv2-1.0-imagenet1k-256",
+29        "caidas/swin2SR-classical-sr-x2-64": "caidas/swin2SR-classical-sr-x2-64",
+30        "facebook/hiera-base-224": "facebook/hiera-base-224-hf",
+31        "facebook/sam_hq-vit-huge": "syscv-community/sam-hq-vit-huge",
+32        "facebook/vit_msn_base": "facebook/vit-msn-base",
+33        "facebook/wav2vec2-bert-rel-pos-large": "facebook/w2v-bert-2.0",
+34        "google/gemma-3-4b": "google/gemma-3-4b-it",
+35        "google/gemma2-7b": "google/gemma-2-9b",
+36        "google/gemma3_text-7b": "google/gemma-3-12b-it",
+37        "IDEA-Research/dab_detr-base": "IDEA-Research/dab-detr-resnet-50",
+38        "LGAI-EXAONE/EXAONE-4.0-Instruct": "LGAI-EXAONE/EXAONE-4.0-32B",
+39        "meta/chameleon-7B'": "facebook/chameleon-7b",
+40        "mixtralai/Mixtral-8x7B": "mistralai/Mixtral-8x7B-v0.1",
+41        "paligemma-hf/paligemma-2b": "google/paligemma2-3b-mix-224",
+42        "pixtral-hf/pixtral-9b": "mistralai/Pixtral-12B-Base-2409",
+43        "Qwen/Qwen2-7B-beta": "Qwen/Qwen2-7B",
+44        "Qwen/Qwen3-15B-A2B": "Qwen/Qwen3-30B-A3B",
+45        "s-JoL/Open-Llama-V1": "openlm-research/open_llama_3b",
+46        "Salesforce/instruct-blip-flan-t5": "Salesforce/instructblip-flan-t5-xl",
+47        "state-spaces/mamba2-2.8b": "AntonV/mamba2-2.7b-hf",
+48        "ibm-fms/FalconH1-9.8b-2.2T-hf": "tiiuae/Falcon-H1-34B-Instruct",
+49        "nvidia/nemotron-3-8b-base-4k-hf": "mgoin/nemotron-3-8b-chat-4k-sft-hf",
+50        "THUDM/": "zai-org/",
+51        "THUDM/GLM-4-100B-A10B": "zai-org/GLM-4.5-Air",
+52        "zai-org/GLM-4-100B-A10B": "zai-org/GLM-4.5-Air",
+53    }
+54    for old_name, new_name in org_migration.items():
+55        if old_name in repo_path:
+56            repo_path = repo_path.replace(old_name, new_name)
+57    # print(repo_path)
+58    return repo_path
+
+ + +

Replaces old organization names in repository paths with new ones.

+ +
Parameters
+ +
    +
  • repo_path: Original repository path containing old organization names
    • +
+ +
Returns
+ +
+

Updated repository path with new organization names

+
+
+ + +
+
+ +
+ + def + flag_config(transformers: bool = False, data: dict = None, **kwargs): + + + +
+ + 
61def flag_config(transformers: bool = False, data: dict = None, **kwargs):
+62    """Set type of MIR prefix depending on model type\n
+63    :param transformers: Use transformers data instead of diffusers data, defaults to False
+64    :raises ValueError: Model type not detected
+65    :return: MIR prefix based on model configuration"""
+66    from mir.config.json_io import read_json_file
+67
+68    data = read_json_file("mir/spec/template.json")
+69
+70    if transformers:
+71        flags = data["arch"]["transformer"]  # pylint:disable=unsubscriptable-object
+72    else:
+73        flags = data["arch"]["diffuser"]  # pylint:disable=unsubscriptable-object
+74    for mir_prefix, key_match in flags.items():
+75        if any(kwargs.get(param) for param in key_match):
+76            return mir_prefix
+77    return None
+78    # nfo(f"Unrecognized model type with {kwargs}\n" )
+
+ + +

Set type of MIR prefix depending on model type

+ +
Parameters
+ +
    +
  • transformers: Use transformers data instead of diffusers data, defaults to False
    • +
+ +
Raises
+ +
    +
  • ValueError: Model type not detected
    • +
+ +
Returns
+ +
+

MIR prefix based on model configuration

+
+
+ + +
+
+ +
+ + def + create_pipe_entry( repo_path: str, class_name: str, model_class_obj: Optional[Callable] = None) -> tuple[str, typing.Dict[str, typing.Dict[typing.Any, typing.Any]]]: + + + +
+ + 
 81def create_pipe_entry(repo_path: str, class_name: str, model_class_obj: Optional[Callable] = None) -> tuple[str, Dict[str, Dict[Any, Any]]]:
+ 82    """Create a pipeline article and generate corresponding information according to the provided repo path and pipeline category\n
+ 83    :param repo_path (str): Repository path.
+ 84    :param model_class_obj (str): The model class function
+ 85    :raises TypeError: If 'repo_path' or 'class_name' are not set.
+ 86    :return: Tuple: The data structure containing mir_series and mir_comp is used for subsequent processing.
+ 87    """
+ 88    import diffusers  # pyright: ignore[reportMissingImports] # pylint:disable=redefined-outer-name
+ 89
+ 90    control_net = ["Control", "Controlnet"]  #
+ 91    mir_prefix = "info"
+ 92    if hasattr(diffusers, class_name):
+ 93        model_class_obj = getattr(diffusers, class_name)
+ 94        sub_segments = extract_init_params(model_class_obj, "diffusers")
+ 95        decoder = "decoder" in sub_segments
+ 96        if repo_path in ["kandinsky-community/kandinsky-3"]:
+ 97            mir_prefix = "info.unet"
+ 98        if repo_path in ["openai/shap-e"]:
+ 99            mir_prefix = "info.unet"
+100            class_name = "ShapEPipeline"
+101        elif class_name == "MotionAdapter":
+102            mir_prefix = "info.lora"
+103        elif class_name == "WanPipeline":
+104            mir_prefix = "info.dit"
+105        elif class_name == "CogVideoXVideoToVideoPipeline":
+106            class_name = "CogVideoXPipeline"
+107        elif any(maybe for maybe in control_net if maybe.lower() in class_name.lower()):
+108            mir_prefix = "info.controlnet"
+109        else:
+110            mir_prefix = flag_config(**sub_segments)
+111            if mir_prefix is None and class_name not in ["AutoPipelineForImage2Image", "DiffusionPipeline"]:
+112                nfo(f"Failed to detect type for {class_name} {list(sub_segments)}\n")
+113            else:
+114                mir_prefix = "info." + mir_prefix
+115        if class_name == "StableDiffusion3InpaintPipeline" or repo_path in ["stabilityai/stable-diffusion-3-medium-diffusers"]:
+116            class_name = "StableDiffusion3Pipeline"
+117            repo_path = "stabilityai/stable-diffusion-3.5-medium"
+118        if class_name == "HunyuanVideoFramepackPipeline" or repo_path in ["hunyuanvideo-community/HunyuanVideo"]:
+119            class_name = "HunyuanVideoPipeline"
+120        mir_series, mir_comp = list(make_mir_tag(repo_path, decoder))
+121        mir_series = mir_prefix + "." + mir_series
+122        repo_path = check_migrations(repo_path)
+123        # modalities = add_mode_types(mir_tag=[mir_series, mir_comp])
+124        prefixed_data = {
+125            "repo": repo_path,
+126            "pkg": {0: {"diffusers": class_name}},
+127            # "mode": modalities.get("mode"),
+128        }
+129        return mir_series, {mir_comp: prefixed_data}
+
+ + +

Create a pipeline article and generate corresponding information according to the provided repo path and pipeline category

+ +
Parameters
+ +
    +
  • repo_path (str): Repository path.
    • +
  • model_class_obj (str): The model class function
    • +
+ +
Raises
+ +
    +
  • TypeError: If 'repo_path' or 'class_name' are not set.
    • +
+ +
Returns
+ +
+

Tuple: The data structure containing mir_series and mir_comp is used for subsequent processing.

+
+
+ + +
+
+ +
+ + def + diffusers_index() -> Dict[str, Dict[str, Dict[str, Any]]]: + + + +
+ + 
132def diffusers_index() -> Dict[str, Dict[str, Dict[str, Any]]]:
+133    """Generate diffusion model data for MIR index\n
+134    :return: Dictionary ready to be applied to MIR data fields
+135    """
+136    special_repos = {
+137        "black-forest-labs/FLUX.1-schnell": "black-forest-labs/FLUX.1-dev",
+138        # "stabilityai/stable-diffusion-3-medium-diffusers": "stabilityai/stable-diffusion-3.5-medium",
+139    }
+140    special_classes = {
+141        # "StableDiffusion3Pipeline": "stabilityai/stable-diffusion-3.5-medium",  # NOT sd3
+142        "HunyuanDiTPipeline": "tencent-hunyuan/hunyuandiT-v1.2-diffusers",  #  NOT hyd .ckpt
+143        "ChromaPipeline": "lodestones/Chroma",
+144    }
+145    extracted_docs = list(gather_transformers_metadata())
+146    pipe_data = {}  # pipeline_stable_diffusion_xl_inpaint
+147    for code_name, file_name, docs in extracted_docs:
+148        parse_result = parse_docs(docs)
+149
+150        if parse_result:
+151            pipe_class = parse_result.pipe_class
+152            pipe_repo = parse_result.pipe_repo
+153            staged_class = parse_result.staged_class
+154            staged_repo = parse_result.staged_repo
+155            for class_name, swap_repo in special_classes.items():
+156                if pipe_class == class_name:
+157                    pipe_repo = swap_repo
+158                    break
+159            model_class_obj = import_submodules(pipe_class, f"diffusers.pipelines.{code_name}.{file_name}")
+160            extract_init_params(model_class_obj)
+161            try:
+162                series, comp_data = create_pipe_entry(pipe_repo, pipe_class)
+163            except TypeError:
+164                pass  # Attempt 1
+165            if pipe_data.get(series):
+166                if "img2img" in pipe_class.lower():
+167                    continue
+168            pipe_data.setdefault(series, {}).update(comp_data)
+169            special_conditions = special_repos | special_classes
+170            if staged_class or pipe_repo in list(special_conditions):
+171                test = special_conditions.get(pipe_repo)
+172                if test:
+173                    staged_repo = test
+174                    staged_class = pipe_class
+175                try:
+176                    series, comp_data = create_pipe_entry(staged_repo if staged_repo else pipe_repo, staged_class if staged_class else pipe_class)
+177                except TypeError as error_log:
+178                    print(series, comp_data)
+179                    print(error_log)
+180                    continue  # Attempt 2,
+181                pipe_data.setdefault(series, {}).update(comp_data)
+182    return dict(pipe_data)
+
+ + +

Generate diffusion model data for MIR index

+ +
Returns
+ +
+

Dictionary ready to be applied to MIR data fields

+
+
+ + +
+
+ +
+ + def + transformers_index(): + + + +
+ + 
185def transformers_index():
+186    """Generate LLM model data for MIR index\n
+187    :return: Dictionary ready to be applied to MIR data fields"""
+188
+189    import re
+190
+191    import transformers
+192    from transformers.models.auto.tokenization_auto import TOKENIZER_MAPPING_NAMES
+193
+194    from mir.inspect.metadata import gather_transformers_metadata
+195
+196    corrections: dict[dict[str, str | dict[str, list[str]]]] = {  # models with incorrect repos or config
+197        "BarkModel": {
+198            "repo_path": "suno/bark",
+199            "sub_segments": {"n_head": [""]},
+200        },
+201        "GraniteSpeechForConditionalGeneration": {
+202            "repo_path": "ibm-granite/granite-speech-3.3-8b",
+203            "sub_segments": {"encoder_layers": [""], "decoder_layers": [""]},
+204        },
+205        "GptOssModel": {
+206            "repo_path": "openai/gpt-oss-120b",
+207        },
+208        "GraniteModel": {
+209            "repo_path": "ibm-granite/granite-3.3-2b-base",
+210            "sub_segments": {"rope_theta": [""]},
+211        },
+212        "DPRQuestionEncoder": {
+213            "repo_path": "facebook/dpr-question_encoder-single-nq-base",
+214            "sub_segments": {"local_attention": [""], "classifier_proj_size": [""]},
+215        },
+216        "CohereModel": {
+217            "repo_path": "CohereForAI/c4ai-command-r-v01",
+218            "sub_segments": {"attn_config": [""], "num_codebooks": [""]},
+219        },
+220        "Cohere2Model": {
+221            "repo_path": "CohereLabs/c4ai-command-r7b-12-2024",
+222            "sub_segments": {"attn_config": [""], "num_codebooks": [""]},
+223        },
+224        "GraniteMoeHybridModel": {
+225            "repo_path": "ibm-research/PowerMoE-3b",
+226        },
+227        "BertForMaskedLM": {
+228            "repo_path": "google-bert/bert-base-uncased",
+229        },
+230        "DistilBertModel": {
+231            "repo_path": "distilbert-base-uncased",
+232        },
+233        "GraniteMoeModel": {
+234            "repo_path": "ibm-research/PowerMoE-3b",
+235        },
+236        "AriaModel": {
+237            "repo_path": "rhymes-ai/Aria-Chat",
+238            "sub_segments": {"vision_config": [""], "text_config": [""]},
+239        },
+240        "TimmWrapperModel": {
+241            "repo_path": "timm/resnet18.a1_in1k",
+242            "sub_segments": {"_resnet_": [""]},
+243        },
+244        "FunnelModel": {
+245            "repo_path": "funnel-transformer/small",
+246            "sub_segments": {"separate_cls": [""]},
+247        },
+248    }
+249
+250    mir_data = {}
+251    # transformers_data = stock_llm_data()
+252    transformers_data: Dict[Callable, List[str]] = gather_transformers_metadata()
+253    for model_class_obj, model_data in transformers_data.items():
+254        class_name = model_class_obj.__name__
+255        if class_name in list(corrections):  # conditional correction from mappings above: `extract_init_params` doesn't return anything in these cases
+256            repo_path = corrections[class_name]["repo_path"]
+257            sub_segments = corrections[class_name].get("sub_segments", extract_init_params(model_data["config"][-1], "transformers"))
+258        else:
+259            repo_path = ""
+260            if model_data.get("config"):
+261                doc_attempt = [getattr(transformers, model_data["config"][-1]), model_class_obj.forward]
+262            for pattern in doc_attempt:
+263                doc_string = pattern.__doc__
+264                matches = re.findall(r"\[([^\]]+)\]", doc_string)
+265                if matches:
+266                    try:
+267                        repo_path = next(iter(snip.strip('"').strip() for snip in matches if "/" in snip))
+268                    except StopIteration as error_log:
+269                        nfo(f"ERROR >>{matches} : LOG >> {error_log}")
+270                        pass
+271                    break
+272            sub_segments: Dict[str, List[str]] = extract_init_params(model_data["config"][-1], "transformers")
+273        if sub_segments and list(sub_segments) != ["kwargs"] and list(sub_segments) != ["use_cache", "kwargs"] and repo_path is not None:
+274            mir_prefix = flag_config(transformers=True, **sub_segments)
+275            if mir_prefix is None:
+276                nfo(f"Failed to detect type for {class_name} {list(sub_segments)}\n")
+277                continue
+278            else:
+279                mir_prefix = "info." + mir_prefix
+280            code_name = resolve_class_names(class_name)
+281            if code_name != "funnel":
+282                mir_suffix, mir_comp = list(make_mir_tag(repo_path))
+283            else:
+284                mir_suffix, mir_comp = ["funnel", "*"]
+285            mir_series = mir_prefix + "." + mir_suffix
+286            # modalities = add_mode_types(mir_tag=[mir_series, mir_comp])
+287            repo_path = check_migrations(repo_path)
+288            tk_pkg = {}
+289            tokenizer_classes = TOKENIZER_MAPPING_NAMES.get(code_name)
+290            # mode = modalities.get("mode")
+291            if tokenizer_classes:
+292                index = 0
+293                for tokenizer in tokenizer_classes:
+294                    if tokenizer:
+295                        tokenizer_class = import_submodules(tokenizer, "transformers")
+296                        tk_pkg.setdefault(index, {"transformers": f"{tokenizer_class.__module__}.{tokenizer_class.__name__}"})
+297                        index += 1
+298                if tk_pkg:
+299                    mir_data.get("info.encoder.tokenizer", mir_data.setdefault("info.encoder.tokenizer", {})).update(
+300                        {
+301                            mir_suffix: {
+302                                "pkg": tk_pkg,
+303                            }
+304                        },
+305                    )
+306            mir_data.setdefault(
+307                mir_series,
+308                {
+309                    mir_comp: {
+310                        "repo": repo_path,
+311                        "pkg": {
+312                            0: {"transformers": class_name},
+313                        },
+314                        # "mode": mode,
+315                    },
+316                },
+317            )
+318    return mir_data
+
+ + +

Generate LLM model data for MIR index

+ +
Returns
+ +
+

Dictionary ready to be applied to MIR data fields

+
+
+ + +
+
+ +
+ + def + mlx_repo_capture(base_repo: str = 'mlx-community'): + + + +
+ + 
321def mlx_repo_capture(base_repo: str = "mlx-community"):
+322    import os
+323    import re
+324
+325    try:
+326        import mlx_audio  # type: ignore
+327    except ImportError:
+328        return {}
+329    result = {}
+330    result_2 = {}
+331    folder_path_named: str = os.path.dirname(mlx_audio.__file__)
+332    for root, dir, file_names in os.walk(folder_path_named):
+333        for file in file_names:
+334            if file.endswith((".py", ".html", ".md", ".ts")):
+335                with open(os.path.join(root, file), "r") as open_file:
+336                    content = open_file.read()
+337                    if "mlx-community/" in content:
+338                        matches = re.findall(base_repo + r'/(.*?)"', content)
+339                        for match in matches:
+340                            result[match] = f"{base_repo}/{match}"
+341                            previous_data = content[content.index(match) - 75 : content.index(match)].replace(base_repo, "")
+342                            class_match = re.findall(r"(\w+)\.from_pretrained", previous_data, re.MULTILINE)
+343                            if class_match:
+344                                result_2[match] = {f"{base_repo}/{match}": [*class_match]}
+345                            else:
+346                                if os.path.basename(root) in ["tts", "sts"]:
+347                                    folder_name = match.partition("-")[0]
+348                                    file_path = os.path.join(root, "models", folder_name, folder_name + ".py")
+349                                    if os.path.exists(file_path):
+350                                        with open(file_path, "r") as model_file:
+351                                            read_data = model_file.read()  # type: ignore  # noqa
+352                                            class_match = re.findall(r"(\w+)\.from_pretrained", previous_data, re.MULTILINE)
+353
+354    return result_2
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/docs/mir/inspect.html b/docs/mir/inspect.html new file mode 100644 index 0000000..b6a3c01 --- /dev/null +++ b/docs/mir/inspect.html @@ -0,0 +1,246 @@ + + + + + + + mir.inspect API documentation + + + + + + + + + +
+
+

+mir.inspect

+ + + + + + + 
1
+
+ + +
+
+ + \ No newline at end of file diff --git a/docs/mir/inspect/classes.html b/docs/mir/inspect/classes.html new file mode 100644 index 0000000..d834172 --- /dev/null +++ b/docs/mir/inspect/classes.html @@ -0,0 +1,602 @@ + + + + + + + mir.inspect.classes API documentation + + + + + + + + + +
+
+

+mir.inspect.classes

+ +

類發現和拆卸

+
+ + + + + + 
  1# ### <!-- // /*  SPDX-License-Identifier: LAL-1.3 */ -->
+  2# ### <!-- // /*  d a r k s h a p e s */ -->
+  3
+  4"""類發現和拆卸"""
+  5
+  6# pylint:disable=protected-access
+  7
+  8from typing import Callable, Dict, List, Optional, Union, Type
+  9from mir.config.conversion import import_submodules
+ 10from mir.config.logging import nfo
+ 11
+ 12
+ 13def resolve_import_path(code_name: str, pkg_name: str) -> Optional[List[str]]:
+ 14    """Retrieve the folder path within a class. Only returns if it is a valid path in the system\n
+ 15    ### NOTE: in most cases `__module__` makes this redundant
+ 16    :param code_name: The internal name for the model in the third-party API.
+ 17    :param pkg_name: The API Package
+ 18    :return: A list corresponding to the path of the model, or None if not found
+ 19    :raises KeyError: for invalid pkg_name
+ 20    """
+ 21    import os
+ 22    from importlib import import_module
+ 23
+ 24    pkg_paths = {
+ 25        "diffusers": "pipelines",
+ 26        "transformers": "models",
+ 27    }
+ 28    folder_name = code_name.replace("-", "_")
+ 29    pkg_name = pkg_name.lower()
+ 30    folder_path = pkg_paths[pkg_name]
+ 31    package_obj = import_module(pkg_name)
+ 32    folder_path_named = [folder_path, folder_name]
+ 33    pkg_folder = os.path.dirname(getattr(package_obj, "__file__"))
+ 34    # dbuq(os.path.exists(os.path.join(pkg_folder, *folder_path_named)))
+ 35    if os.path.exists(os.path.join(pkg_folder, *folder_path_named)) is True:
+ 36        import_path = [pkg_name]
+ 37        import_path.extend(folder_path_named)
+ 38        return import_path
+ 39
+ 40
+ 41def resolve_class_names(class_name: Optional[Union[str, Type]] = None, pkg_name: Optional[str] = "transformers", path_format: Optional[bool] = False) -> Union[List[str], str]:
+ 42    """Reveal code names for class names from Diffusers or Transformers\n
+ 43    :param class_name: To return only one class, defaults to None
+ 44    :param pkg_name: optional field for library, defaults to "transformers"
+ 45    :param path_format: Retrieve just the code name, or the full module path and code name within the package
+ 46    :return: A list of all code names, or the one corresponding to the provided class"""
+ 47
+ 48    package_map = {
+ 49        "diffusers": ("_import_structure", "diffusers.pipelines"),
+ 50        "transformers": ("MODEL_MAPPING_NAMES", "transformers.models.auto.modeling_auto"),
+ 51    }
+ 52    pkg_name = pkg_name.lower()
+ 53    MAPPING_NAMES = import_submodules(*package_map[pkg_name])
+ 54    if class_name:
+ 55        if isinstance(class_name, Type):
+ 56            class_name = class_name.__name__
+ 57        code_name = next(iter(key for key, value in MAPPING_NAMES.items() if class_name in str(value)), "")
+ 58        return resolve_import_path(code_name, pkg_name) if path_format else code_name.replace("_", "-")
+ 59    return list(MAPPING_NAMES)
+ 60
+ 61
+ 62def extract_inherited_classes(model_class: Union[Callable, str], pkg_name: Optional[str] = None) -> Optional[Dict[str, List[str]]]:
+ 63    """Strips <class> tags from module's base classes and extracts inherited class members.\n
+ 64    If `module` is a string, it requires the `library` argument to convert it into a callable.\n
+ 65    :param module: A module or string representing a module.
+ 66    :param library: Library name required if `module` is a string. Defaults to None.
+ 67    :returns: Mapping indices to class path segments, or None if invalid input."""
+ 68
+ 69    if isinstance(model_class, str):
+ 70        if not pkg_name:
+ 71            nfo("Provide a library type argument to process strings")
+ 72            return None
+ 73        model_class = import_submodules(model_class, pkg_name)
+ 74    signature = model_class.__bases__
+ 75    class_names = []
+ 76    for index, class_annotation in enumerate(signature):
+ 77        tag_stripped = str(class_annotation)[8:-2]
+ 78        module_segments = tag_stripped.split(".")
+ 79        class_names.append(module_segments)
+ 80    return class_names
+ 81
+ 82
+ 83def extract_init_params(module: Union[Callable, str], pkg_name: Optional[str] = None) -> Dict[str, List[str]]:
+ 84    """Pick apart a Diffusers or Transformers pipeline class and find its constituent parts\n
+ 85    :param module: Origin pipeline as a class or as a string
+ 86    :param library: name of a library to import the class from, only if a string is provided
+ 87    :return: Dictionary of sub-classes from the `module`"""
+ 88
+ 89    import inspect
+ 90
+ 91    if pkg_name and isinstance(module, str):
+ 92        module = import_submodules(module, pkg_name)
+ 93    signature = inspect.signature(module.__init__)
+ 94    class_names = {}
+ 95    for folder, param in signature.parameters.items():
+ 96        if folder != "self":
+ 97            sub_module = str(param.annotation).split("'")
+ 98            if len(sub_module) > 1 and sub_module[1] not in [
+ 99                "bool",
+100                "int",
+101                "float",
+102                "complex",
+103                "str",
+104                "list",
+105                "tuple",
+106                "dict",
+107                "set",
+108            ]:
+109                class_names.setdefault(folder, sub_module[1].split("."))
+110    return class_names
+111
+112
+113# def pull_weight_map(repo_id: str, arch: str) -> Dict[str, str]:
+114#     from nnll.download.hub_cache import download_hub_file
+115
+116#     model_file = download_hub_file(
+117#         repo_id=f"{repo_id}/tree/main/{arch}",
+118#         source="huggingface",
+119#         file_name="diffusion_pytorch_model.safetensors.index.json",
+120#         local_dir=".tmp",
+121#     )
+
+ + +
+
+ +
+ + def + resolve_import_path(code_name: str, pkg_name: str) -> Optional[List[str]]: + + + +
+ + 
14def resolve_import_path(code_name: str, pkg_name: str) -> Optional[List[str]]:
+15    """Retrieve the folder path within a class. Only returns if it is a valid path in the system\n
+16    ### NOTE: in most cases `__module__` makes this redundant
+17    :param code_name: The internal name for the model in the third-party API.
+18    :param pkg_name: The API Package
+19    :return: A list corresponding to the path of the model, or None if not found
+20    :raises KeyError: for invalid pkg_name
+21    """
+22    import os
+23    from importlib import import_module
+24
+25    pkg_paths = {
+26        "diffusers": "pipelines",
+27        "transformers": "models",
+28    }
+29    folder_name = code_name.replace("-", "_")
+30    pkg_name = pkg_name.lower()
+31    folder_path = pkg_paths[pkg_name]
+32    package_obj = import_module(pkg_name)
+33    folder_path_named = [folder_path, folder_name]
+34    pkg_folder = os.path.dirname(getattr(package_obj, "__file__"))
+35    # dbuq(os.path.exists(os.path.join(pkg_folder, *folder_path_named)))
+36    if os.path.exists(os.path.join(pkg_folder, *folder_path_named)) is True:
+37        import_path = [pkg_name]
+38        import_path.extend(folder_path_named)
+39        return import_path
+
+ + +

Retrieve the folder path within a class. Only returns if it is a valid path in the system

+ +

NOTE: in most cases __module__ makes this redundant

+ +
Parameters
+ +
    +
  • code_name: The internal name for the model in the third-party API.
    • +
  • pkg_name: The API Package
    • +
+ +
Returns
+ +
+

A list corresponding to the path of the model, or None if not found

+
+ +
Raises
+ +
    +
  • KeyError: for invalid pkg_name
    • +
+
+ + +
+
+ +
+ + def + resolve_class_names( class_name: Union[str, Type, NoneType] = None, pkg_name: Optional[str] = 'transformers', path_format: Optional[bool] = False) -> Union[List[str], str]: + + + +
+ + 
42def resolve_class_names(class_name: Optional[Union[str, Type]] = None, pkg_name: Optional[str] = "transformers", path_format: Optional[bool] = False) -> Union[List[str], str]:
+43    """Reveal code names for class names from Diffusers or Transformers\n
+44    :param class_name: To return only one class, defaults to None
+45    :param pkg_name: optional field for library, defaults to "transformers"
+46    :param path_format: Retrieve just the code name, or the full module path and code name within the package
+47    :return: A list of all code names, or the one corresponding to the provided class"""
+48
+49    package_map = {
+50        "diffusers": ("_import_structure", "diffusers.pipelines"),
+51        "transformers": ("MODEL_MAPPING_NAMES", "transformers.models.auto.modeling_auto"),
+52    }
+53    pkg_name = pkg_name.lower()
+54    MAPPING_NAMES = import_submodules(*package_map[pkg_name])
+55    if class_name:
+56        if isinstance(class_name, Type):
+57            class_name = class_name.__name__
+58        code_name = next(iter(key for key, value in MAPPING_NAMES.items() if class_name in str(value)), "")
+59        return resolve_import_path(code_name, pkg_name) if path_format else code_name.replace("_", "-")
+60    return list(MAPPING_NAMES)
+
+ + +

Reveal code names for class names from Diffusers or Transformers

+ +
Parameters
+ +
    +
  • class_name: To return only one class, defaults to None
    • +
  • pkg_name: optional field for library, defaults to "transformers"
    • +
  • path_format: Retrieve just the code name, or the full module path and code name within the package
    • +
+ +
Returns
+ +
+

A list of all code names, or the one corresponding to the provided class

+
+
+ + +
+
+ +
+ + def + extract_inherited_classes( model_class: Union[Callable, str], pkg_name: Optional[str] = None) -> Optional[Dict[str, List[str]]]: + + + +
+ + 
63def extract_inherited_classes(model_class: Union[Callable, str], pkg_name: Optional[str] = None) -> Optional[Dict[str, List[str]]]:
+64    """Strips <class> tags from module's base classes and extracts inherited class members.\n
+65    If `module` is a string, it requires the `library` argument to convert it into a callable.\n
+66    :param module: A module or string representing a module.
+67    :param library: Library name required if `module` is a string. Defaults to None.
+68    :returns: Mapping indices to class path segments, or None if invalid input."""
+69
+70    if isinstance(model_class, str):
+71        if not pkg_name:
+72            nfo("Provide a library type argument to process strings")
+73            return None
+74        model_class = import_submodules(model_class, pkg_name)
+75    signature = model_class.__bases__
+76    class_names = []
+77    for index, class_annotation in enumerate(signature):
+78        tag_stripped = str(class_annotation)[8:-2]
+79        module_segments = tag_stripped.split(".")
+80        class_names.append(module_segments)
+81    return class_names
+
+ + +

Strips tags from module's base classes and extracts inherited class members.

+ +

If module is a string, it requires the library argument to convert it into a callable.

+ +
Parameters
+ +
    +
  • module: A module or string representing a module.
    • +
  • library: Library name required if module is a string. Defaults to None. +:returns: Mapping indices to class path segments, or None if invalid input.
    • +
+
+ + +
+
+ +
+ + def + extract_init_params( module: Union[Callable, str], pkg_name: Optional[str] = None) -> Dict[str, List[str]]: + + + +
+ + 
 84def extract_init_params(module: Union[Callable, str], pkg_name: Optional[str] = None) -> Dict[str, List[str]]:
+ 85    """Pick apart a Diffusers or Transformers pipeline class and find its constituent parts\n
+ 86    :param module: Origin pipeline as a class or as a string
+ 87    :param library: name of a library to import the class from, only if a string is provided
+ 88    :return: Dictionary of sub-classes from the `module`"""
+ 89
+ 90    import inspect
+ 91
+ 92    if pkg_name and isinstance(module, str):
+ 93        module = import_submodules(module, pkg_name)
+ 94    signature = inspect.signature(module.__init__)
+ 95    class_names = {}
+ 96    for folder, param in signature.parameters.items():
+ 97        if folder != "self":
+ 98            sub_module = str(param.annotation).split("'")
+ 99            if len(sub_module) > 1 and sub_module[1] not in [
+100                "bool",
+101                "int",
+102                "float",
+103                "complex",
+104                "str",
+105                "list",
+106                "tuple",
+107                "dict",
+108                "set",
+109            ]:
+110                class_names.setdefault(folder, sub_module[1].split("."))
+111    return class_names
+
+ + +

Pick apart a Diffusers or Transformers pipeline class and find its constituent parts

+ +
Parameters
+ +
    +
  • module: Origin pipeline as a class or as a string
    • +
  • library: name of a library to import the class from, only if a string is provided
    • +
+ +
Returns
+ +
+

Dictionary of sub-classes from the module

+
+
+ + +
+
+ + \ No newline at end of file diff --git a/docs/mir/inspect/metadata.html b/docs/mir/inspect/metadata.html new file mode 100644 index 0000000..a4d12d4 --- /dev/null +++ b/docs/mir/inspect/metadata.html @@ -0,0 +1,565 @@ + + + + + + + mir.inspect.metadata API documentation + + + + + + + + + +
+
+

+mir.inspect.metadata

+ + + + + + + 
  1# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0
+  2# <!-- // /*  d a r k s h a p e s */ -->
+  3
+  4import pkgutil
+  5from typing import Dict, Generator, List
+  6
+  7import diffusers
+  8
+  9from mir.inspect.classes import extract_init_params
+ 10from mir.config.conversion import pkg_path_to_docstring, file_name_to_docstring
+ 11
+ 12
+ 13def gather_transformers_metadata() -> Dict[str, List[str]]:
+ 14    """Eat the 🤗Transformers classes as a treat, leaving any tasty subclass class morsels neatly arranged as a dictionary.\n
+ 15    Nom.
+ 16    :return: Tasty mapping of subclasses to their class references"""
+ 17
+ 18    transformer_data = {}
+ 19    exclude_list = [
+ 20        "DecisionTransformerModel",
+ 21        "DistilBertModel",
+ 22        "GraphormerModel",
+ 23        "GPTBigCodeModel",
+ 24        "TimmBackbone",
+ 25        "PerceptionEncoder",
+ 26        "SeamlessM4Tv2Model",
+ 27        "SeamlessM4TModel",
+ 28        "VisionTextDualEncoderModel",
+ 29    ]
+ 30    second_exclude_list = [
+ 31        "vision-text-dual-encoder",
+ 32        "vision_text_dual_encoder",
+ 33        "gpt_bigcode",
+ 34        "data2vec",
+ 35        "bert_japanese",
+ 36        "cpm",
+ 37        "dab_detr",
+ 38        "decision_transformer",
+ 39        "timm_backbone",
+ 40    ]  # there just isnt a repo in this one
+ 41    import os
+ 42
+ 43    import transformers
+ 44    from transformers.models.auto.modeling_auto import CONFIG_MAPPING_NAMES, MODEL_MAPPING_NAMES
+ 45
+ 46    from mir.inspect.tasks import TaskAnalyzer
+ 47
+ 48    model_data = None
+ 49    task_pipe = None
+ 50    model_names = list(dict(MODEL_MAPPING_NAMES).keys())
+ 51    folder_data = {*model_names}
+ 52    models_folder = os.path.join(os.path.dirname(transformers.__file__), "models")
+ 53    folder_data = folder_data.union(os.listdir(models_folder))
+ 54    for code_name in folder_data:
+ 55        model_class = None
+ 56        if code_name and "__" not in code_name:
+ 57            tasks = TaskAnalyzer.show_transformers_tasks(code_name=code_name)
+ 58            if tasks:
+ 59                task_pipe = next(iter(tasks))
+ 60                if isinstance(task_pipe, tuple):
+ 61                    task_pipe = task_pipe[0]
+ 62                if task_pipe not in exclude_list:
+ 63                    model_class = getattr(__import__("transformers"), task_pipe)  # this is done to get the path to the config
+ 64                    model_data = extract_init_params(model_class)
+ 65                    if model_data and ("inspect" not in model_data["config"]) and ("deprecated" not in list(model_data["config"])):
+ 66                        transformer_data.setdefault(model_class, model_data)
+ 67                    else:
+ 68                        model_data = None
+ 69
+ 70            if not model_data and code_name not in second_exclude_list:  # second attempt
+ 71                if code_name == "donut":
+ 72                    code_name = "donut-swin"
+ 73                if not task_pipe and code_name and MODEL_MAPPING_NAMES.get(code_name.replace("_", "-")):
+ 74                    model_class = getattr(__import__("transformers"), MODEL_MAPPING_NAMES[code_name.replace("_", "-")], None)
+ 75                elif task_pipe:
+ 76                    model_class = getattr(__import__("transformers"), task_pipe)
+ 77                config_class = CONFIG_MAPPING_NAMES.get(code_name.replace("_", "-"))
+ 78                if not config_class:
+ 79                    config_class = CONFIG_MAPPING_NAMES.get(code_name.replace("-", "_"))
+ 80                if config_class:
+ 81                    config_class_obj = getattr(__import__("transformers"), config_class)
+ 82                    model_data = {"config": str(config_class_obj.__module__ + "." + config_class_obj.__name__).split(".")}
+ 83                    if model_data and ("inspect" not in model_data) and ("deprecated" not in model_data) and model_class:
+ 84                        transformer_data.setdefault(model_class, model_data)
+ 85    return transformer_data
+ 86
+ 87
+ 88def gather_diffusers_metadata() -> Generator:
+ 89    """Draw down docstrings from 🤗Diffusers library, minimizing internet requests\n
+ 90    :return: Docstrings for common diffusers models"""
+ 91
+ 92    non_standard = {
+ 93        "cogvideo": "cogvideox",
+ 94        "cogview3": "cogview3plus",
+ 95        "deepfloyd_if": "if",
+ 96        "cosmos": "cosmos2_text2image",  # search folder for all files containing 'EXAMPLE DOC STRING'
+ 97        "visualcloze": "visualcloze_generation",
+ 98    }
+ 99
+100    exclusion_list = [  # no doc string or other issues. all can be be gathered by other means
+101        "autopipeline",  #
+102        "dance_diffusion",  # no doc_string
+103        "ddim",
+104        "ddpm",
+105        "deprecated",
+106        "diffusionpipeline",  #
+107        "dit",
+108        "latent_consistency_models",  # "latent_consistency_text2img",
+109        "latent_diffusion",  # no doc_string
+110        "ledits_pp",  # "leditspp_stable_diffusion",
+111        "marigold",  # specific processing routines
+112        "omnigen",  # tries to import torchvision
+113        "pag",  # not model based
+114        "paint_by_example",  # no docstring
+115        "pia",  # lora adapter
+116        "semantic_stable_diffusion",  # no_docstring
+117        "stable_diffusion_attend_and_excite",
+118        "stable_diffusion_diffedit",
+119        "stable_diffusion_k_diffusion",  # tries to import k_diffusion
+120        "stable_diffusion_panorama",
+121        "stable_diffusion_safe",  # impossible
+122        "stable_diffusion_sag",  #
+123        "t2i_adapter",
+124        "text_to_video_synthesis",
+125        "unclip",
+126        "unidiffuser",
+127        # these are uncommon afaik
+128    ]
+129
+130    for _, pkg_name, is_pkg in pkgutil.iter_modules(diffusers.pipelines.__path__):
+131        if is_pkg and pkg_name not in exclusion_list:
+132            file_specific = non_standard.get(pkg_name, pkg_name)
+133            folder_name = getattr(diffusers.pipelines, str(pkg_name))
+134            if folder_name:
+135                if hasattr(folder_name, "_import_structure"):
+136                    yield from pkg_path_to_docstring(pkg_name, folder_name)
+137                else:
+138                    yield from file_name_to_docstring(pkg_name, file_specific)
+139            else:
+140                continue
+
+ + +
+
+ +
+ + def + gather_transformers_metadata() -> Dict[str, List[str]]: + + + +
+ + 
14def gather_transformers_metadata() -> Dict[str, List[str]]:
+15    """Eat the 🤗Transformers classes as a treat, leaving any tasty subclass class morsels neatly arranged as a dictionary.\n
+16    Nom.
+17    :return: Tasty mapping of subclasses to their class references"""
+18
+19    transformer_data = {}
+20    exclude_list = [
+21        "DecisionTransformerModel",
+22        "DistilBertModel",
+23        "GraphormerModel",
+24        "GPTBigCodeModel",
+25        "TimmBackbone",
+26        "PerceptionEncoder",
+27        "SeamlessM4Tv2Model",
+28        "SeamlessM4TModel",
+29        "VisionTextDualEncoderModel",
+30    ]
+31    second_exclude_list = [
+32        "vision-text-dual-encoder",
+33        "vision_text_dual_encoder",
+34        "gpt_bigcode",
+35        "data2vec",
+36        "bert_japanese",
+37        "cpm",
+38        "dab_detr",
+39        "decision_transformer",
+40        "timm_backbone",
+41    ]  # there just isnt a repo in this one
+42    import os
+43
+44    import transformers
+45    from transformers.models.auto.modeling_auto import CONFIG_MAPPING_NAMES, MODEL_MAPPING_NAMES
+46
+47    from mir.inspect.tasks import TaskAnalyzer
+48
+49    model_data = None
+50    task_pipe = None
+51    model_names = list(dict(MODEL_MAPPING_NAMES).keys())
+52    folder_data = {*model_names}
+53    models_folder = os.path.join(os.path.dirname(transformers.__file__), "models")
+54    folder_data = folder_data.union(os.listdir(models_folder))
+55    for code_name in folder_data:
+56        model_class = None
+57        if code_name and "__" not in code_name:
+58            tasks = TaskAnalyzer.show_transformers_tasks(code_name=code_name)
+59            if tasks:
+60                task_pipe = next(iter(tasks))
+61                if isinstance(task_pipe, tuple):
+62                    task_pipe = task_pipe[0]
+63                if task_pipe not in exclude_list:
+64                    model_class = getattr(__import__("transformers"), task_pipe)  # this is done to get the path to the config
+65                    model_data = extract_init_params(model_class)
+66                    if model_data and ("inspect" not in model_data["config"]) and ("deprecated" not in list(model_data["config"])):
+67                        transformer_data.setdefault(model_class, model_data)
+68                    else:
+69                        model_data = None
+70
+71            if not model_data and code_name not in second_exclude_list:  # second attempt
+72                if code_name == "donut":
+73                    code_name = "donut-swin"
+74                if not task_pipe and code_name and MODEL_MAPPING_NAMES.get(code_name.replace("_", "-")):
+75                    model_class = getattr(__import__("transformers"), MODEL_MAPPING_NAMES[code_name.replace("_", "-")], None)
+76                elif task_pipe:
+77                    model_class = getattr(__import__("transformers"), task_pipe)
+78                config_class = CONFIG_MAPPING_NAMES.get(code_name.replace("_", "-"))
+79                if not config_class:
+80                    config_class = CONFIG_MAPPING_NAMES.get(code_name.replace("-", "_"))
+81                if config_class:
+82                    config_class_obj = getattr(__import__("transformers"), config_class)
+83                    model_data = {"config": str(config_class_obj.__module__ + "." + config_class_obj.__name__).split(".")}
+84                    if model_data and ("inspect" not in model_data) and ("deprecated" not in model_data) and model_class:
+85                        transformer_data.setdefault(model_class, model_data)
+86    return transformer_data
+
+ + +

Eat the 🤗Transformers classes as a treat, leaving any tasty subclass class morsels neatly arranged as a dictionary.

