From 4d3f7ebc60930610460e8104cfa521f387a70142 Mon Sep 17 00:00:00 2001 From: iiiCpu <40638625+iiiCpu@users.noreply.github.com> Date: Sun, 12 Jan 2025 12:30:50 +0700 Subject: [PATCH 1/6] Updated microsoft/TRELLIS to revision eeacb0bf6a7d25058232d746bef4e5e880b130ff (2024.12.27 19:37:55) Updated ComfyUI-3D-Pack\nodes.py to match general flow. Pulled out some hidden parameters of TRELLIS sampler to UI. --- .../trellis/modules/attention/__init__.py | 2 +- .../trellis/modules/sparse/__init__.py | 2 +- .../trellis/pipelines/samplers/flow_euler.py | 5 +- .../trellis/pipelines/trellis_image_to_3d.py | 95 +- .../trellis/renderers/gaussian_render.py | 14 +- .../gaussian/gaussian_model.py | 21 +- .../trellis/representations/mesh/cube2mesh.py | 5 +- .../mesh/flexicubes/LICENSE.txt | 90 + .../representations/mesh/flexicubes/README.md | 110 + .../examples/data/inputmodels/block.obj | 6420 +++++++++++++++++ .../mesh/flexicubes/examples/download_data.py | 41 + .../mesh/flexicubes/examples/extraction.ipynb | 1668 +++++ .../mesh/flexicubes/examples/loss.py | 95 + .../flexicubes/examples/optimization.ipynb | 801 ++ .../mesh/flexicubes/examples/optimize.py | 150 + .../mesh/flexicubes/examples/render.py | 267 + .../mesh/flexicubes/examples/util.py | 122 + .../mesh/flexicubes/flexicubes.py | 35 +- .../mesh/flexicubes/images/ablate_L_dev.jpg | Bin 0 -> 73011 bytes .../mesh/flexicubes/images/block_final.png | Bin 0 -> 55999 bytes .../mesh/flexicubes/images/block_init.png | Bin 0 -> 198533 bytes .../mesh/flexicubes/images/teaser_top.png | Bin 0 -> 3562986 bytes .../trellis/utils/postprocessing_utils.py | 158 +- Gen_3D_Modules/TRELLIS/trellis_/__init__.py | 6 + .../TRELLIS/trellis_/models/__init__.py | 70 + .../trellis_/models/sparse_structure_flow.py | 200 + .../trellis_/models/sparse_structure_vae.py | 306 + .../trellis_/models/structured_latent_flow.py | 262 + .../models/structured_latent_vae/__init__.py | 4 + .../models/structured_latent_vae/base.py | 117 + .../structured_latent_vae/decoder_gs.py | 122 + .../structured_latent_vae/decoder_mesh.py | 167 + .../structured_latent_vae/decoder_rf.py | 104 + .../models/structured_latent_vae/encoder.py | 72 + .../trellis_/modules/attention/__init__.py | 36 + .../trellis_/modules/attention/full_attn.py | 140 + .../trellis_/modules/attention/modules.py | 146 + .../TRELLIS/trellis_/modules/norm.py | 25 + .../trellis_/modules/sparse/__init__.py | 102 + .../modules/sparse/attention/__init__.py | 4 + .../modules/sparse/attention/full_attn.py | 215 + .../modules/sparse/attention/modules.py | 139 + .../sparse/attention/serialized_attn.py | 193 + .../modules/sparse/attention/windowed_attn.py | 135 + .../TRELLIS/trellis_/modules/sparse/basic.py | 459 ++ .../trellis_/modules/sparse/conv/__init__.py | 21 + .../modules/sparse/conv/conv_spconv.py | 80 + .../modules/sparse/conv/conv_torchsparse.py | 38 + .../TRELLIS/trellis_/modules/sparse/linear.py | 15 + .../trellis_/modules/sparse/nonlinearity.py | 35 + .../TRELLIS/trellis_/modules/sparse/norm.py | 58 + .../trellis_/modules/sparse/spatial.py | 110 + .../modules/sparse/transformer/__init__.py | 2 + .../modules/sparse/transformer/blocks.py | 151 + .../modules/sparse/transformer/modulated.py | 166 + .../TRELLIS/trellis_/modules/spatial.py | 48 + .../trellis_/modules/transformer/__init__.py | 2 + .../trellis_/modules/transformer/blocks.py | 182 + .../trellis_/modules/transformer/modulated.py | 157 + .../TRELLIS/trellis_/modules/utils.py | 54 + .../TRELLIS/trellis_/pipelines/__init__.py | 24 + .../TRELLIS/trellis_/pipelines/base.py | 66 + .../trellis_/pipelines/samplers/__init__.py | 2 + .../trellis_/pipelines/samplers/base.py | 20 + .../classifier_free_guidance_mixin.py | 12 + .../trellis_/pipelines/samplers/flow_euler.py | 202 + .../samplers/guidance_interval_mixin.py | 15 + .../trellis_/pipelines/trellis_image_to_3d.py | 283 + .../TRELLIS/trellis_/renderers/__init__.py | 31 + .../trellis_/renderers/gaussian_render.py | 235 + .../trellis_/renderers/mesh_renderer.py | 133 + .../trellis_/renderers/octree_renderer.py | 300 + .../TRELLIS/trellis_/renderers/sh_utils.py | 118 + .../trellis_/representations/__init__.py | 4 + .../representations/gaussian/__init__.py | 1 + .../gaussian/gaussian_model.py | 194 + .../representations/gaussian/general_utils.py | 133 + .../trellis_/representations/mesh/__init__.py | 1 + .../representations/mesh/cube2mesh.py | 146 + .../mesh/flexicubes/flexicubes.py | 417 ++ .../representations/mesh/flexicubes/tables.py | 791 ++ .../representations/mesh/utils_cube.py | 61 + .../representations/octree/__init__.py | 1 + .../representations/octree/octree_dfs.py | 362 + .../radiance_field/__init__.py | 1 + .../representations/radiance_field/strivec.py | 28 + .../TRELLIS/trellis_/utils/__init__.py | 0 .../TRELLIS/trellis_/utils/general_utils.py | 187 + .../trellis_/utils/postprocessing_utils.py | 467 ++ .../TRELLIS/trellis_/utils/random_utils.py | 30 + .../TRELLIS/trellis_/utils/render_utils.py | 116 + nodes.py | 74 +- 92 files changed, 18614 insertions(+), 85 deletions(-) create mode 100644 Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/LICENSE.txt create mode 100644 Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/README.md create mode 100644 Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/data/inputmodels/block.obj create mode 100644 Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/download_data.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/extraction.ipynb create mode 100644 Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/loss.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/optimization.ipynb create mode 100644 Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/optimize.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/render.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/util.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/images/ablate_L_dev.jpg create mode 100644 Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/images/block_final.png create mode 100644 Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/images/block_init.png create mode 100644 Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/images/teaser_top.png create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/__init__.