diff --git a/docs/index.html b/docs/index.html deleted file mode 100644 index 6142692..0000000 --- a/docs/index.html +++ /dev/null @@ -1,7 +0,0 @@ - - - - - - - diff --git a/docs/mir.html b/docs/mir.html deleted file mode 100644 index a59c9f2..0000000 --- a/docs/mir.html +++ /dev/null @@ -1,315 +0,0 @@ - - - - - - - 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 deleted file mode 100644 index 7138ce5..0000000 --- a/docs/mir/automata.html +++ /dev/null @@ -1,7179 +0,0 @@ - - - - - - - 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 deleted file mode 100644 index eb02fc1..0000000 --- a/docs/mir/config.html +++ /dev/null @@ -1,245 +0,0 @@ - - - - - - - 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 deleted file mode 100644 index d80c8c8..0000000 --- a/docs/mir/config/constants.html +++ /dev/null @@ -1,786 +0,0 @@ - - - - - - - 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 deleted file mode 100644 index 4e9ab93..0000000 --- a/docs/mir/config/conversion.html +++ /dev/null @@ -1,719 +0,0 @@ - - - - - - - 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 deleted file mode 100644 index 509bd8a..0000000 --- a/docs/mir/config/json_io.html +++ /dev/null @@ -1,381 +0,0 @@ - - - - - - - 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 deleted file mode 100644 index 772dbcd..0000000 --- a/docs/mir/doc_parser.html +++ /dev/null @@ -1,925 +0,0 @@ - - - - - - - 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 deleted file mode 100644 index 48893a9..0000000 --- a/docs/mir/indexers.html +++ /dev/null @@ -1,1183 +0,0 @@ - - - - - - - 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 deleted file mode 100644 index b6a3c01..0000000 --- a/docs/mir/inspect.html +++ /dev/null @@ -1,246 +0,0 @@ - - - - - - - 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 deleted file mode 100644 index d834172..0000000 --- a/docs/mir/inspect/classes.html +++ /dev/null @@ -1,602 +0,0 @@ - - - - - - - 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 deleted file mode 100644 index a4d12d4..0000000 --- a/docs/mir/inspect/metadata.html +++ /dev/null @@ -1,565 +0,0 @@ - - - - - - - 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 deleted file mode 100644 index 76a5108..0000000 --- a/docs/mir/inspect/parenting.html +++ /dev/null @@ -1,342 +0,0 @@ - - - - - - - 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 deleted file mode 100644 index 3dc111c..0000000 --- a/docs/mir/inspect/pipes.html +++ /dev/null @@ -1,367 +0,0 @@ - - - - - - - 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 deleted file mode 100644 index 481e1e4..0000000 --- a/docs/mir/inspect/tasks.html +++ /dev/null @@ -1,1331 +0,0 @@ - - - - - - - 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 deleted file mode 100644 index 40f5d87..0000000 --- a/docs/mir/maid.html +++ /dev/null @@ -1,1130 +0,0 @@ - - - - - - - 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 deleted file mode 100644 index a56a87c..0000000 --- a/docs/mir/run.html +++ /dev/null @@ -1,423 +0,0 @@ - - - - - - - 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 deleted file mode 100644 index 28e10a5..0000000 --- a/docs/mir/tag.html +++ /dev/null @@ -1,582 +0,0 @@ - - - - - - - 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 deleted file mode 100644 index f3d0c8b..0000000 --- a/docs/search.js +++ /dev/null @@ -1,46 +0,0 @@ -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. This may cause problems when serialising the index.\n",e)},t.Pipeline.load=function(e){var n=new t.Pipeline;return e.forEach(function(e){var i=t.Pipeline.getRegisteredFunction(e);if(!i)throw new Error("Cannot load un-registered function: "+e);n.add(i)}),n},t.Pipeline.prototype.add=function(){var e=Array.prototype.slice.call(arguments);e.forEach(function(e){t.Pipeline.warnIfFunctionNotRegistered(e),this._queue.push(e)},this)},t.Pipeline.prototype.after=function(e,n){t.Pipeline.warnIfFunctionNotRegistered(n);var i=this._queue.indexOf(e);if(-1===i)throw new Error("Cannot find existingFn");this._queue.splice(i+1,0,n)},t.Pipeline.prototype.before=function(e,n){t.Pipeline.warnIfFunctionNotRegistered(n);var i=this._queue.indexOf(e);if(-1===i)throw new Error("Cannot find existingFn");this._queue.splice(i,0,n)},t.Pipeline.prototype.remove=function(e){var t=this._queue.indexOf(e);-1!==t&&this._queue.splice(t,1)},t.Pipeline.prototype.run=function(e){for(var t=[],n=e.length,i=this._queue.length,o=0;n>o;o++){for(var r=e[o],s=0;i>s&&(r=this._queue[s](r,o,e),void 0!==r&&null!