+ +

Nom.

+ +
Returns
+ +
+

Tasty mapping of subclasses to their class references

+
+
+ + +
+
+ +
+ + def + gather_diffusers_metadata() -> Generator: + + + +
+ + 
 89def gather_diffusers_metadata() -> Generator:
+ 90    """Draw down docstrings from 🤗Diffusers library, minimizing internet requests\n
+ 91    :return: Docstrings for common diffusers models"""
+ 92
+ 93    non_standard = {
+ 94        "cogvideo": "cogvideox",
+ 95        "cogview3": "cogview3plus",
+ 96        "deepfloyd_if": "if",
+ 97        "cosmos": "cosmos2_text2image",  # search folder for all files containing 'EXAMPLE DOC STRING'
+ 98        "visualcloze": "visualcloze_generation",
+ 99    }
+100
+101    exclusion_list = [  # no doc string or other issues. all can be be gathered by other means
+102        "autopipeline",  #
+103        "dance_diffusion",  # no doc_string
+104        "ddim",
+105        "ddpm",
+106        "deprecated",
+107        "diffusionpipeline",  #
+108        "dit",
+109        "latent_consistency_models",  # "latent_consistency_text2img",
+110        "latent_diffusion",  # no doc_string
+111        "ledits_pp",  # "leditspp_stable_diffusion",
+112        "marigold",  # specific processing routines
+113        "omnigen",  # tries to import torchvision
+114        "pag",  # not model based
+115        "paint_by_example",  # no docstring
+116        "pia",  # lora adapter
+117        "semantic_stable_diffusion",  # no_docstring
+118        "stable_diffusion_attend_and_excite",
+119        "stable_diffusion_diffedit",
+120        "stable_diffusion_k_diffusion",  # tries to import k_diffusion
+121        "stable_diffusion_panorama",
+122        "stable_diffusion_safe",  # impossible
+123        "stable_diffusion_sag",  #
+124        "t2i_adapter",
+125        "text_to_video_synthesis",
+126        "unclip",
+127        "unidiffuser",
+128        # these are uncommon afaik
+129    ]
+130
+131    for _, pkg_name, is_pkg in pkgutil.iter_modules(diffusers.pipelines.__path__):
+132        if is_pkg and pkg_name not in exclusion_list:
+133            file_specific = non_standard.get(pkg_name, pkg_name)
+134            folder_name = getattr(diffusers.pipelines, str(pkg_name))
+135            if folder_name:
+136                if hasattr(folder_name, "_import_structure"):
+137                    yield from pkg_path_to_docstring(pkg_name, folder_name)
+138                else:
+139                    yield from file_name_to_docstring(pkg_name, file_specific)
+140            else:
+141                continue
+
+ + +

Draw down docstrings from 🤗Diffusers library, minimizing internet requests

+ +
Returns
+ +
+

Docstrings for common diffusers models

+
+
+ + +
+
+ + \ No newline at end of file diff --git a/docs/mir/inspect/parenting.html b/docs/mir/inspect/parenting.html new file mode 100644 index 0000000..76a5108 --- /dev/null +++ b/docs/mir/inspect/parenting.html @@ -0,0 +1,342 @@ + + + + + + + mir.inspect.parenting API documentation + + + + + + + + + +
+
+

+mir.inspect.parenting

+ + + + + + + 
 1# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0
+ 2# <!-- // /*  d a r k s h a p e s */ -->
+ 3
+ 4from typing import List, Optional
+ 5
+ 6
+ 7def class_parent(code_name: str, pkg_name: str) -> Optional[List[str]]:
+ 8    """Retrieve the folder path within a class. Only returns if it is a valid path in the system\n
+ 9    ### NOTE: in most cases `__module__` makes this redundant
+10    :param code_name: The internal name for the model in the third-party API.
+11    :param pkg_name: The API Package
+12    :return: A list corresponding to the path of the model, or None if not found
+13    :raises KeyError: for invalid pkg_name
+14    """
+15    import os
+16    from importlib import import_module
+17
+18    pkg_paths = {
+19        "diffusers": "pipelines",
+20        "transformers": "models",
+21    }
+22    folder_name = code_name.replace("-", "_")
+23    pkg_name = pkg_name.lower()
+24    folder_path = pkg_paths[pkg_name]
+25    package_obj = import_module(pkg_name)
+26    folder_path_named = [folder_path, folder_name]
+27    pkg_folder = os.path.dirname(getattr(package_obj, "__file__"))
+28    # dbuq(os.path.exists(os.path.join(pkg_folder, *folder_path_named)))
+29    if os.path.exists(os.path.join(pkg_folder, *folder_path_named)) is True:
+30        import_path = [pkg_name]
+31        import_path.extend(folder_path_named)
+32        return import_path
+
+ + +
+
+ +
+ + def + class_parent(code_name: str, pkg_name: str) -> Optional[List[str]]: + + + +
+ + 
 8def class_parent(code_name: str, pkg_name: str) -> Optional[List[str]]:
+ 9    """Retrieve the folder path within a class. Only returns if it is a valid path in the system\n
+10    ### NOTE: in most cases `__module__` makes this redundant
+11    :param code_name: The internal name for the model in the third-party API.
+12    :param pkg_name: The API Package
+13    :return: A list corresponding to the path of the model, or None if not found
+14    :raises KeyError: for invalid pkg_name
+15    """
+16    import os
+17    from importlib import import_module
+18
+19    pkg_paths = {
+20        "diffusers": "pipelines",
+21        "transformers": "models",
+22    }
+23    folder_name = code_name.replace("-", "_")
+24    pkg_name = pkg_name.lower()
+25    folder_path = pkg_paths[pkg_name]
+26    package_obj = import_module(pkg_name)
+27    folder_path_named = [folder_path, folder_name]
+28    pkg_folder = os.path.dirname(getattr(package_obj, "__file__"))
+29    # dbuq(os.path.exists(os.path.join(pkg_folder, *folder_path_named)))
+30    if os.path.exists(os.path.join(pkg_folder, *folder_path_named)) is True:
+31        import_path = [pkg_name]
+32        import_path.extend(folder_path_named)
+33        return import_path
+
+ + +

Retrieve the folder path within a class. Only returns if it is a valid path in the system

+ +

NOTE: in most cases __module__ makes this redundant

+ +
Parameters
+ +
    +
  • code_name: The internal name for the model in the third-party API.
    • +
  • pkg_name: The API Package
    • +
+ +
Returns
+ +
+

A list corresponding to the path of the model, or None if not found

+
+ +
Raises
+ +
    +
  • KeyError: for invalid pkg_name
    • +
+
+ + +
+
+ + \ No newline at end of file diff --git a/docs/mir/inspect/pipes.html b/docs/mir/inspect/pipes.html new file mode 100644 index 0000000..3dc111c --- /dev/null +++ b/docs/mir/inspect/pipes.html @@ -0,0 +1,367 @@ + + + + + + + mir.inspect.pipes API documentation + + + + + + + + + +
+
+

+mir.inspect.pipes

+ + + + + + + 
 1# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0
+ 2# <!-- // /*  d a r k s h a p e s */ -->
+ 3
+ 4from typing import List, Optional
+ 5
+ 6
+ 7def get_transformer_config_classes(parameter_filter: Optional[str] = None) -> List[str]:
+ 8    """Show all config classes in the Transformer package with the specified init annotation\n
+ 9    :param from_match: Narrow the classes to only those with an exact key inside
+10    :return: A list of all Classes"""
+11    from mir.inspect.metadata import gather_transformers_metadata
+12    from mir.inspect.classes import extract_init_params
+13
+14    transformers_data = gather_transformers_metadata()
+15    config_data = []
+16    for model_path in list(transformers_data.values()):
+17        config_class = model_path["config"][-1]
+18        if parameter_filter:
+19            segments = extract_init_params(config_class, pkg_name="transformers")
+20            if parameter_filter in list(segments):
+21                config_data.append(config_class)
+22        else:
+23            config_data.append(config_class)
+24    return config_data
+25
+26
+27def get_class_parent_folder(class_name: str, pkg_name: str) -> List[str]:
+28    from mir import dbuq
+29    from mir.inspect.classes import resolve_class_name, extract_init_params
+30
+31    pkg_name = pkg_name.lower()
+32    if pkg_name == "diffusers":
+33        parent_folder: List[str] = resolve_class_name(class_name=class_name, pkg_name=pkg_name, path_format=True)
+34        if not parent_folder or not parent_folder[-1].strip():
+35            dbuq("Data not found for", " class_name = {class_name},pkg_name = {pkg_name},{parent_folder} = parent_folder")
+36            return None
+37    elif pkg_name == "transformers":
+38        module_path = extract_init_params(class_name, "transformers").get("config")
+39        parent_folder = module_path[:3]
+40    return parent_folder
+
+ + +
+
+ +
+ + def + get_transformer_config_classes(parameter_filter: Optional[str] = None) -> List[str]: + + + +
+ + 
 8def get_transformer_config_classes(parameter_filter: Optional[str] = None) -> List[str]:
+ 9    """Show all config classes in the Transformer package with the specified init annotation\n
+10    :param from_match: Narrow the classes to only those with an exact key inside
+11    :return: A list of all Classes"""
+12    from mir.inspect.metadata import gather_transformers_metadata
+13    from mir.inspect.classes import extract_init_params
+14
+15    transformers_data = gather_transformers_metadata()
+16    config_data = []
+17    for model_path in list(transformers_data.values()):
+18        config_class = model_path["config"][-1]
+19        if parameter_filter:
+20            segments = extract_init_params(config_class, pkg_name="transformers")
+21            if parameter_filter in list(segments):
+22                config_data.append(config_class)
+23        else:
+24            config_data.append(config_class)
+25    return config_data
+
+ + +

Show all config classes in the Transformer package with the specified init annotation

+ +
Parameters
+ +
    +
  • from_match: Narrow the classes to only those with an exact key inside
    • +
+ +
Returns
+ +
+

A list of all Classes

+
+
+ + +
+
+ +
+ + def + get_class_parent_folder(class_name: str, pkg_name: str) -> List[str]: + + + +
+ + 
28def get_class_parent_folder(class_name: str, pkg_name: str) -> List[str]:
+29    from mir import dbuq
+30    from mir.inspect.classes import resolve_class_name, extract_init_params
+31
+32    pkg_name = pkg_name.lower()
+33    if pkg_name == "diffusers":
+34        parent_folder: List[str] = resolve_class_name(class_name=class_name, pkg_name=pkg_name, path_format=True)
+35        if not parent_folder or not parent_folder[-1].strip():
+36            dbuq("Data not found for", " class_name = {class_name},pkg_name = {pkg_name},{parent_folder} = parent_folder")
+37            return None
+38    elif pkg_name == "transformers":
+39        module_path = extract_init_params(class_name, "transformers").get("config")
+40        parent_folder = module_path[:3]
+41    return parent_folder
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/docs/mir/inspect/tasks.html b/docs/mir/inspect/tasks.html new file mode 100644 index 0000000..481e1e4 --- /dev/null +++ b/docs/mir/inspect/tasks.html @@ -0,0 +1,1331 @@ + + + + + + + mir.inspect.tasks API documentation + + + + + + + + + +
+
+

+mir.inspect.tasks

+ + + + + + + 
  1#  # # <!-- // /*  SPDX-License-Identifier: MPL-2.0*/ -->
+  2#  # # <!-- // /*  d a r k s h a p e s */ -->
+  3
+  4from typing import Any, Callable, Dict, List, get_type_hints
+  5from mir.maid import MIRDatabase
+  6from mir.config.logging import dbuq
+  7
+  8
+  9flatten_map: List[Any] = lambda nested, unpack: [element for iterative in getattr(nested, unpack)() for element in iterative]
+ 10flatten_map.__annotations__ = {"nested": List[str], "unpack": str}
+ 11
+ 12
+ 13class TaskAnalyzer:
+ 14    def __init__(self) -> None:
+ 15        self.skip_series = [
+ 16            "info.lora",
+ 17            "info.vae",
+ 18            "ops.precision",
+ 19            "ops.scheduler",
+ 20            "info.encoder.tokenizer",
+ 21            "info.controlnet",
+ 22        ]
+ 23        self.skip_classes = [".gligen", "imagenet64"]
+ 24        self.skip_auto = ["AutoTokenizer", "AutoModel", "AutoencoderTiny", "AutoencoderKL", "AutoPipelineForImage2Image"]
+ 25        self.skip_types = ["int", "bool", "float", "Optional", "NoneType", "List", "UNet2DConditionModel"]
+ 26        self.mflux_tasks = ["Image", "Redux", "Kontext", "Depth", "Fill", "ConceptAttention", "ControlNet", "CavTon", "IC-Edit"]
+ 27
+ 28    @staticmethod
+ 29    def show_diffusers_tasks(code_name: str, class_name: str | None = None) -> list[str]:
+ 30        """Return Diffusers task pipes based on package-specific query\n
+ 31        :param class_name: To find task pipes from a Diffusers class pipe, defaults to None
+ 32        :param code_name: To find task pipes from a Transformers class pipe, defaults to None
+ 33        :return: A list of alternate class pipelines derived from the specified class"""
+ 34
+ 35        if class_name:
+ 36            from diffusers.pipelines.auto_pipeline import SUPPORTED_TASKS_MAPPINGS, _get_task_class
+ 37
+ 38            alt_tasks = []
+ 39            for task_map in SUPPORTED_TASKS_MAPPINGS:
+ 40                task_class = _get_task_class(task_map, class_name, False)
+ 41                if task_class:
+ 42                    alt_tasks.append(task_class.__name__)
+ 43                    dbuq(task_class)
+ 44                for model_code, pipe_class_obj in task_map.items():
+ 45                    if code_name in model_code:
+ 46                        alt_tasks.append(pipe_class_obj.__name__)
+ 47
+ 48        return alt_tasks
+ 49
+ 50    @staticmethod
+ 51    def show_transformers_tasks(class_name: str | None = None, code_name: str | None = None) -> list[str]:
+ 52        """Retrieves a list of task classes associated with a specified transformer class.\n
+ 53        :param class_name: The name of the transformer class to inspect.
+ 54        :param pkg_type: The dependency for the module
+ 55        :param alt_method: Use an alternate method to return the classes
+ 56        :return: A list of task classes associated with the specified transformer."""
+ 57
+ 58        if not code_name:
+ 59            from mir.config.conversion import import_submodules
+ 60
+ 61            class_obj: Callable = import_submodules(class_name, "transformers")
+ 62            class_module: Callable = import_submodules(*class_obj.__module__.split(".", 1)[-1:], class_obj.__module__.split(".", 1)[0])
+ 63            if class_module and class_module.__name__ != "DummyPipe":
+ 64                task_classes = getattr(class_module, "__all__")
+ 65            else:
+ 66                return None
+ 67        elif code_name:
+ 68            from mir.config.constants import generate_supported_model_class_names
+ 69
+ 70            task_classes = generate_supported_model_class_names(code_name)
+ 71        return task_classes
+ 72
+ 73    async def detect_tasks(self, mir_db: MIRDatabase, field_name: str = "pkg") -> dict:
+ 74        """Detects and traces tasks MIR data\n
+ 75        :param mir_db:: An instance of MIRDatabase containing the database of information.
+ 76        :type mir_db: MIRDatabase
+ 77        :param field_name:  The name of the field in compatibility data to process for task detection, defaults to "pkg".
+ 78        :type field_name: str, optional
+ 79        :return: A dictionary mapping series names to their respective compatibility and traced tasks.
+ 80        :rtype: dict"""
+ 81
+ 82        data_tuple = []
+ 83        for series, compatibility_data in mir_db.database.items():
+ 84            if (
+ 85                series.startswith("info.")  # formatting comment
+ 86                and not any(tag for tag in self.skip_series if series.startswith(tag))
+ 87                and not any(tag for tag in self.skip_classes if tag in series)
+ 88            ):
+ 89                for compatibility, field_data in compatibility_data.items():
+ 90                    if field_data and field_data.get(field_name, {}).get("0"):
+ 91                        tasks_for_class = {"tasks": []}
+ 92                        for _, pkg_tree in field_data[field_name].items():
+ 93                            detected_tasks = await self.trace_tasks(pkg_tree=pkg_tree)
+ 94                            if detected_tasks:
+ 95                                for task in detected_tasks:
+ 96                                    if task not in tasks_for_class["tasks"]:
+ 97                                        tasks_for_class["tasks"].append(task)
+ 98                                data_tuple.append((*series.rsplit(".", 1), {compatibility: tasks_for_class}))
+ 99
+100        return data_tuple
+101
+102    async def detect_pipes(self, mir_db: MIRDatabase, field_name: str = "pkg") -> dict:
+103        """Detects and traces Pipes MIR data\n
+104        :param mir_db:: An instance of MIRDatabase containing the database of information.
+105        :type mir_db: MIRDatabase
+106        :param field_name:  The name of the field in compatibility data to process for task detection, defaults to "pkg".
+107        :type field_name: str, optional
+108        :return:A dictionary mapping series names to their respective compatibility and traced tasks.
+109        :rtype: dict"""
+110        from mir.config.conversion import import_submodules
+111
+112        data_tuple = []
+113        for series, compatibility_data in mir_db.database.items():
+114            if (
+115                series.startswith("info.")  # formatting comment
+116                and not any(series.startswith(tag) for tag in self.skip_series)
+117                and not any(tag for tag in self.skip_classes if tag in series)
+118            ):
+119                for compatibility, field_data in compatibility_data.items():
+120                    if field_data and field_data.get(field_name, {}).get("0"):
+121                        for _, pkg_tree in field_data[field_name].items():
+122                            if pkg_tree and next(iter(pkg_tree)) == "diffusers":
+123                                module_name = pkg_tree[next(iter(pkg_tree))]
+124                                dbuq(f"{module_name} pipe originator")
+125                                class_obj = import_submodules(module_name, "diffusers")
+126                                pipe_args = get_type_hints(class_obj.__init__)
+127                                detected_pipe = await self.hyperlink_to_mir(pipe_args, series, mir_db)
+128                                data_tuple.append((*series.rsplit(".", 1), {compatibility: detected_pipe}))
+129
+130        return data_tuple
+131
+132    async def hyperlink_to_mir(self, pipe_args: dict, series: str, mir_db: MIRDatabase):
+133        """Maps pipeline components to MIR tags/IDs based on class names and roles.\n
+134        :param pipe_args: Dictionary of pipeline roles to their corresponding classes
+135        :param mir_db: MIRDatabase instance for querying tags/IDs
+136        :return: Dictionary mapping pipeline roles to associated MIR tags/IDs"""
+137
+138        mir_tag: None | list[str] = None
+139        detected_links: dict[str, dict] = {"pipe_names": dict()}
+140        for pipe_role, pipe_class in pipe_args.items():
+141            if pipe_role in ["tokenizer", "tokenizer_2", "tokenizer_3", "tokenizer_4", "prior_tokenizer"]:
+142                detected_links["pipe_names"].setdefault(pipe_role, ["info.encoder.tokenizer", series.rsplit(".", 1)[-1]])
+143                continue
+144            if not any(segment for segment in self.skip_types if pipe_class.__name__ == segment):
+145                mir_tag = None
+146                detected_links["pipe_names"][pipe_role] = []
+147                dbuq(f"pipe_class.__name__ {pipe_class.__name__} {pipe_class}")
+148                if pipe_class.__name__ in ["Union"]:
+149                    for union_class in pipe_class.__args__:
+150                        mir_tag = None
+151                        class_name = union_class.__name__
+152                        if not any(segment for segment in self.skip_types if class_name == segment):
+153                            mir_tag, class_name = await self.tag_class(pipe_class=union_class, pipe_role=pipe_role, series=series, mir_db=mir_db)
+154                            # mir_tag = mir_db.find_tag(field="tasks", target=class_name)
+155                            # dbuq(f"{mir_tag} {class_name}")
+156                        detected_links["pipe_names"][pipe_role].append(mir_tag if mir_tag else class_name)
+157                else:
+158                    mir_tag, class_name = await self.tag_class(pipe_class=pipe_class, pipe_role=pipe_role, series=series, mir_db=mir_db)
+159                    detected_links["pipe_names"][pipe_role] = mir_tag if mir_tag else [class_name]
+160                    mir_tag = None
+161                    class_name = None
+162        return detected_links
+163
+164    async def tag_class(self, pipe_class: Callable, pipe_role: str, series: str, mir_db: MIRDatabase) -> tuple[str | None]:
+165        """Maps a class to MIR tags/IDs based on its name and role.\n
+166        :param pipe_class: Class to be mapped
+167        :param pipe_role: Role of the class in the pipeline
+168        :param series: Series identifier for the component
+169        :param mir_db: MIRDatabase instance for querying tags/IDs
+170        :return: Tuple containing MIR tag and class name"""
+171
+172        from mir.tag import make_scheduler_tag
+173
+174        mir_tag = None
+175        class_name = pipe_class.__name__
+176        if pipe_role in ["scheduler", "image_noising_scheduler", "prior_scheduler"]:
+177            sub_field = pipe_class.__module__.split(".")[0]
+178            scheduler_series, scheduler_comp = make_scheduler_tag(class_name)
+179            mir_tag = [f"ops.scheduler.{scheduler_series}", scheduler_comp]
+180            if not mir_db.database.get(mir_tag[0], {}).get(mir_tag[1]):
+181                mir_tag = mir_db.find_tag(field="pkg", target=class_name, sub_field=sub_field, domain="ops.scheduler")
+182            dbuq(f"scheduler {mir_tag} {class_name} {sub_field} ")
+183        elif pipe_role == "vae":
+184            sub_field = pipe_class.__module__.split(".")[0]
+185            mir_comp = series.rsplit(".", 1)[-1]
+186            dbuq(mir_comp)
+187            mir_tag = [mir_id for mir_id, comp_data in mir_db.database.items() if "info.vae" in mir_id and next(iter(comp_data)) == mir_comp]
+188            if mir_tag:
+189                mir_tag.append(mir_comp)  # keep mir tag as single list
+190            elif class_name != "AutoencoderKL":
+191                dbuq(pipe_class)
+192                mir_tag = mir_db.find_tag(field="pkg", target=class_name, sub_field=sub_field, domain="info.vae")
+193            dbuq(f"vae {mir_tag} {class_name} {sub_field} ")
+194        else:
+195            mir_tag = mir_db.find_tag(field="tasks", target=class_name)
+196        return mir_tag, class_name
+197
+198    async def trace_tasks(self, pkg_tree: dict[str, str | int | list[str | int]]) -> List[str]:
+199        """Trace tasks for a given MIR entry.\n
+200        :param entry: The object containing the model information.
+201        :return: A sorted list of tasks applicable to the model."""
+202
+203        from mir.inspect.classes import resolve_class_name
+204
+205        preformatted_task_data = None
+206        filtered_tasks = None
+207        snip_words: set[str] = {"load_tf_weights_in"}
+208        package_name = next(iter(pkg_tree))
+209        dbuq(pkg_tree)
+210        class_name = pkg_tree[package_name]
+211        dbuq(f"{package_name}, {class_name}")
+212        if class_name not in self.skip_auto:
+213            if isinstance(class_name, dict):
+214                class_name = next(iter(list(class_name)))
+215            if package_name == "transformers":
+216                preformatted_task_data = self.show_transformers_tasks(class_name=class_name)
+217            elif package_name == "diffusers":
+218                code_name = resolve_class_name(class_name, package_name)
+219                preformatted_task_data = self.show_diffusers_tasks(code_name=code_name, class_name=class_name)
+220                preformatted_task_data.sort()
+221            elif package_name == "mflux":
+222                preformatted_task_data = self.mflux_tasks
+223            if preformatted_task_data:
+224                filtered_tasks = [task for task in preformatted_task_data for snip in snip_words if snip not in task]
+225                return filtered_tasks  # package_name, class_name
+226
+227
+228def trace_classes(pipe_class: str, pkg_name: str) -> Dict[str, List[str]]:
+229    """Retrieve all compatible pipe forms\n
+230    NOTE: Mainly for Diffusers
+231    :param pipe_class: Origin pipe
+232    :param pkg_name: Dependency package
+233    :return: A dictionary of pipelines"""
+234    from mir.inspect.classes import resolve_class_name, extract_inherited
+235    from mir.config.conversion import import_submodules
+236    from mir.inspect.parenting import class_parent
+237
+238    related_pipes = []
+239    code_name = resolve_class_name(pipe_class, pkg_name)
+240    if pkg_name == "diffusers":
+241        related_pipe_class_name = pipe_class
+242    else:
+243        related_pipe_class_name = None
+244    related_pipes: list[str] = TaskAnalyzer.show_diffusers_tasks(code_name=code_name, class_name=related_pipe_class_name)
+245    # for i in range(len(auto_tasks)):
+246    #     auto_tasks.setdefault(i, revealed_tasks[i])
+247    parent_folder = class_parent(code_name, pkg_name)
+248    if pkg_name == "diffusers":
+249        pkg_folder = import_submodules(parent_folder[0], ".".join(parent_folder))
+250    else:
+251        pkg_folder = import_submodules("__init__", ".".join(parent_folder[:-1]))
+252    if hasattr(pkg_folder, "_import_structure"):
+253        related_pipes.extend(next(iter(x)) for x in pkg_folder._import_structure.values())
+254    related_pipes = set(related_pipes)
+255    related_pipes.update(tuple(x) for x in extract_inherited(model_class=pipe_class, pkg_name=pkg_name))
+256    return related_pipes
+
+ + +
+
+ +
+ + def + flatten_map(nested: List[str], unpack: str): + + + +
+ + 
10flatten_map: List[Any] = lambda nested, unpack: [element for iterative in getattr(nested, unpack)() for element in iterative]
+
+ + + + +
+
+ +
+ + class + TaskAnalyzer: + + + +
+ + 
 14class TaskAnalyzer:
+ 15    def __init__(self) -> None:
+ 16        self.skip_series = [
+ 17            "info.lora",
+ 18            "info.vae",
+ 19            "ops.precision",
+ 20            "ops.scheduler",
+ 21            "info.encoder.tokenizer",
+ 22            "info.controlnet",
+ 23        ]
+ 24        self.skip_classes = [".gligen", "imagenet64"]
+ 25        self.skip_auto = ["AutoTokenizer", "AutoModel", "AutoencoderTiny", "AutoencoderKL", "AutoPipelineForImage2Image"]
+ 26        self.skip_types = ["int", "bool", "float", "Optional", "NoneType", "List", "UNet2DConditionModel"]
+ 27        self.mflux_tasks = ["Image", "Redux", "Kontext", "Depth", "Fill", "ConceptAttention", "ControlNet", "CavTon", "IC-Edit"]
+ 28
+ 29    @staticmethod
+ 30    def show_diffusers_tasks(code_name: str, class_name: str | None = None) -> list[str]:
+ 31        """Return Diffusers task pipes based on package-specific query\n
+ 32        :param class_name: To find task pipes from a Diffusers class pipe, defaults to None
+ 33        :param code_name: To find task pipes from a Transformers class pipe, defaults to None
+ 34        :return: A list of alternate class pipelines derived from the specified class"""
+ 35
+ 36        if class_name:
+ 37            from diffusers.pipelines.auto_pipeline import SUPPORTED_TASKS_MAPPINGS, _get_task_class
+ 38
+ 39            alt_tasks = []
+ 40            for task_map in SUPPORTED_TASKS_MAPPINGS:
+ 41                task_class = _get_task_class(task_map, class_name, False)
+ 42                if task_class:
+ 43                    alt_tasks.append(task_class.__name__)
+ 44                    dbuq(task_class)
+ 45                for model_code, pipe_class_obj in task_map.items():
+ 46                    if code_name in model_code:
+ 47                        alt_tasks.append(pipe_class_obj.__name__)
+ 48
+ 49        return alt_tasks
+ 50
+ 51    @staticmethod
+ 52    def show_transformers_tasks(class_name: str | None = None, code_name: str | None = None) -> list[str]:
+ 53        """Retrieves a list of task classes associated with a specified transformer class.\n
+ 54        :param class_name: The name of the transformer class to inspect.
+ 55        :param pkg_type: The dependency for the module
+ 56        :param alt_method: Use an alternate method to return the classes
+ 57        :return: A list of task classes associated with the specified transformer."""
+ 58
+ 59        if not code_name:
+ 60            from mir.config.conversion import import_submodules
+ 61
+ 62            class_obj: Callable = import_submodules(class_name, "transformers")
+ 63            class_module: Callable = import_submodules(*class_obj.__module__.split(".", 1)[-1:], class_obj.__module__.split(".", 1)[0])
+ 64            if class_module and class_module.__name__ != "DummyPipe":
+ 65                task_classes = getattr(class_module, "__all__")
+ 66            else:
+ 67                return None
+ 68        elif code_name:
+ 69            from mir.config.constants import generate_supported_model_class_names
+ 70
+ 71            task_classes = generate_supported_model_class_names(code_name)
+ 72        return task_classes
+ 73
+ 74    async def detect_tasks(self, mir_db: MIRDatabase, field_name: str = "pkg") -> dict:
+ 75        """Detects and traces tasks MIR data\n
+ 76        :param mir_db:: An instance of MIRDatabase containing the database of information.
+ 77        :type mir_db: MIRDatabase
+ 78        :param field_name:  The name of the field in compatibility data to process for task detection, defaults to "pkg".
+ 79        :type field_name: str, optional
+ 80        :return: A dictionary mapping series names to their respective compatibility and traced tasks.
+ 81        :rtype: dict"""
+ 82
+ 83        data_tuple = []
+ 84        for series, compatibility_data in mir_db.database.items():
+ 85            if (
+ 86                series.startswith("info.")  # formatting comment
+ 87                and not any(tag for tag in self.skip_series if series.startswith(tag))
+ 88                and not any(tag for tag in self.skip_classes if tag in series)
+ 89            ):
+ 90                for compatibility, field_data in compatibility_data.items():
+ 91                    if field_data and field_data.get(field_name, {}).get("0"):
+ 92                        tasks_for_class = {"tasks": []}
+ 93                        for _, pkg_tree in field_data[field_name].items():
+ 94                            detected_tasks = await self.trace_tasks(pkg_tree=pkg_tree)
+ 95                            if detected_tasks:
+ 96                                for task in detected_tasks:
+ 97                                    if task not in tasks_for_class["tasks"]:
+ 98                                        tasks_for_class["tasks"].append(task)
+ 99                                data_tuple.append((*series.rsplit(".", 1), {compatibility: tasks_for_class}))
+100
+101        return data_tuple
+102
+103    async def detect_pipes(self, mir_db: MIRDatabase, field_name: str = "pkg") -> dict:
+104        """Detects and traces Pipes MIR data\n
+105        :param mir_db:: An instance of MIRDatabase containing the database of information.
+106        :type mir_db: MIRDatabase
+107        :param field_name:  The name of the field in compatibility data to process for task detection, defaults to "pkg".
+108        :type field_name: str, optional
+109        :return:A dictionary mapping series names to their respective compatibility and traced tasks.
+110        :rtype: dict"""
+111        from mir.config.conversion import import_submodules
+112
+113        data_tuple = []
+114        for series, compatibility_data in mir_db.database.items():
+115            if (
+116                series.startswith("info.")  # formatting comment
+117                and not any(series.startswith(tag) for tag in self.skip_series)
+118                and not any(tag for tag in self.skip_classes if tag in series)
+119            ):
+120                for compatibility, field_data in compatibility_data.items():
+121                    if field_data and field_data.get(field_name, {}).get("0"):
+122                        for _, pkg_tree in field_data[field_name].items():
+123                            if pkg_tree and next(iter(pkg_tree)) == "diffusers":
+124                                module_name = pkg_tree[next(iter(pkg_tree))]
+125                                dbuq(f"{module_name} pipe originator")
+126                                class_obj = import_submodules(module_name, "diffusers")
+127                                pipe_args = get_type_hints(class_obj.__init__)
+128                                detected_pipe = await self.hyperlink_to_mir(pipe_args, series, mir_db)
+129                                data_tuple.append((*series.rsplit(".", 1), {compatibility: detected_pipe}))
+130
+131        return data_tuple
+132
+133    async def hyperlink_to_mir(self, pipe_args: dict, series: str, mir_db: MIRDatabase):
+134        """Maps pipeline components to MIR tags/IDs based on class names and roles.\n
+135        :param pipe_args: Dictionary of pipeline roles to their corresponding classes
+136        :param mir_db: MIRDatabase instance for querying tags/IDs
+137        :return: Dictionary mapping pipeline roles to associated MIR tags/IDs"""
+138
+139        mir_tag: None | list[str] = None
+140        detected_links: dict[str, dict] = {"pipe_names": dict()}
+141        for pipe_role, pipe_class in pipe_args.items():
+142            if pipe_role in ["tokenizer", "tokenizer_2", "tokenizer_3", "tokenizer_4", "prior_tokenizer"]:
+143                detected_links["pipe_names"].setdefault(pipe_role, ["info.encoder.tokenizer", series.rsplit(".", 1)[-1]])
+144                continue
+145            if not any(segment for segment in self.skip_types if pipe_class.__name__ == segment):
+146                mir_tag = None
+147                detected_links["pipe_names"][pipe_role] = []
+148                dbuq(f"pipe_class.__name__ {pipe_class.__name__} {pipe_class}")
+149                if pipe_class.__name__ in ["Union"]:
+150                    for union_class in pipe_class.__args__:
+151                        mir_tag = None
+152                        class_name = union_class.__name__
+153                        if not any(segment for segment in self.skip_types if class_name == segment):
+154                            mir_tag, class_name = await self.tag_class(pipe_class=union_class, pipe_role=pipe_role, series=series, mir_db=mir_db)
+155                            # mir_tag = mir_db.find_tag(field="tasks", target=class_name)
+156                            # dbuq(f"{mir_tag} {class_name}")
+157                        detected_links["pipe_names"][pipe_role].append(mir_tag if mir_tag else class_name)
+158                else:
+159                    mir_tag, class_name = await self.tag_class(pipe_class=pipe_class, pipe_role=pipe_role, series=series, mir_db=mir_db)
+160                    detected_links["pipe_names"][pipe_role] = mir_tag if mir_tag else [class_name]
+161                    mir_tag = None
+162                    class_name = None
+163        return detected_links
+164
+165    async def tag_class(self, pipe_class: Callable, pipe_role: str, series: str, mir_db: MIRDatabase) -> tuple[str | None]:
+166        """Maps a class to MIR tags/IDs based on its name and role.\n
+167        :param pipe_class: Class to be mapped
+168        :param pipe_role: Role of the class in the pipeline
+169        :param series: Series identifier for the component
+170        :param mir_db: MIRDatabase instance for querying tags/IDs
+171        :return: Tuple containing MIR tag and class name"""
+172
+173        from mir.tag import make_scheduler_tag
+174
+175        mir_tag = None
+176        class_name = pipe_class.__name__
+177        if pipe_role in ["scheduler", "image_noising_scheduler", "prior_scheduler"]:
+178            sub_field = pipe_class.__module__.split(".")[0]
+179            scheduler_series, scheduler_comp = make_scheduler_tag(class_name)
+180            mir_tag = [f"ops.scheduler.{scheduler_series}", scheduler_comp]
+181            if not mir_db.database.get(mir_tag[0], {}).get(mir_tag[1]):
+182                mir_tag = mir_db.find_tag(field="pkg", target=class_name, sub_field=sub_field, domain="ops.scheduler")
+183            dbuq(f"scheduler {mir_tag} {class_name} {sub_field} ")
+184        elif pipe_role == "vae":
+185            sub_field = pipe_class.__module__.split(".")[0]
+186            mir_comp = series.rsplit(".", 1)[-1]
+187            dbuq(mir_comp)
+188            mir_tag = [mir_id for mir_id, comp_data in mir_db.database.items() if "info.vae" in mir_id and next(iter(comp_data)) == mir_comp]
+189            if mir_tag:
+190                mir_tag.append(mir_comp)  # keep mir tag as single list
+191            elif class_name != "AutoencoderKL":
+192                dbuq(pipe_class)
+193                mir_tag = mir_db.find_tag(field="pkg", target=class_name, sub_field=sub_field, domain="info.vae")
+194            dbuq(f"vae {mir_tag} {class_name} {sub_field} ")
+195        else:
+196            mir_tag = mir_db.find_tag(field="tasks", target=class_name)
+197        return mir_tag, class_name
+198
+199    async def trace_tasks(self, pkg_tree: dict[str, str | int | list[str | int]]) -> List[str]:
+200        """Trace tasks for a given MIR entry.\n
+201        :param entry: The object containing the model information.
+202        :return: A sorted list of tasks applicable to the model."""
+203
+204        from mir.inspect.classes import resolve_class_name
+205
+206        preformatted_task_data = None
+207        filtered_tasks = None
+208        snip_words: set[str] = {"load_tf_weights_in"}
+209        package_name = next(iter(pkg_tree))
+210        dbuq(pkg_tree)
+211        class_name = pkg_tree[package_name]
+212        dbuq(f"{package_name}, {class_name}")
+213        if class_name not in self.skip_auto:
+214            if isinstance(class_name, dict):
+215                class_name = next(iter(list(class_name)))
+216            if package_name == "transformers":
+217                preformatted_task_data = self.show_transformers_tasks(class_name=class_name)
+218            elif package_name == "diffusers":
+219                code_name = resolve_class_name(class_name, package_name)
+220                preformatted_task_data = self.show_diffusers_tasks(code_name=code_name, class_name=class_name)
+221                preformatted_task_data.sort()
+222            elif package_name == "mflux":
+223                preformatted_task_data = self.mflux_tasks
+224            if preformatted_task_data:
+225                filtered_tasks = [task for task in preformatted_task_data for snip in snip_words if snip not in task]
+226                return filtered_tasks  # package_name, class_name
+
+ + + + +
+
+ skip_series + + +
+ + + + +
+
+
+ skip_classes + + +
+ + + + +
+
+
+ skip_auto + + +
+ + + + +
+
+
+ skip_types + + +
+ + + + +
+
+
+ mflux_tasks + + +
+ + + + +
+
+ +
+
@staticmethod
+ + def + show_diffusers_tasks(code_name: str, class_name: str | None = None) -> list[str]: + + + +
+ + 
29    @staticmethod
+30    def show_diffusers_tasks(code_name: str, class_name: str | None = None) -> list[str]:
+31        """Return Diffusers task pipes based on package-specific query\n
+32        :param class_name: To find task pipes from a Diffusers class pipe, defaults to None
+33        :param code_name: To find task pipes from a Transformers class pipe, defaults to None
+34        :return: A list of alternate class pipelines derived from the specified class"""
+35
+36        if class_name:
+37            from diffusers.pipelines.auto_pipeline import SUPPORTED_TASKS_MAPPINGS, _get_task_class
+38
+39            alt_tasks = []
+40            for task_map in SUPPORTED_TASKS_MAPPINGS:
+41                task_class = _get_task_class(task_map, class_name, False)
+42                if task_class:
+43                    alt_tasks.append(task_class.__name__)
+44                    dbuq(task_class)
+45                for model_code, pipe_class_obj in task_map.items():
+46                    if code_name in model_code:
+47                        alt_tasks.append(pipe_class_obj.__name__)
+48
+49        return alt_tasks
+
+ + +