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/models/__init__.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/models/sparse_structure_flow.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/models/sparse_structure_vae.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_flow.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/__init__.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/base.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/decoder_gs.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/decoder_mesh.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/decoder_rf.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/encoder.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/attention/__init__.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/attention/full_attn.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/attention/modules.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/norm.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/__init__.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/__init__.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/full_attn.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/modules.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/serialized_attn.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/windowed_attn.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/basic.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/conv/__init__.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/conv/conv_spconv.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/conv/conv_torchsparse.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/linear.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/nonlinearity.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/norm.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/spatial.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/transformer/__init__.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/transformer/blocks.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/transformer/modulated.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/spatial.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/transformer/__init__.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/transformer/blocks.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/transformer/modulated.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/utils.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/pipelines/__init__.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/pipelines/base.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/__init__.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/base.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/classifier_free_guidance_mixin.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/flow_euler.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/guidance_interval_mixin.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/pipelines/trellis_image_to_3d.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/renderers/__init__.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/renderers/gaussian_render.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/renderers/mesh_renderer.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/renderers/octree_renderer.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/renderers/sh_utils.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/representations/__init__.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/representations/gaussian/__init__.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/representations/gaussian/gaussian_model.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/representations/gaussian/general_utils.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/__init__.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/cube2mesh.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/flexicubes/flexicubes.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/flexicubes/tables.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/utils_cube.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/representations/octree/__init__.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/representations/octree/octree_dfs.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/representations/radiance_field/__init__.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/representations/radiance_field/strivec.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/utils/__init__.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/utils/general_utils.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/utils/postprocessing_utils.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/utils/random_utils.py create mode 100644 Gen_3D_Modules/TRELLIS/trellis_/utils/render_utils.py diff --git a/Gen_3D_Modules/TRELLIS/trellis/modules/attention/__init__.py b/Gen_3D_Modules/TRELLIS/trellis/modules/attention/__init__.py index b77197d6..f452320d 100644 --- a/Gen_3D_Modules/TRELLIS/trellis/modules/attention/__init__.py +++ b/Gen_3D_Modules/TRELLIS/trellis/modules/attention/__init__.py @@ -1,6 +1,6 @@ from typing import * -BACKEND = 'xformers' +BACKEND = 'flash_attn' DEBUG = False def __from_env(): diff --git a/Gen_3D_Modules/TRELLIS/trellis/modules/sparse/__init__.py b/Gen_3D_Modules/TRELLIS/trellis/modules/sparse/__init__.py index 77108a63..726756c1 100644 --- a/Gen_3D_Modules/TRELLIS/trellis/modules/sparse/__init__.py +++ b/Gen_3D_Modules/TRELLIS/trellis/modules/sparse/__init__.py @@ -2,7 +2,7 @@ BACKEND = 'spconv' DEBUG = False -ATTN = 'xformers' +ATTN = 'flash_attn' def __from_env(): import os diff --git a/Gen_3D_Modules/TRELLIS/trellis/pipelines/samplers/flow_euler.py b/Gen_3D_Modules/TRELLIS/trellis/pipelines/samplers/flow_euler.py index b2d48607..d79124cf 100644 --- a/Gen_3D_Modules/TRELLIS/trellis/pipelines/samplers/flow_euler.py +++ b/Gen_3D_Modules/TRELLIS/trellis/pipelines/samplers/flow_euler.py @@ -2,7 +2,6 @@ import torch import numpy as np from tqdm import tqdm -import comfy.utils from easydict import EasyDict as edict from .base import Sampler from .classifier_free_guidance_mixin import ClassifierFreeGuidanceSamplerMixin @@ -109,13 +108,11 @@ def sample( t_seq = rescale_t * t_seq / (1 + (rescale_t - 1) * t_seq) t_pairs = list((t_seq[i], t_seq[i + 1]) for i in range(steps)) ret = edict({"samples": None, "pred_x_t": [], "pred_x_0": []}) - comfy_pbar = comfy.utils.ProgressBar(steps) - for i, (t, t_prev) in enumerate(tqdm(t_pairs, desc="Sampling", disable=not verbose)): + for t, t_prev in tqdm(t_pairs, desc="Sampling", disable=not verbose): out = self.sample_once(model, sample, t, t_prev, cond, **kwargs) sample = out.pred_x_prev ret.pred_x_t.append(out.pred_x_prev) ret.pred_x_0.append(out.pred_x_0) - comfy_pbar.update_absolute(i + 1) ret.samples = sample return ret diff --git a/Gen_3D_Modules/TRELLIS/trellis/pipelines/trellis_image_to_3d.py b/Gen_3D_Modules/TRELLIS/trellis/pipelines/trellis_image_to_3d.py index 033083e0..f781e348 100644 --- a/Gen_3D_Modules/TRELLIS/trellis/pipelines/trellis_image_to_3d.py +++ b/Gen_3D_Modules/TRELLIS/trellis/pipelines/trellis_image_to_3d.py @@ -1,4 +1,5 @@ from typing import * +from contextlib import contextmanager import torch import torch.nn as nn import torch.nn.functional as F @@ -7,6 +8,7 @@ from easydict import EasyDict as edict from torchvision import transforms from PIL import Image +import rembg from .base import Pipeline from . import samplers from ..modules import sparse as sp @@ -93,7 +95,6 @@ def preprocess_image(self, input: Image.Image) -> Image.Image: if has_alpha: output = input else: - import rembg input = input.convert('RGB') max_size = max(input.size) scale = min(1, 1024 / max_size) @@ -281,3 +282,95 @@ def run( coords = self.sample_sparse_structure(cond, num_samples, sparse_structure_sampler_params) slat = self.sample_slat(cond, coords, slat_sampler_params) return self.decode_slat(slat, formats) + + @contextmanager + def inject_sampler_multi_image( + self, + sampler_name: str, + num_images: int, + num_steps: int, + mode: Literal['stochastic', 'multidiffusion'] = 'stochastic', + ): + """ + Inject a sampler with multiple images as condition. + + Args: + sampler_name (str): The name of the sampler to inject. + num_images (int): The number of images to condition on. + num_steps (int): The number of steps to run the sampler for. + """ + sampler = getattr(self, sampler_name) + setattr(sampler, f'_old_inference_model', sampler._inference_model) + + if mode == 'stochastic': + if num_images > num_steps: + print(f"\033[93mWarning: number of conditioning images is greater than number of steps for {sampler_name}. " + "This may lead to performance degradation.\033[0m") + + cond_indices = (np.arange(num_steps) % num_images).tolist() + def _new_inference_model(self, model, x_t, t, cond, **kwargs): + cond_idx = cond_indices.pop(0) + cond_i = cond[cond_idx:cond_idx+1] + return self._old_inference_model(model, x_t, t, cond=cond_i, **kwargs) + + elif mode =='multidiffusion': + from .samplers import FlowEulerSampler + def _new_inference_model(self, model, x_t, t, cond, neg_cond, cfg_strength, cfg_interval, **kwargs): + if cfg_interval[0] <= t <= cfg_interval[1]: + preds = [] + for i in range(len(cond)): + preds.append(FlowEulerSampler._inference_model(self, model, x_t, t, cond[i:i+1], **kwargs)) + pred = sum(preds) / len(preds) + neg_pred = FlowEulerSampler._inference_model(self, model, x_t, t, neg_cond, **kwargs) + return (1 + cfg_strength) * pred - cfg_strength * neg_pred + else: + preds = [] + for i in range(len(cond)): + preds.append(FlowEulerSampler._inference_model(self, model, x_t, t, cond[i:i+1], **kwargs)) + pred = sum(preds) / len(preds) + return pred + + else: + raise ValueError(f"Unsupported mode: {mode}") + + sampler._inference_model = _new_inference_model.__get__(sampler, type(sampler)) + + yield + + sampler._inference_model = sampler._old_inference_model + delattr(sampler, f'_old_inference_model') + + @torch.no_grad() + def run_multi_image( + self, + images: List[Image.Image], + num_samples: int = 1, + seed: int = 42, + sparse_structure_sampler_params: dict = {}, + slat_sampler_params: dict = {}, + formats: List[str] = ['mesh', 'gaussian', 'radiance_field'], + preprocess_image: bool = True, + mode: Literal['stochastic', 'multidiffusion'] = 'stochastic', + ) -> dict: + """ + Run the pipeline with multiple images as condition + + Args: + images (List[Image.Image]): The multi-view images of the assets + num_samples (int): The number of samples to generate. + sparse_structure_sampler_params (dict): Additional parameters for the sparse structure sampler. + slat_sampler_params (dict): Additional parameters for the structured latent sampler. + preprocess_image (bool): Whether to preprocess the image. + """ + if preprocess_image: + images = [self.preprocess_image(image) for image in images] + cond = self.get_cond(images) + cond['neg_cond'] = cond['neg_cond'][:1] + torch.manual_seed(seed) + ss_steps = {**self.sparse_structure_sampler_params, **sparse_structure_sampler_params}.get('steps') + with self.inject_sampler_multi_image('sparse_structure_sampler', len(images), ss_steps, mode=mode): + coords = self.sample_sparse_structure(cond, num_samples, sparse_structure_sampler_params) + slat_steps = {**self.slat_sampler_params, **slat_sampler_params}.get('steps') + with self.inject_sampler_multi_image('slat_sampler', len(images), slat_steps, mode=mode): + slat = self.sample_slat(cond, coords, slat_sampler_params) + return self.decode_slat(slat, formats) diff --git a/Gen_3D_Modules/TRELLIS/trellis/renderers/gaussian_render.py b/Gen_3D_Modules/TRELLIS/trellis/renderers/gaussian_render.py index ef3ef8c4..57108e3c 100644 --- a/Gen_3D_Modules/TRELLIS/trellis/renderers/gaussian_render.py +++ b/Gen_3D_Modules/TRELLIS/trellis/renderers/gaussian_render.py @@ -52,10 +52,6 @@ def render(viewpoint_camera, pc : Gaussian, pipe, bg_color : torch.Tensor, scali Render the scene. Background tensor (bg_color) must be on