==r);s++);void 0!==r&&null!==r&&t.push(r)}return t},t.Pipeline.prototype.reset=function(){this._queue=[]},t.Pipeline.prototype.get=function(){return this._queue},t.Pipeline.prototype.toJSON=function(){return this._queue.map(function(e){return t.Pipeline.warnIfFunctionNotRegistered(e),e.label})},t.Index=function(){this._fields=[],this._ref="id",this.pipeline=new t.Pipeline,this.documentStore=new t.DocumentStore,this.index={},this.eventEmitter=new t.EventEmitter,this._idfCache={},this.on("add","remove","update",function(){this._idfCache={}}.bind(this))},t.Index.prototype.on=function(){var e=Array.prototype.slice.call(arguments);return this.eventEmitter.addListener.apply(this.eventEmitter,e)},t.Index.prototype.off=function(e,t){return this.eventEmitter.removeListener(e,t)},t.Index.load=function(e){e.version!==t.version&&t.utils.warn("version mismatch: current "+t.version+" importing "+e.version);var n=new this;n._fields=e.fields,n._ref=e.ref,n.documentStore=t.DocumentStore.load(e.documentStore),n.pipeline=t.Pipeline.load(e.pipeline),n.index={};for(var i in e.index)n.index[i]=t.InvertedIndex.load(e.index[i]);return n},t.Index.prototype.addField=function(e){return this._fields.push(e),this.index[e]=new t.InvertedIndex,this},t.Index.prototype.setRef=function(e){return this._ref=e,this},t.Index.prototype.saveDocument=function(e){return this.documentStore=new t.DocumentStore(e),this},t.Index.prototype.addDoc=function(e,n){if(e){var n=void 0===n?!0:n,i=e[this._ref];this.documentStore.addDoc(i,e),this._fields.forEach(function(n){var o=this.pipeline.run(t.tokenizer(e[n]));this.documentStore.addFieldLength(i,n,o.length);var r={};o.forEach(function(e){e in r?r[e]+=1:r[e]=1},this);for(var s in r){var u=r[s];u=Math.sqrt(u),this.index[n].addToken(s,{ref:i,tf:u})}},this),n&&this.eventEmitter.emit("add",e,this)}},t.Index.prototype.removeDocByRef=function(e){if(e&&this.documentStore.isDocStored()!==!1&&this.documentStore.hasDoc(e)){var t=this.documentStore.getDoc(e);this.removeDoc(t,!1)}},t.Index.prototype.removeDoc=function(e,n){if(e){var n=void 0===n?!0:n,i=e[this._ref];this.documentStore.hasDoc(i)&&(this.documentStore.removeDoc(i),this._fields.forEach(function(n){var o=this.pipeline.run(t.tokenizer(e[n]));o.forEach(function(e){this.index[n].removeToken(e,i)},this)},this),n&&this.eventEmitter.emit("remove",e,this))}},t.Index.prototype.updateDoc=function(e,t){var t=void 0===t?!0:t;this.removeDocByRef(e[this._ref],!1),this.addDoc(e,!1),t&&this.eventEmitter.emit("update",e,this)},t.Index.prototype.idf=function(e,t){var n="@"+t+"/"+e;if(Object.prototype.hasOwnProperty.call(this._idfCache,n))return this._idfCache[n];var 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configuration"),this.buildDefaultConfig(n)}},t.Configuration.prototype.buildDefaultConfig=function(e){this.reset(),e.forEach(function(e){this.config[e]={boost:1,bool:"OR",expand:!1}},this)},t.Configuration.prototype.buildUserConfig=function(e,n){var i="OR",o=!1;if(this.reset(),"bool"in e&&(i=e.bool||i),"expand"in e&&(o=e.expand||o),"fields"in e)for(var r in e.fields)if(n.indexOf(r)>-1){var s=e.fields[r],u=o;void 0!=s.expand&&(u=s.expand),this.config[r]={boost:s.boost||0===s.boost?s.boost:1,bool:s.bool||i,expand:u}}else t.utils.warn("field name in user configuration not found in index instance fields");else this.addAllFields2UserConfig(i,o,n)},t.Configuration.prototype.addAllFields2UserConfig=function(e,t,n){n.forEach(function(n){this.config[n]={boost:1,bool:e,expand:t}},this)},t.Configuration.prototype.get=function(){return this.config},t.Configuration.prototype.reset=function(){this.config={}},lunr.SortedSet=function(){this.length=0,this.elements=[]},lunr.SortedSet.load=function(e){var t=new this;return t.elements=e,t.length=e.length,t},lunr.SortedSet.prototype.add=function(){var e,t;for(e=0;e1;){if(r===e)return o;e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o]}return r===e?o:-1},lunr.SortedSet.prototype.locationFor=function(e){for(var t=0,n=this.elements.length,i=n-t,o=t+Math.floor(i/2),r=this.elements[o];i>1;)e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o];return r>e?o:e>r?o+1:void 0},lunr.SortedSet.prototype.intersect=function(e){for(var t=new lunr.SortedSet,n=0,i=0,o=this.length,r=e.length,s=this.elements,u=e.elements;;){if(n>o-1||i>r-1)break;s[n]!==u[i]?s[n]u[i]&&i++:(t.add(s[n]),n++,i++)}return t},lunr.SortedSet.prototype.clone=function(){var e=new lunr.SortedSet;return e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();o