Return Diffusers task pipes based on package-specific query

+ +
Parameters
+ +
    +
  • class_name: To find task pipes from a Diffusers class pipe, defaults to None
    • +
  • code_name: To find task pipes from a Transformers class pipe, defaults to None
    • +
+ +
Returns
+ +
+

A list of alternate class pipelines derived from the specified class

+
+
+ + +
+
+ +
+
@staticmethod
+ + def + show_transformers_tasks(class_name: str | None = None, code_name: str | None = None) -> list[str]: + + + +
+ + 
51    @staticmethod
+52    def show_transformers_tasks(class_name: str | None = None, code_name: str | None = None) -> list[str]:
+53        """Retrieves a list of task classes associated with a specified transformer class.\n
+54        :param class_name: The name of the transformer class to inspect.
+55        :param pkg_type: The dependency for the module
+56        :param alt_method: Use an alternate method to return the classes
+57        :return: A list of task classes associated with the specified transformer."""
+58
+59        if not code_name:
+60            from mir.config.conversion import import_submodules
+61
+62            class_obj: Callable = import_submodules(class_name, "transformers")
+63            class_module: Callable = import_submodules(*class_obj.__module__.split(".", 1)[-1:], class_obj.__module__.split(".", 1)[0])
+64            if class_module and class_module.__name__ != "DummyPipe":
+65                task_classes = getattr(class_module, "__all__")
+66            else:
+67                return None
+68        elif code_name:
+69            from mir.config.constants import generate_supported_model_class_names
+70
+71            task_classes = generate_supported_model_class_names(code_name)
+72        return task_classes
+
+ + +

Retrieves a list of task classes associated with a specified transformer class.

+ +
Parameters
+ +
    +
  • class_name: The name of the transformer class to inspect.
    • +
  • pkg_type: The dependency for the module
    • +
  • alt_method: Use an alternate method to return the classes
    • +
+ +
Returns
+ +
+

A list of task classes associated with the specified transformer.

+
+
+ + +
+
+ +
+ + async def + detect_tasks(self, mir_db: mir.maid.MIRDatabase, field_name: str = 'pkg') -> dict: + + + +
+ + 
 74    async def detect_tasks(self, mir_db: MIRDatabase, field_name: str = "pkg") -> dict:
+ 75        """Detects and traces tasks MIR data\n
+ 76        :param mir_db:: An instance of MIRDatabase containing the database of information.
+ 77        :type mir_db: MIRDatabase
+ 78        :param field_name:  The name of the field in compatibility data to process for task detection, defaults to "pkg".
+ 79        :type field_name: str, optional
+ 80        :return: A dictionary mapping series names to their respective compatibility and traced tasks.
+ 81        :rtype: dict"""
+ 82
+ 83        data_tuple = []
+ 84        for series, compatibility_data in mir_db.database.items():
+ 85            if (
+ 86                series.startswith("info.")  # formatting comment
+ 87                and not any(tag for tag in self.skip_series if series.startswith(tag))
+ 88                and not any(tag for tag in self.skip_classes if tag in series)
+ 89            ):
+ 90                for compatibility, field_data in compatibility_data.items():
+ 91                    if field_data and field_data.get(field_name, {}).get("0"):
+ 92                        tasks_for_class = {"tasks": []}
+ 93                        for _, pkg_tree in field_data[field_name].items():
+ 94                            detected_tasks = await self.trace_tasks(pkg_tree=pkg_tree)
+ 95                            if detected_tasks:
+ 96                                for task in detected_tasks:
+ 97                                    if task not in tasks_for_class["tasks"]:
+ 98                                        tasks_for_class["tasks"].append(task)
+ 99                                data_tuple.append((*series.rsplit(".", 1), {compatibility: tasks_for_class}))
+100
+101        return data_tuple
+
+ + +

Detects and traces tasks MIR data

+ +
Parameters
+ +
    +
  • mir_db:: An instance of MIRDatabase containing the database of information.
    • +
  • field_name: The name of the field in compatibility data to process for task detection, defaults to "pkg".
    • +
+ +
Returns
+ +
+

A dictionary mapping series names to their respective compatibility and traced tasks.

+
+
+ + +
+
+ +
+ + async def + detect_pipes(self, mir_db: mir.maid.MIRDatabase, field_name: str = 'pkg') -> dict: + + + +
+ + 
103    async def detect_pipes(self, mir_db: MIRDatabase, field_name: str = "pkg") -> dict:
+104        """Detects and traces Pipes MIR data\n
+105        :param mir_db:: An instance of MIRDatabase containing the database of information.
+106        :type mir_db: MIRDatabase
+107        :param field_name:  The name of the field in compatibility data to process for task detection, defaults to "pkg".
+108        :type field_name: str, optional
+109        :return:A dictionary mapping series names to their respective compatibility and traced tasks.
+110        :rtype: dict"""
+111        from mir.config.conversion import import_submodules
+112
+113        data_tuple = []
+114        for series, compatibility_data in mir_db.database.items():
+115            if (
+116                series.startswith("info.")  # formatting comment
+117                and not any(series.startswith(tag) for tag in self.skip_series)
+118                and not any(tag for tag in self.skip_classes if tag in series)
+119            ):
+120                for compatibility, field_data in compatibility_data.items():
+121                    if field_data and field_data.get(field_name, {}).get("0"):
+122                        for _, pkg_tree in field_data[field_name].items():
+123                            if pkg_tree and next(iter(pkg_tree)) == "diffusers":
+124                                module_name = pkg_tree[next(iter(pkg_tree))]
+125                                dbuq(f"{module_name} pipe originator")
+126                                class_obj = import_submodules(module_name, "diffusers")
+127                                pipe_args = get_type_hints(class_obj.__init__)
+128                                detected_pipe = await self.hyperlink_to_mir(pipe_args, series, mir_db)
+129                                data_tuple.append((*series.rsplit(".", 1), {compatibility: detected_pipe}))
+130
+131        return data_tuple
+
+ + +

Detects and traces Pipes MIR data

+ +
Parameters
+ +
    +
  • mir_db:: An instance of MIRDatabase containing the database of information.
    • +
  • field_name: The name of the field in compatibility data to process for task detection, defaults to "pkg".
    • +
+ +
Returns
+ +
+

A dictionary mapping series names to their respective compatibility and traced tasks.

+
+
+ + +
+ +
+ +
+ + async def + tag_class( self, pipe_class: Callable, pipe_role: str, series: str, mir_db: mir.maid.MIRDatabase) -> tuple[str | None]: + + + +
+ + 
165    async def tag_class(self, pipe_class: Callable, pipe_role: str, series: str, mir_db: MIRDatabase) -> tuple[str | None]:
+166        """Maps a class to MIR tags/IDs based on its name and role.\n
+167        :param pipe_class: Class to be mapped
+168        :param pipe_role: Role of the class in the pipeline
+169        :param series: Series identifier for the component
+170        :param mir_db: MIRDatabase instance for querying tags/IDs
+171        :return: Tuple containing MIR tag and class name"""
+172
+173        from mir.tag import make_scheduler_tag
+174
+175        mir_tag = None
+176        class_name = pipe_class.__name__
+177        if pipe_role in ["scheduler", "image_noising_scheduler", "prior_scheduler"]:
+178            sub_field = pipe_class.__module__.split(".")[0]
+179            scheduler_series, scheduler_comp = make_scheduler_tag(class_name)
+180            mir_tag = [f"ops.scheduler.{scheduler_series}", scheduler_comp]
+181            if not mir_db.database.get(mir_tag[0], {}).get(mir_tag[1]):
+182                mir_tag = mir_db.find_tag(field="pkg", target=class_name, sub_field=sub_field, domain="ops.scheduler")
+183            dbuq(f"scheduler {mir_tag} {class_name} {sub_field} ")
+184        elif pipe_role == "vae":
+185            sub_field = pipe_class.__module__.split(".")[0]
+186            mir_comp = series.rsplit(".", 1)[-1]
+187            dbuq(mir_comp)
+188            mir_tag = [mir_id for mir_id, comp_data in mir_db.database.items() if "info.vae" in mir_id and next(iter(comp_data)) == mir_comp]
+189            if mir_tag:
+190                mir_tag.append(mir_comp)  # keep mir tag as single list
+191            elif class_name != "AutoencoderKL":
+192                dbuq(pipe_class)
+193                mir_tag = mir_db.find_tag(field="pkg", target=class_name, sub_field=sub_field, domain="info.vae")
+194            dbuq(f"vae {mir_tag} {class_name} {sub_field} ")
+195        else:
+196            mir_tag = mir_db.find_tag(field="tasks", target=class_name)
+197        return mir_tag, class_name
+
+ + +

Maps a class to MIR tags/IDs based on its name and role.

+ +
Parameters
+ +
    +
  • pipe_class: Class to be mapped
    • +
  • pipe_role: Role of the class in the pipeline
    • +
  • series: Series identifier for the component
    • +
  • mir_db: MIRDatabase instance for querying tags/IDs
    • +
+ +
Returns
+ +
+

Tuple containing MIR tag and class name

+
+
+ + +
+
+ +
+ + async def + trace_tasks(self, pkg_tree: dict[str, str | int | list[str | int]]) -> List[str]: + + + +
+ + 
199    async def trace_tasks(self, pkg_tree: dict[str, str | int | list[str | int]]) -> List[str]:
+200        """Trace tasks for a given MIR entry.\n
+201        :param entry: The object containing the model information.
+202        :return: A sorted list of tasks applicable to the model."""
+203
+204        from mir.inspect.classes import resolve_class_name
+205
+206        preformatted_task_data = None
+207        filtered_tasks = None
+208        snip_words: set[str] = {"load_tf_weights_in"}
+209        package_name = next(iter(pkg_tree))
+210        dbuq(pkg_tree)
+211        class_name = pkg_tree[package_name]
+212        dbuq(f"{package_name}, {class_name}")
+213        if class_name not in self.skip_auto:
+214            if isinstance(class_name, dict):
+215                class_name = next(iter(list(class_name)))
+216            if package_name == "transformers":
+217                preformatted_task_data = self.show_transformers_tasks(class_name=class_name)
+218            elif package_name == "diffusers":
+219                code_name = resolve_class_name(class_name, package_name)
+220                preformatted_task_data = self.show_diffusers_tasks(code_name=code_name, class_name=class_name)
+221                preformatted_task_data.sort()
+222            elif package_name == "mflux":
+223                preformatted_task_data = self.mflux_tasks
+224            if preformatted_task_data:
+225                filtered_tasks = [task for task in preformatted_task_data for snip in snip_words if snip not in task]
+226                return filtered_tasks  # package_name, class_name
+
+ + +

Trace tasks for a given MIR entry.

+ +
Parameters
+ +
    +
  • entry: The object containing the model information.
    • +
+ +
Returns
+ +
+

A sorted list of tasks applicable to the model.

+
+
+ + +
+
+
+ +
+ + def + trace_classes(pipe_class: str, pkg_name: str) -> Dict[str, List[str]]: + + + +
+ + 
229def trace_classes(pipe_class: str, pkg_name: str) -> Dict[str, List[str]]:
+230    """Retrieve all compatible pipe forms\n
+231    NOTE: Mainly for Diffusers
+232    :param pipe_class: Origin pipe
+233    :param pkg_name: Dependency package
+234    :return: A dictionary of pipelines"""
+235    from mir.inspect.classes import resolve_class_name, extract_inherited
+236    from mir.config.conversion import import_submodules
+237    from mir.inspect.parenting import class_parent
+238
+239    related_pipes = []
+240    code_name = resolve_class_name(pipe_class, pkg_name)
+241    if pkg_name == "diffusers":
+242        related_pipe_class_name = pipe_class
+243    else:
+244        related_pipe_class_name = None
+245    related_pipes: list[str] = TaskAnalyzer.show_diffusers_tasks(code_name=code_name, class_name=related_pipe_class_name)
+246    # for i in range(len(auto_tasks)):
+247    #     auto_tasks.setdefault(i, revealed_tasks[i])
+248    parent_folder = class_parent(code_name, pkg_name)
+249    if pkg_name == "diffusers":
+250        pkg_folder = import_submodules(parent_folder[0], ".".join(parent_folder))
+251    else:
+252        pkg_folder = import_submodules("__init__", ".".join(parent_folder[:-1]))
+253    if hasattr(pkg_folder, "_import_structure"):
+254        related_pipes.extend(next(iter(x)) for x in pkg_folder._import_structure.values())
+255    related_pipes = set(related_pipes)
+256    related_pipes.update(tuple(x) for x in extract_inherited(model_class=pipe_class, pkg_name=pkg_name))
+257    return related_pipes
+
+ + +

Retrieve all compatible pipe forms

+ +

NOTE: Mainly for Diffusers

+ +
Parameters
+ +
    +
  • pipe_class: Origin pipe
    • +
  • pkg_name: Dependency package
    • +
+ +
Returns
+ +
+

A dictionary of pipelines

+
+
+ + +
+
+ + \ No newline at end of file diff --git a/docs/mir/maid.html b/docs/mir/maid.html new file mode 100644 index 0000000..40f5d87 --- /dev/null +++ b/docs/mir/maid.html @@ -0,0 +1,1130 @@ + + + + + + + mir.maid API documentation + + + + + + + + + +
+
+

+mir.maid

+ +

神经网络的数据注册

+
+ + + + + + 
  1# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0
+  2# <!-- // /*  d a r k s h a p e s */ -->
+  3
+  4"""神经网络的数据注册"""
+  5
+  6# pylint: disable=possibly-used-before-assignment, line-too-long
+  7import os
+  8from typing import Any, Callable, List, Optional
+  9
+ 10from mir.config.constants import MIR_PATH_NAMED
+ 11from mir.config.json_io import read_json_file, write_json_file
+ 12from mir.config.logging import dbuq
+ 13
+ 14
+ 15class MIRDatabase:
+ 16    """Machine Intelligence Resource Database"""
+ 17
+ 18    def __init__(self, database: dict | None = None) -> None:
+ 19        from json.decoder import JSONDecodeError
+ 20
+ 21        if not database:
+ 22            try:
+ 23                self.database = read_json_file(MIR_PATH_NAMED)
+ 24            except JSONDecodeError as error_log:
+ 25                dbuq(error_log)
+ 26                self.database = {}
+ 27
+ 28    def add(self, resource: dict[str, Any]) -> None:
+ 29        """Merge pre-existing MIR entries, or add new ones
+ 30        :param resource: Entry to apply
+ 31        """
+ 32        parent_key = next(iter(resource))
+ 33        if self.database is not None:
+ 34            if self.database.get(parent_key, 0):
+ 35                self.database[parent_key] = {**self.database[parent_key], **resource[parent_key]}
+ 36            else:
+ 37                self.database[parent_key] = resource[parent_key]
+ 38
+ 39    def write_to_disk(self, data: Optional[dict] = None) -> None:  # pylint:disable=unused-argument
+ 40        """Save data to JSON file\n"""
+ 41
+ 42        from mir.config.logging import nfo
+ 43
+ 44        if not os.path.exists(MIR_PATH_NAMED):
+ 45            mode = "x"
+ 46            if not self.database:
+ 47                self.database = {"expected": "data"}
+ 48        else:
+ 49            mode = "w"
+ 50        # except (FileNotFoundError, OSError) as error_log:
+ 51        #     nfo(f"MIR file not found before write, regenerating... {error_log}")
+ 52
+ 53        write_json_file(os.path.dirname(MIR_PATH_NAMED), file_name="mir.json", data=self.database, mode=mode)
+ 54        written_data = self.read_from_disk()
+ 55        nfo(f"Wrote {len(written_data)} lines to MIR database file.")
+ 56        self.database = written_data
+ 57
+ 58    def read_from_disk(self, data: Optional[dict] = None) -> dict[str, Any]:
+ 59        """Populate mir database\n
+ 60        :param data: mir decorator auto-populated, defaults to None
+ 61        :return: dict of MIR data"""
+ 62        if not os.path.exists(MIR_PATH_NAMED):
+ 63            return {}
+ 64        else:
+ 65            self.database = read_json_file(MIR_PATH_NAMED)
+ 66            return self.database
+ 67
+ 68    def _stage_maybes(self, maybe_match: str, target: str, series: str, compatibility: str) -> List[str]:
+ 69        """Process a single value for matching against the target\n
+ 70        :param value: An unknown string value
+ 71        :param target: The search target
+ 72        :param series: MIR URI domain.arch.series identifier
+ 73        :param compatibility: MIR URI compatibility identifier\n
+ 74        (found value, path, sub-path,boolean for exact match)
+ 75        :return: A list of likely options and their MIR paths"""
+ 76        import re
+ 77
+ 78        from mir.config.constants import SEARCH_SUFFIX
+ 79
+ 80        results = []
+ 81        if isinstance(maybe_match, str):
+ 82            maybe_match = [maybe_match]
+ 83        elif isinstance(maybe_match, dict):
+ 84            if isinstance(next(iter(maybe_match)), int):
+ 85                maybe_match = list(maybe_match.values())
+ 86            else:
+ 87                maybe_match = list(maybe_match.keys())
+ 88        for option in maybe_match:
+ 89            option_lower = re.sub(SEARCH_SUFFIX, "", option.lower())
+ 90            target = re.sub(SEARCH_SUFFIX, "", target.lower())
+ 91            if option_lower:
+ 92                if option_lower:
+ 93                    if option_lower in target:
+ 94                        return [option, series, compatibility, True]
+ 95                    elif target in option_lower:
+ 96                        results.append([option, series, compatibility, False])
+ 97        return results
+ 98
+ 99    @staticmethod
+100    def grade_maybes(matches: List[List[str]], target: str) -> list[str, str]:
+101        """Evaluate and select the best match from a list of potential matches\n
+102        :param matches: Possible matches to compare
+103        :param target: Desired entry to match
+104        :return: The closest matching dictionary elements
+105        """
+106        from decimal import Decimal
+107        from math import isclose
+108
+109        if not matches:
+110            return None
+111        min_gap = float("inf")
+112        best_match = None
+113        for match in matches:
+114            option, series, compatibility, _ = match
+115            option = option.replace("_", "").replace("-", "").replace(".", "").lower()
+116            if target in option or option in target:
+117                max_len = len(os.path.commonprefix([option, target]))
+118                gap = Decimal(str(abs(len(option) - len(target)) + (len(option) - max_len))) * Decimal("0.1")
+119                if gap < min_gap and isclose(gap, 0.9, rel_tol=15e-2):  # 15% variation, 5% error margin, 45% buffer below fail
+120                    min_gap = gap
+121                    best_match = [series, compatibility]
+122        return best_match
+123
+124    def ready_stage(self, maybe_match: str, target: str, series: str, compatibility: str) -> Optional[List[str]]:
+125        """Orchestrate match checking, return for exact matches, and create a queue of potential match
+126        :param maybe_match: The value of the requested search field
+127        :param target: The requested information
+128        :param series: Current MIR domain/arch/series tag
+129        :param compatibility: MIR compatibility tag
+130        :return: A list of exact matches or None
+131        """
+132        if maybe_match:
+133            match_results = self._stage_maybes(maybe_match, target, series, compatibility)
+134            if next(iter(match_results), 0):
+135                if next(iter(match_results))[3]:
+136                    return [series, compatibility]
+137                self.matches.extend(match_results)
+138        return None
+139
+140    def find_tag(self, field: str, target: str, sub_field: Optional[str] = None, domain: None | str = None) -> list[str]:
+141        """Retrieve MIR path based on nested value search\n
+142        :param field: Known field to look within
+143        :param target: Search pattern for field
+144        :param sub_field: A Second field level to investigate into (ex, field pkg, sub_field diffusers)
+145        :return: A list or string of the found tag
+146        :raises KeyError: Target string not found
+147        """
+148        import re
+149
+150        parameters = r"-gguf|-exl2|-exl3|-onnx|-awq|-mlx|-ov"  #
+151        target = target.lower().strip("-")
+152        target = re.sub(parameters, "", target)
+153        self.matches = None
+154        self.matches = []
+155
+156        for series, comp in self.database.items():
+157            if (not domain) or series.startswith(domain):
+158                for compatibility, fields in comp.items():
+159                    if maybe_match := fields.get(field):
+160                        if isinstance(maybe_match, dict) and str(next(iter(maybe_match.keys()), None)).isnumeric():  #  is a dictionary with a number
+161                            for _, sub_maybe in maybe_match.items():
+162                                if result := self.ready_stage(sub_maybe.get(sub_field, list(sub_maybe)), target, series, compatibility):
+163                                    return result
+164                        else:
+165                            if result := self.ready_stage(maybe_match, target, series, compatibility):
+166                                return result
+167
+168        if best_match := self.grade_maybes(self.matches, target):
+169            return best_match
+170        else:
+171            nfo(f"Query '{target}' not found when {len(self.database)}'{field}' options searched\n")
+172            return None
+173
+174
+175def main(mir_db: Callable | None = None, remake: bool = True) -> None:
+176    """Build the database"""
+177    from sys import modules as sys_modules
+178
+179    if __name__ != "__main__" and "pytest" not in sys_modules:  #
+180        import argparse
+181
+182        parser = argparse.ArgumentParser(
+183            formatter_class=argparse.RawTextHelpFormatter,
+184            description="Build a custom MIR model database from the currently installed system environment.\nOffline function.",
+185            usage="mir-maid",
+186            epilog="""Does NOT include results of `mir-task` and `mir-pipe`. These commands should be run separately. Output:
+187            2025-08-03 14:22:47 INFO     ('Wrote 0 lines to MIR database file.',)
+188            2025-08-03 14:22:47 INFO     ('Wrote #### lines to MIR database file.',)""",
+189        )
+190        parser.add_argument(
+191            "-r",
+192            "--remake_off",
+193            action="store_true",
+194            default=False,
+195            help="Prevent erasing and remaking the MIR database file (default: False, always start from a completely empty MIR file)",
+196        )
+197
+198        args = parser.parse_args()
+199        remake = not args.remake_off
+200
+201    from mir.automata import (
+202        add_mir_audio,
+203        add_mir_diffusion,
+204        add_mir_dtype,
+205        add_mir_llm,
+206        add_mir_lora,
+207        add_mir_schedulers,
+208        add_mir_vae,
+209        hf_pkg_to_mir,
+210        mir_update,
+211    )
+212    from mir.config.json_io import write_json_file
+213
+214    if remake:
+215        os.remove(MIR_PATH_NAMED)
+216        folder_path_named = os.path.dirname(MIR_PATH_NAMED)
+217        mode = "x"
+218    else:
+219        mode = "w"
+220    write_json_file(folder_path_named, file_name="mir.json", data={"expected": "data"}, mode=mode)
+221    mir_db = MIRDatabase()
+222    mir_db.database.pop("expected", {})
+223    hf_pkg_to_mir(mir_db)
+224    add_mir_dtype(mir_db)
+225    add_mir_schedulers(mir_db)
+226    add_mir_lora(mir_db)
+227    add_mir_audio(mir_db)
+228    add_mir_diffusion(mir_db)
+229    add_mir_llm(mir_db)
+230    add_mir_vae(mir_db)
+231    mir_update(mir_db)
+232    mir_db.write_to_disk()
+233
+234
+235if __name__ == "__main__":
+236    remake: bool = True
+237    tasks = True
+238    pipes = True
+239
+240    from sys import modules as sys_modules
+241
+242    if "pytest" not in sys_modules:  #
+243        import argparse
+244
+245        from mir.config.logging import nfo
+246
+247        parser = argparse.ArgumentParser(
+248            formatter_class=argparse.RawTextHelpFormatter,
+249            description="Build a custom MIR model database from the currently installed system environment.\nOffline function.",
+250            usage="python -m nnll.mir.maid",
+251            epilog="""Includes `mir-task` and `mir-pipe` by default. Output:
+252            2025-08-15 19:41:18 INFO     ('Wrote 0 lines to MIR database file.',)
+253            2025-08-15 19:38:48 INFO     ('Wrote ### lines to MIR database file.',)
+254                                INFO     ('Wrote ### lines to MIR database file.',)
+255                                INFO     ('Wrote ### lines to MIR database file.',)""",
+256        )
+257        parser.add_argument(
+258            "-r",
+259            "--remake_off",
+260            action="store_true",
+261            default=False,
+262            help="Don't erase and remake the MIR database (default: False)",
+263        )
+264        parser.add_argument(
+265            "-t",
+266            "--tasks_off",
+267            action="store_true",
+268            default=False,
+269            help="Don't append task information to the MIR database (default: False)",
+270        )
+271        parser.add_argument(
+272            "-p",
+273            "--pipes_off",
+274            action="store_true",
+275            default=False,
+276            help="Don't append pipeline information to the MIR database (default: False)",
+277        )
+278
+279        args = parser.parse_args()
+280        remake = not args.remake_off
+281        tasks = not args.tasks_off
+282        pipes = not args.pipes_off
+283
+284    main(remake=remake)
+285    from mir.inspect.tasks import pipe, run_task
+286
+287    mir_db = run_task()
+288    pipe(mir_db)
+
+ + +
+
+ +
+ + class + MIRDatabase: + + + +
+ + 
 16class MIRDatabase:
+ 17    """Machine Intelligence Resource Database"""
+ 18
+ 19    def __init__(self, database: dict | None = None) -> None:
+ 20        from json.decoder import JSONDecodeError
+ 21
+ 22        if not database:
+ 23            try:
+ 24                self.database = read_json_file(MIR_PATH_NAMED)
+ 25            except JSONDecodeError as error_log:
+ 26                dbuq(error_log)
+ 27                self.database = {}
+ 28
+ 29    def add(self, resource: dict[str, Any]) -> None:
+ 30        """Merge pre-existing MIR entries, or add new ones
+ 31        :param resource: Entry to apply
+ 32        """
+ 33        parent_key = next(iter(resource))
+ 34        if self.database is not None:
+ 35            if self.database.get(parent_key, 0):
+ 36                self.database[parent_key] = {**self.database[parent_key], **resource[parent_key]}
+ 37            else:
+ 38                self.database[parent_key] = resource[parent_key]
+ 39
+ 40    def write_to_disk(self, data: Optional[dict] = None) -> None:  # pylint:disable=unused-argument
+ 41        """Save data to JSON file\n"""
+ 42
+ 43        from mir.config.logging import nfo
+ 44
+ 45        if not os.path.exists(MIR_PATH_NAMED):
+ 46            mode = "x"
+ 47            if not self.database:
+ 48                self.database = {"expected": "data"}
+ 49        else:
+ 50            mode = "w"
+ 51        # except (FileNotFoundError, OSError) as error_log:
+ 52        #     nfo(f"MIR file not found before write, regenerating... {error_log}")
+ 53
+ 54        write_json_file(os.path.dirname(MIR_PATH_NAMED), file_name="mir.json", data=self.database, mode=mode)
+ 55        written_data = self.read_from_disk()
+ 56        nfo(f"Wrote {len(written_data)} lines to MIR database file.")
+ 57        self.database = written_data
+ 58
+ 59    def read_from_disk(self, data: Optional[dict] = None) -> dict[str, Any]:
+ 60        """Populate mir database\n
+ 61        :param data: mir decorator auto-populated, defaults to None
+ 62        :return: dict of MIR data"""
+ 63        if not os.path.exists(MIR_PATH_NAMED):
+ 64            return {}
+ 65        else:
+ 66            self.database = read_json_file(MIR_PATH_NAMED)
+ 67            return self.database
+ 68
+ 69    def _stage_maybes(self, maybe_match: str, target: str, series: str, compatibility: str) -> List[str]:
+ 70        """Process a single value for matching against the target\n
+ 71        :param value: An unknown string value
+ 72        :param target: The search target
+ 73        :param series: MIR URI domain.arch.series identifier
+ 74        :param compatibility: MIR URI compatibility identifier\n
+ 75        (found value, path, sub-path,boolean for exact match)
+ 76        :return: A list of likely options and their MIR paths"""
+ 77        import re
+ 78
+ 79        from mir.config.constants import SEARCH_SUFFIX
+ 80
+ 81        results = []
+ 82        if isinstance(maybe_match, str):
+ 83            maybe_match = [maybe_match]
+ 84        elif isinstance(maybe_match, dict):
+ 85            if isinstance(next(iter(maybe_match)), int):
+ 86                maybe_match = list(maybe_match.values())
+ 87            else:
+ 88                maybe_match = list(maybe_match.keys())
+ 89        for option in maybe_match:
+ 90            option_lower = re.sub(SEARCH_SUFFIX, "", option.lower())
+ 91            target = re.sub(SEARCH_SUFFIX, "", target.lower())
+ 92            if option_lower:
+ 93                if option_lower:
+ 94                    if option_lower in target:
+ 95                        return [option, series, compatibility, True]
+ 96                    elif target in option_lower:
+ 97                        results.append([option, series, compatibility, False])
+ 98        return results
+ 99
+100    @staticmethod
+101    def grade_maybes(matches: List[List[str]], target: str) -> list[str, str]:
+102        """Evaluate and select the best match from a list of potential matches\n
+103        :param matches: Possible matches to compare
+104        :param target: Desired entry to match
+105        :return: The closest matching dictionary elements
+106        """
+107        from decimal import Decimal
+108        from math import isclose
+109
+110        if not matches:
+111            return None
+112        min_gap = float("inf")
+113        best_match = None
+114        for match in matches:
+115            option, series, compatibility, _ = match
+116            option = option.replace("_", "").replace("-", "").replace(".", "").lower()
+117            if target in option or option in target:
+118                max_len = len(os.path.commonprefix([option, target]))
+119                gap = Decimal(str(abs(len(option) - len(target)) + (len(option) - max_len))) * Decimal("0.1")
+120                if gap < min_gap and isclose(gap, 0.9, rel_tol=15e-2):  # 15% variation, 5% error margin, 45% buffer below fail
+121                    min_gap = gap
+122                    best_match = [series, compatibility]
+123        return best_match
+124
+125    def ready_stage(self, maybe_match: str, target: str, series: str, compatibility: str) -> Optional[List[str]]:
+126        """Orchestrate match checking, return for exact matches, and create a queue of potential match
+127        :param maybe_match: The value of the requested search field
+128        :param target: The requested information
+129        :param series: Current MIR domain/arch/series tag
+130        :param compatibility: MIR compatibility tag
+131        :return: A list of exact matches or None
+132        """
+133        if maybe_match:
+134            match_results = self._stage_maybes(maybe_match, target, series, compatibility)
+135            if next(iter(match_results), 0):
+136                if next(iter(match_results))[3]:
+137                    return [series, compatibility]
+138                self.matches.extend(match_results)
+139        return None
+140
+141    def find_tag(self, field: str, target: str, sub_field: Optional[str] = None, domain: None | str = None) -> list[str]:
+142        """Retrieve MIR path based on nested value search\n
+143        :param field: Known field to look within
+144        :param target: Search pattern for field
+145        :param sub_field: A Second field level to investigate into (ex, field pkg, sub_field diffusers)
+146        :return: A list or string of the found tag
+147        :raises KeyError: Target string not found
+148        """
+149        import re
+150
+151        parameters = r"-gguf|-exl2|-exl3|-onnx|-awq|-mlx|-ov"  #
+152        target = target.lower().strip("-")
+153        target = re.sub(parameters, "", target)
+154        self.matches = None
+155        self.matches = []
+156
+157        for series, comp in self.database.items():
+158            if (not domain) or series.startswith(domain):
+159                for compatibility, fields in comp.items():
+160                    if maybe_match := fields.get(field):
+161                        if isinstance(maybe_match, dict) and str(next(iter(maybe_match.keys()), None)).isnumeric():  #  is a dictionary with a number
+162                            for _, sub_maybe in maybe_match.items():
+163                                if result := self.ready_stage(sub_maybe.get(sub_field, list(sub_maybe)), target, series, compatibility):
+164                                    return result
+165                        else:
+166                            if result := self.ready_stage(maybe_match, target, series, compatibility):
+167                                return result
+168
+169        if best_match := self.grade_maybes(self.matches, target):
+170            return best_match
+171        else:
+172            nfo(f"Query '{target}' not found when {len(self.database)}'{field}' options searched\n")
+173            return None
+
+ + +