GPU! - - Original code use the Differential Gaussian Rasterization from https://github.com/autonomousvision/mip-splatting/tree/main/submodules/diff-gaussian-rasterization - Modified to use the GaussianRasterizer from https://github.com/ashawkey/diff-gaussian-rasterization - Only changes are the inputs to GaussianRasterizationSettings: kernel_size and subpixel_offset are commented out. """ # lazy import if 'GaussianRasterizer' not in globals(): @@ -71,16 +67,16 @@ def render(viewpoint_camera, pc : Gaussian, pipe, bg_color : torch.Tensor, scali tanfovx = math.tan(viewpoint_camera.FoVx * 0.5) tanfovy = math.tan(viewpoint_camera.FoVy * 0.5) - #kernel_size = pipe.kernel_size - #subpixel_offset = torch.zeros((int(viewpoint_camera.image_height), int(viewpoint_camera.image_width), 2), dtype=torch.float32, device="cuda") + kernel_size = pipe.kernel_size + subpixel_offset = torch.zeros((int(viewpoint_camera.image_height), int(viewpoint_camera.image_width), 2), dtype=torch.float32, device="cuda") raster_settings = GaussianRasterizationSettings( image_height=int(viewpoint_camera.image_height), image_width=int(viewpoint_camera.image_width), tanfovx=tanfovx, tanfovy=tanfovy, - #kernel_size=kernel_size, - #subpixel_offset=subpixel_offset, + kernel_size=kernel_size, + subpixel_offset=subpixel_offset, bg=bg_color, scale_modifier=scaling_modifier, viewmatrix=viewpoint_camera.world_view_transform, @@ -125,7 +121,7 @@ def render(viewpoint_camera, pc : Gaussian, pipe, bg_color : torch.Tensor, scali colors_precomp = override_color # Rasterize visible Gaussians to image, obtain their radii (on screen). - rendered_image, radii, rendered_depth, rendered_alpha = rasterizer( + rendered_image, radii = rasterizer( means3D = means3D, means2D = means2D, shs = shs, diff --git a/Gen_3D_Modules/TRELLIS/trellis/representations/gaussian/gaussian_model.py b/Gen_3D_Modules/TRELLIS/trellis/representations/gaussian/gaussian_model.py index 2dc70552..54ba16f1 100644 --- a/Gen_3D_Modules/TRELLIS/trellis/representations/gaussian/gaussian_model.py +++ b/Gen_3D_Modules/TRELLIS/trellis/representations/gaussian/gaussian_model.py @@ -2,6 +2,7 @@ import numpy as np from plyfile import PlyData, PlyElement from .general_utils import inverse_sigmoid, strip_symmetric, build_scaling_rotation +import utils3d class Gaussian: @@ -120,14 +121,21 @@ def construct_list_of_attributes(self): for i in range(self._rotation.shape[1]): l.append('rot_{}'.format(i)) return l - - def save_ply(self, path): + + def save_ply(self, path, transform=[[1, 0, 0], [0, 0, -1], [0, 1, 0]]): xyz = self.get_xyz.detach().cpu().numpy() normals = np.zeros_like(xyz) f_dc = self._features_dc.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy() opacities = inverse_sigmoid(self.get_opacity).detach().cpu().numpy() scale = torch.log(self.get_scaling).detach().cpu().numpy() rotation = (self._rotation + self.rots_bias[None, :]).detach().cpu().numpy() + + if transform is not None: + transform = np.array(transform) + xyz = np.matmul(xyz, transform.T) + rotation = utils3d.numpy.quaternion_to_matrix(rotation) + rotation = np.matmul(transform, rotation) + rotation = utils3d.numpy.matrix_to_quaternion(rotation) dtype_full = [(attribute, 'f4') for attribute in self.construct_list_of_attributes()] @@ -137,7 +145,7 @@ def save_ply(self, path): el = PlyElement.describe(elements, 'vertex') PlyData([el]).write(path) - def load_ply(self, path): + def load_ply(self, path, transform=[[1, 0, 0], [0, 0, -1], [0, 1, 0]]): plydata = PlyData.read(path) xyz = np.stack((np.asarray(plydata.elements[0]["x"]), @@ -172,6 +180,13 @@ def load_ply(self, path): for idx, attr_name in enumerate(rot_names): rots[:, idx] = np.asarray(plydata.elements[0][attr_name]) + if transform is not None: + transform = np.array(transform) + xyz = np.matmul(xyz, transform) + rotation = utils3d.numpy.quaternion_to_matrix(rotation) + rotation = np.matmul(rotation, transform) + rotation = utils3d.numpy.matrix_to_quaternion(rotation) + # convert to actual gaussian attributes xyz = torch.tensor(xyz, dtype=torch.float, device=self.device) features_dc = torch.tensor(features_dc, dtype=torch.float, device=self.device).transpose(1, 2).contiguous() diff --git a/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/cube2mesh.py b/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/cube2mesh.py index fe2cca76..44e8776f 100644 --- a/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/cube2mesh.py +++ b/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/cube2mesh.py @@ -2,10 +2,7 @@ from ...modules.sparse import SparseTensor from easydict import EasyDict as edict from .utils_cube import * -try: - from .flexicubes.flexicubes import FlexiCubes -except: - print("Please install kaolin and diso to use the mesh extractor.") +from .flexicubes.flexicubes import FlexiCubes class MeshExtractResult: diff --git a/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/LICENSE.txt b/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/LICENSE.txt new file mode 100644 index 00000000..40e8f765 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/LICENSE.txt @@ -0,0 +1,90 @@ +Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + + +NVIDIA Source Code License for FlexiCubes + + +======================================================================= + +1. Definitions + +“Licensor” means any person or entity that distributes its Work. + +“Work” means (a) the original work of authorship made available under +this license, which may include software, documentation, or other files, +and (b) any additions to or derivative works thereof that are made +available under this license. + +The terms “reproduce,” “reproduction,” “derivative works,” and +“distribution” have the meaning as provided under U.S. copyright law; +provided, however, that for the purposes of this license, derivative works +shall not include works that remain separable from, or merely link +(or bind by name) to the interfaces of, the Work. + +Works are “made available” under this license by including in or with +the Work either (a) a copyright notice referencing the applicability of +this license to the Work, or (b) a copy of this license. + +2. License Grant + + 2.1 Copyright Grant. Subject to the terms and conditions of this license, + each Licensor grants to you a perpetual, worldwide, non-exclusive, + royalty-free, copyright license to use, reproduce, prepare derivative + works of, publicly display, publicly perform, sublicense and distribute + its Work and any resulting derivative works in any form. + +3. Limitations + + 3.1 Redistribution. You may reproduce or distribute the Work only if + (a) you do so under this license, (b) you include a complete copy of + this license with your distribution, and (c) you retain without + modification any copyright, patent, trademark, or attribution notices + that are present in the Work. + + 3.2 Derivative Works. You may specify that additional or different terms + apply to the use, reproduction, and distribution of your derivative + works of the Work (“Your Terms”) only if (a) Your Terms provide that the + use limitation in Section 3.3 applies to your derivative works, and (b) + you identify the specific derivative works that are subject to Your Terms. + Notwithstanding Your Terms, this license (including the redistribution + requirements in Section 3.1) will continue to apply to the Work itself. + + 3.3 Use Limitation. The Work and any derivative works thereof only may be + used or intended for use non-commercially. Notwithstanding the foregoing, + NVIDIA Corporation and its affiliates may use the Work and any derivative + works commercially. As used herein, “non-commercially” means for research + or evaluation purposes only. + + 3.4 Patent Claims. If you bring or threaten to bring a patent claim against + any Licensor (including any claim, cross-claim or counterclaim in a lawsuit) + to enforce any patents that you allege are infringed by any Work, then your + rights under this license from such Licensor (including the grant in + Section 2.1) will terminate immediately. + + 3.5 Trademarks. This license does not grant any rights to use any Licensor’s + or its affiliates’ names, logos, or trademarks, except as necessary to + reproduce the notices described in this license. + + 3.6 Termination. If you violate any term of this license, then your rights + under this license (including the grant in Section 2.1) will terminate + immediately. + +4. Disclaimer of Warranty. + +THE WORK IS PROVIDED “AS IS” WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, +EITHER EXPRESS OR IMPLIED, INCLUDING WARRANTIES OR CONDITIONS OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE OR NON-INFRINGEMENT. +YOU BEAR THE RISK OF UNDERTAKING ANY ACTIVITIES UNDER THIS LICENSE. + +5. Limitation of Liability. + +EXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL THEORY, +WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE SHALL ANY +LICENSOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT, INDIRECT, SPECIAL, +INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF OR RELATED TO THIS LICENSE, +THE USE OR INABILITY TO USE THE WORK (INCLUDING BUT NOT LIMITED TO LOSS OF +GOODWILL, BUSINESS INTERRUPTION, LOST PROFITS OR DATA, COMPUTER FAILURE OR +MALFUNCTION, OR ANY OTHER DAMAGES OR LOSSES), EVEN IF THE LICENSOR HAS BEEN +ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. + +======================================================================= \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/README.md b/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/README.md new file mode 100644 index 00000000..8f8b4606 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/README.md @@ -0,0 +1,110 @@ +## Flexible Isosurface Extraction for Gradient-Based Mesh Optimization (FlexiCubes)
Official PyTorch implementation + +![Teaser image]() + +FlexiCubes is a high-quality isosurface representation specifically designed for gradient-based mesh optimization with respect to geometric, visual, or even physical objectives. For more details, please refer to our [paper](https://arxiv.org/abs/2308.05371) and [project page](https://research.nvidia.com/labs/toronto-ai/flexicubes/). + +## Highlights +* [Getting started](https://github.com/nv-tlabs/FlexiCubes#getting-started) +* [Basic workflow](https://github.com/nv-tlabs/FlexiCubes#example-usage) +* [nvdiffrec: image-based reconstruction example](https://github.com/NVlabs/nvdiffrec#news) +* [GET3D: generative AI example](https://github.com/nv-tlabs/GET3D#employing-flexicubes) +* [Bibtex](https://github.com/nv-tlabs/FlexiCubes#citation) + +## Getting Started + +The core functions of FlexiCubes are now in [Kaolin](https://github.com/NVIDIAGameWorks/kaolin/) starting from v0.15.0. See installation instructions [here](https://kaolin.readthedocs.io/en/latest/notes/installation.html) and API documentations [here](https://kaolin.readthedocs.io/en/latest/modules/kaolin.non_commercial.html#kaolin.non_commercial.FlexiCubes) + +The original code of the paper is still visible in `flexicube.py`. + +## Example Usage + +### Gradient-Based Mesh Optimization +We provide examples demonstrating how to use FlexiCubes for reconstructing unknown meshes through gradient-based optimization. Specifically, starting from randomly initialized SDF, we optimize the shape towards the reference mesh by minimizing their geometric difference, measured by multiview mask and depth losses. This workflow is a simplified version of `nvdiffrec` with code largely borrowed from the [nvdiffrec GitHub](https://github.com/NVlabs/nvdiffrec). We use the same pipeline to conduct the analysis in Section 3 and the main experiments described in Section 5 of our paper. We provide a detailed tutorial in `examples/optimization.ipynb`, along with an optimization script in `examples/optimize.py` which accepts command-line arguments. + + +To run the examples, it is suggested to install the Conda environment as detailed below: +```sh +conda create -n flexicubes python=3.9 +conda activate flexicubes +conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.3 -c pytorch +pip install imageio trimesh tqdm matplotlib torch_scatter ninja +pip install