\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
\n", "signature": "(\tmir_db: mir.maid.MIRDatabase,\tdata_tuple: List[Tuple[Dict[str, <built-in function any>]]]) -> None:", "funcdef": "def"}, "mir.automata.hf_pkg_to_mir": {"fullname": "mir.automata.hf_pkg_to_mir", "modulename": "mir.automata", "qualname": "hf_pkg_to_mir", "kind": "function", "doc": "

Generate MIR HF Hub model database

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Create mir info database

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Create mir info database

\n", "signature": "(mir_db: mir.maid.MIRDatabase):", "funcdef": "def"}, "mir.automata.mir_update": {"fullname": "mir.automata.mir_update", "modulename": "mir.automata", "qualname": "mir_update", "kind": "function", "doc": "

Create mir unet info database

\n", "signature": "(\tmir_db: mir.maid.MIRDatabase,\ttask_list: list = None,\tpipe_list: list = None):", "funcdef": "def"}, "mir.automata.add_mir_diffusion": {"fullname": "mir.automata.add_mir_diffusion", "modulename": "mir.automata", "qualname": "add_mir_diffusion", "kind": "function", "doc": "

Create MIR entries missing from the database

\n", "signature": "(mir_db: mir.maid.MIRDatabase):", "funcdef": "def"}, "mir.automata.add_mir_llm": {"fullname": "mir.automata.add_mir_llm", "modulename": "mir.automata", "qualname": "add_mir_llm", "kind": "function", "doc": "

\n", "signature": "(mir_db: mir.maid.MIRDatabase):", "funcdef": "def"}, "mir.automata.add_mir_audio": {"fullname": "mir.automata.add_mir_audio", "modulename": "mir.automata", "qualname": "add_mir_audio", "kind": "function", "doc": "