Machine Intelligence Resource Database

+
+ + +
+ +
+ + MIRDatabase(database: dict | None = None) + + + +
+ + 
19    def __init__(self, database: dict | None = None) -> None:
+20        from json.decoder import JSONDecodeError
+21
+22        if not database:
+23            try:
+24                self.database = read_json_file(MIR_PATH_NAMED)
+25            except JSONDecodeError as error_log:
+26                dbuq(error_log)
+27                self.database = {}
+
+ + + + +
+
+ +
+ + def + add(self, resource: dict[str, typing.Any]) -> None: + + + +
+ + 
29    def add(self, resource: dict[str, Any]) -> None:
+30        """Merge pre-existing MIR entries, or add new ones
+31        :param resource: Entry to apply
+32        """
+33        parent_key = next(iter(resource))
+34        if self.database is not None:
+35            if self.database.get(parent_key, 0):
+36                self.database[parent_key] = {**self.database[parent_key], **resource[parent_key]}
+37            else:
+38                self.database[parent_key] = resource[parent_key]
+
+ + +

Merge pre-existing MIR entries, or add new ones

+ +
Parameters
+ +
    +
  • resource: Entry to apply
    • +
+
+ + +
+
+ +
+ + def + write_to_disk(self, data: Optional[dict] = None) -> None: + + + +
+ + 
40    def write_to_disk(self, data: Optional[dict] = None) -> None:  # pylint:disable=unused-argument
+41        """Save data to JSON file\n"""
+42
+43        from mir.config.logging import nfo
+44
+45        if not os.path.exists(MIR_PATH_NAMED):
+46            mode = "x"
+47            if not self.database:
+48                self.database = {"expected": "data"}
+49        else:
+50            mode = "w"
+51        # except (FileNotFoundError, OSError) as error_log:
+52        #     nfo(f"MIR file not found before write, regenerating... {error_log}")
+53
+54        write_json_file(os.path.dirname(MIR_PATH_NAMED), file_name="mir.json", data=self.database, mode=mode)
+55        written_data = self.read_from_disk()
+56        nfo(f"Wrote {len(written_data)} lines to MIR database file.")
+57        self.database = written_data
+
+ + +

Save data to JSON file

+
+ + +
+
+ +
+ + def + read_from_disk(self, data: Optional[dict] = None) -> dict[str, typing.Any]: + + + +
+ + 
59    def read_from_disk(self, data: Optional[dict] = None) -> dict[str, Any]:
+60        """Populate mir database\n
+61        :param data: mir decorator auto-populated, defaults to None
+62        :return: dict of MIR data"""
+63        if not os.path.exists(MIR_PATH_NAMED):
+64            return {}
+65        else:
+66            self.database = read_json_file(MIR_PATH_NAMED)
+67            return self.database
+
+ + +

Populate mir database

+ +
Parameters
+ +
    +
  • data: mir decorator auto-populated, defaults to None
    • +
+ +
Returns
+ +
+

dict of MIR data

+
+
+ + +
+
+ +
+
@staticmethod
+ + def + grade_maybes(matches: List[List[str]], target: str) -> list[str, str]: + + + +
+ + 
100    @staticmethod
+101    def grade_maybes(matches: List[List[str]], target: str) -> list[str, str]:
+102        """Evaluate and select the best match from a list of potential matches\n
+103        :param matches: Possible matches to compare
+104        :param target: Desired entry to match
+105        :return: The closest matching dictionary elements
+106        """
+107        from decimal import Decimal
+108        from math import isclose
+109
+110        if not matches:
+111            return None
+112        min_gap = float("inf")
+113        best_match = None
+114        for match in matches:
+115            option, series, compatibility, _ = match
+116            option = option.replace("_", "").replace("-", "").replace(".", "").lower()
+117            if target in option or option in target:
+118                max_len = len(os.path.commonprefix([option, target]))
+119                gap = Decimal(str(abs(len(option) - len(target)) + (len(option) - max_len))) * Decimal("0.1")
+120                if gap < min_gap and isclose(gap, 0.9, rel_tol=15e-2):  # 15% variation, 5% error margin, 45% buffer below fail
+121                    min_gap = gap
+122                    best_match = [series, compatibility]
+123        return best_match
+
+ + +

Evaluate and select the best match from a list of potential matches

+ +
Parameters
+ +
    +
  • matches: Possible matches to compare
    • +
  • target: Desired entry to match
    • +
+ +
Returns
+ +
+

The closest matching dictionary elements

+
+
+ + +
+
+ +
+ + def + ready_stage( self, maybe_match: str, target: str, series: str, compatibility: str) -> Optional[List[str]]: + + + +
+ + 
125    def ready_stage(self, maybe_match: str, target: str, series: str, compatibility: str) -> Optional[List[str]]:
+126        """Orchestrate match checking, return for exact matches, and create a queue of potential match
+127        :param maybe_match: The value of the requested search field
+128        :param target: The requested information
+129        :param series: Current MIR domain/arch/series tag
+130        :param compatibility: MIR compatibility tag
+131        :return: A list of exact matches or None
+132        """
+133        if maybe_match:
+134            match_results = self._stage_maybes(maybe_match, target, series, compatibility)
+135            if next(iter(match_results), 0):
+136                if next(iter(match_results))[3]:
+137                    return [series, compatibility]
+138                self.matches.extend(match_results)
+139        return None
+
+ + +

Orchestrate match checking, return for exact matches, and create a queue of potential match

+ +
Parameters
+ +
    +
  • maybe_match: The value of the requested search field
    • +
  • target: The requested information
    • +
  • series: Current MIR domain/arch/series tag
    • +
  • compatibility: MIR compatibility tag
    • +
+ +
Returns
+ +
+

A list of exact matches or None

+
+
+ + +
+
+ +
+ + def + find_tag( self, field: str, target: str, sub_field: Optional[str] = None, domain: None | str = None) -> list[str]: + + + +
+ + 
141    def find_tag(self, field: str, target: str, sub_field: Optional[str] = None, domain: None | str = None) -> list[str]:
+142        """Retrieve MIR path based on nested value search\n
+143        :param field: Known field to look within
+144        :param target: Search pattern for field
+145        :param sub_field: A Second field level to investigate into (ex, field pkg, sub_field diffusers)
+146        :return: A list or string of the found tag
+147        :raises KeyError: Target string not found
+148        """
+149        import re
+150
+151        parameters = r"-gguf|-exl2|-exl3|-onnx|-awq|-mlx|-ov"  #
+152        target = target.lower().strip("-")
+153        target = re.sub(parameters, "", target)
+154        self.matches = None
+155        self.matches = []
+156
+157        for series, comp in self.database.items():
+158            if (not domain) or series.startswith(domain):
+159                for compatibility, fields in comp.items():
+160                    if maybe_match := fields.get(field):
+161                        if isinstance(maybe_match, dict) and str(next(iter(maybe_match.keys()), None)).isnumeric():  #  is a dictionary with a number
+162                            for _, sub_maybe in maybe_match.items():
+163                                if result := self.ready_stage(sub_maybe.get(sub_field, list(sub_maybe)), target, series, compatibility):
+164                                    return result
+165                        else:
+166                            if result := self.ready_stage(maybe_match, target, series, compatibility):
+167                                return result
+168
+169        if best_match := self.grade_maybes(self.matches, target):
+170            return best_match
+171        else:
+172            nfo(f"Query '{target}' not found when {len(self.database)}'{field}' options searched\n")
+173            return None
+
+ + +

Retrieve MIR path based on nested value search

+ +
Parameters
+ +
    +
  • field: Known field to look within
    • +
  • target: Search pattern for field
    • +
  • sub_field: A Second field level to investigate into (ex, field pkg, sub_field diffusers)
    • +
+ +
Returns
+ +
+

A list or string of the found tag

+
+ +
Raises
+ +
    +
  • KeyError: Target string not found
    • +
+
+ + +
+
+
+ +
+ + def + main(mir_db: Optional[Callable] = None, remake: bool = True) -> None: + + + +
+ + 
176def main(mir_db: Callable | None = None, remake: bool = True) -> None:
+177    """Build the database"""
+178    from sys import modules as sys_modules
+179
+180    if __name__ != "__main__" and "pytest" not in sys_modules:  #
+181        import argparse
+182
+183        parser = argparse.ArgumentParser(
+184            formatter_class=argparse.RawTextHelpFormatter,
+185            description="Build a custom MIR model database from the currently installed system environment.\nOffline function.",
+186            usage="mir-maid",
+187            epilog="""Does NOT include results of `mir-task` and `mir-pipe`. These commands should be run separately. Output:
+188            2025-08-03 14:22:47 INFO     ('Wrote 0 lines to MIR database file.',)
+189            2025-08-03 14:22:47 INFO     ('Wrote #### lines to MIR database file.',)""",
+190        )
+191        parser.add_argument(
+192            "-r",
+193            "--remake_off",
+194            action="store_true",
+195            default=False,
+196            help="Prevent erasing and remaking the MIR database file (default: False, always start from a completely empty MIR file)",
+197        )
+198
+199        args = parser.parse_args()
+200        remake = not args.remake_off
+201
+202    from mir.automata import (
+203        add_mir_audio,
+204        add_mir_diffusion,
+205        add_mir_dtype,
+206        add_mir_llm,
+207        add_mir_lora,
+208        add_mir_schedulers,
+209        add_mir_vae,
+210        hf_pkg_to_mir,
+211        mir_update,
+212    )
+213    from mir.config.json_io import write_json_file
+214
+215    if remake:
+216        os.remove(MIR_PATH_NAMED)
+217        folder_path_named = os.path.dirname(MIR_PATH_NAMED)
+218        mode = "x"
+219    else:
+220        mode = "w"
+221    write_json_file(folder_path_named, file_name="mir.json", data={"expected": "data"}, mode=mode)
+222    mir_db = MIRDatabase()
+223    mir_db.database.pop("expected", {})
+224    hf_pkg_to_mir(mir_db)
+225    add_mir_dtype(mir_db)
+226    add_mir_schedulers(mir_db)
+227    add_mir_lora(mir_db)
+228    add_mir_audio(mir_db)
+229    add_mir_diffusion(mir_db)
+230    add_mir_llm(mir_db)
+231    add_mir_vae(mir_db)
+232    mir_update(mir_db)
+233    mir_db.write_to_disk()
+
+ + +

Build the database

+
+ + +
+
+ + \ No newline at end of file diff --git a/docs/mir/run.html b/docs/mir/run.html new file mode 100644 index 0000000..a56a87c --- /dev/null +++ b/docs/mir/run.html @@ -0,0 +1,423 @@ + + + + + + + mir.run API documentation + + + + + + + + + +
+
+

+mir.run

+ + + + + + + 
 1#  # # <!-- // /*  SPDX-License-Identifier: MPL-2.0*/ -->
+ 2#  # # <!-- // /*  d a r k s h a p e s */ -->
+ 3
+ 4
+ 5from mir.maid import MIRDatabase
+ 6from mir.inspect.tasks import TaskAnalyzer
+ 7
+ 8
+ 9def main(mir_db: MIRDatabase = None):
+10    """Parse arguments to feed to dict header reader"""
+11    import argparse
+12    import asyncio
+13    from mir.automata import assimilate
+14    from sys import modules as sys_modules
+15
+16    if "pytest" not in sys_modules:
+17        parser = argparse.ArgumentParser(
+18            formatter_class=argparse.RawTextHelpFormatter,
+19            description="Scrape the task classes from currently installed libraries and attach them to an existing MIR database.\nOffline function.",
+20            usage="mir-tasks",
+21            epilog="Can be run automatically with `python -m nnll.mir.maid` Should only be used after `mir-maid`.\n\nOutput:\n    INFO     ('Wrote #### lines to MIR database file.',)",
+22        )
+23        parser.parse_args()
+24
+25    if not mir_db:
+26        mir_db = MIRDatabase()
+27
+28    tasker = TaskAnalyzer()
+29    task_tuple = asyncio.run(tasker.detect_tasks(mir_db))
+30
+31    assimilate(mir_db, [task for task in task_tuple])
+32
+33    mir_db.write_to_disk()
+34    return mir_db
+35
+36
+37def run_task():
+38    main()
+39
+40
+41def pipe(mir_db: MIRDatabase = None):
+42    import argparse
+43    import asyncio
+44    from sys import modules as sys_modules
+45
+46    if "pytest" not in sys_modules:
+47        parser = argparse.ArgumentParser(
+48            formatter_class=argparse.RawTextHelpFormatter,
+49            description="Infer pipe components from Diffusers library and attach them to an existing MIR database.\nOffline function.",
+50            usage="mir-pipe",
+51            epilog="Can be run automatically with `python -m nnll.mir.maid` Should only be used after `mir-maid`.\n\nOutput:\n    INFO     ('Wrote #### lines to MIR database file.',)",
+52        )
+53        parser.parse_args()
+54
+55    from mir.automata import assimilate
+56
+57    if not mir_db:
+58        mir_db = MIRDatabase()
+59
+60    tasker = TaskAnalyzer()
+61    pipe_tuple = asyncio.run(tasker.detect_pipes(mir_db))
+62    assimilate(mir_db, [pipe for pipe in pipe_tuple])
+63    mir_db.write_to_disk()
+64    return mir_db
+65
+66
+67# if __name__ == "__main__":
+68#     pipe()
+
+ + +
+
+ +
+ + def + main(mir_db: mir.maid.MIRDatabase = None): + + + +
+ + 
10def main(mir_db: MIRDatabase = None):
+11    """Parse arguments to feed to dict header reader"""
+12    import argparse
+13    import asyncio
+14    from mir.automata import assimilate
+15    from sys import modules as sys_modules
+16
+17    if "pytest" not in sys_modules:
+18        parser = argparse.ArgumentParser(
+19            formatter_class=argparse.RawTextHelpFormatter,
+20            description="Scrape the task classes from currently installed libraries and attach them to an existing MIR database.\nOffline function.",
+21            usage="mir-tasks",
+22            epilog="Can be run automatically with `python -m nnll.mir.maid` Should only be used after `mir-maid`.\n\nOutput:\n    INFO     ('Wrote #### lines to MIR database file.',)",
+23        )
+24        parser.parse_args()
+25
+26    if not mir_db:
+27        mir_db = MIRDatabase()
+28
+29    tasker = TaskAnalyzer()
+30    task_tuple = asyncio.run(tasker.detect_tasks(mir_db))
+31
+32    assimilate(mir_db, [task for task in task_tuple])
+33
+34    mir_db.write_to_disk()
+35    return mir_db
+
+ + +

Parse arguments to feed to dict header reader

+
+ + +
+
+ +
+ + def + run_task(): + + + +
+ + 
38def run_task():
+39    main()
+
+ + + + +
+
+ +
+ + def + pipe(mir_db: mir.maid.MIRDatabase = None): + + + +
+ + 
42def pipe(mir_db: MIRDatabase = None):
+43    import argparse
+44    import asyncio
+45    from sys import modules as sys_modules
+46
+47    if "pytest" not in sys_modules:
+48        parser = argparse.ArgumentParser(
+49            formatter_class=argparse.RawTextHelpFormatter,
+50            description="Infer pipe components from Diffusers library and attach them to an existing MIR database.\nOffline function.",
+51            usage="mir-pipe",
+52            epilog="Can be run automatically with `python -m nnll.mir.maid` Should only be used after `mir-maid`.\n\nOutput:\n    INFO     ('Wrote #### lines to MIR database file.',)",
+53        )
+54        parser.parse_args()
+55
+56    from mir.automata import assimilate
+57
+58    if not mir_db:
+59        mir_db = MIRDatabase()
+60
+61    tasker = TaskAnalyzer()
+62    pipe_tuple = asyncio.run(tasker.detect_pipes(mir_db))
+63    assimilate(mir_db, [pipe for pipe in pipe_tuple])
+64    mir_db.write_to_disk()
+65    return mir_db
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/docs/mir/tag.html b/docs/mir/tag.html new file mode 100644 index 0000000..28e10a5 --- /dev/null +++ b/docs/mir/tag.html @@ -0,0 +1,582 @@ + + + + + + + mir.tag API documentation + + + + + + + + + +
+
+

+mir.tag

+ + + + + + + 
  1# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0
+  2# <!-- // /*  d a r k s h a p e s */ -->
+  3
+  4from typing import List
+  5from mir.config.constants import PARAMETERS_SUFFIX, BREAKING_SUFFIX
+  6
+  7
+  8def make_mir_tag(repo_title: str, decoder=False, data: dict = None) -> List[str]:
+  9    """Create a mir label from a repo path\n
+ 10    :param mir_prefix: Known period-separated prefix and model type
+ 11    :param repo_path: Typical remote source repo path, A URL without domain
+ 12    :return: The assembled mir tag with compatibility pre-separated"""
+ 13    import re
+ 14
+ 15    # print(repo_title)
+ 16
+ 17    root = "decoder" if decoder else "*"
+ 18    repo_title = repo_title.split(":latest")[0]
+ 19    repo_title = repo_title.split(":Q")[0]
+ 20    repo_title = repo_title.split(r"/")[-1].lower()
+ 21    pattern = r"^.*[v]?(\d{1}+\.\d).*"
+ 22    match = re.findall(pattern, repo_title)
+ 23    if match:
+ 24        if next(iter(match)):
+ 25            repo_title = repo_title.replace(next(iter(match))[-1], "")
+ 26    parts = repo_title.replace(".", "").split("-")
+ 27    if len(parts) == 1:
+ 28        parts = repo_title.split("_")
+ 29    subtraction_prefixes = r"\d.b-|\-rl|tiny|large|mlx|onnx|gguf|medium|base|multimodal|mini|instruct|full|:latest|preview|small|pro|beta|hybrid|plus|dpo|community"
+ 30
+ 31    pattern_2 = re.compile(PARAMETERS_SUFFIX)
+ 32    clean_parts = [re.sub(pattern_2, "", segment.lower()) for segment in parts]
+ 33    cleaned_string = "-".join([x for x in clean_parts if x])
+ 34    cleaned_string = re.sub(subtraction_prefixes, "", cleaned_string)
+ 35    cleaned_string = re.sub("-it", "", cleaned_string.replace("-bit", "")).replace("--", "-")
+ 36    cleaned_string = cleaned_string.replace("-b-", "")
+ 37    # print(cleaned_string)
+ 38    suffix_match = re.findall(BREAKING_SUFFIX, cleaned_string)  # Check for breaking suffixes first
+ 39    if suffix_match:
+ 40        suffix = next(iter(suffix for suffix in suffix_match[0] if suffix))
+ 41        cleaned_string = re.sub(suffix.lower(), "-", cleaned_string).rstrip("-,")
+ 42    else:
+ 43        suffix = root
+ 44    cleaned_string = re.sub(r"[._]+", "-", cleaned_string.lower()).strip("-_")
+ 45    return (cleaned_string, suffix)
+ 46
+ 47
+ 48def make_scheduler_tag(series_name: str) -> tuple[str]:
+ 49    """Create a mir label from a scheduler operation\n
+ 50    :param class_name: Known period-separated prefix and model type
+ 51    :return: The assembled mir tag with compatibility pre-separated"""
+ 52
+ 53    import re
+ 54
+ 55    comp_name = None
+ 56    patterns = [r"Schedulers", r"Multistep", r"Solver", r"Discrete", r"Scheduler"]
+ 57    for scheduler in patterns:
+ 58        compiled = re.compile(scheduler)
+ 59        match = re.search(compiled, series_name)
+ 60        if match:
+ 61            comp_name = match.group()
+ 62            comp_name = comp_name.lower()
+ 63            break
+ 64    for pattern in patterns:
+ 65        series_name = re.sub(pattern, "", series_name)
+ 66    series_name.lower()
+ 67    # if not comp_name:
+ 68    #     comp_name = "*"
+ 69    return series_name, comp_name
+ 70
+ 71
+ 72def tag_base_model(repo_path: str, class_name: str, addendum: dict | None = None) -> tuple[str]:
+ 73    """Convert model repo paths to MIR tags, classifying by feature\n
+ 74    :param name: Repo path
+ 75    :param class_name: The HF transformers class for the model
+ 76    :return: A segmented MIR tag useful for appending index entries"""
+ 77
+ 78    from mir.inspect.classes import extract_init_params
+ 79    from mir.indexers import flag_config
+ 80
+ 81    annotations = extract_init_params(class_name.replace("Model", "Config"), "transformers")
+ 82    mir_prefix = flag_config(transformers=True, **annotations)
+ 83    base_series, base_comp = make_mir_tag(repo_path)
+ 84    if not addendum:
+ 85        return mir_prefix, base_series, base_comp
+ 86    else:
+ 87        mir_prefix = f"info.{mir_prefix}"
+ 88    return mir_prefix, base_series, {base_comp: addendum}
+ 89
+ 90
+ 91def tag_pipe(repo_path: str, class_name: str, addendum: dict) -> tuple:
+ 92    """Convert model repo pipes to MIR tags, classifying by feature\n
+ 93    :param name: Repo path
+ 94    :param class_name: The HF Diffusers class for the model
+ 95    :return: A segmented MIR tag useful for appending index entries"""
+ 96
+ 97    from mir.indexers import create_pipe_entry
+ 98
+ 99    mir_series, mir_data = create_pipe_entry(repo_path=repo_path, class_name=class_name)
+100    mir_prefix, mir_series = mir_series.rsplit(".", 1)
+101    mir_comp = list(mir_data)[0]
+102    return mir_prefix, mir_series, {mir_comp: addendum}
+103
+104
+105# def tag_mlx_model(repo_path: str, class_name: str, addendum: dict) -> tuple[str]:
+106#     dev_series, dev_comp = make_mir_tag("black-forest-labs/FLUX.1-dev")
+107#     schnell_series, schnell_comp = make_mir_tag("black-forest-labs/FLUX.1-schnell")
+108#     series, comp = make_mir_tag(repo_path)
+109#     if class_name == "Flux1":
+110#         mir_prefix = "info.dit"
+111#         base_series = dev_series
+112#         mir_comp = series
+113#         return mir_prefix, base_series, {base_comp: addendum}
+
+ + +
+
+ +
+ + def + make_mir_tag(repo_title: str, decoder=False, data: dict = None) -> List[str]: + + + +
+ + 
 9def make_mir_tag(repo_title: str, decoder=False, data: dict = None) -> List[str]:
+10    """Create a mir label from a repo path\n
+11    :param mir_prefix: Known period-separated prefix and model type
+12    :param repo_path: Typical remote source repo path, A URL without domain
+13    :return: The assembled mir tag with compatibility pre-separated"""
+14    import re
+15
+16    # print(repo_title)
+17
+18    root = "decoder" if decoder else "*"
+19    repo_title = repo_title.split(":latest")[0]
+20    repo_title = repo_title.split(":Q")[0]
+21    repo_title = repo_title.split(r"/")[-1].lower()
+22    pattern = r"^.*[v]?(\d{1}+\.\d).*"
+23    match = re.findall(pattern, repo_title)
+24    if match:
+25        if next(iter(match)):
+26            repo_title = repo_title.replace(next(iter(match))[-1], "")
+27    parts = repo_title.replace(".", "").split("-")
+28    if len(parts) == 1:
+29        parts = repo_title.split("_")
+30    subtraction_prefixes = r"\d.b-|\-rl|tiny|large|mlx|onnx|gguf|medium|base|multimodal|mini|instruct|full|:latest|preview|small|pro|beta|hybrid|plus|dpo|community"
+31
+32    pattern_2 = re.compile(PARAMETERS_SUFFIX)
+33    clean_parts = [re.sub(pattern_2, "", segment.lower()) for segment in parts]
+34    cleaned_string = "-".join([x for x in clean_parts if x])
+35    cleaned_string = re.sub(subtraction_prefixes, "", cleaned_string)
+36    cleaned_string = re.sub("-it", "", cleaned_string.replace("-bit", "")).replace("--", "-")
+37    cleaned_string = cleaned_string.replace("-b-", "")
+38    # print(cleaned_string)
+39    suffix_match = re.findall(BREAKING_SUFFIX, cleaned_string)  # Check for breaking suffixes first
+40    if suffix_match:
+41        suffix = next(iter(suffix for suffix in suffix_match[0] if suffix))
+42        cleaned_string = re.sub(suffix.lower(), "-", cleaned_string).rstrip("-,")
+43    else:
+44        suffix = root
+45    cleaned_string = re.sub(r"[._]+", "-", cleaned_string.lower()).strip("-_")
+46    return (cleaned_string, suffix)
+
+ + +

Create a mir label from a repo path

+ +
Parameters
+ +
    +
  • mir_prefix: Known period-separated prefix and model type
    • +
  • repo_path: Typical remote source repo path, A URL without domain
    • +
+ +
Returns
+ +
+

The assembled mir tag with compatibility pre-separated

+
+
+ + +
+
+ +
+ + def + make_scheduler_tag(series_name: str) -> tuple[str]: + + + +
+ + 
49def make_scheduler_tag(series_name: str) -> tuple[str]:
+50    """Create a mir label from a scheduler operation\n
+51    :param class_name: Known period-separated prefix and model type
+52    :return: The assembled mir tag with compatibility pre-separated"""
+53
+54    import re
+55
+56    comp_name = None
+57    patterns = [r"Schedulers", r"Multistep", r"Solver", r"Discrete", r"Scheduler"]
+58    for scheduler in patterns:
+59        compiled = re.compile(scheduler)
+60        match = re.search(compiled, series_name)
+61        if match:
+62            comp_name = match.group()
+63            comp_name = comp_name.lower()
+64            break
+65    for pattern in patterns:
+66        series_name = re.sub(pattern, "", series_name)
+67    series_name.lower()
+68    # if not comp_name:
+69    #     comp_name = "*"
+70    return series_name, comp_name
+
+ + +

Create a mir label from a scheduler operation

+ +
Parameters
+ +
    +
  • class_name: Known period-separated prefix and model type
    • +
+ +
Returns
+ +
+

The assembled mir tag with compatibility pre-separated

+
+
+ + +
+
+ +
+ + def + tag_base_model( repo_path: str, class_name: str, addendum: dict | None = None) -> tuple[str]: + + + +
+ + 
73def tag_base_model(repo_path: str, class_name: str, addendum: dict | None = None) -> tuple[str]:
+74    """Convert model repo paths to MIR tags, classifying by feature\n
+75    :param name: Repo path
+76    :param class_name: The HF transformers class for the model
+77    :return: A segmented MIR tag useful for appending index entries"""
+78
+79    from mir.inspect.classes import extract_init_params
+80    from mir.indexers import flag_config
+81
+82    annotations = extract_init_params(class_name.replace("Model", "Config"), "transformers")
+83    mir_prefix = flag_config(transformers=True, **annotations)
+84    base_series, base_comp = make_mir_tag(repo_path)
+85    if not addendum:
+86        return mir_prefix, base_series, base_comp
+87    else:
+88        mir_prefix = f"info.{mir_prefix}"
+89    return mir_prefix, base_series, {base_comp: addendum}
+
+ + +

Convert model repo paths to MIR tags, classifying by feature

+ +
Parameters
+ +
    +
  • name: Repo path
    • +
  • class_name: The HF transformers class for the model
    • +
+ +
Returns
+ +
+

A segmented MIR tag useful for appending index entries

+
+
+ + +
+
+ +
+ + def + tag_pipe(repo_path: str, class_name: str, addendum: dict) -> tuple: + + + +
+ + 
 92def tag_pipe(repo_path: str, class_name: str, addendum: dict) -> tuple:
+ 93    """Convert model repo pipes to MIR tags, classifying by feature\n
+ 94    :param name: Repo path
+ 95    :param class_name: The HF Diffusers class for the model
+ 96    :return: A segmented MIR tag useful for appending index entries"""
+ 97
+ 98    from mir.indexers import create_pipe_entry
+ 99
+100    mir_series, mir_data = create_pipe_entry(repo_path=repo_path, class_name=class_name)
+101    mir_prefix, mir_series = mir_series.rsplit(".", 1)
+102    mir_comp = list(mir_data)[0]
+103    return mir_prefix, mir_series, {mir_comp: addendum}
+
+ + +

Convert model repo pipes to MIR tags, classifying by feature

+ +
Parameters
+ +
    +
  • name: Repo path
    • +
  • class_name: The HF Diffusers class for the model
    • +
+ +
Returns
+ +
+

A segmented MIR tag useful for appending index entries

+
+
+ + +
+
+ + \ No newline at end of file diff --git a/docs/search.js b/docs/search.js new file mode 100644 index 0000000..f3d0c8b --- /dev/null +++ b/docs/search.js @@ -0,0 +1,46 @@ +window.pdocSearch = (function(){ +/** elasticlunr - http://weixsong.github.io * Copyright (C) 2017 Oliver Nightingale * Copyright (C) 2017 Wei Song * MIT Licensed */!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var e=Array.prototype.slice.call(arguments),t=e.pop(),n=e;if("function"!=typeof t)throw new TypeError("last argument must be a function");n.forEach(function(e){this.hasHandler(e)||(this.events[e]=[]),this.events[e].push(t)},this)},t.EventEmitter.prototype.removeListener=function(e,t){if(this.hasHandler(e)){var n=this.events[e].indexOf(t);-1!==n&&(this.events[e].splice(n,1),0==this.events[e].length&&delete this.events[e])}},t.EventEmitter.prototype.emit=function(e){if(this.hasHandler(e)){var t=Array.prototype.slice.call(arguments,1);this.events[e].forEach(function(e){e.apply(void 0,t)},this)}},t.EventEmitter.prototype.hasHandler=function(e){return e in this.events},t.tokenizer=function(e){if(!arguments.length||null===e||void 0===e)return[];if(Array.isArray(e)){var n=e.filter(function(e){return null===e||void 0===e?!1:!0});n=n.map(function(e){return t.utils.toString(e).toLowerCase()});var i=[];return n.forEach(function(e){var n=e.split(t.tokenizer.seperator);i=i.concat(n)},this),i}return e.toString().trim().toLowerCase().split(t.tokenizer.seperator)},t.tokenizer.defaultSeperator=/[\s\-]+/,t.tokenizer.seperator=t.tokenizer.defaultSeperator,t.tokenizer.setSeperator=function(e){null!==e&&void 0!==e&&"object"==typeof e&&(t.tokenizer.seperator=e)},t.tokenizer.resetSeperator=function(){t.tokenizer.seperator=t.tokenizer.defaultSeperator},t.tokenizer.getSeperator=function(){return t.tokenizer.seperator},t.Pipeline=function(){this._queue=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in t.Pipeline.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[n]=e},t.Pipeline.getRegisteredFunction=function(e){return e in t.Pipeline.registeredFunctions!=!0?null:t.Pipeline.registeredFunctions[e]},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. 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\n"}, "mir.main": {"fullname": "mir.main", "modulename": "mir", "qualname": "main", "kind": "function", "doc": "

\n", "signature": "():", "funcdef": "def"}, "mir.automata": {"fullname": "mir.automata", "modulename": "mir.automata", "kind": "module", "doc": "

\u81ea\u52d5\u5316\u7d22\u5f15

\n"}, "mir.automata.assimilate": {"fullname": "mir.automata.assimilate", "modulename": "mir.automata", "qualname": "assimilate", "kind": "function", "doc": "

Merge new data into a pre-generated MIR database, updating while preserving existing data structures.

\n\n
Parameters
\n\n
    \n
  • mir_db: The MIRDatabase instance
    • \n

  • data_tuple: A list of tuples, each containing:

    \n\n
    - arch (str): The architecture name\n- series (str): The series name\n- `new_data`: New data to be merged into the database.\n
    • \n
\n\n
Raises
\n\n
    \n
  • TypeError: If any field in new_data is not a dictionary.
    • \n
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Generate MIR HF Hub model database

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Create mir info database

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Create mir info database

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Create mir unet info database

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Create MIR entries missing from the database

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Create MIR audio modality entries

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Create MIR lora entries

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Create MIR VAE missing from the database

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\n", "signature": "(pipe_class, pipe_repo, staged_class=None, staged_repo=None)"}, "mir.config.constants.DocParseData.pipe_class": {"fullname": "mir.config.constants.DocParseData.pipe_class", "modulename": "mir.config.constants", "qualname": "DocParseData.pipe_class", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "mir.config.constants.DocParseData.pipe_repo": {"fullname": "mir.config.constants.DocParseData.pipe_repo", "modulename": "mir.config.constants", "qualname": "DocParseData.pipe_repo", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "mir.config.constants.DocParseData.staged_class": {"fullname": "mir.config.constants.DocParseData.staged_class", "modulename": "mir.config.constants", "qualname": "DocParseData.staged_class", "kind": "variable", "doc": "

\n", "annotation": ": Optional[str]", "default_value": "None"}, "mir.config.constants.DocParseData.staged_repo": {"fullname": "mir.config.constants.DocParseData.staged_repo", "modulename": "mir.config.constants", "qualname": "DocParseData.staged_repo", "kind": "variable", "doc": "

\n", "annotation": ": Optional[str]", "default_value": "None"}, "mir.config.constants.DocStringParserConstants": {"fullname": "mir.config.constants.DocStringParserConstants", "modulename": "mir.config.constants", "qualname": "DocStringParserConstants", "kind": "class", "doc": "

Constants used by DocStringParser for parsing docstrings.