git+https://github.com/NVlabs/nvdiffrast/ +pip install kaolin==0.15.0 -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-1.12.0_cu113.html +``` + +Then download the dataset collected by [Myles et al.](https://vcg.isti.cnr.it/Publications/2014/MPZ14/) as follows. We include one shape in 'examples/data/inputmodels/block.obj' if you want to test without downloading the full dataset. + +```sh +cd examples +python download_data.py +``` + +After downloading the data, run shape optimization with the following example command: +```sh +python optimize.py --ref_mesh data/inputmodels/block.obj --out_dir out/block +``` +You can find visualization and output meshes in the `out/block`. Below, we show the initial and final shapes during optimization, with the reference shape on the right. + +block_init + +block_final + + +To further demonstrate the flexibility of our FlexiCubes representation, which can accommodates both reconstruction objectives and regularizers defined on the extracted mesh, you can add a developability regularizer (proposed by [Stein et al.](https://www.cs.cmu.edu/~kmcrane/Projects/DiscreteDevelopable/)) to the previous reconstruction pipeline to encourage fabricability from panels: +```sh +python optimize.py --ref_mesh data/inputmodels/david.obj --out_dir out/david_dev --develop_reg True --iter=1250 +``` + +### Extract mesh from known signed distance field +While not its designated use case, our function can extract a mesh from a known Signed Distance Field (SDF) without optimization. Please refer to the tutorial found in `examples/extraction.ipynb` for details. + +## Tips for using FlexiCubes +### Regularization losses: +We commonly use three regularizers in our mesh optimization pipelines, referenced in lines `L104-L106` in `examples/optimize.py`. The weights of these regularizers should be scaled according to the your application objectives. Initially, it is suggested to employ low weights because strong regularization can hinder convergence. You can incrementally increase the weights if you notice artifacts appearing in the optimized meshes. Specifically: + +* The loss function at `L104` helps to remove floaters in areas of the shape that are not supervised by the application objective, such as internal faces when using image supervision only. +* The L_dev loss at `L105` can be increased if you observe artifacts in flat areas, as illustrated in the image below. +* Generally, the L1 regularizer on flexible weights at `L106` does not have a significant impact during the optimization of a single shape. However, we found it to be effective in stabilizing training in generative pipelines such as GET3D. +Ablating L_dev + +### Resolution of voxel grid vs. tetrahedral grid: +If you are switching from our previous work, DMTet, it's important to note the difference in grid resolution when compared to FlexiCubes. In both implementations, the resolution is defined by the edge length: a grid resolution of `n` means the grid edge length is 1/n for both the voxel and tetrahedral grids. However, a tetrahedral grid with a resolution of `n` contains only `(n/2+1)³` grid vertices, in contrast to the `(n+1)³` vertices in a voxel grid. Consequently, if you are switching from DMTet to FlexiCubes while maintaining the same resolution, you will notice not only a denser output mesh but also a substantial increase in computational cost. To align the triangle count in the output meshes more closely, we recommend adopting a 4:5 resolution ratio between the voxel grid and the tetrahedral grid. For instance, in our paper, `64³` FlexiCubes generate approximately the same number of triangles as `80³` DMTet. + +## Applications +FlexiCubes is now integrated into NVIDIA applications as a drop-in replacement for DMTet. You can visit their GitHub pages to see how FlexiCubes is used in advanced photogrammetry and 3D generative pipelines. + +[Extracting Triangular 3D Models, Materials, and Lighting From Images (nvdiffrec)](https://github.com/NVlabs/nvdiffrec#news) + +[GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images](https://github.com/nv-tlabs/GET3D#employing-flexicubes) + + + +## License +Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + +This work is made available under the [Nvidia Source Code License](LICENSE.txt). + +For business inquiries, please visit our website and submit the form: [NVIDIA Research Licensing](https://www.nvidia.com/en-us/research/inquiries/). + +## Citation +```bibtex +@article{shen2023flexicubes, +author = {Shen, Tianchang and Munkberg, Jacob and Hasselgren, Jon and Yin, Kangxue and Wang, Zian + and Chen, Wenzheng and Gojcic, Zan and Fidler, Sanja and Sharp, Nicholas and Gao, Jun}, +title = {Flexible Isosurface Extraction for Gradient-Based Mesh Optimization}, +year = {2023}, +issue_date = {August 2023}, +publisher = {Association for Computing Machinery}, +address = {New York, NY, USA}, +volume = {42}, +number = {4}, +issn = {0730-0301}, +url = {https://doi.org/10.1145/3592430}, +doi = {10.1145/3592430}, +journal = {ACM Trans. Graph.}, +month = {jul}, +articleno = {37}, +numpages = {16} +} +``` diff --git a/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/data/inputmodels/block.obj b/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/data/inputmodels/block.obj new file mode 100644 index 00000000..2e047a63 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/data/inputmodels/block.obj @@ -0,0 +1,6420 @@ +#### +# +# OBJ File Generated by Meshlab +# +#### +# Object block.obj +# +# Vertices: 2132 +# Faces: 4272 +# +#### +v -0.165639 -5.573223 -18.999933 +v -0.461723 -6.632682 -18.999977 +v -1.123145 -5.856486 -18.999767 +v 1.337052 -5.619015 -19.000029 +v 0.762372 -6.350244 -18.999895 +v 2.820900 -5.742386 -18.999819 +v 2.408428 -6.366748 -18.999821 +v 4.120584 -5.888971 -18.999855 +v 4.075361 -6.620109 -18.999723 +v 5.162054 -5.604235 -18.999794 +v 4.564273 -6.432123 -18.999977 +v 5.177588 -6.145572 -18.999548 +v -1.476656 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+f 2047 2046 2128 +f 2129 2047 2128 +f 2048 2047 2129 +f 2130 2048 2129 +f 2049 