Create MIR audio modality entries

\n", "signature": "(mir_db: mir.maid.MIRDatabase):", "funcdef": "def"}, "mir.automata.add_mir_lora": {"fullname": "mir.automata.add_mir_lora", "modulename": "mir.automata", "qualname": "add_mir_lora", "kind": "function", "doc": "

Create MIR lora entries

\n", "signature": "(mir_db: mir.maid.MIRDatabase):", "funcdef": "def"}, "mir.automata.add_mir_vae": {"fullname": "mir.automata.add_mir_vae", "modulename": "mir.automata", "qualname": "add_mir_vae", "kind": "function", "doc": "

Create MIR VAE missing from the database

\n", "signature": "(mir_db: mir.maid.MIRDatabase):", "funcdef": "def"}, "mir.config": {"fullname": "mir.config", "modulename": "mir.config", "kind": "module", "doc": "

\n"}, "mir.config.constants": {"fullname": "mir.config.constants", "modulename": "mir.config.constants", "kind": "module", "doc": "

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

\n", "signature": "(\tmodel_name: type[transformers.configuration_utils.PreTrainedConfig],\tsupported_tasks: Union[str, list[str], NoneType] = None) -> list[str]:", "funcdef": "def"}, "mir.config.constants.DocParseData": {"fullname": "mir.config.constants.DocParseData", "modulename": "mir.config.constants", "qualname": "DocParseData", "kind": "class", "doc": "

\n"}, "mir.config.constants.DocParseData.__init__": {"fullname": "mir.config.constants.DocParseData.__init__", "modulename": "mir.config.constants", "qualname": "DocParseData.__init__", "kind": "function", "doc": "

\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.

\n"}, "mir.config.constants.DocStringParserConstants.pipe_prefixes": {"fullname": "mir.config.constants.DocStringParserConstants.pipe_prefixes", "modulename": "mir.config.constants", "qualname": "DocStringParserConstants.pipe_prefixes", "kind": "variable", "doc": "