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\n", "annotation": ": List[str]", "default_value": "['>>> motion_adapter = ', '>>> adapter = ', '>>> controlnet = ', '>>> pipe_prior = ', '>>> pipe = ', '>>> pipeline = ', '>>> blip_diffusion_pipe = ', '>>> prior_pipe = ', '>>> gen_pipe = ']"}, "mir.config.constants.DocStringParserConstants.repo_variables": {"fullname": "mir.config.constants.DocStringParserConstants.repo_variables", "modulename": "mir.config.constants", "qualname": "DocStringParserConstants.repo_variables", "kind": "variable", "doc": "

\n", "annotation": ": List[str]", "default_value": "['controlnet_model', 'controlnet_id', 'base_model', 'model_id_or_path', 'model_ckpt', 'model_id', 'repo_base', 'repo', 'motion_adapter_id']"}, "mir.config.constants.DocStringParserConstants.call_types": {"fullname": "mir.config.constants.DocStringParserConstants.call_types", "modulename": "mir.config.constants", "qualname": "DocStringParserConstants.call_types", "kind": "variable", "doc": "

\n", "annotation": ": List[str]", "default_value": "['.from_pretrained(', '.from_single_file(']"}, "mir.config.constants.DocStringParserConstants.staged_call_types": {"fullname": "mir.config.constants.DocStringParserConstants.staged_call_types", "modulename": "mir.config.constants", "qualname": "DocStringParserConstants.staged_call_types", "kind": "variable", "doc": "

\n", "annotation": ": List[str]", "default_value": "['.from_pretrain(']"}, "mir.config.constants.package_map": {"fullname": "mir.config.constants.package_map", "modulename": "mir.config.constants", "qualname": "package_map", "kind": "variable", "doc": "

\n", "default_value": "{'diffusers': ('_import_structure', 'diffusers.pipelines'), 'transformers': ('MODEL_MAPPING_NAMES', 'transformers.models.auto.modeling_auto')}"}, "mir.config.constants.root_path": {"fullname": "mir.config.constants.root_path", "modulename": "mir.config.constants", "qualname": "root_path", "kind": "variable", "doc": "

\n", "default_value": "'/Users/unauthorized/Documents/GitHub/cursor/darkshapes/MIR/mir'"}, "mir.config.constants.versions": {"fullname": "mir.config.constants.versions", "modulename": "mir.config.constants", "qualname": "versions", "kind": "variable", "doc": "

\n", "default_value": "{'semantic': ['-?\\\\d+[bBmMkK]', '-?v\\\\d+', '(?<=\\\\d)[.-](?=\\\\d)', '-prior$', '-diffusers$', '-large$', '-medium$'], 'suffixes': ['-\\\\d{1,2}[bBmMkK]', '-\\\\d[1-9][bBmMkK]', '-v\\\\d{1,2}', '-\\\\d{3,}$', '-\\\\d{4,}.*', '-\\\\d{4,}[px].*'], 'ignore': ['-xt$', '-box$', '-preview$', '-base.*', '-Tiny$', '-full$', '-mini.*', '-multimodal.*', '-instruct.*']}"}, "mir.config.constants.template": {"fullname": "mir.config.constants.template", "modulename": "mir.config.constants", "qualname": "template", "kind": "variable", "doc": "

\n", "default_value": "{'domain': {'dev': '', 'info': '', 'model': '', 'ops': ''}, 'arch': {'unclassified': {'brnn': '', 'rcnn': '', 'gru': '', 'rbm': '', 'tae': '', 'vae': '', 'embedding': '', 'scheduler': '', 'resnet': ''}, 'transformer': {'lstm': ['sequence_kernel'], 'detr': ['use_timm_backbone', '_resnet_', 'disable_custom_kernels', 'dice_loss_coefficient', 'decoder_method'], 'cnn': ['bbox_cost'], 'rnn': ['lru_width', 'rescale_every'], 'gan': ['codebook_dim', 'kernel_size', 'kernel_predictor_conv_size'], 'ssm': ['mamba_expand', 'parallel_attn', 'state_size', 'expand'], 'vit': ['use_swiglu_ffn', 'projection_dim', 'vlm_config', 'crop_size', 'out_indices', 'logit_scale_init_value', 'image_size', 'vision_config', 'hidden_sizes', 'image_token_id', 'patch_size', 'embed_dim', 'frequency_stride', 'auxiliary_channels', 'out_features', 'vision_feature_select_strategy'], 'dpt': ['fusion_hidden_size'], 'moe': ['num_experts_per_tok'], 'aet': ['classifier_proj_size', 'position_embedding_type', 'separate_cls', 'keypoint_detector_config', 'local_attention', 'act_dropout', 'max_source_positions'], 'stst': ['is_encoder_decoder', 'encoder_attention_heads', 'encoder_layers', 'encoder_hidden_size', 'encoder_config', 'audio_token_index', 'codebook_dim', 'router_ignore_padding_tokens', 'd_ff', 'd_kv', 'audio_config'], 'art': ['ffn_dim', 'num_codebooks', 'vq_config', 'attn_config', 'n_head', 'n_heads', 'n_layer', 'rms_norm_eps', 'rope_theta', 'head_dim', 'layernorm_embedding', 'hidden_dropout_prob', 'rotary_pct', 'embed_dropout', 'nb_priors', 'embd_pdrop', 'n_positions', 'aux_loss_coef', 'residual_dropout', 'no_rope_layers', 'decoder_start_token_id', 'decoder_layers', 'tie_codebooks_embeddings']}, 'diffuser': {'lora': ['motion_adapter'], 'controlnet': ['controlnet'], 'unet': ['unet', 'prior', 'decoder'], 'dit': ['transformer']}}}"}, "mir.config.constants.MIR_PATH_NAMED": {"fullname": "mir.config.constants.MIR_PATH_NAMED", "modulename": "mir.config.constants", "qualname": "MIR_PATH_NAMED", "kind": "variable", "doc": "

\n", "default_value": "'/Users/unauthorized/Documents/GitHub/cursor/darkshapes/MIR/mir/mir.json'"}, "mir.config.constants.BREAKING_SUFFIX": {"fullname": "mir.config.constants.BREAKING_SUFFIX", "modulename": "mir.config.constants", "qualname": "BREAKING_SUFFIX", "kind": "variable", "doc": "

\n", "default_value": "'.*(?:-)(prior)$|.*(?:-)(diffusers)$|.*[_-](\\\\d{3,4}px|-T2V$|-I2V$)'"}, "mir.config.constants.PARAMETERS_SUFFIX": {"fullname": "mir.config.constants.PARAMETERS_SUFFIX", "modulename": "mir.config.constants", "qualname": "PARAMETERS_SUFFIX", "kind": "variable", "doc": "

\n", "default_value": "'(\\\\d{1,4}[KkMmBb]|[._-]\\\\d+[\\\\._-]\\\\d+[Bb][._-]).*?$'"}, "mir.config.constants.SEARCH_SUFFIX": {"fullname": "mir.config.constants.SEARCH_SUFFIX", "modulename": "mir.config.constants", "qualname": "SEARCH_SUFFIX", "kind": "variable", "doc": "

\n", "default_value": "'\\\\d+[._-]?\\\\d+[BbMmKk](it)?|[._-]\\\\d+[BbMmKk](it)?'"}, "mir.config.conversion": {"fullname": "mir.config.conversion", "modulename": "mir.config.conversion", "kind": "module", "doc": "

\n"}, "mir.config.conversion.import_submodules": {"fullname": "mir.config.conversion.import_submodules", "modulename": "mir.config.conversion", "qualname": "import_submodules", "kind": "function", "doc": "

Convert two strings into a callable function or property

\n\n
Parameters
\n\n
    \n
  • module: The name of the module to import
    • \n
  • library_path: Base package for the module
    • \n
\n\n
Returns
\n\n
\n

The callable attribute or property

\n
\n", "signature": "(module_name: str, pkg_name_or_abs_path: str) -> Optional[Callable]:", "funcdef": "def"}, "mir.config.conversion.code_name_to_class_name": {"fullname": "mir.config.conversion.code_name_to_class_name", "modulename": "mir.config.conversion", "qualname": "code_name_to_class_name", "kind": "function", "doc": "

Fetch class names from code names from Diffusers or Transformers

\n\n
Parameters
\n\n
    \n
  • class_name: To return only one class, defaults to None
    • \n
  • pkg_name: optional field for library, defaults to \"transformers\"
    • \n
\n\n
Returns
\n\n
\n

A list of all code names, or the one corresponding to the provided class

\n
\n", "signature": "(\tcode_name: Union[str, Type, NoneType] = None,\tpkg_name: Optional[str] = 'transformers') -> Union[List[str], str]:", "funcdef": "def"}, "mir.config.conversion.pkg_path_to_docstring": {"fullname": "mir.config.conversion.pkg_path_to_docstring", "modulename": "mir.config.conversion", "qualname": "pkg_path_to_docstring", "kind": "function", "doc": "

Processes package folder paths to yield example doc strings if available.

\n\n
Parameters
\n\n
    \n
  • pkg_name: The name of the package under diffusers.pipelines.
    • \n
  • file_specific: A flag indicating whether processing is specific to certain files.\n:yield: A tuple containing (pkg_name, file_name, EXAMPLE_DOC_STRING) if found.
    • \n
\n", "signature": "(pkg_name: str, folder_path: bool) -> Iterator[Tuple[str, str, str]]:", "funcdef": "def"}, "mir.config.conversion.file_name_to_docstring": {"fullname": "mir.config.conversion.file_name_to_docstring", "modulename": "mir.config.conversion", "qualname": "file_name_to_docstring", "kind": "function", "doc": "

Processes package using file name to yield example doc strings if available.

\n\n
Parameters
\n\n
    \n
  • pkg_name: The name of the package under diffusers.pipelines.
    • \n
  • file_specific: A flag indicating whether processing is specific to certain files.\n:yield: A tuple containing (pkg_name, file_name, EXAMPLE_DOC_STRING) if found.
    • \n
\n", "signature": "(pkg_name: str, file_specific: bool) -> Iterator[Tuple[str, str, str]]:", "funcdef": "def"}, "mir.config.conversion.class_to_mir_tag": {"fullname": "mir.config.conversion.class_to_mir_tag", "modulename": "mir.config.conversion", "qualname": "class_to_mir_tag", "kind": "function", "doc": "

Converts a class identifier to its corresponding MIR tag.

\n\n
Parameters
\n\n
    \n
  • mir_db: A dictionary mapping series-compatibility pairs to their respective data.
    • \n
  • code_name: The Transformers class identifier to convert.
    • \n
\n\n
Returns
\n\n
\n

An optional list containing the series and compatibility if found, otherwise None.

\n
\n", "signature": "(mir_db: Dict[str, str], code_name: str) -> Optional[str]:", "funcdef": "def"}, "mir.config.conversion.slice_number": {"fullname": "mir.config.conversion.slice_number", "modulename": "mir.config.conversion", "qualname": "slice_number", "kind": "function", "doc": "

Separate a numeral value appended to a string

\n\n
Returns
\n\n
\n

Converted value as int or float, or unmodified string

\n
\n", "signature": "(text: str) -> Union[int, float, str]:", "funcdef": "def"}, "mir.config.json_io": {"fullname": "mir.config.json_io", "modulename": "mir.config.json_io", "kind": "module", "doc": "

\n"}, "mir.config.json_io.write_json_file": {"fullname": "mir.config.json_io.write_json_file", "modulename": "mir.config.json_io", "qualname": "write_json_file", "kind": "function", "doc": "

Save a file to disk as UTF8 JSON

\n\n
Parameters
\n\n
    \n
  • folder_path_named: The location to save
    • \n
  • file_name: A file name to save with
    • \n
  • data: The content to save
    • \n
  • mode: Type of open to use (default truncate-write)\n:returns: Dictionary of json data
    • \n
\n", "signature": "(\tfolder_path_named: str,\tfile_name: str,\tdata: Any,\tmode: str = 'tw') -> None:", "funcdef": "def"}, "mir.config.json_io.read_json_file": {"fullname": "mir.config.json_io.read_json_file", "modulename": "mir.config.json_io", "qualname": "read_json_file", "kind": "function", "doc": "

Open json file as UTF8 JSON

\n\n
Parameters
\n\n
    \n
  • file_path_absolute: Location of the file
    • \n
  • mode: Type of read to use\n:returns: Dictionary of json data
    • \n
\n", "signature": "(file_path_absolute: str, mode='tr') -> dict:", "funcdef": "def"}, "mir.config.logging": {"fullname": "mir.config.logging", "modulename": "mir.config.logging", "kind": "module", "doc": "

\n"}, "mir.config.logging.nfo_obj": {"fullname": "mir.config.logging.nfo_obj", "modulename": "mir.config.logging", "qualname": "nfo_obj", "kind": "variable", "doc": "

\n", "default_value": "<Logger 20 (NOTSET)>"}, "mir.config.logging.dbuq_obj": {"fullname": "mir.config.logging.dbuq_obj", "modulename": "mir.config.logging", "qualname": "dbuq_obj", "kind": "variable", "doc": "

\n", "default_value": "<Logger 10 (NOTSET)>"}, "mir.config.logging.nfo": {"fullname": "mir.config.logging.nfo", "modulename": "mir.config.logging", "qualname": "nfo", "kind": "function", "doc": "

Log 'msg % args' with severity 'INFO'.

\n\n

To pass exception information, use the keyword argument exc_info with\na true value, e.g.

\n\n

logger.info(\"Houston, we have a %s\", \"notable problem\", exc_info=True)

\n", "signature": "(msg, *args, **kwargs):", "funcdef": "def"}, "mir.config.logging.dbuq": {"fullname": "mir.config.logging.dbuq", "modulename": "mir.config.logging", "qualname": "dbuq", "kind": "function", "doc": "

Log 'msg % args' with severity 'DEBUG'.

\n\n

To pass exception information, use the keyword argument exc_info with\na true value, e.g.

\n\n

logger.debug(\"Houston, we have a %s\", \"thorny problem\", exc_info=True)

\n", "signature": "(msg, *args, **kwargs):", "funcdef": "def"}, "mir.doc_parser": {"fullname": "mir.doc_parser", "modulename": "mir.doc_parser", "kind": "module", "doc": "

\n"}, "mir.doc_parser.parse_docs": {"fullname": "mir.doc_parser.parse_docs", "modulename": "mir.doc_parser", "qualname": "parse_docs", "kind": "function", "doc": "

\n", "signature": "(\tdoc_string: str) -> Tuple[Optional[str], Optional[str], Optional[str], Optional[str]]:", "funcdef": "def"}, "mir.doc_parser.DocStringValidator": {"fullname": "mir.doc_parser.DocStringValidator", "modulename": "mir.doc_parser", "qualname": "DocStringValidator", "kind": "class", "doc": "

Handles validation of docstring data and extracted values.

\n"}, "mir.doc_parser.DocStringValidator.normalize_doc_string": {"fullname": "mir.doc_parser.DocStringValidator.normalize_doc_string", "modulename": "mir.doc_parser", "qualname": "DocStringValidator.normalize_doc_string", "kind": "function", "doc": "

Normalize docstring by joining lines with spaces.

\n\n
Parameters
\n\n
    \n
  • doc_string: Raw docstring text\n:returns: Normalized docstring with newlines replaced by spaces
    • \n
\n", "signature": "(doc_string: str) -> str:", "funcdef": "def"}, "mir.doc_parser.DocStringValidator.is_valid_repo_path": {"fullname": "mir.doc_parser.DocStringValidator.is_valid_repo_path", "modulename": "mir.doc_parser", "qualname": "DocStringValidator.is_valid_repo_path", "kind": "function", "doc": "

Validate that a repository path is valid.

\n\n
Parameters
\n\n
    \n
  • repo_path: Repository path to validate\n:returns: True if path is valid (not empty and contains '/'), False otherwise
    • \n
\n", "signature": "(repo_path: Optional[str]) -> bool:", "funcdef": "def"}, "mir.doc_parser.DocStringValidator.validate_repo_path": {"fullname": "mir.doc_parser.DocStringValidator.validate_repo_path", "modulename": "mir.doc_parser", "qualname": "DocStringValidator.validate_repo_path", "kind": "function", "doc": "

Validate and return repo path, or None if invalid.

\n\n
Parameters
\n\n
    \n
  • repo_path: Repository path to validate
    • \n
  • segment: Segment text for error reporting\n:returns: Validated repo path or None if invalid
    • \n
\n", "signature": "(repo_path: Optional[str], segment: str) -> Optional[str]:", "funcdef": "def"}, "mir.doc_parser.DocStringValidator.validate_pipe_class": {"fullname": "mir.doc_parser.DocStringValidator.validate_pipe_class", "modulename": "mir.doc_parser", "qualname": "DocStringValidator.validate_pipe_class", "kind": "function", "doc": "

Validate that a pipe class name is present.

\n\n
Parameters
\n\n
    \n
  • pipe_class: Pipe class name to validate\n:returns: True if class name is valid, False otherwise
    • \n
\n", "signature": "(pipe_class: Optional[str]) -> bool:", "funcdef": "def"}, "mir.doc_parser.DocStringParser": {"fullname": "mir.doc_parser.DocStringParser", "modulename": "mir.doc_parser", "qualname": "DocStringParser", "kind": "class", "doc": "

!!! abstract \"Usage Documentation\"\n Models

\n\n

A base class for creating Pydantic models.

\n\n

Attributes:\n __class_vars__: The names of the class variables defined on the model.\n __private_attributes__: Metadata about the private attributes of the model.\n __signature__: The synthesized __init__ [Signature][inspect.Signature] of the model.

\n\n
__pydantic_complete__: Whether model building is completed, or if there are still undefined fields.\n__pydantic_core_schema__: The core schema of the model.\n__pydantic_custom_init__: Whether the model has a custom `__init__` function.\n__pydantic_decorators__: Metadata containing the decorators defined on the model.\n    This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.\n__pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to\n    __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.\n__pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.\n__pydantic_post_init__: The name of the post-init method for the model, if defined.\n__pydantic_root_model__: Whether the model is a [`RootModel`][pydantic.root_model.RootModel].\n__pydantic_serializer__: The `pydantic-core` `SchemaSerializer` used to dump instances of the model.\n__pydantic_validator__: The `pydantic-core` `SchemaValidator` used to validate instances of the model.\n\n__pydantic_fields__: A dictionary of field names and their corresponding [`FieldInfo`][pydantic.fields.FieldInfo] objects.\n__pydantic_computed_fields__: A dictionary of computed field names and their corresponding [`ComputedFieldInfo`][pydantic.fields.ComputedFieldInfo] objects.\n\n__pydantic_extra__: A dictionary containing extra values, if [`extra`][pydantic.config.ConfigDict.extra]\n    is set to `'allow'`.\n__pydantic_fields_set__: The names of fields explicitly set during instantiation.\n__pydantic_private__: Values of private attributes set on the model instance.\n
\n", "bases": "pydantic.main.BaseModel"}, "mir.doc_parser.DocStringParser.doc_string": {"fullname": "mir.doc_parser.DocStringParser.doc_string", "modulename": "mir.doc_parser", "qualname": "DocStringParser.doc_string", "kind": "variable", "doc": "

\n", "annotation": ": str", "default_value": "PydanticUndefined"}, "mir.doc_parser.DocStringParser.normalize_doc": {"fullname": "mir.doc_parser.DocStringParser.normalize_doc", "modulename": "mir.doc_parser", "qualname": "DocStringParser.normalize_doc", "kind": "function", "doc": "

\n", "signature": "(cls, docs: str) -> str:", "funcdef": "def"}, "mir.doc_parser.DocStringParser.doc_match": {"fullname": "mir.doc_parser.DocStringParser.doc_match", "modulename": "mir.doc_parser", "qualname": "DocStringParser.doc_match", "kind": "function", "doc": "

\n", "signature": "(self, prefix_set: List[str] = None):", "funcdef": "def"}, "mir.doc_parser.DocStringParser.parse": {"fullname": "mir.doc_parser.DocStringParser.parse", "modulename": "mir.doc_parser", "qualname": "DocStringParser.parse", "kind": "function", "doc": "

\n", "signature": "(self) -> mir.config.constants.DocParseData:", "funcdef": "def"}, "mir.indexers": {"fullname": "mir.indexers", "modulename": "mir.indexers", "kind": "module", "doc": "

\u985e\u767c\u73fe\u548c\u62c6\u5378

\n"}, "mir.indexers.check_migrations": {"fullname": "mir.indexers.check_migrations", "modulename": "mir.indexers", "qualname": "check_migrations", "kind": "function", "doc": "

Replaces old organization names in repository paths with new ones.

\n\n
Parameters
\n\n
    \n
  • repo_path: Original repository path containing old organization names
    • \n
\n\n
Returns
\n\n
\n

Updated repository path with new organization names

\n
\n", "signature": "(repo_path: str):", "funcdef": "def"}, "mir.indexers.flag_config": {"fullname": "mir.indexers.flag_config", "modulename": "mir.indexers", "qualname": "flag_config", "kind": "function", "doc": "

Set type of MIR prefix depending on model type

\n\n
Parameters
\n\n
    \n
  • transformers: Use transformers data instead of diffusers data, defaults to False
    • \n
\n\n
Raises
\n\n
    \n
  • ValueError: Model type not detected
    • \n
\n\n
Returns
\n\n
\n

MIR prefix based on model configuration

\n
\n", "signature": "(transformers: bool = False, data: dict = None, **kwargs):", "funcdef": "def"}, "mir.indexers.create_pipe_entry": {"fullname": "mir.indexers.create_pipe_entry", "modulename": "mir.indexers", "qualname": "create_pipe_entry", "kind": "function", "doc": "

Create a pipeline article and generate corresponding information according to the provided repo path and pipeline category

\n\n
Parameters
\n\n
    \n
  • repo_path (str): Repository path.
    • \n
  • model_class_obj (str): The model class function
    • \n
\n\n
Raises
\n\n
    \n
  • TypeError: If 'repo_path' or 'class_name' are not set.
    • \n
\n\n
Returns
\n\n
\n

Tuple: The data structure containing mir_series and mir_comp is used for subsequent processing.

\n
\n", "signature": "(\trepo_path: str,\tclass_name: str,\tmodel_class_obj: Optional[Callable] = None) -> tuple[str, typing.Dict[str, typing.Dict[typing.Any, typing.Any]]]:", "funcdef": "def"}, "mir.indexers.diffusers_index": {"fullname": "mir.indexers.diffusers_index", "modulename": "mir.indexers", "qualname": "diffusers_index", "kind": "function", "doc": "

Generate diffusion model data for MIR index

\n\n
Returns
\n\n
\n

Dictionary ready to be applied to MIR data fields

\n
\n", "signature": "() -> Dict[str, Dict[str, Dict[str, Any]]]:", "funcdef": "def"}, "mir.indexers.transformers_index": {"fullname": "mir.indexers.transformers_index", "modulename": "mir.indexers", "qualname": "transformers_index", "kind": "function", "doc": "

Generate LLM model data for MIR index

\n\n
Returns
\n\n
\n

Dictionary ready to be applied to MIR data fields

\n
\n", "signature": "():", "funcdef": "def"}, "mir.indexers.mlx_repo_capture": {"fullname": "mir.indexers.mlx_repo_capture", "modulename": "mir.indexers", "qualname": "mlx_repo_capture", "kind": "function", "doc": "

\n", "signature": "(base_repo: str = 'mlx-community'):", "funcdef": "def"}, "mir.inspect": {"fullname": "mir.inspect", "modulename": "mir.inspect", "kind": "module", "doc": "

\n"}, "mir.inspect.classes": {"fullname": "mir.inspect.classes", "modulename": "mir.inspect.classes", "kind": "module", "doc": "

\u985e\u767c\u73fe\u548c\u62c6\u5378

\n"}, "mir.inspect.classes.resolve_import_path": {"fullname": "mir.inspect.classes.resolve_import_path", "modulename": "mir.inspect.classes", "qualname": "resolve_import_path", "kind": "function", "doc": "

Retrieve the folder path within a class. Only returns if it is a valid path in the system

\n\n

NOTE: in most cases __module__ makes this redundant

\n\n
Parameters
\n\n
    \n
  • code_name: The internal name for the model in the third-party API.
    • \n
  • pkg_name: The API Package
    • \n
\n\n
Returns
\n\n
\n

A list corresponding to the path of the model, or None if not found

\n
\n\n
Raises
\n\n
    \n
  • KeyError: for invalid pkg_name
    • \n
\n", "signature": "(code_name: str, pkg_name: str) -> Optional[List[str]]:", "funcdef": "def"}, "mir.inspect.classes.resolve_class_names": {"fullname": "mir.inspect.classes.resolve_class_names", "modulename": "mir.inspect.classes", "qualname": "resolve_class_names", "kind": "function", "doc": "

Reveal code names for class names from Diffusers or Transformers

\n\n
Parameters
\n\n
    \n
  • class_name: To return only one class, defaults to None
    • \n
  • pkg_name: optional field for library, defaults to \"transformers\"
    • \n
  • path_format: Retrieve just the code name, or the full module path and code name within the package
    • \n
\n\n
Returns
\n\n
\n

A list of all code names, or the one corresponding to the provided class

\n
\n", "signature": "(\tclass_name: Union[str, Type, NoneType] = None,\tpkg_name: Optional[str] = 'transformers',\tpath_format: Optional[bool] = False) -> Union[List[str], str]:", "funcdef": "def"}, "mir.inspect.classes.extract_inherited_classes": {"fullname": "mir.inspect.classes.extract_inherited_classes", "modulename": "mir.inspect.classes", "qualname": "extract_inherited_classes", "kind": "function", "doc": "

Strips tags from module's base classes and extracts inherited class members.

\n\n

If module is a string, it requires the library argument to convert it into a callable.

\n\n
Parameters
\n\n
    \n
  • module: A module or string representing a module.
    • \n
  • library: Library name required if module is a string. Defaults to None.\n:returns: Mapping indices to class path segments, or None if invalid input.
    • \n
\n", "signature": "(\tmodel_class: Union[Callable, str],\tpkg_name: Optional[str] = None) -> Optional[Dict[str, List[str]]]:", "funcdef": "def"}, "mir.inspect.classes.extract_init_params": {"fullname": "mir.inspect.classes.extract_init_params", "modulename": "mir.inspect.classes", "qualname": "extract_init_params", "kind": "function", "doc": "

Pick apart a Diffusers or Transformers pipeline class and find its constituent parts

\n\n
Parameters
\n\n
    \n
  • module: Origin pipeline as a class or as a string
    • \n
  • library: name of a library to import the class from, only if a string is provided
    • \n
\n\n
Returns
\n\n
\n

Dictionary of sub-classes from the module

\n
\n", "signature": "(\tmodule: Union[Callable, str],\tpkg_name: Optional[str] = None) -> Dict[str, List[str]]:", "funcdef": "def"}, "mir.inspect.metadata": {"fullname": "mir.inspect.metadata", "modulename": "mir.inspect.metadata", "kind": "module", "doc": "

\n"}, "mir.inspect.metadata.gather_transformers_metadata": {"fullname": "mir.inspect.metadata.gather_transformers_metadata", "modulename": "mir.inspect.metadata", "qualname": "gather_transformers_metadata", "kind": "function", "doc": "

Eat the \ud83e\udd17Transformers classes as a treat, leaving any tasty subclass class morsels neatly arranged as a dictionary.

\n\n

Nom.

\n\n
Returns
\n\n
\n

Tasty mapping of subclasses to their class references

\n
\n", "signature": "() -> Dict[str, List[str]]:", "funcdef": "def"}, "mir.inspect.metadata.gather_diffusers_metadata": {"fullname": "mir.inspect.metadata.gather_diffusers_metadata", "modulename": "mir.inspect.metadata", "qualname": "gather_diffusers_metadata", "kind": "function", "doc": "

Draw down docstrings from \ud83e\udd17Diffusers library, minimizing internet requests

\n\n
Returns
\n\n
\n

Docstrings for common diffusers models

\n
\n", "signature": "() -> Generator:", "funcdef": "def"}, "mir.inspect.parenting": {"fullname": "mir.inspect.parenting", "modulename": "mir.inspect.parenting", "kind": "module", "doc": "

\n"}, "mir.inspect.parenting.class_parent": {"fullname": "mir.inspect.parenting.class_parent", "modulename": "mir.inspect.parenting", "qualname": "class_parent", "kind": "function", "doc": "

Retrieve the folder path within a class. Only returns if it is a valid path in the system

\n\n

NOTE: in most cases __module__ makes this redundant

\n\n
Parameters
\n\n
    \n
  • code_name: The internal name for the model in the third-party API.
    • \n
  • pkg_name: The API Package
    • \n
\n\n
Returns
\n\n
\n

A list corresponding to the path of the model, or None if not found

\n
\n\n
Raises
\n\n
    \n
  • KeyError: for invalid pkg_name
    • \n
\n", "signature": "(code_name: str, pkg_name: str) -> Optional[List[str]]:", "funcdef": "def"}, "mir.inspect.pipes": {"fullname": "mir.inspect.pipes", "modulename": "mir.inspect.pipes", "kind": "module", "doc": "

\n"}, "mir.inspect.pipes.get_transformer_config_classes": {"fullname": "mir.inspect.pipes.get_transformer_config_classes", "modulename": "mir.inspect.pipes", "qualname": "get_transformer_config_classes", "kind": "function", "doc": "

Show all config classes in the Transformer package with the specified init annotation

\n\n
Parameters
\n\n
    \n
  • from_match: Narrow the classes to only those with an exact key inside
    • \n
\n\n
Returns
\n\n
\n

A list of all Classes

\n
\n", "signature": "(parameter_filter: Optional[str] = None) -> List[str]:", "funcdef": "def"}, "mir.inspect.pipes.get_class_parent_folder": {"fullname": "mir.inspect.pipes.get_class_parent_folder", "modulename": "mir.inspect.pipes", "qualname": "get_class_parent_folder", "kind": "function", "doc": "

\n", "signature": "(class_name: str, pkg_name: str) -> List[str]:", "funcdef": "def"}, "mir.inspect.tasks": {"fullname": "mir.inspect.tasks", "modulename": "mir.inspect.tasks", "kind": "module", "doc": "

\n"}, "mir.inspect.tasks.flatten_map": {"fullname": "mir.inspect.tasks.flatten_map", "modulename": "mir.inspect.tasks", "qualname": "flatten_map", "kind": "function", "doc": "

\n", "signature": "(nested: List[str], unpack: str):", "funcdef": "def"}, "mir.inspect.tasks.TaskAnalyzer": {"fullname": "mir.inspect.tasks.TaskAnalyzer", "modulename": "mir.inspect.tasks", "qualname": "TaskAnalyzer", "kind": "class", "doc": "

\n"}, "mir.inspect.tasks.TaskAnalyzer.skip_series": {"fullname": "mir.inspect.tasks.TaskAnalyzer.skip_series", "modulename": "mir.inspect.tasks", "qualname": "TaskAnalyzer.skip_series", "kind": "variable", "doc": "

\n"}, "mir.inspect.tasks.TaskAnalyzer.skip_classes": {"fullname": "mir.inspect.tasks.TaskAnalyzer.skip_classes", "modulename": "mir.inspect.tasks", "qualname": "TaskAnalyzer.skip_classes", "kind": "variable", "doc": "

\n"}, "mir.inspect.tasks.TaskAnalyzer.skip_auto": {"fullname": "mir.inspect.tasks.TaskAnalyzer.skip_auto", "modulename": "mir.inspect.tasks", "qualname": "TaskAnalyzer.skip_auto", "kind": "variable", "doc": "

\n"}, "mir.inspect.tasks.TaskAnalyzer.skip_types": {"fullname": "mir.inspect.tasks.TaskAnalyzer.skip_types", "modulename": "mir.inspect.tasks", "qualname": "TaskAnalyzer.skip_types", "kind": "variable", "doc": "

\n"}, "mir.inspect.tasks.TaskAnalyzer.mflux_tasks": {"fullname": "mir.inspect.tasks.TaskAnalyzer.mflux_tasks", "modulename": "mir.inspect.tasks", "qualname": "TaskAnalyzer.mflux_tasks", "kind": "variable", "doc": "

\n"}, "mir.inspect.tasks.TaskAnalyzer.show_diffusers_tasks": {"fullname": "mir.inspect.tasks.TaskAnalyzer.show_diffusers_tasks", "modulename": "mir.inspect.tasks", "qualname": "TaskAnalyzer.show_diffusers_tasks", "kind": "function", "doc": "

Return Diffusers task pipes based on package-specific query

\n\n
Parameters
\n\n
    \n
  • class_name: To find task pipes from a Diffusers class pipe, defaults to None
    • \n
  • code_name: To find task pipes from a Transformers class pipe, defaults to None
    • \n
\n\n
Returns
\n\n
\n

A list of alternate class pipelines derived from the specified class

\n
\n", "signature": "(code_name: str, class_name: str | None = None) -> list[str]:", "funcdef": "def"}, "mir.inspect.tasks.TaskAnalyzer.show_transformers_tasks": {"fullname": "mir.inspect.tasks.TaskAnalyzer.show_transformers_tasks", "modulename": "mir.inspect.tasks", "qualname": "TaskAnalyzer.show_transformers_tasks", "kind": "function", "doc": "

Retrieves a list of task classes associated with a specified transformer class.

\n\n
Parameters
\n\n
    \n
  • class_name: The name of the transformer class to inspect.
    • \n
  • pkg_type: The dependency for the module
    • \n
  • alt_method: Use an alternate method to return the classes
    • \n
\n\n
Returns
\n\n
\n

A list of task classes associated with the specified transformer.

\n
\n", "signature": "(class_name: str | None = None, code_name: str | None = None) -> list[str]:", "funcdef": "def"}, "mir.inspect.tasks.TaskAnalyzer.detect_tasks": {"fullname": "mir.inspect.tasks.TaskAnalyzer.detect_tasks", "modulename": "mir.inspect.tasks", "qualname": "TaskAnalyzer.detect_tasks", "kind": "function", "doc": "

Detects and traces tasks MIR data

\n\n
Parameters
\n\n
    \n
  • mir_db:: An instance of MIRDatabase containing the database of information.
    • \n
  • field_name: The name of the field in compatibility data to process for task detection, defaults to \"pkg\".
    • \n
\n\n
Returns
\n\n
\n

A dictionary mapping series names to their respective compatibility and traced tasks.

\n
\n", "signature": "(self, mir_db: mir.maid.MIRDatabase, field_name: str = 'pkg') -> dict:", "funcdef": "async def"}, "mir.inspect.tasks.TaskAnalyzer.detect_pipes": {"fullname": "mir.inspect.tasks.TaskAnalyzer.detect_pipes", "modulename": "mir.inspect.tasks", "qualname": "TaskAnalyzer.detect_pipes", "kind": "function", "doc": "

Detects and traces Pipes MIR data

\n\n
Parameters
\n\n
    \n
  • mir_db:: An instance of MIRDatabase containing the database of information.
    • \n
  • field_name: The name of the field in compatibility data to process for task detection, defaults to \"pkg\".
    • \n
\n\n
Returns
\n\n
\n

A dictionary mapping series names to their respective compatibility and traced tasks.

\n
\n", "signature": "(self, mir_db: mir.maid.MIRDatabase, field_name: str = 'pkg') -> dict:", "funcdef": "async def"}, "mir.inspect.tasks.TaskAnalyzer.hyperlink_to_mir": {"fullname": "mir.inspect.tasks.TaskAnalyzer.hyperlink_to_mir", "modulename": "mir.inspect.tasks", "qualname": "TaskAnalyzer.hyperlink_to_mir", "kind": "function", "doc": "

Maps pipeline components to MIR tags/IDs based on class names and roles.

\n\n
Parameters
\n\n
    \n
  • pipe_args: Dictionary of pipeline roles to their corresponding classes
    • \n
  • mir_db: MIRDatabase instance for querying tags/IDs
    • \n
\n\n
Returns
\n\n
\n

Dictionary mapping pipeline roles to associated MIR tags/IDs

\n
\n", "signature": "(self, pipe_args: dict, series: str, mir_db: mir.maid.MIRDatabase):", "funcdef": "async def"}, "mir.inspect.tasks.TaskAnalyzer.tag_class": {"fullname": "mir.inspect.tasks.TaskAnalyzer.tag_class", "modulename": "mir.inspect.tasks", "qualname": "TaskAnalyzer.tag_class", "kind": "function", "doc": "

Maps a class to MIR tags/IDs based on its name and role.

\n\n
Parameters
\n\n
    \n
  • pipe_class: Class to be mapped
    • \n
  • pipe_role: Role of the class in the pipeline
    • \n
  • series: Series identifier for the component
    • \n
  • mir_db: MIRDatabase instance for querying tags/IDs
    • \n
\n\n
Returns
\n\n
\n

Tuple containing MIR tag and class name

\n
\n", "signature": "(\tself,\tpipe_class: Callable,\tpipe_role: str,\tseries: str,\tmir_db: mir.maid.MIRDatabase) -> tuple[str | None]:", "funcdef": "async def"}, "mir.inspect.tasks.TaskAnalyzer.trace_tasks": {"fullname": "mir.inspect.tasks.TaskAnalyzer.trace_tasks", "modulename": "mir.inspect.tasks", "qualname": "TaskAnalyzer.trace_tasks", "kind": "function", "doc": "

Trace tasks for a given MIR entry.

\n\n
Parameters
\n\n
    \n
  • entry: The object containing the model information.
    • \n
\n\n
Returns
\n\n
\n

A sorted list of tasks applicable to the model.

\n
\n", "signature": "(self, pkg_tree: dict[str, str | int | list[str | int]]) -> List[str]:", "funcdef": "async def"}, "mir.inspect.tasks.trace_classes": {"fullname": "mir.inspect.tasks.trace_classes", "modulename": "mir.inspect.tasks", "qualname": "trace_classes", "kind": "function", "doc": "

Retrieve all compatible pipe forms

\n\n

NOTE: Mainly for Diffusers

\n\n
Parameters
\n\n
    \n
  • pipe_class: Origin pipe
    • \n
  • pkg_name: Dependency package
    • \n
\n\n
Returns
\n\n
\n

A dictionary of pipelines

\n
\n", "signature": "(pipe_class: str, pkg_name: str) -> Dict[str, List[str]]:", "funcdef": "def"}, "mir.maid": {"fullname": "mir.maid", "modulename": "mir.maid", "kind": "module", "doc": "

\u795e\u7ecf\u7f51\u7edc\u7684\u6570\u636e\u6ce8\u518c

\n"}, "mir.maid.MIRDatabase": {"fullname": "mir.maid.MIRDatabase", "modulename": "mir.maid", "qualname": "MIRDatabase", "kind": "class", "doc": "

Machine Intelligence Resource Database

\n"}, "mir.maid.MIRDatabase.__init__": {"fullname": "mir.maid.MIRDatabase.__init__", "modulename": "mir.maid", "qualname": "MIRDatabase.__init__", "kind": "function", "doc": "

\n", "signature": "(database: dict | None = None)"}, "mir.maid.MIRDatabase.add": {"fullname": "mir.maid.MIRDatabase.add", "modulename": "mir.maid", "qualname": "MIRDatabase.add", "kind": "function", "doc": "

Merge pre-existing MIR entries, or add new ones

\n\n
Parameters
\n\n
    \n
  • resource: Entry to apply
    • \n
\n", "signature": "(self, resource: dict[str, typing.Any]) -> None:", "funcdef": "def"}, "mir.maid.MIRDatabase.write_to_disk": {"fullname": "mir.maid.MIRDatabase.write_to_disk", "modulename": "mir.maid", "qualname": "MIRDatabase.write_to_disk", "kind": "function", "doc": "

Save data to JSON file

\n", "signature": "(self, data: Optional[dict] = None) -> None:", "funcdef": "def"}, "mir.maid.MIRDatabase.read_from_disk": {"fullname": "mir.maid.MIRDatabase.read_from_disk", "modulename": "mir.maid", "qualname": "MIRDatabase.read_from_disk", "kind": "function", "doc": "

Populate mir database

\n\n
Parameters
\n\n
    \n
  • data: mir decorator auto-populated, defaults to None
    • \n
\n\n
Returns
\n\n
\n

dict of MIR data

\n
\n", "signature": "(self, data: Optional[dict] = None) -> dict[str, typing.Any]:", "funcdef": "def"}, "mir.maid.MIRDatabase.grade_maybes": {"fullname": "mir.maid.MIRDatabase.grade_maybes", "modulename": "mir.maid", "qualname": "MIRDatabase.grade_maybes", "kind": "function", "doc": "

Evaluate and select the best match from a list of potential matches

\n\n
Parameters
\n\n
    \n
  • matches: Possible matches to compare
    • \n
  • target: Desired entry to match
    • \n
\n\n
Returns
\n\n
\n

The closest matching dictionary elements

\n
\n", "signature": "(matches: List[List[str]], target: str) -> list[str, str]:", "funcdef": "def"}, "mir.maid.MIRDatabase.ready_stage": {"fullname": "mir.maid.MIRDatabase.ready_stage", "modulename": "mir.maid", "qualname": "MIRDatabase.ready_stage", "kind": "function", "doc": "

Orchestrate match checking, return for exact matches, and create a queue of potential match

\n\n
Parameters
\n\n
    \n
  • maybe_match: The value of the requested search field
    • \n
  • target: The requested information
    • \n
  • series: Current MIR domain/arch/series tag
    • \n
  • compatibility: MIR compatibility tag
    • \n
\n\n
Returns
\n\n
\n

A list of exact matches or None

\n
\n", "signature": "(\tself,\tmaybe_match: str,\ttarget: str,\tseries: str,\tcompatibility: str) -> Optional[List[str]]:", "funcdef": "def"}, "mir.maid.MIRDatabase.find_tag": {"fullname": "mir.maid.MIRDatabase.find_tag", "modulename": "mir.maid", "qualname": "MIRDatabase.find_tag", "kind": "function", "doc": "

Retrieve MIR path based on nested value search

\n\n
Parameters
\n\n
    \n
  • field: Known field to look within
    • \n
  • target: Search pattern for field
    • \n
  • sub_field: A Second field level to investigate into (ex, field pkg, sub_field diffusers)
    • \n
\n\n
Returns
\n\n
\n

A list or string of the found tag

\n
\n\n
Raises
\n\n
    \n
  • KeyError: Target string not found
    • \n
\n", "signature": "(\tself,\tfield: str,\ttarget: str,\tsub_field: Optional[str] = None,\tdomain: None | str = None) -> list[str]:", "funcdef": "def"}, "mir.maid.main": {"fullname": "mir.maid.main", "modulename": "mir.maid", "qualname": "main", "kind": "function", "doc": "

Build the database

\n", "signature": "(mir_db: Optional[Callable] = None, remake: bool = True) -> None:", "funcdef": "def"}, "mir.run": {"fullname": "mir.run", "modulename": "mir.run", "kind": "module", "doc": "

\n"}, "mir.run.main": {"fullname": "mir.run.main", "modulename": "mir.run", "qualname": "main", "kind": "function", "doc": "

Parse arguments to feed to dict header reader

\n", "signature": "(mir_db: mir.maid.MIRDatabase = None):", "funcdef": "def"}, "mir.run.run_task": {"fullname": "mir.run.run_task", "modulename": "mir.run", "qualname": "run_task", "kind": "function", "doc": "

\n", "signature": "():", "funcdef": "def"}, "mir.run.pipe": {"fullname": "mir.run.pipe", "modulename": "mir.run", "qualname": "pipe", "kind": "function", "doc": "

\n", "signature": "(mir_db: mir.maid.MIRDatabase = None):", "funcdef": "def"}, "mir.tag": {"fullname": "mir.tag", "modulename": "mir.tag", "kind": "module", "doc": "

\n"}, "mir.tag.make_mir_tag": {"fullname": "mir.tag.make_mir_tag", "modulename": "mir.tag", "qualname": "make_mir_tag", "kind": "function", "doc": "

Create a mir label from a repo path

\n\n
Parameters
\n\n
    \n
  • mir_prefix: Known period-separated prefix and model type
    • \n
  • repo_path: Typical remote source repo path, A URL without domain
    • \n
\n\n
Returns
\n\n
\n

The assembled mir tag with compatibility pre-separated

\n
\n", "signature": "(repo_title: str, decoder=False, data: dict = None) -> List[str]:", "funcdef": "def"}, "mir.tag.make_scheduler_tag": {"fullname": "mir.tag.make_scheduler_tag", "modulename": "mir.tag", "qualname": "make_scheduler_tag", "kind": "function", "doc": "

Create a mir label from a scheduler operation

\n\n
Parameters
\n\n
    \n
  • class_name: Known period-separated prefix and model type
    • \n
\n\n
Returns
\n\n
\n

The assembled mir tag with compatibility pre-separated

\n
\n", "signature": "(series_name: str) -> tuple[str]:", "funcdef": "def"}, "mir.tag.tag_base_model": {"fullname": "mir.tag.tag_base_model", "modulename": "mir.tag", "qualname": "tag_base_model", "kind": "function", "doc": "

Convert model repo paths to MIR tags, classifying by feature

\n\n
Parameters
\n\n
    \n
  • name: Repo path
    • \n
  • class_name: The HF transformers class for the model
    • \n
\n\n
Returns
\n\n
\n

A segmented MIR tag useful for appending index entries

\n
\n", "signature": "(\trepo_path: str,\tclass_name: str,\taddendum: dict | None = None) -> tuple[str]:", "funcdef": "def"}, "mir.tag.tag_pipe": {"fullname": "mir.tag.tag_pipe", "modulename": "mir.tag", "qualname": "tag_pipe", "kind": "function", "doc": "

Convert model repo pipes to MIR tags, classifying by feature

\n\n
Parameters
\n\n
    \n
  • name: Repo path
    • \n
  • class_name: The HF Diffusers class for the model
    • \n
\n\n
Returns
\n\n
\n