\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, "signature": 0, "bases": 0, "doc": 3}, "mir.config.constants.DocParseData.staged_repo": {"qualname": 3, "fullname": 6, "annotation": 2, "default_value": 1, "signature": 0, "bases": 0, "doc": 3}, "mir.config.constants.DocStringParserConstants": {"qualname": 1, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 10}, "mir.config.constants.DocStringParserConstants.pipe_prefixes": {"qualname": 3, "fullname": 6, "annotation": 2, "default_value": 62, "signature": 0, "bases": 0, "doc": 3}, "mir.config.constants.DocStringParserConstants.repo_variables": {"qualname": 3, "fullname": 6, "annotation": 2, "default_value": 40, "signature": 0, "bases": 0, "doc": 3}, "mir.config.constants.DocStringParserConstants.call_types": {"qualname": 3, "fullname": 6, "annotation": 2, "default_value": 11, "signature": 0, "bases": 0, "doc": 3}, "mir.config.constants.DocStringParserConstants.staged_call_types": {"qualname": 4, "fullname": 7, "annotation": 2, "default_value": 6, "signature": 0, "bases": 0, "doc": 3}, "mir.config.constants.package_map": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 30, "signature": 0, "bases": 0, "doc": 3}, "mir.config.constants.root_path": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 5, "signature": 0, "bases": 0, "doc": 3}, "mir.config.constants.versions": {"qualname": 1, "fullname": 4, "annotation": 0, "default_value": 103, "signature": 0, "bases": 0, "doc": 3}, "mir.config.constants.template": {"qualname": 1, "fullname": 4, "annotation": 0, "default_value": 550, "signature": 0, "bases": 0, "doc": 3}, "mir.config.constants.MIR_PATH_NAMED": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 6, "signature": 0, "bases": 0, "doc": 3}, "mir.config.constants.BREAKING_SUFFIX": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 18, "signature": 0, "bases": 0, "doc": 3}, "mir.config.constants.PARAMETERS_SUFFIX": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 10, "signature": 0, "bases": 0, "doc": 3}, "mir.config.constants.SEARCH_SUFFIX": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 11, "signature": 0, "bases": 0, "doc": 3}, "mir.config.conversion": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mir.config.conversion.import_submodules": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 40, "bases": 0, "doc": 55}, "mir.config.conversion.code_name_to_class_name": {"qualname": 5, "fullname": 8, "annotation": 0, "default_value": 0, "signature": 91, "bases": 0, "doc": 70}, "mir.config.conversion.pkg_path_to_docstring": {"qualname": 4, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 53, "bases": 0, "doc": 66}, "mir.config.conversion.file_name_to_docstring": {"qualname": 4, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 53, "bases": 0, "doc": 67}, "mir.config.conversion.class_to_mir_tag": {"qualname": 4, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 49, "bases": 0, "doc": 71}, "mir.config.conversion.slice_number": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 37, "bases": 0, "doc": 28}, "mir.config.json_io": {"qualname": 0, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mir.config.json_io.write_json_file": {"qualname": 3, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 67, "bases": 0, "doc": 67}, "mir.config.json_io.read_json_file": {"qualname": 3, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 35, "bases": 0, "doc": 41}, "mir.config.logging": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mir.config.logging.nfo_obj": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 7, "signature": 0, "bases": 0, "doc": 3}, "mir.config.logging.dbuq_obj": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 7, "signature": 0, "bases": 0, "doc": 3}, "mir.config.logging.nfo": {"qualname": 1, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 25, "bases": 0, "doc": 44}, "mir.config.logging.dbuq": {"qualname": 1, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 25, "bases": 0, "doc": 44}, "mir.doc_parser": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mir.doc_parser.parse_docs": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 61, "bases": 0, "doc": 3}, "mir.doc_parser.DocStringValidator": {"qualname": 1, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 11}, "mir.doc_parser.DocStringValidator.normalize_doc_string": {"qualname": 4, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 20, "bases": 0, "doc": 34}, "mir.doc_parser.DocStringValidator.is_valid_repo_path": {"qualname": 5, "fullname": 8, "annotation": 0, "default_value": 0, "signature": 26, "bases": 0, "doc": 40}, "mir.doc_parser.DocStringValidator.validate_repo_path": {"qualname": 4, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 42, "bases": 0, "doc": 47}, "mir.doc_parser.DocStringValidator.validate_pipe_class": {"qualname": 4, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 26, "bases": 0, "doc": 38}, "mir.doc_parser.DocStringParser": {"qualname": 1, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 3, "doc": 308}, "mir.doc_parser.DocStringParser.doc_string": {"qualname": 3, "fullname": 6, "annotation": 2, "default_value": 1, "signature": 0, "bases": 0, "doc": 3}, "mir.doc_parser.DocStringParser.normalize_doc": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 24, "bases": 0, "doc": 3}, "mir.doc_parser.DocStringParser.doc_match": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 35, "bases": 0, "doc": 3}, "mir.doc_parser.DocStringParser.parse": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 29, "bases": 0, "doc": 3}, "mir.indexers": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mir.indexers.check_migrations": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 17, "bases": 0, "doc": 49}, "mir.indexers.flag_config": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 47, "bases": 0, "doc": 64}, "mir.indexers.create_pipe_entry": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 111, "bases": 0, "doc": 97}, "mir.indexers.diffusers_index": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 42, "bases": 0, "doc": 27}, "mir.indexers.transformers_index": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 7, "bases": 0, "doc": 27}, "mir.indexers.mlx_repo_capture": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 29, "bases": 0, "doc": 3}, "mir.inspect": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mir.inspect.classes": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mir.inspect.classes.resolve_import_path": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 41, "bases": 0, "doc": 107}, "mir.inspect.classes.resolve_class_names": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 116, "bases": 0, "doc": 92}, "mir.inspect.classes.extract_inherited_classes": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 78, "bases": 0, "doc": 91}, "mir.inspect.classes.extract_init_params": 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"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