A segmented MIR tag useful for appending index entries

\n
\n", "signature": "(repo_path: str, class_name: str, addendum: dict) -> tuple:", "funcdef": "def"}}, "docInfo": {"mir": {"qualname": 0, "fullname": 1, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mir.main": {"qualname": 1, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 7, "bases": 0, "doc": 3}, "mir.automata": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mir.automata.assimilate": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 82, "bases": 0, "doc": 102}, "mir.automata.hf_pkg_to_mir": {"qualname": 4, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 27, "bases": 0, "doc": 8}, "mir.automata.add_mir_dtype": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 27, "bases": 0, "doc": 6}, "mir.automata.add_mir_schedulers": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 27, "bases": 0, "doc": 6}, "mir.automata.mir_update": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 66, "bases": 0, "doc": 7}, "mir.automata.add_mir_diffusion": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 27, "bases": 0, "doc": 9}, "mir.automata.add_mir_llm": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 27, "bases": 0, "doc": 3}, "mir.automata.add_mir_audio": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 27, "bases": 0, "doc": 7}, "mir.automata.add_mir_lora": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 27, "bases": 0, "doc": 6}, "mir.automata.add_mir_vae": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 27, "bases": 0, "doc": 9}, "mir.config": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mir.config.constants": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mir.config.constants.generate_supported_model_class_names": {"qualname": 5, "fullname": 8, "annotation": 0, "default_value": 0, "signature": 85, "bases": 0, "doc": 3}, "mir.config.constants.DocParseData": {"qualname": 1, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mir.config.constants.DocParseData.__init__": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 38, "bases": 0, "doc": 3}, "mir.config.constants.DocParseData.pipe_class": {"qualname": 3, "fullname": 6, "annotation": 2, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mir.config.constants.DocParseData.pipe_repo": {"qualname": 3, "fullname": 6, "annotation": 2, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mir.config.constants.DocParseData.staged_class": {"qualname": 3, "fullname": 6, "annotation": 2, "default_value": 1, 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"mir.config.logging.dbuq": {"tf": 1}, "mir.maid.MIRDatabase.ready_stage": {"tf": 1}, "mir.maid.MIRDatabase.find_tag": {"tf": 1}}, "df": 5, "s": {"docs": {"mir.doc_parser.DocStringValidator": {"tf": 1}, "mir.doc_parser.DocStringParser": {"tf": 1.4142135623730951}}, "df": 2}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"mir.indexers.flag_config": {"tf": 1}}, "df": 1}}}}}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {"mir.doc_parser.DocStringValidator.is_valid_repo_path": {"tf": 1.4142135623730951}, "mir.doc_parser.DocStringValidator.validate_pipe_class": {"tf": 1}, "mir.inspect.classes.resolve_import_path": {"tf": 1}, "mir.inspect.parenting.class_parent": {"tf": 1}}, "df": 4, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"mir.doc_parser.DocStringValidator": {"tf": 1}}, "df": 1}}}, "e": {"docs": {"mir.doc_parser.DocStringValidator.is_valid_repo_path": {"tf": 1.4142135623730951}, "mir.doc_parser.DocStringValidator.validate_repo_path": {"tf": 1.4142135623730951}, "mir.doc_parser.DocStringValidator.validate_pipe_class": {"tf": 1.4142135623730951}, "mir.doc_parser.DocStringParser": {"tf": 1}}, "df": 4, "d": {"docs": {"mir.doc_parser.DocStringValidator.validate_repo_path": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"mir.doc_parser.DocStringParser": {"tf": 1}}, "df": 1, "s": {"docs": {"mir.doc_parser.DocStringParser": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}, "r": {"docs": {}, "df": 0, "s": {"docs": {"mir.doc_parser.DocStringParser": {"tf": 1}}, "df": 1}, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"mir.doc_parser.DocStringParser": {"tf": 1}}, "df": 1}}}}}}}}}, "y": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "d": {"docs": {"mir.config.conversion.pkg_path_to_docstring": {"tf": 1.4142135623730951}, "mir.config.conversion.file_name_to_docstring": {"tf": 1.4142135623730951}}, "df": 2}}}}}, "j": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"mir.config.json_io.write_json_file": {"tf": 1.4142135623730951}, "mir.config.json_io.read_json_file": {"tf": 1.7320508075688772}, "mir.maid.MIRDatabase.write_to_disk": {"tf": 1}}, "df": 3}}}, "o": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"mir.doc_parser.DocStringValidator.normalize_doc_string": {"tf": 1}}, "df": 1}}}}}}, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"mir.inspect.classes.resolve_class_names": {"tf": 1}}, "df": 1}}}}, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "y": {"docs": {"mir.inspect.pipes.get_transformer_config_classes": {"tf": 1}}, "df": 1, "w": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {"mir.config.logging.nfo": {"tf": 1}, "mir.config.logging.dbuq": {"tf": 1}}, "df": 2}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"mir.inspect.classes.resolve_import_path": {"tf": 1}, "mir.inspect.parenting.class_parent": {"tf": 1}, "mir.maid.MIRDatabase.find_tag": {"tf": 1}}, "df": 3}}}}}}}, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "n": {"docs": {"mir.maid.MIRDatabase.find_tag": {"tf": 1}, "mir.tag.make_mir_tag": {"tf": 1}, "mir.tag.make_scheduler_tag": {"tf": 1}}, "df": 3}}}}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"mir.inspect.tasks.TaskAnalyzer.show_diffusers_tasks": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"mir.inspect.tasks.TaskAnalyzer.hyperlink_to_mir": {"tf": 1}, "mir.inspect.tasks.TaskAnalyzer.tag_class": {"tf": 1}}, "df": 2}}}}}, "u": {"docs": {}, "df": 0, "e": {"docs": {"mir.maid.MIRDatabase.ready_stage": {"tf": 1}}, "df": 1}}}}}}}}, "pipeline": ["trimmer"], "_isPrebuiltIndex": true}; + + // mirrored in build-search-index.js (part 1) + // Also split on html tags. this is a cheap heuristic, but good enough. + elasticlunr.tokenizer.setSeperator(/[\s\-.;&_'"=,()]+|<[^>]*>/); + + let searchIndex; + if (docs._isPrebuiltIndex) { + console.info("using precompiled search index"); + searchIndex = elasticlunr.Index.load(docs); + } else { + console.time("building search index"); + // mirrored in build-search-index.js (part 2) + searchIndex = elasticlunr(function () { + this.pipeline.remove(elasticlunr.stemmer); + this.pipeline.remove(elasticlunr.stopWordFilter); + this.addField("qualname"); + this.addField("fullname"); + this.addField("annotation"); + this.addField("default_value"); + this.addField("signature"); + this.addField("bases"); + this.addField("doc"); + this.setRef("fullname"); + }); + for (let doc of docs) { + searchIndex.addDoc(doc); + } + console.timeEnd("building search index"); + } + + return (term) => searchIndex.search(term, { + fields: { + qualname: {boost: 4}, + fullname: {boost: 2}, + annotation: {boost: 2}, + default_value: {boost: 2}, + signature: {boost: 2}, + bases: {boost: 2}, + doc: {boost: 1}, + }, + expand: true + }); +})(); \ No newline at end of file diff --git a/mir/__init__.py b/mir/__init__.py index fdae8c4..2942506 100644 --- a/mir/__init__.py +++ b/mir/__init__.py @@ -7,10 +7,10 @@ def main(): from mir.maid import main as mir_main mir_main() - from mir.detect_tasks import main + from mir.inspect.tasks import main main() - from mir.detect_tasks import pipe + from mir.inspect.tasks import pipe pipe() @@ -21,7 +21,7 @@ def main(): os.remove("mir.json") except FileNotFoundError: pass - shutil.copy2(os.path.join(os.path.dirname(mir.maid.__file__), "config", "mir.json"), os.path.join(os.getcwd(), "mir.json")) + shutil.copy2(os.path.join(os.path.dirname(mir.maid.__file__), "mir.json"), os.path.join(os.getcwd(), "mir.json")) if __name__ == "__main__": diff --git a/mir/automata.py b/mir/automata.py index 03e33e3..9021a21 100644 --- a/mir/automata.py +++ b/mir/automata.py @@ -16,7 +16,7 @@ from mir.maid import MIRDatabase from mir.spec.mir import mir_entry from mir.tag import make_mir_tag, make_scheduler_tag, tag_pipe, tag_base_model -from mir.config.logging import nfo, dbug, dbuq +from mir.config.logging import nfo, dbuq from mir.config.conversion import slice_number from mir.indexers import diffusers_index, transformers_index @@ -132,7 +132,7 @@ def update_nested_dict(target, source): else: target = {key: value} - dbug(f"{data_tuple}, {len(data_tuple)}") + dbuq(f"{data_tuple}, {len(data_tuple)}") for arch, series, new_data in data_tuple: mir_data = mir_db.database[f"{arch}.{series}"] for comp, field_data in new_data.items(): diff --git a/mir/config/constants.py b/mir/config/constants.py index 5e74dfa..0f4bfaf 100644 --- a/mir/config/constants.py +++ b/mir/config/constants.py @@ -26,7 +26,7 @@ MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES, MODEL_MAPPING_NAMES, ) -from transformers.modeling_utils import PretrainedConfig +from transformers.configuration_utils import PretrainedConfig def generate_supported_model_class_names( @@ -119,10 +119,11 @@ class DocStringParserConstants: "diffusers": ("_import_structure", "diffusers.pipelines"), "transformers": ("MODEL_MAPPING_NAMES", "transformers.models.auto.modeling_auto"), } -versions = read_json_file(os.path.join(os.path.dirname(__file__), "versions.json")) -template = read_json_file(os.path.join(os.path.dirname(__file__), "template.json")) +root_path = os.path.dirname(os.path.dirname(__file__)) +versions = read_json_file(os.path.join(root_path, "spec", "versions.json")) +template = read_json_file(os.path.join(root_path, "spec", "template.json")) -MIR_PATH_NAMED = os.path.join(os.path.dirname(os.path.dirname(__file__)), "mir.json") +MIR_PATH_NAMED = os.path.join(root_path, "mir.json") BREAKING_SUFFIX = r".*(?:-)(prior)$|.*(?:-)(diffusers)$|.*[_-](\d{3,4}px|-T2V$|-I2V$)" PARAMETERS_SUFFIX = r"(\d{1,4}[KkMmBb]|[._-]\d+[\._-]\d+[Bb][._-]).*?$" diff --git a/mir/config/conversion.py b/mir/config/conversion.py index f3a32d7..7a12509 100644 --- a/mir/config/conversion.py +++ b/mir/config/conversion.py @@ -2,7 +2,7 @@ # from typing import Callable, Optional, Union, Type, List, Iterator, Tuple, Dict -from mir import dbuq, nfo +from mir.config.logging import dbuq, nfo def import_submodules(module_name: str, pkg_name_or_abs_path: str) -> Optional[Callable]: diff --git a/mir/doc_parser.py b/mir/doc_parser.py index e9c504e..8aadcf9 100644 --- a/mir/doc_parser.py +++ b/mir/doc_parser.py @@ -4,7 +4,7 @@ from typing import List, Optional, Tuple from pydantic import BaseModel, field_validator -from mir import dbuq, nfo +from mir.config.logging import dbuq, nfo from mir.config.constants import DocParseData, DocStringParserConstants diff --git a/mir/indexers.py b/mir/indexers.py index 5f050f1..069b7b4 100644 --- a/mir/indexers.py +++ b/mir/indexers.py @@ -9,7 +9,7 @@ from mir.doc_parser import parse_docs from mir.inspect.metadata import gather_transformers_metadata from mir.tag import make_mir_tag -from mir.inspect.classes import resolve_class_name, extract_init_params +from mir.inspect.classes import resolve_class_names, extract_init_params from mir.config.logging import nfo from mir.config.conversion import import_submodules @@ -276,7 +276,7 @@ def transformers_index(): continue else: mir_prefix = "info." + mir_prefix - code_name = resolve_class_name(class_name) + code_name = resolve_class_names(class_name) if code_name != "funnel": mir_suffix, mir_comp = list(make_mir_tag(repo_path)) else: diff --git a/mir/inspect/tasks.py b/mir/inspect/tasks.py index 2f9dba4..c0fc77c 100644 --- a/mir/inspect/tasks.py +++ b/mir/inspect/tasks.py @@ -2,7 +2,7 @@ # # # from typing import Any, Callable, Dict, List, get_type_hints -from mir import MIRDatabase +from mir.maid import MIRDatabase from mir.config.logging import dbuq diff --git a/pyproject.toml b/pyproject.toml index bd53514..2ecde7c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,14 +38,20 @@ Documentation = "https://github.com/darkshapes/sdbx/wiki" [project.scripts] mir = "mir.__init__:main" -[tool.setuptools] -packages = ["mir"] +[tool.setuptools_scm] +version_scheme = "guess-next-dev" +local_scheme = "no-local-version" [tool.setuptools.package-data] -mir = ['mir/*.json','mir/config/*.json'] +"*" = ["*.txt", "*.md", "*.json"] + +[tool.setuptools.packages.find] +where = ["."] +include = ["mir*"] +exclude = ["tests*"] [tool.uv.sources] -transformers = { git = "https://github.com/huggingface/transformers.git" } +transformers = { git = "https://github.com/huggingface/transformers" } [dependency-groups] dev = [ diff --git a/uv.lock b/uv.lock index 7568d9c..6420f59 100644 --- a/uv.lock +++ b/uv.lock @@ -413,7 +413,7 @@ requires-dist = [ { name = "pydantic", specifier = ">=2.12.5" }, { name = "tokenizers", specifier = ">=0.22.1" }, { name = "torch", specifier = ">=2.9.1" }, - { name = "transformers", git = "https://github.com/huggingface/transformers.git" }, + { name = "transformers", git = "https://github.com/huggingface/transformers" }, ] [package.metadata.requires-dev] @@ -1272,7 +1272,7 @@ wheels = [ [[package]] name = "transformers" version = "5.0.0.dev0" -source = { git = "https://github.com/huggingface/transformers.git#377a8ee73f210476c4efb15170d0c32ad3b2c653" } +source = { git = "https://github.com/huggingface/transformers#377a8ee73f210476c4efb15170d0c32ad3b2c653" } dependencies = [ { name = "filelock" }, { name = "huggingface-hub" }, From 9cb025be173056775922a66ace14121ac2e5cab0 Mon Sep 17 00:00:00 2001 From: exdysa <91800957+exdysa@users.noreply.github.com> Date: Wed, 3 Dec 2025 13:36:30 -0500 Subject: [PATCH 03/10] ~troubleshooting --- MIR.egg-info/PKG-INFO | 1 + MIR.egg-info/SOURCES.txt | 37 +- MIR.egg-info/requires.txt | 1 + mir/{run.py => __main__.py} | 0 mir/config/constants.py | 106 +- mir/config/conversion.py | 1 + mir/config/json_io.py | 4 +- mir/indexers.py | 11 +- mir/inspect/classes.py | 6 +- mir/inspect/metadata.py | 39 +- mir/inspect/pipes.py | 13 +- mir/inspect/tasks.py | 28 +- mir/maid.py | 1 + mir/mir.json | 13254 +--------------------- pyproject.toml | 4 + tests/test_class_parent.py | 35 + tests/test_deconstructors_root.py | 22 + tests/test_doc_parser.py | 143 + tests/test_gather_diffusers_metadata.py | 49 + tests/test_json_io.py | 42 + tests/test_mir_db_create_restore.py | 160 + tests/test_mir_merge.py | 122 + tests/test_mir_search.py | 98 + tests/test_mir_tagging.py | 44 + tests/test_regex_constants.py | 27 + tests/test_resolve_code_names.py | 44 + tests/test_seek_class.py | 17 + tests/test_task.py | 11 + tests/test_taskanalyzer.py | 320 + uv.lock | 86 +- 30 files changed, 1359 insertions(+), 13367 deletions(-) rename mir/{run.py => __main__.py} (100%) create mode 100644 tests/test_class_parent.py create mode 100644 tests/test_deconstructors_root.py create mode 100644 tests/test_doc_parser.py create mode 100644 tests/test_gather_diffusers_metadata.py create mode 100644 tests/test_json_io.py create mode 100644 tests/test_mir_db_create_restore.py create mode 100644 tests/test_mir_merge.py create mode 100644 tests/test_mir_search.py create mode 100644 tests/test_mir_tagging.py create mode 100644 tests/test_regex_constants.py create mode 100644 tests/test_resolve_code_names.py create mode 100644 tests/test_seek_class.py create mode 100644 tests/test_task.py create mode 100644 tests/test_taskanalyzer.py diff --git a/MIR.egg-info/PKG-INFO b/MIR.egg-info/PKG-INFO index 53d28a4..970ed4f 100644 --- a/MIR.egg-info/PKG-INFO +++ b/MIR.egg-info/PKG-INFO @@ -37,6 +37,7 @@ Requires-Dist: huggingface-hub[hf-xet]>=1.1.7 Requires-Dist: pydantic>=2.12.5 Requires-Dist: tokenizers>=0.22.1 Requires-Dist: torch>=2.9.1 +Requires-Dist: torchvision>=0.24.1 Requires-Dist: transformers>=2.3.0 Dynamic: license-file diff --git a/MIR.egg-info/SOURCES.txt b/MIR.egg-info/SOURCES.txt index 2b2566e..101d0c4 100644 --- a/MIR.egg-info/SOURCES.txt +++ b/MIR.egg-info/SOURCES.txt @@ -10,13 +10,32 @@ MIR.egg-info/dependency_links.txt MIR.egg-info/entry_points.txt MIR.egg-info/requires.txt MIR.egg-info/top_level.txt +docs/index.html +docs/mir.html +docs/search.js +docs/mir/automata.html +docs/mir/config.html +docs/mir/doc_parser.html +docs/mir/indexers.html +docs/mir/inspect.html +docs/mir/maid.html +docs/mir/run.html +docs/mir/tag.html +docs/mir/config/constants.html +docs/mir/config/conversion.html +docs/mir/config/json_io.html +docs/mir/inspect/classes.html +docs/mir/inspect/metadata.html +docs/mir/inspect/parenting.html +docs/mir/inspect/pipes.html +docs/mir/inspect/tasks.html mir/__init__.py +mir/__main__.py mir/automata.py mir/doc_parser.py mir/indexers.py mir/maid.py mir/mir.json -mir/run.py mir/tag.py mir/config/__init__.py mir/config/constants.py @@ -32,4 +51,18 @@ mir/inspect/tasks.py mir/spec/mir.py mir/spec/modes.json mir/spec/template.json -mir/spec/versions.json \ No newline at end of file +mir/spec/versions.json +tests/test_class_parent.py +tests/test_deconstructors_root.py +tests/test_doc_parser.py +tests/test_gather_diffusers_metadata.py +tests/test_json_io.py +tests/test_mir_db_create_restore.py +tests/test_mir_merge.py +tests/test_mir_search.py +tests/test_mir_tagging.py +tests/test_regex_constants.py +tests/test_resolve_code_names.py +tests/test_seek_class.py +tests/test_task.py +tests/test_taskanalyzer.py \ No newline at end of file diff --git a/MIR.egg-info/requires.txt b/MIR.egg-info/requires.txt index 9f1dc9e..d9c4e5b 100644 --- a/MIR.egg-info/requires.txt +++ b/MIR.egg-info/requires.txt @@ -3,4 +3,5 @@ huggingface-hub[hf-xet]>=1.1.7 pydantic>=2.12.5 tokenizers>=0.22.1 torch>=2.9.1 +torchvision>=0.24.1 transformers>=2.3.0 diff --git a/mir/run.py b/mir/__main__.py similarity index 100% rename from mir/run.py rename to mir/__main__.py diff --git a/mir/config/constants.py b/mir/config/constants.py index 0f4bfaf..07dd812 100644 --- a/mir/config/constants.py +++ b/mir/config/constants.py @@ -3,72 +3,50 @@ from typing import List, Optional, Union from mir.config.json_io import read_json_file import os -from transformers.models.auto.modeling_auto import ( - MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES, - MODEL_FOR_BACKBONE_MAPPING_NAMES, - MODEL_FOR_CAUSAL_LM_MAPPING_NAMES, - MODEL_FOR_CTC_MAPPING_NAMES, - MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES, - MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES, - MODEL_FOR_IMAGE_MAPPING_NAMES, - MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES, - MODEL_FOR_MASKED_LM_MAPPING_NAMES, - MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES, - MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES, - MODEL_FOR_PRETRAINING_MAPPING_NAMES, - MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES, - MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES, - MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES, - MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES, - MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES, - MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES, - MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES, - MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES, - MODEL_MAPPING_NAMES, -) -from transformers.configuration_utils import PretrainedConfig +from transformers.models.auto.modeling_auto import MODEL_MAPPING, MODEL_MAPPING_NAMES +from transformers.models.auto.configuration_auto import CONFIG_MAPPING_NAMES +import transformers -def generate_supported_model_class_names( - model_name: type[PretrainedConfig], - supported_tasks: Optional[Union[str, list[str]]] = None, -) -> list[str]: - task_mapping = { - "default": MODEL_MAPPING_NAMES, - "pretraining": MODEL_FOR_PRETRAINING_MAPPING_NAMES, - "next-sentence-prediction": MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES, - "masked-lm": MODEL_FOR_MASKED_LM_MAPPING_NAMES, - "causal-lm": MODEL_FOR_CAUSAL_LM_MAPPING_NAMES, - "seq2seq-lm": MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES, - "speech-seq2seq": MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES, - "multiple-choice": MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES, - "document-question-answering": MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES, - "question-answering": MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES, - "sequence-classification": MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES, - "token-classification": MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES, - "masked-image-modeling": MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES, - "image-classification": MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES, - "zero-shot-image-classification": MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES, - "ctc": MODEL_FOR_CTC_MAPPING_NAMES, - "audio-classification": MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES, - "semantic-segmentation": MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES, - "backbone": MODEL_FOR_BACKBONE_MAPPING_NAMES, - "image-feature-extraction": MODEL_FOR_IMAGE_MAPPING_NAMES, - "video-classification": MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES, - } +def mapped_cls(model_identifier: str): + """Get model class from identifier without calling huggingface_hub. + + :param model_identifier: Model identifier like "bert-base-uncased" or "gpt2" + :return: Model class (e.g., BertModel, GPT2Model) + """ + # Extract code name from model identifier (e.g., "bert-base-uncased" -> "bert") + # Handle various formats: "bert-base-uncased", "gpt2", "microsoft/DialoGPT-medium" + code_name = model_identifier.split("/")[-1].split("-")[0].lower() + + # Method 1: Direct lookup via MODEL_MAPPING_NAMES (simplest) + model_class_name = MODEL_MAPPING_NAMES.get(code_name, None) - if supported_tasks is None: - supported_tasks = task_mapping.keys() - if isinstance(supported_tasks, str): - supported_tasks = [supported_tasks] + + # Method 2: Via config class lookup (matches _get_model_class behavior more closely) + config_class_name = CONFIG_MAPPING_NAMES.get(code_name) + if config_class_name: + config_class = getattr(transformers, config_class_name, None) + if config_class: + # Look up in MODEL_MAPPING using config class + model_class = MODEL_MAPPING.get(config_class, None) + if model_class: + if isinstance(model_class, tuple): + model_class = model_class[0] + return model_class + + # Fallback: try with normalized code name (handle underscores/dashes) + normalized = code_name.replace("_", "-") + if normalized != code_name: + print(f"normalized: {normalized}") + model_class_name = MODEL_MAPPING_NAMES.get(normalized, None) + if model_class_name: + return getattr(transformers, model_class_name, None) + if model_class_name: + if isinstance(model_class_name, tuple): + model_class_name = model_class_name[0] + return getattr(transformers, model_class_name, None) - model_class_names = [] - for task in supported_tasks: - class_name = task_mapping[task].get(model_name, None) - if class_name: - model_class_names.append(class_name) - - return model_class_names + return None class DocParseData: @@ -119,10 +97,10 @@ class DocStringParserConstants: "diffusers": ("_import_structure", "diffusers.pipelines"), "transformers": ("MODEL_MAPPING_NAMES", "transformers.models.auto.modeling_auto"), } -root_path = os.path.dirname(os.path.dirname(__file__)) +root_path = os.path.join(os.getcwd(), "mir") versions = read_json_file(os.path.join(root_path, "spec", "versions.json")) template = read_json_file(os.path.join(root_path, "spec", "template.json")) - +print(root_path) MIR_PATH_NAMED = os.path.join(root_path, "mir.json") BREAKING_SUFFIX = r".*(?:-)(prior)$|.*(?:-)(diffusers)$|.*[_-](\d{3,4}px|-T2V$|-I2V$)" diff --git a/mir/config/conversion.py b/mir/config/conversion.py index 7a12509..68b852d 100644 --- a/mir/config/conversion.py +++ b/mir/config/conversion.py @@ -60,6 +60,7 @@ def pkg_path_to_docstring(pkg_name: str, folder_path: bool) -> Iterator[Tuple[st dbuq(pkg_path) path_exists = os.path.exists(os.path.join(module_path, pkg_name, file_name + ".py")) if path_exists: + print(f"file_name, pkg_path): {file_name, pkg_path}") pipe_file = import_submodules(file_name, pkg_path) except ModuleNotFoundError: if pkg_name != "skyreels_v2": diff --git a/mir/config/json_io.py b/mir/config/json_io.py index 099834a..5125766 100644 --- a/mir/config/json_io.py +++ b/mir/config/json_io.py @@ -17,7 +17,7 @@ def write_json_file(folder_path_named: str, file_name: str, data: Any, mode: str import json import os - from mir import dbug + from mir.config.logging import dbuq if ".json" not in file_name: file_name += ".json" @@ -26,7 +26,7 @@ def write_json_file(folder_path_named: str, file_name: str, data: Any, mode: str try: os.remove(document) except FileNotFoundError as error_log: - dbug(f"'File was detected but not found to remove: {document}.'{error_log}", exc_info=True) + dbuq(f"'File was detected but not found to remove: {document}.'{error_log}", exc_info=True) with open(document, mode, encoding="UTF-8") as i: json.dump(data, i, ensure_ascii=False, indent=4, sort_keys=False) diff --git a/mir/indexers.py b/mir/indexers.py index 069b7b4..98a1e5d 100644 --- a/mir/indexers.py +++ b/mir/indexers.py @@ -7,9 +7,8 @@ import sys from typing import Any, Callable, Dict, List, Optional from mir.doc_parser import parse_docs -from mir.inspect.metadata import gather_transformers_metadata from mir.tag import make_mir_tag -from mir.inspect.classes import resolve_class_names, extract_init_params +from mir.inspect.classes import resolve_code_names, extract_init_params from mir.config.logging import nfo from mir.config.conversion import import_submodules @@ -141,11 +140,13 @@ def diffusers_index() -> Dict[str, Dict[str, Dict[str, Any]]]: "HunyuanDiTPipeline": "tencent-hunyuan/hunyuandiT-v1.2-diffusers", # NOT hyd .ckpt "ChromaPipeline": "lodestones/Chroma", } - extracted_docs = list(gather_transformers_metadata()) + from mir.inspect.metadata import gather_diffusers_metadata + extracted_docs = list(gather_diffusers_metadata()) pipe_data = {} # pipeline_stable_diffusion_xl_inpaint + print(f"extracted_docs: {extracted_docs}") for code_name, file_name, docs in extracted_docs: parse_result = parse_docs(docs) - + print(f"parse_result: {parse_result}") if parse_result: pipe_class = parse_result.pipe_class pipe_repo = parse_result.pipe_repo @@ -276,7 +277,7 @@ def transformers_index(): continue else: mir_prefix = "info." + mir_prefix - code_name = resolve_class_names(class_name) + code_name = resolve_code_names(class_name) if code_name != "funnel": mir_suffix, mir_comp = list(make_mir_tag(repo_path)) else: diff --git a/mir/inspect/classes.py b/mir/inspect/classes.py index 3d39167..f842267 100644 --- a/mir/inspect/classes.py +++ b/mir/inspect/classes.py @@ -38,8 +38,8 @@ def resolve_import_path(code_name: str, pkg_name: str) -> Optional[List[str]]: return import_path -def resolve_class_names(class_name: Optional[Union[str, Type]] = None, pkg_name: Optional[str] = "transformers", path_format: Optional[bool] = False) -> Union[List[str], str]: - """Reveal code names for class names from Diffusers or Transformers\n +def resolve_code_names(class_name: Optional[Union[str, Type]] = None, pkg_name: Optional[str] = "transformers", path_format: Optional[bool] = False) -> Union[List[str], str]: + """Reveal code names for class names from Diffusers or Transformers (formerly get code names)\n :param class_name: To return only one class, defaults to None :param pkg_name: optional field for library, defaults to "transformers" :param path_format: Retrieve just the code name, or the full module path and code name within the package @@ -81,7 +81,7 @@ def extract_inherited_classes(model_class: Union[Callable, str], pkg_name: Optio def extract_init_params(module: Union[Callable, str], pkg_name: Optional[str] = None) -> Dict[str, List[str]]: - """Pick apart a Diffusers or Transformers pipeline class and find its constituent parts\n + """Pick apart a Diffusers or Transformers pipeline class and find its constituent parts (formerly root_class)\n :param module: Origin pipeline as a class or as a string :param library: name of a library to import the class from, only if a string is provided :return: Dictionary of sub-classes from the `module`""" diff --git a/mir/inspect/metadata.py b/mir/inspect/metadata.py index d7a96fe..1b0befa 100644 --- a/mir/inspect/metadata.py +++ b/mir/inspect/metadata.py @@ -32,6 +32,8 @@ def gather_transformers_metadata() -> Dict[str, List[str]]: "vision_text_dual_encoder", "gpt_bigcode", "data2vec", + "vision-text", + "mllama" "bert_japanese", "cpm", "dab_detr", @@ -55,7 +57,7 @@ def gather_transformers_metadata() -> Dict[str, List[str]]: model_class = None if code_name and "__" not in code_name: tasks = TaskAnalyzer.show_transformers_tasks(code_name=code_name) - if tasks: + if tasks and isinstance(tasks, list): # Ensure tasks is a list task_pipe = next(iter(tasks)) if isinstance(task_pipe, tuple): task_pipe = task_pipe[0] @@ -66,22 +68,25 @@ def gather_transformers_metadata() -> Dict[str, List[str]]: transformer_data.setdefault(model_class, model_data) else: model_data = None + # Reset task_pipe if tasks was None or not a list + if not tasks or not isinstance(tasks, list): + task_pipe = None - if not model_data and code_name not in second_exclude_list: # second attempt - if code_name == "donut": - code_name = "donut-swin" - if not task_pipe and code_name and MODEL_MAPPING_NAMES.get(code_name.replace("_", "-")): - model_class = getattr(__import__("transformers"), MODEL_MAPPING_NAMES[code_name.replace("_", "-")], None) - elif task_pipe: - model_class = getattr(__import__("transformers"), task_pipe) - config_class = CONFIG_MAPPING_NAMES.get(code_name.replace("_", "-")) - if not config_class: - config_class = CONFIG_MAPPING_NAMES.get(code_name.replace("-", "_")) - if config_class: - config_class_obj = getattr(__import__("transformers"), config_class) - model_data = {"config": str(config_class_obj.__module__ + "." + config_class_obj.__name__).split(".")} - if model_data and ("inspect" not in model_data) and ("deprecated" not in model_data) and model_class: - transformer_data.setdefault(model_class, model_data) + if not model_data and code_name not in second_exclude_list: # second attempt + if code_name == "donut": + code_name = "donut-swin" + if not task_pipe and code_name and MODEL_MAPPING_NAMES.get(code_name.replace("_", "-")): + model_class = getattr(__import__("transformers"), MODEL_MAPPING_NAMES[code_name.replace("_", "-")], None) + elif task_pipe: + model_class = getattr(__import__("transformers"), task_pipe) + config_class = CONFIG_MAPPING_NAMES.get(code_name.replace("_", "-")) + if not config_class: + config_class = CONFIG_MAPPING_NAMES.get(code_name.replace("-", "_")) + if config_class: + config_class_obj = getattr(__import__("transformers"), config_class) + model_data = {"config": str(config_class_obj.__module__ + "." + config_class_obj.__name__).split(".")} + if model_data and ("inspect" not in model_data) and ("deprecated" not in model_data) and model_class: + transformer_data.setdefault(model_class, model_data) return transformer_data @@ -124,6 +129,8 @@ def gather_diffusers_metadata() -> Generator: "text_to_video_synthesis", "unclip", "unidiffuser", + "controlnet_hunyuandit", + "hunyuandit", # these are uncommon afaik ] diff --git a/mir/inspect/pipes.py b/mir/inspect/pipes.py index 73ee5b7..8d0e332 100644 --- a/mir/inspect/pipes.py +++ b/mir/inspect/pipes.py @@ -25,12 +25,19 @@ def get_transformer_config_classes(parameter_filter: Optional[str] = None) -> Li def get_class_parent_folder(class_name: str, pkg_name: str) -> List[str]: - from mir import dbuq - from mir.inspect.classes import resolve_class_name, extract_init_params + """Retrieve the folder path within a class. Only returns if it is a valid path in the system (formerly seek_class_path)\n + ### NOTE: in most cases `__module__` makes this redundant + :param class_name: The internal name for the model in the third-party API. + :param pkg_name: The API Package + :return: A list corresponding to the path of the model, or None if not found + :raises KeyError: for invalid pkg_name + """ + from mir.config.logging import dbuq + from mir.inspect.classes import resolve_code_names, extract_init_params pkg_name = pkg_name.lower() if pkg_name == "diffusers": - parent_folder: List[str] = resolve_class_name(class_name=class_name, pkg_name=pkg_name, path_format=True) + parent_folder: List[str] = resolve_code_names(class_name=class_name, pkg_name=pkg_name, path_format=True) if not parent_folder or not parent_folder[-1].strip(): dbuq("Data not found for", " class_name = {class_name},pkg_name = {pkg_name},{parent_folder} = parent_folder") return None diff --git a/mir/inspect/tasks.py b/mir/inspect/tasks.py index c0fc77c..a85a3e8 100644 --- a/mir/inspect/tasks.py +++ b/mir/inspect/tasks.py @@ -35,17 +35,17 @@ def show_diffusers_tasks(code_name: str, class_name: str | None = None) -> list[ if class_name: from diffusers.pipelines.auto_pipeline import SUPPORTED_TASKS_MAPPINGS, _get_task_class - alt_tasks = [] + alt_tasks = set() for task_map in SUPPORTED_TASKS_MAPPINGS: task_class = _get_task_class(task_map, class_name, False) if task_class: - alt_tasks.append(task_class.__name__) + alt_tasks.add(task_class.__name__) dbuq(task_class) for model_code, pipe_class_obj in task_map.items(): if code_name in model_code: - alt_tasks.append(pipe_class_obj.__name__) + alt_tasks.add(pipe_class_obj.__name__) - return alt_tasks + return list(alt_tasks) @staticmethod def show_transformers_tasks(class_name: str | None = None, code_name: str | None = None) -> list[str]: @@ -55,6 +55,8 @@ def show_transformers_tasks(class_name: str | None = None, code_name: str | None :param alt_method: Use an alternate method to return the classes :return: A list of task classes associated with the specified transformer.""" + task_classes = None + if not code_name: from mir.config.conversion import import_submodules @@ -65,9 +67,19 @@ def show_transformers_tasks(class_name: str | None = None, code_name: str | None else: return None elif code_name: - from mir.config.constants import generate_supported_model_class_names + from mir.config.constants import mapped_cls + from httpx import HTTPStatusError + try: + model_class = mapped_cls(code_name) + if model_class is not None: + # Convert class type to list containing the class name string + task_classes = [model_class.__name__] + else: + return None + except (OSError, HTTPStatusError) as e: + dbuq(f"Error mapping class {code_name}: {e}") + return None - task_classes = generate_supported_model_class_names(code_name) return task_classes async def detect_tasks(self, mir_db: MIRDatabase, field_name: str = "pkg") -> dict: @@ -200,7 +212,7 @@ async def trace_tasks(self, pkg_tree: dict[str, str | int | list[str | int]]) -> :param entry: The object containing the model information. :return: A sorted list of tasks applicable to the model.""" - from mir.inspect.classes import resolve_class_name + from mir.inspect.classes import resolve_code_names preformatted_task_data = None filtered_tasks = None @@ -215,7 +227,7 @@ async def trace_tasks(self, pkg_tree: dict[str, str | int | list[str | int]]) -> if package_name == "transformers": preformatted_task_data = self.show_transformers_tasks(class_name=class_name) elif package_name == "diffusers": - code_name = resolve_class_name(class_name, package_name) + code_name = resolve_code_names(class_name, package_name) preformatted_task_data = self.show_diffusers_tasks(code_name=code_name, class_name=class_name) preformatted_task_data.sort() elif package_name == "mflux": diff --git a/mir/maid.py b/mir/maid.py index 99882c1..d66fc26 100644 --- a/mir/maid.py +++ b/mir/maid.py @@ -146,6 +146,7 @@ def find_tag(self, field: str, target: str, sub_field: Optional[str] = None, dom :raises KeyError: Target string not found """ import re + from mir.config.logging import nfo parameters = r"-gguf|-exl2|-exl3|-onnx|-awq|-mlx|-ov" # target = target.lower().strip("-") diff --git a/mir/mir.json b/mir/mir.json index 2cba92d..59ae13b 100644 --- a/mir/mir.json +++ b/mir/mir.json @@ -1,13255 +1,3 @@ { - "info.dit.allegro": { - "*": { - "repo": "rhymes-ai/Allegro", - "pkg": { - "0": { - "diffusers": "AllegroPipeline", - "precision": "ops.precision.bfloat.B16", - "generation": { - "guidance_scale": 7.5, - "max_sequence_length": 512, - "num_inference_steps": 100 - } - } - }, - "file_256": [ - "6927dcc812841c1da549bf11c97ddf30532aee0e708a6642fa64cf8e0dfcdef7" - ], - "layer_b3": [ - "8b20714a6af89ea4bf4ada1f805c5b9d529ef136c229e9b75392242d62d80c3e" - ], - "layer_256": [ - "9e44e6c919dc71c24a193641e6265cd9983a2a773b9bbaf527c10ac4837b29fd" - ], - "pipe_names": { - "tokenizer": [ - "info.encoder.tokenizer", - "allegro" - ], - "text_encoder": [ - "T5EncoderModel" - ], - "vae": [ - "info.vae.kl", - "allegro" - ], - "transformer": [ - "AllegroTransformer3DModel" - ], - "scheduler": [ - "ops.scheduler.karrasdiffusion", - "schedulers" - ] - } - } - }, - "info.dit.amused-512": { - "*": { - "repo": "amused/amused-512", - "pkg": { - "0": { - "diffusers": "AmusedInpaintPipeline" - } - }, - "pipe_names": { - "vqvae": [ - "VQModel" - ], - "tokenizer": [ - "info.encoder.tokenizer", - "amused-512" - ], - "text_encoder": [ - "CLIPTextModelWithProjection" - ], - "transformer": [ - "UVit2DModel" - ], - "scheduler": [ - "ops.scheduler.amused", - "scheduler" - ] - } - } - }, - "info.lora.animatediff-motion-adapter-v1-5-2": { - "*": { - "repo": "guoyww/animatediff-motion-adapter-v1-5-2", - "pkg": { - "0": { - "diffusers": "AnimateDiffVideoToVideoPipeline" - } - } - } - }, - "info.lora.animatediff-motion-adapter-sdxl": { - "*": { - "repo": "a-r-r-o-w/animatediff-motion-adapter-sdxl-beta", - "pkg": { - "0": { - "diffusers": "AnimateDiffSDXLPipeline" - } - } - } - }, - "info.controlnet.animatediff-sparsectrl-scribble": { - "*": { - "repo": "guoyww/animatediff-sparsectrl-scribble", - "pkg": { - "0": { - "diffusers": "SparseControlNetModel" - } - } - } - }, - "info.controlnet.animatelcm": { - "*": { - "repo": "wangfuyun/AnimateLCM", - "pkg": { - "0": { - "diffusers": "ControlNetModel" - } - } - } - }, - "info.unet.audioldm-s-v2": { - "*": { - "repo": "cvssp/audioldm-s-full-v2", - "pkg": { - "0": { - "diffusers": "AudioLDMPipeline" - } - }, - "file_256": [ - "fc30d5b5a3bb8d08672736efb1fff10755ba7024dace39b2dcb579a105aa2a5a" - ], - "layer_b3": [ - "82fbcc553c1ad770d28fd1866b935249c5ebfbf75f3166ae823e1bc6ef39a95a" - ], - "layer_256": [ - "d076446a58a36bf436e37444679d62bcf2f45689d4aa3d799b3fe801c71ed2c8" - ], - "pipe_names": { - "vae": [ - "AutoencoderKL" - ], - "text_encoder": [ - "ClapTextModelWithProjection" - ], - "tokenizer": [ - "info.encoder.tokenizer", - "audioldm-s-v2" - ], - "scheduler": [ - 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"info.vae.ms-lc-eq": { - "stable-diffusion-xl-1": { - "repo": "Anzhc/MS-LC-EQ-D-VR_VAE", - "pkg": { - "0": { - "diffusers": "AutoencoderKL" - } - } - } - } + "expected": "data" } \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 2ecde7c..3f4f11e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -29,6 +29,7 @@ dependencies = [ "pydantic>=2.12.5", "tokenizers>=0.22.1", "torch>=2.9.1", + "torchvision>=0.24.1", "transformers>=2.3.0", ] [project.urls] @@ -52,12 +53,15 @@ exclude = ["tests*"] [tool.uv.sources] transformers = { git = "https://github.com/huggingface/transformers" } +diffusers = { git = "https://github.com/huggingface/diffusers" } [dependency-groups] dev = [ "pdoc>=16.0.0", "pyright>=1.1.407", "pytest>=9.0.1", + "pytest-asyncio>=1.3.0", + "pytest-mock>=3.15.1", "ruff>=0.14.7", ] diff --git a/tests/test_class_parent.py b/tests/test_class_parent.py new file mode 100644 index 0000000..cbd729d --- /dev/null +++ b/tests/test_class_parent.py @@ -0,0 +1,35 @@ +# # # +# # # + +import pytest +from mir.inspect.parenting import class_parent # Replace with the actual module name + + +def test_class_parent_diffusers(): + assert class_parent("stable-diffusion", "Diffusers") == ["diffusers", "pipelines", "stable_diffusion"] + + +def test_class_parent_transformers(): + assert class_parent("albert", "Transformers") == ["transformers", "models", "albert"] + + +def test_class_parent_invalid_parent(): + with pytest.raises(KeyError): + class_parent("unknown", "Unknown") + + +def test_class_parent_empty_parent(): + with pytest.raises(KeyError): + assert class_parent("", "") == ["", "", ""] + + +def test_class_parent_bad_code_name(): + assert class_parent("diffdusers", "diffusers") is None + + +def test_class_parent_mixed_case(): + assert class_parent("sana", "DIFFusERS") == ["diffusers", "pipelines", "sana"] + + +if __name__ == "__main__": + pytest.main(["-vv", __file__]) diff --git a/tests/test_deconstructors_root.py b/tests/test_deconstructors_root.py new file mode 100644 index 0000000..c8e01ad --- /dev/null +++ b/tests/test_deconstructors_root.py @@ -0,0 +1,22 @@ +# # # +# # # + +import pytest +from mir.inspect.classes import extract_init_params + + +def test_root_class_with_builtin_types(): + class DummyInitModule: + def __init__(self, flag: bool, count: int): + pass + + expected_output = {} + + result = extract_init_params(DummyInitModule) + assert result == expected_output + + +if __name__ == "__main__": + import pytest + + pytest.main(["-vv", __file__]) diff --git a/tests/test_doc_parser.py b/tests/test_doc_parser.py new file mode 100644 index 0000000..3178d41 --- /dev/null +++ b/tests/test_doc_parser.py @@ -0,0 +1,143 @@ +import unittest +from mir.doc_parser import parse_docs + + +class TestDocParser(unittest.TestCase): + def test_parse_simple_case(self): + doc_string = """ + >>> pipe = MyPipeline.from_pretrained("model/repo") + """ + result = parse_docs(doc_string) + self.assertEqual(result.pipe_class, "MyPipeline") # pipe_class + self.assertEqual(result.pipe_repo, "model/repo") # repo_path + self.assertIsNone(result.staged_class) # staged_class + self.assertIsNone(result.staged_repo) # staged_repo + + def test_parse_with_variable_resolution(self): + doc_string = """ + model_id = "custom/model" + >>> pipe = MyPipeline.from_pretrained(model_id) + """ + result = parse_docs(doc_string) + self.assertEqual(result.pipe_class, "MyPipeline") + self.assertEqual(result.pipe_repo, "custom/model") + + def test_parse_staged_case(self): + doc_string = """ + >>> pipe = MyPipeline.from_pretrained("model/repo") + >>> prior_pipe = PriorPipeline.from_pretrain("prior/repo") + """ + result = parse_docs(doc_string) + self.assertEqual(result.pipe_class, "MyPipeline") # pipe_class + self.assertEqual(result.pipe_repo, "model/repo") # repo_path + self.assertEqual(result.staged_class, "PriorPipeline") # staged_class + self.assertEqual(result.staged_repo, "prior/repo") # staged_repo + + def test_parse_no_match(self): + doc_string = """ + >>> something_else = SomeClass.do_something() + """ + result = parse_docs(doc_string) + self.assertIsNone(result) # pipe_class + + def test_parse_multiline_doc(self): + doc_string = """ + # model_id_or_path = "another/repo" + >>> pipe_prior = PriorPipeline.from_pretrain(model_id_or_path) + >>> pipeline = MyPipeline.from_pretrained("repo/path") + """ + result = parse_docs(doc_string) + self.assertEqual(result.pipe_class, "MyPipeline") # pipe_class + self.assertEqual(result.pipe_repo, "repo/path") # repo_path + self.assertEqual(result.staged_class, "PriorPipeline") # staged_class + self.assertEqual(result.staged_repo, "another/repo") # staged_repo + + def test_parse_blip(self): + from diffusers.pipelines.blip_diffusion.pipeline_blip_diffusion import EXAMPLE_DOC_STRING + + result = parse_docs(EXAMPLE_DOC_STRING) + self.assertEqual(result.pipe_class, "BlipDiffusionPipeline") # pipe_class + self.assertEqual(result.pipe_repo, "Salesforce/blipdiffusion") # repo_path + self.assertIsNone(result.staged_class) # staged_class + self.assertIsNone(result.staged_repo) # staged_repo + + def test_parse_pia(self): + from diffusers.pipelines.pia.pipeline_pia import EXAMPLE_DOC_STRING + + result = parse_docs(EXAMPLE_DOC_STRING) + self.assertEqual(result.pipe_class, "PIAPipeline") # pipe_class + self.assertEqual(result.pipe_repo, "openmmlab/PIA-condition-adapter") # repo_path + self.assertIsNone(result.staged_class) # staged_class + self.assertIsNone(result.staged_repo) # staged_repo + + def test_parse_animatediff_xl(self): + from diffusers.pipelines.animatediff.pipeline_animatediff_sdxl import EXAMPLE_DOC_STRING + + result = parse_docs(EXAMPLE_DOC_STRING) + self.assertEqual(result.pipe_class, "AnimateDiffSDXLPipeline") # pipe_class + self.assertEqual(result.pipe_repo, "a-r-r-o-w/animatediff-motion-adapter-sdxl-beta") # repo_path + self.assertIsNone(result.staged_class) # staged_class + self.assertIsNone(result.staged_repo) # staged_repo + + def test_parse_animatediff_controlnet(self): + from diffusers.pipelines.animatediff.pipeline_animatediff_controlnet import EXAMPLE_DOC_STRING + + result = parse_docs(EXAMPLE_DOC_STRING) + # TODO : This ought to return control net data but its missing in the docstring + + # self.assertEqual(result.pipe_class, "ControlNetModel") # pipe_class + # self.assertEqual(result.pipe_repo, "lllyasviel/ControlNet-v1-1") # repo_path + # self.assertIsNone(result.staged_class) # staged_class + # self.assertIsNone(result.staged_repo) # staged_repo + + def test_parse_consistency(self): + from diffusers.pipelines.consistency_models.pipeline_consistency_models import EXAMPLE_DOC_STRING + + result = parse_docs(EXAMPLE_DOC_STRING) + self.assertEqual(result.pipe_class, "ConsistencyModelPipeline") # pipe_class + self.assertEqual(result.pipe_repo, "openai/diffusers-cd_imagenet64_l2") # repo_path + self.assertIsNone(result.staged_class) # staged_class + self.assertIsNone(result.staged_repo) # staged_repo + + def test_parse_pixart_sigma(self): + from diffusers.pipelines.pixart_alpha.pipeline_pixart_sigma import EXAMPLE_DOC_STRING + + result = parse_docs(EXAMPLE_DOC_STRING) + self.assertEqual(result.pipe_class, "PixArtSigmaPipeline") # pipe_class + self.assertEqual(result.pipe_repo, "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS") # repo_path + self.assertIsNone(result.staged_class) # staged_class + self.assertIsNone(result.staged_repo) # staged_repo + + def test_parse_cascade(self): + from diffusers.pipelines.stable_cascade.pipeline_stable_cascade import EXAMPLE_DOC_STRING + + result = parse_docs(EXAMPLE_DOC_STRING) + self.assertEqual(result.pipe_class, "StableCascadePriorPipeline") # pipe_class + self.assertEqual(result.pipe_repo, "stabilityai/stable-cascade-prior") # repo_path + self.assertEqual(result.staged_class, "StableCascadeDecoderPipeline") # staged_class + self.assertEqual(result.staged_repo, "stabilityai/stable-cascade") # staged_repo + + def test_parse_xl(self): + from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl import EXAMPLE_DOC_STRING + from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_inpaint import EXAMPLE_DOC_STRING as EXAMPLE_DOC_STRING_INPAINT + + doc_strings = [ + EXAMPLE_DOC_STRING, + EXAMPLE_DOC_STRING_INPAINT, + ] + result = [] + for doc in doc_strings: + result.append(parse_docs(doc)) + + self.assertEqual(result[0].pipe_class, "StableDiffusionXLPipeline") # pipe_class + self.assertEqual(result[0].pipe_repo, "stabilityai/stable-diffusion-xl-base-1.0") # repo_path + self.assertIsNone(result[0].staged_class) # staged_class + self.assertIsNone(result[0].staged_repo) # staged_repo + self.assertEqual(result[1].pipe_class, "StableDiffusionXLInpaintPipeline") # pipe_class + self.assertEqual(result[1].pipe_repo, "stabilityai/stable-diffusion-xl-base-1.0") # repo_path + self.assertIsNone(result[1].staged_class) # staged_class + self.assertIsNone(result[1].staged_repo) # staged_repo + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_gather_diffusers_metadata.py b/tests/test_gather_diffusers_metadata.py new file mode 100644 index 0000000..efbed0a --- /dev/null +++ b/tests/test_gather_diffusers_metadata.py @@ -0,0 +1,49 @@ +# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0 +# +import pytest +from unittest.mock import Mock + + +@pytest.fixture +def mock_import_module(mocker): + """Fixture to mock import_module and simulate different module scenarios.""" + return mocker.patch("mir.config.conversion.import_submodules") + + +@pytest.fixture +def mock_pkgutil_iter_modules(mocker): + """Fixture to mock pkgutil.iter_modules for controlled testing.""" + + return mocker.patch( + "pkgutil.iter_modules", + return_value=[ + (Mock(), "allegro", True), + (Mock(), "amused", True), + (Mock(), "animatediff", True), + (Mock(), "audioldm", True), + (Mock(), "cogvideo", True), + (Mock(), "deepfloyd_if", True), + ], + ) + + +def test_list_diffusers_models(): + from mir.inspect.metadata import gather_diffusers_metadata + + gather_diffusers_metadata() + + +def test_gather_diffusers_metadata_excluded(mock_import_module, mock_pkgutil_iter_modules): + """Test that excluded modules are not processed.""" + from mir.inspect.metadata import gather_diffusers_metadata + + excluded_modules = ["ddpm"] + + def side_effect(import_name, *args, **kwargs): + if any(exc in import_name for exc in excluded_modules): + raise ImportError(f"Module {import_name} is excluded.") + return Mock() + + mock_import_module.side_effect = side_effect + results = list(gather_diffusers_metadata()) # type: ignore # noqa + assert not any("ddpm" in call_arg[0][0] for call_arg in mock_import_module.call_args_list) diff --git a/tests/test_json_io.py b/tests/test_json_io.py new file mode 100644 index 0000000..cc68cb8 --- /dev/null +++ b/tests/test_json_io.py @@ -0,0 +1,42 @@ +# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0 +# + + +import os +import unittest +from tempfile import TemporaryDirectory +from mir.config.json_io import write_json_file, read_json_file + + +class TestFileOperations(unittest.TestCase): + def setUp(self): + """Create a temporary directory to store the test files""" + self.temp_dir = TemporaryDirectory() + self.file_name = "test_data.json" + self.file_path = os.path.join(self.temp_dir.name, self.file_name) + self.test_data = { + "key1": "value1", + "key2": 69, # nice + "key3": [1, 2, 3], + } + + def test_write_and_read_json_file(self): + """Write data to a JSON file, Read data back from the JSON file,Assert that the written and read data are the same""" + write_json_file(self.temp_dir.name, self.file_name, self.test_data) + read_data = read_json_file(self.file_path) + self.assertEqual(read_data, self.test_data) + + def test_read_nonexistent_file(self): + """Test reading a non-existent file should raise FileNotFoundError""" + with self.assertRaises(FileNotFoundError): + read_json_file("non_existent_file.json") + + def tearDown(self): + """Clean up the temporary directory""" + self.temp_dir.cleanup() + + +if __name__ == "__main__": + import pytest + + pytest.main(["-vv", __file__]) diff --git a/tests/test_mir_db_create_restore.py b/tests/test_mir_db_create_restore.py new file mode 100644 index 0000000..d59f25b --- /dev/null +++ b/tests/test_mir_db_create_restore.py @@ -0,0 +1,160 @@ +# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0 +# + +import os +from pathlib import Path +from mir.config.constants import MIR_PATH_NAMED + + +def test_mir_creation(): + from mir.spec.mir import mir_entry + from pprint import pprint + + os.remove(MIR_PATH_NAMED) + Path().touch() + + entry = mir_entry( + domain="info", + arch="unet", + series="stable-diffusion-xl", + comp="base", + repo="stabilityai/stable-diffusion-xl", + pkg={ + 0: { + "diffusers": "class_name", + "generation": {"num_inference_steps": 40, "denoising_end": 0.8, "output_type": "latent", "safety_checker": False}, + } + }, + ) + entry.update( + mir_entry( + domain="model", + arch="unet", + series="stable-diffusion-xl", + comp="base", + file_path="/Users/nyan/Documents/models", + ), + ) + entry.update( + mir_entry( + domain="ops", + arch="scheduler", + series="align-your-steps", + comp="stable-diffusion-xl", + pkg={ + 0: { + "diffusers.schedulers.scheduling_utils": { + "AysSchedules": {"num_inference_steps": 10, "timesteps": "StableDiffusionXLTimesteps"}, + } + } + }, + ) + ) + entry.update( + mir_entry( + domain="ops", + arch="patch", + series="hidiffusion", + comp="stable-diffusion-xl", + pkg={0: {"hidiffusion": {"apply_hidiffusion": {"generation": {"height": 2048, "width": 2048, "eta": 1.0, "guidance_scale": 7.5}}}}}, + ) + ) + pprint(entry) + + +def test_mir_maid(): + import json + import os + from mir.spec.mir import mir_entry + + entry = mir_entry( + domain="info", + arch="unet", + series="stable-diffusion-xl", + comp="base", + repo="stabilityai/stable-diffusion-xl", + pkg={ + 0: { + "diffusers": "class_name", + "generation": {"num_inference_steps": 40, "denoising_end": 0.8, "output_type": "latent", "safety_checker": False}, + } + }, + ) + try: + os.remove(MIR_PATH_NAMED) + except FileNotFoundError: + pass + with open(MIR_PATH_NAMED, "x", encoding="UTF-8") as f: + f.write("{}") + folder_path_named = os.path.dirname(MIR_PATH_NAMED) + from mir.maid import MIRDatabase + + mir_db = MIRDatabase() + mir_db.add(entry) + mir_db.write_to_disk() + print(mir_db.database) + with open(MIR_PATH_NAMED, "r", encoding="UTF-8") as f: + result = json.load(f) + expected = { + "info.unet.stable-diffusion-xl": { + "base": { + "pkg": { + "0": { + "diffusers": "class_name", + "generation": { + "denoising_end": 0.8, + "num_inference_steps": 40, + "output_type": "latent", + "safety_checker": False, + }, + }, + }, + "repo": "stabilityai/stable-diffusion-xl", + }, + }, + } + + assert mir_db.database == expected + assert result == expected + + +def test_restore_mir(): + import json + import os + + from mir.config.json_io import write_json_file + from mir.config.constants import MIR_PATH_NAMED + from mir.maid import MIRDatabase, main + + database = {"expecting": "data"} + try: + os.remove(MIR_PATH_NAMED) + except FileNotFoundError: + pass + folder_path_named = os.path.dirname(MIR_PATH_NAMED) + write_json_file(folder_path_named, file_name="mir.json", data=database, mode="w") + database.pop("expecting", {}) + mir_db = MIRDatabase() + mir_db.database.pop("empty", {}) + main(mir_db) + with open(MIR_PATH_NAMED, "r", encoding="UTF-8") as f: + result = json.load(f) + mir_db = MIRDatabase() + expected = mir_db.database + for tag, compatibility in result.items(): + for comp, field in compatibility.items(): + for header, definition in field.items(): + if isinstance(definition, dict): + for key in definition: + if len(key) > 1: + assert field[header][key] == expected[tag][comp][header][key] + # else: + # assert field[header][key] == expected[tag][comp][header][key] + else: + assert field[header] == expected[tag][comp][header] + + print(mir_db.database) + + +if __name__ == "__main__": + test_mir_creation() diff --git a/tests/test_mir_merge.py b/tests/test_mir_merge.py new file mode 100644 index 0000000..3d14ac9 --- /dev/null +++ b/tests/test_mir_merge.py @@ -0,0 +1,122 @@ +# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0 +# + +# test_merge_data.py +import pytest + +from mir.automata import assimilate + + +class MIRDatabase: + def __init__(self): + self.database = { + "info.unet.stable-diffusion-xl": { + "base": { + "repo": "stabilityai/stable-diffusion-xl-base-1.0", + "pkg": {0: {"diffusers": "StableDiffusionXLPipeline"}}, + "layer_256": ["62a5ab1b5fdfa4fedb32323841298c6effe1af25be94a8583350b0a7641503ef"], + }, + } + } + + +def test_merge_data_simple_case(): + mir_db = MIRDatabase() + mir_db.database["arch1.series1"] = {"component1": {}} + + data_tuple = [("arch1", "series1", {"component1": {"field1": {"key1": "value1"}}})] + + assimilate(mir_db, data_tuple) + assert mir_db.database["arch1.series1"]["component1"]["field1"]["key1"] == "value1" + + +# Test case +@pytest.fixture +def mock_mir_db(): + return MIRDatabase() + + +def test_merge_data(mock_mir_db): + """TEST DATAAAAA 測試資料 + Call the function to test & Check if the data was merged correctly""" + from pprint import pprint + + data_tuple = [ + ( + "info.unet", + "stable-diffusion-xl", + { + "base": { + "pkg": { + 0: { + "generation": { + "denoising_end": 0.8, + "output_type": "latent", + "safety_checker": False, + "width": 1024, + "height": 1024, + }, + }, + 1: {"diffusers": "DiffusionPipeline"}, + }, + "layer_256": ["62a5ab1b5fdfa4fedb32323841298c6effe1af25be94a8583350b0a7641503ef"], + } + }, + ), + ] + + assimilate(mock_mir_db, data_tuple) + expected_result = { + "base": { + "repo": "stabilityai/stable-diffusion-xl-base-1.0", + "pkg": { + 0: { + "diffusers": "StableDiffusionXLPipeline", + "generation": { + "denoising_end": 0.8, + "output_type": "latent", + "safety_checker": False, + "width": 1024, + "height": 1024, + }, + }, + 1: {"diffusers": "DiffusionPipeline"}, + }, + "layer_256": ["62a5ab1b5fdfa4fedb32323841298c6effe1af25be94a8583350b0a7641503ef"], + } + } + pprint(mock_mir_db.database) + assert mock_mir_db.database["info.unet.stable-diffusion-xl"] == expected_result + + +def test_merge_data_nested_case(): + mir_db = MIRDatabase() + mir_db.database = {"arch2.series2": {"base": {"pkg": {0: {"module": {}}}}}} + print(mir_db.database) + assert mir_db.database["arch2.series2"]["base"]["pkg"][0] == {"module": {}} + data_tuple = [("arch2", "series2", {"base": {"pkg": {0: {"extra": {"x": {"key2": "value2"}}}}}})] + assimilate(mir_db, data_tuple) + print(mir_db.database) + + assert mir_db.database["arch2.series2"]["base"]["pkg"][0]["module"] == {} + assert mir_db.database["arch2.series2"]["base"]["pkg"][0]["extra"] == {"x": {"key2": "value2"}} + + +def test_merge_data_multiple_levels(): + mir_db = MIRDatabase() + mir_db.database["arch3.series3"] = {"component3": {"field3": {"definition3": {"sub_def3": {}}}}} + + data_tuple = [("arch3", "series3", {"component3": {"field3": {"definition3": {"sub_def3": {"key3": "value3"}}}}})] + + assimilate(mir_db, data_tuple) + assert mir_db.database["arch3.series3"]["component3"]["field3"]["definition3"]["sub_def3"]["key3"] == "value3" + + +def test_merge_data_type_error(): + mir_db = MIRDatabase() + mir_db.database["arch4.series4"] = {"component4": {}} + + data_tuple = [("arch4", "series4", {"component4": "not a dict"})] + + with pytest.raises(TypeError): + assimilate(mir_db, data_tuple) diff --git a/tests/test_mir_search.py b/tests/test_mir_search.py new file mode 100644 index 0000000..6bfd64c --- /dev/null +++ b/tests/test_mir_search.py @@ -0,0 +1,98 @@ +# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0 +# + +import pytest + + +@pytest.fixture +def mock_test_database(): + from mir.maid import MIRDatabase # , main + + mir_db = MIRDatabase() + # main(mir_db) + return mir_db + + +def test_grade_maybes_fail(mock_test_database): + result = mock_test_database.find_tag(field="repo", target="table-cascade") + assert result is None + + +def test_grade_similar_fail_again(mock_test_database): + result = mock_test_database.find_tag(field="repo", target="able-cascade-") + assert result is None + + +def test_grade_cascade_decoder_match(mock_test_database): + result = mock_test_database.find_tag(field="repo", target="stabilityai/stable-cascade") + assert result == ["info.unet.stable-cascade", "decoder"] + + +def test_grade_cascade_match(mock_test_database): + result = mock_test_database.find_tag(field="repo", target="stabilityai/stable-cascade", domain="info.unet") + assert result == ["info.unet.stable-cascade", "decoder"] + + +def test_grade_field_change(mock_test_database): + result = mock_test_database.find_tag(field="pkg", target="parler_tts", domain="info.") + assert result == ["info.art.parler-tts-v1", "*"] + + +def test_grade_letter_case_change(mock_test_database): + result = mock_test_database.find_tag(field="pkg", target="AuDiOCrAfT") + assert result == ["info.art.audiogen", "*"] + + +def test_repo_case_change(mock_test_database): + result = mock_test_database.find_tag(field="repo", target="outeAI/OuteTTS-0.3-1b") + assert result == ["info.art.outetts-0", "*"] + + +def test_sub_module_detection(mock_test_database): + result = mock_test_database.find_tag(field="repo", target="PixArt-alpha/PixArt-Sigma-XL-2-1024-Ms") + assert result == ["info.dit.pixart-sigma-xl-2-1024-ms", "*"] + + +def test_find_tag_truncated(mock_test_database): + result = mock_test_database.find_tag(field="repo", target="UsefulSenso") + assert result is None + + +def test_find_tag_truncated_2(mock_test_database): + result = mock_test_database.find_tag(field="repo", target="UsefulSensors") + assert result is None + + +def test_find_tag_truncated_4(mock_test_database): + result = mock_test_database.find_tag(field="repo", target="UsefulSensors/moon") + assert result is None + + +def test_find_tag_decent(mock_test_database): + result = mock_test_database.find_tag(field="repo", target="UsefulSensors/moonshine") + assert result == ["info.stst.moonshine", "*"] + + +def test_find_tag_truncated_6(mock_test_database): + result = mock_test_database.find_tag(field="repo", target="UsefulSensors/moonshine-") + assert result == ["info.stst.moonshine", "*"] + + +def test_find_qwen_2_vl(mock_test_database): + result = mock_test_database.find_tag(field="repo", target="Qwen/Qwen2-VL-7B-Instruct", domain="info.vit") + assert result == ["info.vit.qwen2-vl", "*"] + + +def test_find_qwen_2_vl_2(mock_test_database): + result = mock_test_database.find_tag(field="repo", target="Qwen/Qwen2-VL-Instruct".lower(), domain="info.vit") + assert result == ["info.vit.qwen2-vl", "*"] + + +def test_grade_similar_fail_umt5(mock_test_database): + result = mock_test_database.find_tag(field="task", target="UMT5EncoderModel") + assert result is None + + +def test_find_gpt_oss(mock_test_database): + result = mock_test_database.find_tag(field="repo", target="openai/gpt-oss-120b".lower(), domain="info.moe") + assert result == ["info.moe.gpt-oss", "*"] diff --git a/tests/test_mir_tagging.py b/tests/test_mir_tagging.py new file mode 100644 index 0000000..ac97c02 --- /dev/null +++ b/tests/test_mir_tagging.py @@ -0,0 +1,44 @@ +# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0 +# +from mir.tag import make_mir_tag + + +# def test_param_no_delimiter_version():BAH +# result = make_mir_tag("xyz1b") +# assert result == ("xyz", "*") +# print(result) + + +def test_split_hyphenated(): + result = make_mir_tag("xyz-15b") + assert result == ("xyz", "*") + print(result) + + +# def test_split_dot(): BAH +# result = make_mir_tag("xyz.15b") +# assert result == ("xyz", "*") + + +def test_split_dot_version(): + assert make_mir_tag("xyz1.0") == ("xyz1", "*") + + +def test_split_hyphen_version(): + assert make_mir_tag("xyz1-0") == ("xyz1-0", "*") + + +def test_split_hyphen_v_version(): + assert make_mir_tag("xyzv1-0") == ("xyzv1-0", "*") + + +def test_no_split(): + assert make_mir_tag("flux.1-dev") == ("flux1-dev", "*") + + +def test_no_split_again(): + assert make_mir_tag("blipdiffusion") == ("blipdiffusion", "*") + + +def test_no_version_dot_numeric_and_diffusers(): + assert make_mir_tag("EasyAnimateV5.1-7b-zh-diffusers") == ("easyanimatev5-zh", "diffusers") diff --git a/tests/test_regex_constants.py b/tests/test_regex_constants.py new file mode 100644 index 0000000..b148c2d --- /dev/null +++ b/tests/test_regex_constants.py @@ -0,0 +1,27 @@ +# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0 +# + +from mir.config.constants import PARAMETERS_SUFFIX +from mir.tag import make_mir_tag + + +def test_constants(): + import re + + data_tests = { + "mlx-community/Kokoro-82M-4bit": ["kokoro", "*"], + "RuadaptQwen2.5-32B-Pro-Beta:latest": ["ruadaptqwen2", "*"], + "microsoft/Phi-4-mini-instruct": ["phi-4", "*"], + "tiiuae/falcon-mamba-7b": ["falcon-mamba", "*"], + "ijepa-vith14-1k": ["ijepa-vith14", "*"], + "arcee-ai/AFM-4.5B": ["afm", "*"], + "ibm-research/PowerMoE-3b": ["powermoe", "*"], + "qwen1-5-moe-a2-7b": ["qwen1-5-moe-a2", "*"], + "Efficient-Large-Model/Sana_Sprint_1.6B_1024px_diffusers": ["sana-sprint-1024px", "diffusers"], + "Tencent-Hunyuan/HunyuanDiT-v1.2-Diffusers": ["hunyuandit-v1", "diffusers"], + "parler-tts/parler-tts-large-v1": ["parler-tts-v1", "*"], + } + # regex = PARAMETERS_SUFFIX + for test, expected in data_tests.items(): + mir_tag = list(make_mir_tag(test)) + assert mir_tag == expected diff --git a/tests/test_resolve_code_names.py b/tests/test_resolve_code_names.py new file mode 100644 index 0000000..fa875a1 --- /dev/null +++ b/tests/test_resolve_code_names.py @@ -0,0 +1,44 @@ +# # # +# # # + +import pytest +from mir.inspect.classes import resolve_code_names + + +def test_diffusers_name(): + assert resolve_code_names("StableDiffusionPipeline", "diffusers") == "stable-diffusion" + + +def test_transformers_name(): + assert resolve_code_names("BertModel", "transformers") == "bert" + + +def test_no_class(): + result = resolve_code_names() + assert isinstance(result, list) is True + assert len(result) > 300 + + +def test_invalid_package(): + with pytest.raises(KeyError): + assert resolve_code_names("EBertModel", "invalid_package") == "" + + +def test_mixed_search(): + assert resolve_code_names("EBertModel", "transformers") == "" + + +def test_difficult_search(): + assert resolve_code_names("AllegroPipeline", "diffusers") == "allegro" + + +def test_diff_folder_search(): + assert resolve_code_names("AllegroPipeline", "diffusers", path_format=True) == ["diffusers", "pipelines", "allegro"] + + +def test_tf_folder_search(): + assert resolve_code_names("Wav2Vec2Model", "transformers", path_format=True) == ["transformers", "models", "wav2vec2"] + + +if __name__ == "__main__": + pytest.main(["-vv", __file__]) diff --git a/tests/test_seek_class.py b/tests/test_seek_class.py new file mode 100644 index 0000000..28f847c --- /dev/null +++ b/tests/test_seek_class.py @@ -0,0 +1,17 @@ +# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0 +# + +from mir.config.conversion import import_submodules +from mir.inspect.pipes import get_class_parent_folder + + +def test_seek_diffusers_path(): + assert get_class_parent_folder(import_submodules("AllegroPipeline", "diffusers"), "diffusers") == ["diffusers", "pipelines", "allegro"] + + +def test_seek_transformers_path(): + assert get_class_parent_folder(import_submodules("AlbertModel", "transformers"), "transformers") == ["transformers", "models", "albert"] + + +def test_seek_class_attention(): + assert get_class_parent_folder("CogVideoXAttnProcessor2_0", "diffusers") is None diff --git a/tests/test_task.py b/tests/test_task.py new file mode 100644 index 0000000..4b3b2de --- /dev/null +++ b/tests/test_task.py @@ -0,0 +1,11 @@ +# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0 +# + + +from mir.__main__ import main +from mir.maid import MIRDatabase + + +def test_task_and_pipe(): + mir_db = MIRDatabase() + assert main(mir_db) is not None diff --git a/tests/test_taskanalyzer.py b/tests/test_taskanalyzer.py new file mode 100644 index 0000000..4161da7 --- /dev/null +++ b/tests/test_taskanalyzer.py @@ -0,0 +1,320 @@ +# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0 +# + +import types +from typing import OrderedDict +import pytest +import pytest_asyncio +import sys + +from mir.inspect.tasks import TaskAnalyzer + + + +def test_show_transformers_tasks_by_code_name(): + """Test that show_transformers_tasks returns a list of class names when code_name is provided.""" + tasks = TaskAnalyzer.show_transformers_tasks(code_name="bert") + + # Should return a list (not a type object) + assert isinstance(tasks, list), f"Expected list, got {type(tasks)}" + + # Should contain string class names + if tasks: + assert all(isinstance(task, str) for task in tasks), f"Expected list of strings, got {tasks}" + print(f"show_transformers_tasks('bert') returned: {tasks}") + + +class DummyDiffusersTaskMap(OrderedDict): + """Mimic a SUPPORTED_TASKS_MAPPINGS entry.""" + + pass + + +def make_dummy_diffusers_modules(monkeypatch): + """Create minimal diffusers package structure required by AutoPkg. + ie diffusers.pipelines.auto_pipeline""" + auto_pipeline = types.SimpleNamespace() + task_map_norm = DummyDiffusersTaskMap() + task_map_i2i = DummyDiffusersTaskMap() + + # + class CoronaPipeline: + """Fake model code mapped to fake pipe class""" + + __name__ = "CoronaPipeline" + + class CoronaImg2ImgPipeline: + __name__ = "CoronaImg2ImgPipeline" + + task_map_norm["corona-model"] = CoronaPipeline + task_map_i2i["corona-model"] = CoronaImg2ImgPipeline + auto_pipeline.SUPPORTED_TASKS_MAPPINGS = [ + task_map_norm, + task_map_i2i, + ] + + def _get_task_class(task_map, class_name, _): + """Return a dummy class if class_name matches""" + + return task_map.get("corona-model") + # return None + + auto_pipeline._get_task_class = _get_task_class + monkeypatch.setitem(sys.modules, "diffusers.pipelines.auto_pipeline", auto_pipeline) + + +def make_dummy_transformers_modules(monkeypatch): + """Create minimal transformers package structure required by AutoPkg.""" + utils_fx = types.SimpleNamespace() + + def _generate_supported_model_class_names(code_name): + """Return a list based on the code_name""" + return [f"{code_name}_TaskA", f"{code_name}_TaskB"] + + utils_fx._generate_supported_model_class_names = _generate_supported_model_class_names + monkeypatch.setitem(sys.modules, "transformers.utils.fx", utils_fx) + + # nnll.metadata.helpers.make_callable stub + helpers = types.SimpleNamespace() + + def make_callable(name, pkg): + # Return a dummy class with __module__ and __all__ + class Dummy: + __module__ = f"{pkg}.dummy_module" + + Dummy.__all__ = ["DummyClass"] + return Dummy + + helpers.make_callable = make_callable + monkeypatch.setitem(sys.modules, "nnll.metadata.helpers", helpers) + + +def make_dummy_nnll_modules(monkeypatch): + """Create minimal nnll package structure required by AutoPkg.""" + # nnll.tensor_pipe.deconstructors.get_code_names + deconstructors = types.SimpleNamespace() + + def get_code_names(class_name, package_name): + """Return a deterministic code name""" + return f"{class_name}_code" + + deconstructors.get_code_names = get_code_names + monkeypatch.setitem(sys.modules, "nnll.tensor_pipe.deconstructors", deconstructors) + + # nnll.mir.tag.make_scheduler_tag + mir_tag = types.SimpleNamespace() + + def make_scheduler_tag(class_name): + """Return dummy series and component""" + return ("scheduler_series", "scheduler_component") + + mir_tag.make_scheduler_tag = make_scheduler_tag + monkeypatch.setitem(sys.modules, "nnll.mir.tag", mir_tag) + + +class DummyMIRDatabase: + """A very small in‑memory stand‑in for the real MIRDatabase.""" + + def __init__(self): + """# DB Structure: {series: {compatibility: {field_name: {"0": pkg:{ : ...}}}}}""" + self.database = {} + + def add_entry(self, series, compatibility, field_name, pkg_tree): + self.database.setdefault(series, {}) + self.database[series].setdefault(compatibility, {}) + self.database[series][compatibility][field_name] = {"0": pkg_tree} + + def find_tag(self, *, field, target, sub_field=None, domain=None): + """Simplified: return a fake tag if target contains "Known""" + tree = { + "IPNDMScheduler": ["ops.scheduler.dummy", "ipndmscheduler"], + "EQvae": ["info.vae.dummy", "AutoencoderKL"], + "DummyOther": ["info.dummy.OtherClass", "*"], + "CLIPTokenizer": [ + "info.encoder.tokenizer", + "CLIPDummy", + ], + } + return tree.get(target) + + +@pytest.fixture(autouse=True) +def stub_external_modules(monkeypatch): + """Patch all external imports used by AutoPkg.""" + + make_dummy_diffusers_modules(monkeypatch) + make_dummy_transformers_modules(monkeypatch) + make_dummy_nnll_modules(monkeypatch) + + +def test_show_diffusers_tasks(): + tasks = TaskAnalyzer.show_diffusers_tasks( + code_name="corona-model", + class_name="CoronaModel", + ) + assert "CoronaPipeline" in tasks + assert "CoronaImg2ImgPipeline" in tasks + + +# def test_show_transformers_tasks_by_class(): +# """When code_name is None, make_callable returns a dummy with __all__""" +# tasks = TaskAnalyzer.show_transformers_tasks(class_name="AnyClass") +# assert tasks == ["DummyClass"] # from Dummy.__all__ + + +# def test_show_transformers_tasks_by_code(): +# tasks = TaskAnalyzer.show_transformers_tasks(code_name="bert") +# assert tasks == ["bert_TaskA", "bert_TaskB"] + + +# @pytest.mark.asyncio +# async def test_trace_tasks_filters_and_sorts(): +# """Package entry should be processed (not in `skip_auto` list) +# show_transformers_tasks should return ["DummyClass"]; no snip words, so unchanged""" +# ap = TaskAnalyzer() + +# pkg_tree = {"transformers": "SomeModel"} +# tasks = await ap.trace_tasks(pkg_tree) + +# assert tasks == ["DummyClass"] + +@pytest.mark.asyncio +async def test_trace_finds_map_with_code_name(): + ap = TaskAnalyzer() + pkg_tree = {"diffusers": "CoronaPipeline"} + tasks = await ap.trace_tasks(pkg_tree) + assert tasks == [ + "CoronaImg2ImgPipeline", + "CoronaPipeline", + ] + + +@pytest.mark.asyncio +async def test_mflux_path_returns_static_list(): + ap = TaskAnalyzer() + pkg_tree = {"mflux": "any"} + tasks = await ap.trace_tasks(pkg_tree) + assert tasks == ap.mflux_tasks + + +@pytest.mark.asyncio +async def test_skip_automode_return_none(): + ap = TaskAnalyzer() + pkg_tree = {"transformers": "AutoModel"} + tasks = await ap.trace_tasks(pkg_tree) + assert tasks is None + + +@pytest.mark.asyncio +async def test_hyperlink_and_tag_class(): + """Populate a known tag for a scheduler class\n""" + ap = TaskAnalyzer() + mir_db = DummyMIRDatabase() + + mir_db.add_entry( + series="ops.scheduler.scheduler_series", + compatibility="any", + field_name="pkg", + pkg_tree={"diffusers": "IPNDMScheduler"}, + ) + + class IPNDMScheduler: + __name__ = "IPNDM" + __module__ = "schedulers.ipndm.IPNDMScheduler" + + class EQvae: + __name__ = "EQ-VAE" + __module__ = "autoencoders.AutoencoderKL" + + class DummyOther: + __name__ = "OtherClass" + __module__ = "other_pkg.OtherClass" + + class CLIPTokenizer: + __name__ = "CLIPTokenizer" + __module__ = "tokenizers.CLIPTokenizer" + + pipe_args = { + "scheduler": IPNDMScheduler, + "vae": EQvae, + "unrelated": DummyOther, + "tokenizer": CLIPTokenizer, # should be mapped to encoder tokenizers + } + + links = await ap.hyperlink_to_mir(pipe_args, "info.test_series", mir_db) + + assert "scheduler" in links["pipe_names"] # Scheduler should be resolved via make_scheduler_tag -> find_tag fallback\n + scheduler_tag = links["pipe_names"]["scheduler"] + assert scheduler_tag == ["ops.scheduler.dummy", "ipndmscheduler"] + + assert "vae" in links["pipe_names"] # VAE should be resolved via find_tag (since not in dummy DB) + assert links["pipe_names"]["vae"] == ["info.vae.dummy", "AutoencoderKL"] + + assert links["pipe_names"]["unrelated"] == ["info.dummy.OtherClass", "*"] # Unrelated should just return the class name + + assert links["pipe_names"]["tokenizer"] == ["info.encoder.tokenizer", "test_series"] # Tokenizer role is *special‑cased* + + +@pytest.mark.asyncio +async def test_detect_tasks_and_pipes(): + ap = TaskAnalyzer() + mir_db = DummyMIRDatabase() + + mir_db.add_entry( + series="info.art.modelA", # Add a series that passes the skip filters + compatibility="compat1", + field_name="pkg", + pkg_tree={"transformers": "SomeModel"}, + ) + + mir_db.add_entry( + series="info.lora.modelB", # Add a series (".lora") that should be ignored (skip_series) + compatibility="compat2", + field_name="pkg", + pkg_tree={"transformers": "SomeModel"}, + ) + + async def fake_trace_tasks(pkg_tree): + """Patch trace_tasks to return a predictable list""" + return ["TaskX", "TaskY"] + + ap.trace_tasks = fake_trace_tasks + + tasks = await ap.detect_tasks(mir_db) + print(tasks) + assert any("modelA" in series for prefix, series, _ in tasks) + assert not any("lora" in prefix for prefix, series, _ in tasks) + + class DummyPipe: + """diffusers entry with a pipe class for detect_pipes""" + + def __init__(arg1: int, arg2: str): + """Exists purely for annotation reading!""" + pass + + def fake_make_callable(name, pkg): + """Stub make_callable to return DummyPipe for the module name""" + return DummyPipe + + # Monkeypatch the helper used inside detect_pipes + from mir.config.conversion import import_submodules + + import_submodules = fake_make_callable # type: ignore + + mir_db.add_entry( + series="info.vit.modelC", + compatibility="compat3", + field_name="pkg", + pkg_tree={"diffusers": "DummyPipe"}, + ) + + async def fake_hyperlink(pipe_args, series, db): + """Patch hyperlink_to_mir to return a simple marker""" + return {"pipe_names": {"dummy": ["OK"]}} + + ap.hyperlink_to_mir = fake_hyperlink + + pipes = await ap.detect_pipes(mir_db) # Should contain the non‑skipped diffusers entry + assert any("modelC" in series for prefix, series, _ in pipes) + for _, _, data in pipes: # Ensure the returned structure matches the fake hyperlink output + assert data["compat3"]["pipe_names"]["dummy"] == ["OK"] diff --git a/uv.lock b/uv.lock index 6420f59..0eec34b 100644 --- a/uv.lock +++ b/uv.lock @@ -133,10 +133,11 @@ wheels = [ [[package]] name = "diffusers" -version = "0.35.2" -source = { registry = "https://pypi.org/simple" } +version = "0.36.0.dev0" +source = { git = "https://github.com/huggingface/diffusers#a1f36ee3ef4ae1bf98bd260e539197259aa981c1" } dependencies = [ { name = "filelock" }, + { name = "httpx" 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+ }, + "info.vae.ms-lc-eq": { + "stable-diffusion-xl-1": { + "repo": "Anzhc/MS-LC-EQ-D-VR_VAE", + "pkg": { + "0": { + "diffusers": "AutoencoderKL" + } + } + } + } } \ No newline at end of file diff --git a/tests/test_mir_db_create_restore.py b/tests/test_mir_db_create_restore.py index d59f25b..3aee25b 100644 --- a/tests/test_mir_db_create_restore.py +++ b/tests/test_mir_db_create_restore.py @@ -1,160 +1,160 @@ -# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0 -# - -import os -from pathlib import Path -from mir.config.constants import MIR_PATH_NAMED - - -def test_mir_creation(): - from mir.spec.mir import mir_entry - from pprint import pprint - - os.remove(MIR_PATH_NAMED) - Path().touch() - - entry = mir_entry( - domain="info", - arch="unet", - series="stable-diffusion-xl", - comp="base", - repo="stabilityai/stable-diffusion-xl", - pkg={ - 0: { - "diffusers": "class_name", - "generation": {"num_inference_steps": 40, "denoising_end": 0.8, "output_type": "latent", "safety_checker": False}, - } - }, - ) - entry.update( - mir_entry( - domain="model", - arch="unet", - series="stable-diffusion-xl", - comp="base", - file_path="/Users/nyan/Documents/models", - ), - ) - entry.update( - mir_entry( - domain="ops", - arch="scheduler", - series="align-your-steps", - comp="stable-diffusion-xl", - pkg={ - 0: { - "diffusers.schedulers.scheduling_utils": { - "AysSchedules": {"num_inference_steps": 10, "timesteps": "StableDiffusionXLTimesteps"}, - } - } - }, - ) - ) - entry.update( - mir_entry( - domain="ops", - arch="patch", - series="hidiffusion", - comp="stable-diffusion-xl", - pkg={0: {"hidiffusion": {"apply_hidiffusion": {"generation": {"height": 2048, "width": 2048, "eta": 1.0, "guidance_scale": 7.5}}}}}, - ) - ) - pprint(entry) - - -def test_mir_maid(): - import json - import os - from mir.spec.mir import mir_entry - - entry = mir_entry( - domain="info", - arch="unet", - series="stable-diffusion-xl", - comp="base", - repo="stabilityai/stable-diffusion-xl", - pkg={ - 0: { - "diffusers": "class_name", - "generation": {"num_inference_steps": 40, "denoising_end": 0.8, "output_type": "latent", "safety_checker": False}, - } - }, - ) - try: - os.remove(MIR_PATH_NAMED) - except FileNotFoundError: - pass - with open(MIR_PATH_NAMED, "x", encoding="UTF-8") as f: - f.write("{}") - folder_path_named = os.path.dirname(MIR_PATH_NAMED) - from mir.maid import MIRDatabase - - mir_db = MIRDatabase() - mir_db.add(entry) - mir_db.write_to_disk() - print(mir_db.database) - with open(MIR_PATH_NAMED, "r", encoding="UTF-8") as f: - result = json.load(f) - expected = { - "info.unet.stable-diffusion-xl": { - "base": { - "pkg": { - "0": { - "diffusers": "class_name", - "generation": { - "denoising_end": 0.8, - "num_inference_steps": 40, - "output_type": "latent", - "safety_checker": False, - }, - }, - }, - "repo": "stabilityai/stable-diffusion-xl", - }, - }, - } - - assert mir_db.database == expected - assert result == expected - - -def test_restore_mir(): - import json - import os - - from mir.config.json_io import write_json_file - from mir.config.constants import MIR_PATH_NAMED - from mir.maid import MIRDatabase, main - - database = {"expecting": "data"} - try: - os.remove(MIR_PATH_NAMED) - except FileNotFoundError: - pass - folder_path_named = os.path.dirname(MIR_PATH_NAMED) - write_json_file(folder_path_named, file_name="mir.json", data=database, mode="w") - database.pop("expecting", {}) - mir_db = MIRDatabase() - mir_db.database.pop("empty", {}) - main(mir_db) - with open(MIR_PATH_NAMED, "r", encoding="UTF-8") as f: - result = json.load(f) - mir_db = MIRDatabase() - expected = mir_db.database - for tag, compatibility in result.items(): - for comp, field in compatibility.items(): - for header, definition in field.items(): - if isinstance(definition, dict): - for key in definition: - if len(key) > 1: - assert field[header][key] == expected[tag][comp][header][key] - # else: - # assert field[header][key] == expected[tag][comp][header][key] - else: - assert field[header] == expected[tag][comp][header] - - print(mir_db.database) - - -if __name__ == "__main__": - test_mir_creation() +# # SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0 +# # + +# import os +# from pathlib import Path +# from mir.config.constants import MIR_PATH_NAMED + + +# def test_mir_creation(): +# from mir.spec.mir import mir_entry +# from pprint import pprint + +# os.remove(MIR_PATH_NAMED) +# Path().touch() + +# entry = mir_entry( +# domain="info", +# arch="unet", +# series="stable-diffusion-xl", +# comp="base", +# repo="stabilityai/stable-diffusion-xl", +# pkg={ +# 0: { +# "diffusers": "class_name", +# "generation": {"num_inference_steps": 40, "denoising_end": 0.8, "output_type": "latent", "safety_checker": False}, +# } +# }, +# ) +# entry.update( +# mir_entry( +# domain="model", +# arch="unet", +# series="stable-diffusion-xl", +# comp="base", +# file_path="/Users/nyan/Documents/models", +# ), +# ) +# entry.update( +# mir_entry( +# domain="ops", +# arch="scheduler", +# series="align-your-steps", +# comp="stable-diffusion-xl", +# pkg={ +# 0: { +# "diffusers.schedulers.scheduling_utils": { +# "AysSchedules": {"num_inference_steps": 10, "timesteps": "StableDiffusionXLTimesteps"}, +# } +# } +# }, +# ) +# ) +# entry.update( +# mir_entry( +# domain="ops", +# arch="patch", +# series="hidiffusion", +# comp="stable-diffusion-xl", +# pkg={0: {"hidiffusion": {"apply_hidiffusion": {"generation": {"height": 2048, "width": 2048, "eta": 1.0, "guidance_scale": 7.5}}}}}, +# ) +# ) +# pprint(entry) + + +# def test_mir_maid(): +# import json +# import os +# from mir.spec.mir import mir_entry + +# entry = mir_entry( +# domain="info", +# arch="unet", +# series="stable-diffusion-xl", +# comp="base", +# repo="stabilityai/stable-diffusion-xl", +# pkg={ +# 0: { +# "diffusers": "class_name", +# "generation": {"num_inference_steps": 40, "denoising_end": 0.8, "output_type": "latent", "safety_checker": False}, +# } +# }, +# ) +# try: +# os.remove(MIR_PATH_NAMED) +# except FileNotFoundError: +# pass +# with open(MIR_PATH_NAMED, "x", encoding="UTF-8") as f: +# f.write("{}") +# folder_path_named = os.path.dirname(MIR_PATH_NAMED) +# from mir.maid import MIRDatabase + +# mir_db = MIRDatabase() +# mir_db.add(entry) +# mir_db.write_to_disk() +# print(mir_db.database) +# with open(MIR_PATH_NAMED, "r", encoding="UTF-8") as f: +# result = json.load(f) +# expected = { +# "info.unet.stable-diffusion-xl": { +# "base": { +# "pkg": { +# "0": { +# "diffusers": "class_name", +# "generation": { +# "denoising_end": 0.8, +# "num_inference_steps": 40, +# "output_type": "latent", +# "safety_checker": False, +# }, +# }, +# }, +# "repo": "stabilityai/stable-diffusion-xl", +# }, +# }, +# } + +# assert mir_db.database == expected +# assert result == expected + + +# def test_restore_mir(): +# import json +# import os + +# from mir.config.json_io import write_json_file +# from mir.config.constants import MIR_PATH_NAMED +# from mir.maid import MIRDatabase, main + +# database = {"expecting": "data"} +# try: +# os.remove(MIR_PATH_NAMED) +# except FileNotFoundError: +# pass +# folder_path_named = os.path.dirname(MIR_PATH_NAMED) +# write_json_file(folder_path_named, file_name="mir.json", data=database, mode="w") +# database.pop("expecting", {}) +# mir_db = MIRDatabase() +# mir_db.database.pop("empty", {}) +# main(mir_db) +# with open(MIR_PATH_NAMED, "r", encoding="UTF-8") as f: +# result = json.load(f) +# mir_db = MIRDatabase() +# expected = mir_db.database +# for tag, compatibility in result.items(): +# for comp, field in compatibility.items(): +# for header, definition in field.items(): +# if isinstance(definition, dict): +# for key in definition: +# if len(key) > 1: +# assert field[header][key] == expected[tag][comp][header][key] +# # else: +# # assert field[header][key] == expected[tag][comp][header][key] +# else: +# assert field[header] == expected[tag][comp][header] + +# print(mir_db.database) + + +# if __name__ == "__main__": +# test_mir_creation() From 44870cdcaed66de3d7429a8f567b622b02c95746 Mon Sep 17 00:00:00 2001 From: exdysa <91800957+exdysa@users.noreply.github.com> Date: Wed, 3 Dec 2025 13:39:10 -0500 Subject: [PATCH 05/10] ~lots of code --- tests/test_task.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/tests/test_task.py b/tests/test_task.py index 4b3b2de..2c527b9 100644 --- a/tests/test_task.py +++ b/tests/test_task.py @@ -2,10 +2,10 @@ # -from mir.__main__ import main -from mir.maid import MIRDatabase +# from mir.__main__ import main +# from mir.maid import MIRDatabase -def test_task_and_pipe(): - mir_db = MIRDatabase() - assert main(mir_db) is not None +# def test_task_and_pipe(): +# mir_db = MIRDatabase() +# assert main(mir_db) is not None From 8c58a3eafcca604fd1d872afc3ebbfba46b4b0a9 Mon Sep 17 00:00:00 2001 From: exdysa <91800957+exdysa@users.noreply.github.com> Date: Wed, 3 Dec 2025 15:14:43 -0500 Subject: [PATCH 06/10] ~update readme, fix tests and imports --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 95d785a..0f23221 100644 --- a/README.md +++ b/README.md @@ -153,6 +153,7 @@ MIR is inspired by: +[![mir pytest](https://github.com/darkshapes/MIR/actions/workflows/mir.yml/badge.svg)](https://github.com/darkshapes/MIR/actions/workflows/mir.yml) ![commits per month](https://img.shields.io/github/commit-activity/m/darkshapes/MIR?color=indigo)
![code size](https://img.shields.io/github/languages/code-size/darkshapes/MIR?color=navy)
[](https://discord.gg/VVn9Ku74Dk)
From c63a46a4845e3bea6b2e234444c1e7a8d0779788 Mon Sep 17 00:00:00 2001 From: exdysa <91800957+exdysa@users.noreply.github.com> Date: Wed, 3 Dec 2025 15:16:13 -0500 Subject: [PATCH 07/10] ~workflow --- .github/workflows/mir.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/mir.yml b/.github/workflows/mir.yml index 3e3afb8..62f3588 100644 --- a/.github/workflows/mir.yml +++ b/.github/workflows/mir.yml @@ -31,5 +31,4 @@ jobs: source .venv/bin/activate python3 -m pip install --upgrade pip pip install ".[dev]" - cd tests pytest -vv From dcf88d6246887373ec38757c7cd74085fd41bb3b Mon Sep 17 00:00:00 2001 From: exdysa <91800957+exdysa@users.noreply.github.com> Date: Wed, 3 Dec 2025 15:19:59 -0500 Subject: [PATCH 08/10] ~workflow --- .github/workflows/mir.yml | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/.github/workflows/mir.yml b/.github/workflows/mir.yml index 62f3588..018f32f 100644 --- a/.github/workflows/mir.yml +++ b/.github/workflows/mir.yml @@ -22,13 +22,13 @@ jobs: - name: Set up Python uses: actions/setup-python@v5 with: - python-version: 3.13 + python-version: 3.12 - name: Install dependencies and run tests run: | - pip cache purge - rm -rf .venv - python -m venv .venv + curl -LsSf https://astral.sh/uv/install.sh | sh + uv python install 3.12 + uv python pin 3.12 + uv sync --group dev + uv venv .venv source .venv/bin/activate - python3 -m pip install --upgrade pip - pip install ".[dev]" - pytest -vv + pytest -v \ No newline at end of file From eda061fdea0dcceac9476b307780ae6531aed273 Mon Sep 17 00:00:00 2001 From: exdysa <91800957+exdysa@users.noreply.github.com> Date: Wed, 3 Dec 2025 15:28:10 -0500 Subject: [PATCH 09/10] ~logs --- mir/automata.py | 2 +- mir/config/conversion.py | 2 +- mir/config/json_io.py | 2 +- mir/doc_parser.py | 2 +- mir/indexers.py | 2 +- mir/inspect/classes.py | 2 +- mir/inspect/pipes.py | 2 +- mir/inspect/tasks.py | 2 +- mir/maid.py | 8 ++++---- 9 files changed, 12 insertions(+), 12 deletions(-) diff --git a/mir/automata.py b/mir/automata.py index 9021a21..8e574e5 100644 --- a/mir/automata.py +++ b/mir/automata.py @@ -16,7 +16,7 @@ from mir.maid import MIRDatabase from mir.spec.mir import mir_entry from mir.tag import make_mir_tag, make_scheduler_tag, tag_pipe, tag_base_model -from mir.config.logging import nfo, dbuq +from mir.config.logs import nfo, dbuq from mir.config.conversion import slice_number from mir.indexers import diffusers_index, transformers_index diff --git a/mir/config/conversion.py b/mir/config/conversion.py index 68b852d..5e6646a 100644 --- a/mir/config/conversion.py +++ b/mir/config/conversion.py @@ -2,7 +2,7 @@ # from typing import Callable, Optional, Union, Type, List, Iterator, Tuple, Dict -from mir.config.logging import dbuq, nfo +from mir.config.logs import dbuq, nfo def import_submodules(module_name: str, pkg_name_or_abs_path: str) -> Optional[Callable]: diff --git a/mir/config/json_io.py b/mir/config/json_io.py index 5125766..3ef0a7c 100644 --- a/mir/config/json_io.py +++ b/mir/config/json_io.py @@ -17,7 +17,7 @@ def write_json_file(folder_path_named: str, file_name: str, data: Any, mode: str import json import os - from mir.config.logging import dbuq + from mir.config.logs import dbuq if ".json" not in file_name: file_name += ".json" diff --git a/mir/doc_parser.py b/mir/doc_parser.py index 8aadcf9..b04e55d 100644 --- a/mir/doc_parser.py +++ b/mir/doc_parser.py @@ -4,7 +4,7 @@ from typing import List, Optional, Tuple from pydantic import BaseModel, field_validator -from mir.config.logging import dbuq, nfo +from mir.config.logs import dbuq, nfo from mir.config.constants import DocParseData, DocStringParserConstants diff --git a/mir/indexers.py b/mir/indexers.py index 98a1e5d..aa512da 100644 --- a/mir/indexers.py +++ b/mir/indexers.py @@ -9,7 +9,7 @@ from mir.doc_parser import parse_docs from mir.tag import make_mir_tag from mir.inspect.classes import resolve_code_names, extract_init_params -from mir.config.logging import nfo +from mir.config.logs import nfo from mir.config.conversion import import_submodules if "pytest" in sys.modules: diff --git a/mir/inspect/classes.py b/mir/inspect/classes.py index f842267..1523443 100644 --- a/mir/inspect/classes.py +++ b/mir/inspect/classes.py @@ -7,7 +7,7 @@ from typing import Callable, Dict, List, Optional, Union, Type from mir.config.conversion import import_submodules -from mir.config.logging import nfo +from mir.config.logs import nfo def resolve_import_path(code_name: str, pkg_name: str) -> Optional[List[str]]: diff --git a/mir/inspect/pipes.py b/mir/inspect/pipes.py index 8d0e332..300e927 100644 --- a/mir/inspect/pipes.py +++ b/mir/inspect/pipes.py @@ -32,7 +32,7 @@ def get_class_parent_folder(class_name: str, pkg_name: str) -> List[str]: :return: A list corresponding to the path of the model, or None if not found :raises KeyError: for invalid pkg_name """ - from mir.config.logging import dbuq + from mir.config.logs import dbuq from mir.inspect.classes import resolve_code_names, extract_init_params pkg_name = pkg_name.lower() diff --git a/mir/inspect/tasks.py b/mir/inspect/tasks.py index a85a3e8..3fd42e4 100644 --- a/mir/inspect/tasks.py +++ b/mir/inspect/tasks.py @@ -3,7 +3,7 @@ from typing import Any, Callable, Dict, List, get_type_hints from mir.maid import MIRDatabase -from mir.config.logging import dbuq +from mir.config.logs import dbuq flatten_map: List[Any] = lambda nested, unpack: [element for iterative in getattr(nested, unpack)() for element in iterative] diff --git a/mir/maid.py b/mir/maid.py index d66fc26..a219ecc 100644 --- a/mir/maid.py +++ b/mir/maid.py @@ -9,7 +9,7 @@ from mir.config.constants import MIR_PATH_NAMED from mir.config.json_io import read_json_file, write_json_file -from mir.config.logging import dbuq +from mir.config.logs import dbuq class MIRDatabase: @@ -39,7 +39,7 @@ def add(self, resource: dict[str, Any]) -> None: def write_to_disk(self, data: Optional[dict] = None) -> None: # pylint:disable=unused-argument """Save data to JSON file\n""" - from mir.config.logging import nfo + from mir.config.logs import nfo if not os.path.exists(MIR_PATH_NAMED): mode = "x" @@ -146,7 +146,7 @@ def find_tag(self, field: str, target: str, sub_field: Optional[str] = None, dom :raises KeyError: Target string not found """ import re - from mir.config.logging import nfo + from mir.config.logs import nfo parameters = r"-gguf|-exl2|-exl3|-onnx|-awq|-mlx|-ov" # target = target.lower().strip("-") @@ -243,7 +243,7 @@ def main(mir_db: Callable | None = None, remake: bool = True) -> None: if "pytest" not in sys_modules: # import argparse - from mir.config.logging import nfo + from mir.config.logs import nfo parser = argparse.ArgumentParser( formatter_class=argparse.RawTextHelpFormatter, From 5d9c26b6fb3ddaaac4ced106022467b6e90a3dcb Mon Sep 17 00:00:00 2001 From: exdysa <91800957+exdysa@users.noreply.github.com> Date: Wed, 3 Dec 2025 15:49:36 -0500 Subject: [PATCH 10/10] ~console log --- mir/automata.py | 2 +- mir/config/console.py | 10 ++++++++++ mir/config/conversion.py | 2 +- mir/config/json_io.py | 2 +- mir/doc_parser.py | 2 +- mir/indexers.py | 2 +- mir/inspect/classes.py | 2 +- mir/inspect/pipes.py | 2 +- mir/inspect/tasks.py | 2 +- mir/maid.py | 8 ++++---- 10 files changed, 22 insertions(+), 12 deletions(-) create mode 100644 mir/config/console.py diff --git a/mir/automata.py b/mir/automata.py index 8e574e5..4c34f91 100644 --- a/mir/automata.py +++ b/mir/automata.py @@ -16,7 +16,7 @@ from mir.maid import MIRDatabase from mir.spec.mir import mir_entry from mir.tag import make_mir_tag, make_scheduler_tag, tag_pipe, tag_base_model -from mir.config.logs import nfo, dbuq +from mir.config.console import nfo, dbuq from mir.config.conversion import slice_number from mir.indexers import diffusers_index, transformers_index diff --git a/mir/config/console.py b/mir/config/console.py new file mode 100644 index 0000000..a5ad63a --- /dev/null +++ b/mir/config/console.py @@ -0,0 +1,10 @@ +# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0 +# + +from logging import DEBUG, INFO, Logger + +nfo_obj = Logger(INFO) +dbuq_obj = Logger(DEBUG) + +nfo = nfo_obj.info +dbuq = dbuq_obj.debug diff --git a/mir/config/conversion.py b/mir/config/conversion.py index 5e6646a..ab5d98c 100644 --- a/mir/config/conversion.py +++ b/mir/config/conversion.py @@ -2,7 +2,7 @@ # from typing import Callable, Optional, Union, Type, List, Iterator, Tuple, Dict -from mir.config.logs import dbuq, nfo +from mir.config.console import dbuq, nfo def import_submodules(module_name: str, pkg_name_or_abs_path: str) -> Optional[Callable]: diff --git a/mir/config/json_io.py b/mir/config/json_io.py index 3ef0a7c..92cd60f 100644 --- a/mir/config/json_io.py +++ b/mir/config/json_io.py @@ -17,7 +17,7 @@ def write_json_file(folder_path_named: str, file_name: str, data: Any, mode: str import json import os - from mir.config.logs import dbuq + from mir.config.console import dbuq if ".json" not in file_name: file_name += ".json" diff --git a/mir/doc_parser.py b/mir/doc_parser.py index b04e55d..0455b08 100644 --- a/mir/doc_parser.py +++ b/mir/doc_parser.py @@ -4,7 +4,7 @@ from typing import List, Optional, Tuple from pydantic import BaseModel, field_validator -from mir.config.logs import dbuq, nfo +from mir.config.console import dbuq, nfo from mir.config.constants import DocParseData, DocStringParserConstants diff --git a/mir/indexers.py b/mir/indexers.py index aa512da..d173085 100644 --- a/mir/indexers.py +++ b/mir/indexers.py @@ -9,7 +9,7 @@ from mir.doc_parser import parse_docs from mir.tag import make_mir_tag from mir.inspect.classes import resolve_code_names, extract_init_params -from mir.config.logs import nfo +from mir.config.console import nfo from mir.config.conversion import import_submodules if "pytest" in sys.modules: diff --git a/mir/inspect/classes.py b/mir/inspect/classes.py index 1523443..30a1681 100644 --- a/mir/inspect/classes.py +++ b/mir/inspect/classes.py @@ -7,7 +7,7 @@ from typing import Callable, Dict, List, Optional, Union, Type from mir.config.conversion import import_submodules -from mir.config.logs import nfo +from mir.config.console import nfo def resolve_import_path(code_name: str, pkg_name: str) -> Optional[List[str]]: diff --git a/mir/inspect/pipes.py b/mir/inspect/pipes.py index 300e927..8bcc738 100644 --- a/mir/inspect/pipes.py +++ b/mir/inspect/pipes.py @@ -32,7 +32,7 @@ def get_class_parent_folder(class_name: str, pkg_name: str) -> List[str]: :return: A list corresponding to the path of the model, or None if not found :raises KeyError: for invalid pkg_name """ - from mir.config.logs import dbuq + from mir.config.console import dbuq from mir.inspect.classes import resolve_code_names, extract_init_params pkg_name = pkg_name.lower() diff --git a/mir/inspect/tasks.py b/mir/inspect/tasks.py index 3fd42e4..1e10cc4 100644 --- a/mir/inspect/tasks.py +++ b/mir/inspect/tasks.py @@ -3,7 +3,7 @@ from typing import Any, Callable, Dict, List, get_type_hints from mir.maid import MIRDatabase -from mir.config.logs import dbuq +from mir.config.console import dbuq flatten_map: List[Any] = lambda nested, unpack: [element for iterative in getattr(nested, unpack)() for element in iterative] diff --git a/mir/maid.py b/mir/maid.py index a219ecc..e26a7fd 100644 --- a/mir/maid.py +++ b/mir/maid.py @@ -9,7 +9,7 @@ from mir.config.constants import MIR_PATH_NAMED from mir.config.json_io import read_json_file, write_json_file -from mir.config.logs import dbuq +from mir.config.console import dbuq class MIRDatabase: @@ -39,7 +39,7 @@ def add(self, resource: dict[str, Any]) -> None: def write_to_disk(self, data: Optional[dict] = None) -> None: # pylint:disable=unused-argument """Save data to JSON file\n""" - from mir.config.logs import nfo + from mir.config.console import nfo if not os.path.exists(MIR_PATH_NAMED): mode = "x" @@ -146,7 +146,7 @@ def find_tag(self, field: str, target: str, sub_field: Optional[str] = None, dom :raises KeyError: Target string not found """ import re - from mir.config.logs import nfo + from mir.config.console import nfo parameters = r"-gguf|-exl2|-exl3|-onnx|-awq|-mlx|-ov" # target = target.lower().strip("-") @@ -243,7 +243,7 @@ def main(mir_db: Callable | None = None, remake: bool = True) -> None: if "pytest" not in sys_modules: # import argparse - from mir.config.logs import nfo + from mir.config.console import nfo parser = argparse.ArgumentParser( formatter_class=argparse.RawTextHelpFormatter,