2048 2130 +f 2131 2049 2130 +f 2050 2049 2131 +f 2132 2050 2131 +f 2044 2050 2132 +# 4272 faces, 0 coords texture + +# End of File \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/download_data.py b/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/download_data.py new file mode 100644 index 00000000..3d0ea2d0 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/download_data.py @@ -0,0 +1,41 @@ +# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited. +import requests +from zipfile import ZipFile +from tqdm import tqdm +import os + +def download_file(url, output_path): + response = requests.get(url, stream=True) + response.raise_for_status() + total_size_in_bytes = int(response.headers.get('content-length', 0)) + block_size = 1024 #1 Kibibyte + progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True) + + with open(output_path, 'wb') as file: + for data in response.iter_content(block_size): + progress_bar.update(len(data)) + file.write(data) + progress_bar.close() + if total_size_in_bytes != 0 and progress_bar.n != total_size_in_bytes: + raise Exception("ERROR, something went wrong") + + +url = "https://vcg.isti.cnr.it/Publications/2014/MPZ14/inputmodels.zip" +zip_file_path = './data/inputmodels.zip' + +os.makedirs('./data', exist_ok=True) + +download_file(url, zip_file_path) + +with ZipFile(zip_file_path, 'r') as zip_ref: + zip_ref.extractall('./data') + +os.remove(zip_file_path) + +print("Download and extraction complete.") diff --git a/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/extraction.ipynb b/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/extraction.ipynb new file mode 100644 index 00000000..650ee0d3 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/extraction.ipynb @@ -0,0 +1,1668 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Mesh Extraction from a fixed Signed Distance Field (SDF)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example, we demonstrate how to use FlexiCubes to extract a mesh from a fixed signed distance field (SDF) **without** optimization. Note that in this case, the extraction scheme used is the original Dual Marching Cubes [Nielson 2004] algorithm, with minor improvements in splitting. To begin with, we will establish two functions: one for calculating the SDF of a cube, and another for determining its analytic gradient. In your specific application, the SDF might be predicted by a network, with gradients computed through methods such as finite differences or autograd." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import kaolin as kal\n", + "from matplotlib import pyplot as plt\n", + "\n", + "import render" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def cube_sdf(x_nx3):\n", + " sdf_values = 0.5 - torch.abs(x_nx3)\n", + " sdf_values = torch.clamp(sdf_values, min=0.0)\n", + " sdf_values = sdf_values[:, 0] * sdf_values[:, 1] * sdf_values[:, 2]\n", + " sdf_values = -1.0 * sdf_values\n", + "\n", + " return sdf_values\n", + "\n", + "\n", + "def cube_sdf_gradient(x_nx3):\n", + " gradients = []\n", + " for i in range(x_nx3.shape[0]):\n", + " x, y, z = x_nx3[i]\n", + " grad_x, grad_y, grad_z = 0, 0, 0\n", + "\n", + " max_val = max(abs(x) - 0.5, abs(y) - 0.5, abs(z) - 0.5)\n", + "\n", + " if max_val == abs(x) - 0.5:\n", + " grad_x = 1.0 if x > 0 else -1.0\n", + " if max_val == abs(y) - 0.5:\n", + " grad_y = 1.0 if y > 0 else -1.0\n", + " if max_val == abs(z) - 0.5:\n", + " grad_z = 1.0 if z > 0 else -1.0\n", + "\n", + " gradients.append(torch.tensor([grad_x, grad_y, grad_z]))\n", + "\n", + " return torch.stack(gradients).to(x_nx3.device)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let's call upon FlexiCubes to extract the mesh from this SDF, both with and without providing the gradient information." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "res = 5\n", + "device='cuda'\n", + "fc = kal.non_commercial.FlexiCubes(device)\n", + "voxelgrid_vertices, cube_idx = fc.construct_voxel_grid(res)\n", + "voxelgrid_vertices *= 1.1 # add small margin to boundary\n", + "scalar_field = cube_sdf(voxelgrid_vertices)\n", + "\n", + "mesh_with_grad_v, mesh_with_grad_f, _ = fc(\n", + " voxelgrid_vertices, scalar_field, cube_idx, res, grad_func=cube_sdf_gradient)\n", + "\n", + "mesh_with_grad = kal.rep.SurfaceMesh(vertices=mesh_with_grad_v, faces=mesh_with_grad_f)\n", + "mesh_no_grad_v, mesh_no_grad_f, _ = fc(\n", + " voxelgrid_vertices, scalar_field, cube_idx, res)\n", + "\n", + "mesh_no_grad = kal.rep.SurfaceMesh(vertices=mesh_no_grad_v, faces=mesh_no_grad_f)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we visualize the two meshes. Without the gradient information (left), the extracted vertex locations are positioned at the centroids of the primal (Marching Cubes) mesh. Consequently, this method fails to reconstruct the sharp features present in the cube." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "camera = render.get_rotate_camera(0, iter_res=[512, 512], device=device)\n", + "f, ax = plt.subplots(1, 2)\n", + "output = render.render_mesh(mesh_no_grad, camera, [512, 512], return_types=['normals'])\n", + "ax[0].imshow(((output['normals'][0] + 1) / 2.).cpu())\n", + "output = render.render_mesh(mesh_with_grad, camera, [512, 512], return_types=['normals'])\n", + "ax[1].imshow(((output['normals'][0] + 1) / 2.).cpu())\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also visualize interactively with [kaolin's interactive visualizer](https://kaolin.readthedocs.io/en/latest/modules/kaolin.visualize.html), by moving around the camera and adjusting a wireframe to see the topology of the meshes." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + 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+ "encoding": "base64", + "path": [ + "value" + ] + } + ], + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ImageModel", + "state": { + "layout": "IPY_MODEL_570818bdbfe7490abbd09a27602e7dde" + } + } + }, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/loss.py b/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/loss.py new file mode 100644 index 00000000..ba507f08 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/loss.py @@ -0,0 +1,95 @@ +# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited. +import torch +import torch_scatter + +############################################################################### +# Pytorch implementation of the developability regularizer introduced in paper +# "Developability of Triangle Meshes" by Stein et al. +############################################################################### +def mesh_developable_reg(mesh): + + verts = mesh.vertices + tris = mesh.faces + + device = verts.device + V = verts.shape[0] + F = tris.shape[0] + + POS_EPS = 1e-6 + REL_EPS = 1e-6 + + def normalize(vecs): + return vecs / (torch.linalg.norm(vecs, dim=-1, keepdim=True) + POS_EPS) + + tri_pos = verts[tris] + + vert_normal_covariance_sum = torch.zeros((V, 9), device=device) + vert_area = torch.zeros(V, device=device) + vert_degree = torch.zeros(V, dtype=torch.int32, device=device) + + for iC in range(3): # loop over three corners of each triangle + + # gather tri verts + pRoot = tri_pos[:, iC, :] + pA = tri_pos[:, (iC + 1) % 3, :] + pB = tri_pos[:, (iC + 2) % 3, :] + + # compute the corner angle & normal + vA = pA - pRoot + vAn = normalize(vA) + vB = pB - pRoot + vBn = normalize(vB) + area_normal = torch.linalg.cross(vA, vB, dim=-1) + face_area = 0.5 * torch.linalg.norm(area_normal, dim=-1) + normal = normalize(area_normal) + corner_angle = torch.acos(torch.clamp(torch.sum(vAn * vBn, dim=-1), min=-1., max=1.)) + + # add up the contribution to the covariance matrix + outer = normal[:, :, None] @ normal[:, None, :] + contrib = corner_angle[:, None] * outer.reshape(-1, 9) + + # scatter the result to the appropriate matrices + vert_normal_covariance_sum = torch_scatter.scatter_add(src=contrib, + index=tris[:, iC], + dim=-2, + out=vert_normal_covariance_sum) + + vert_area = torch_scatter.scatter_add(src=face_area / 3., + index=tris[:, iC], + dim=-1, + out=vert_area) + + vert_degree = torch_scatter.scatter_add(src=torch.ones(F, dtype=torch.int32, device=device), + index=tris[:, iC], + dim=-1, + out=vert_degree) + + # The energy is the smallest eigenvalue of the outer-product matrix + vert_normal_covariance_sum = vert_normal_covariance_sum.reshape( + -1, 3, 3) # reshape to a batch of matrices + vert_normal_covariance_sum = vert_normal_covariance_sum + torch.eye( + 3, device=device)[None, :, :] * REL_EPS + + min_eigvals = torch.min(torch.linalg.eigvals(vert_normal_covariance_sum).abs(), dim=-1).values + + # Mask out degree-3 vertices + vert_area = torch.where(vert_degree == 3, torch.tensor(0, dtype=vert_area.dtype,device=vert_area.device), vert_area) + + # Adjust the vertex area weighting so it is unit-less, and 1 on average + vert_area = vert_area * (V / torch.sum(vert_area, dim=-1, keepdim=True)) + + return vert_area * min_eigvals + +def sdf_reg_loss(sdf, all_edges): + sdf_f1x6x2 = sdf[all_edges.reshape(-1)].reshape(-1,2) + mask = torch.sign(sdf_f1x6x2[...,0]) != torch.sign(sdf_f1x6x2[...,1]) + sdf_f1x6x2 = sdf_f1x6x2[mask] + sdf_diff = torch.nn.functional.binary_cross_entropy_with_logits(sdf_f1x6x2[...,0], (sdf_f1x6x2[...,1] > 0).float()) + \ + torch.nn.functional.binary_cross_entropy_with_logits(sdf_f1x6x2[...,1], (sdf_f1x6x2[...,0] > 0).float()) + return sdf_diff \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/optimization.ipynb b/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/optimization.ipynb new file mode 100644 index 00000000..21f153b3 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/optimization.ipynb @@ -0,0 +1,801 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Gradient-Based Mesh Optimization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "FlexiCubes is an isosurface representation designed for gradient-based mesh optimization, where we iteratively\n", + "optimize for a 3D surface mesh by representing it as the isosurface of a scalar field. Essentially, this paradigm allows objectives to be directly evaluated on the extracted surface, while offering the flexibility to optimize over meshes with different topologies.\n", + "\n", + "In this tutorial, we demonstrate how to reconstruct an unknown mesh using multiview masks and depth supervision with FlexiCubes. Note that in our paper, we demonstrate more objectives that FlexiCubes can optimize for a variety of applications." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We begin by importing the necessary packages and defining the hyperparameters for optimization." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import tqdm\n", + "import numpy as np\n", + "import kaolin as kal\n", + "from matplotlib import pyplot as plt\n", + "\n", + "import render\n", + "import loss\n", + "\n", + "iter = 1000\n", + "batch = 8\n", + "train_res = [2048, 2048]\n", + "learning_rate = 0.01\n", + "voxel_grid_res = 64\n", + "device = 'cuda'\n", + "sdf_regularizer = 0.2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we load the reference mesh and initialize a FlexiCubes object. We will be optimizing its SDF, weights, and deformations to fit the reference mesh. In this example, we are directly applying gradient descents on these parameters. Alternatively, you can parameterize them using a network of your choice and optimize the network weights instead (Please refer to the GET3D GitHub page for more details)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "gt_mesh = kal.io.obj.import_mesh('data/inputmodels/block.obj').cuda()\n", + "vertices = gt_mesh.vertices\n", + "vmin, vmax = vertices.min(dim=0)[0], vertices.max(dim=0)[0]\n", + "scale = 1.8 / torch.max(vmax - vmin).item()\n", + "vertices = vertices - (vmax + vmin) / 2 # Center mesh on origin\n", + "gt_mesh.vertices = vertices * scale # Rescale to [-0.9, 0.9]\n", + "\n", + "fc = kal.non_commercial.FlexiCubes(device)\n", + "x_nx3, cube_fx8 = fc.construct_voxel_grid(voxel_grid_res)\n", + "x_nx3 *= 2 # scale up the grid so that it's larger than the target object\n", + "sdf = torch.rand_like(x_nx3[:,0]) - 0.1 # randomly initialize SDF\n", + "sdf = torch.nn.Parameter(sdf.clone().detach(), requires_grad=True)\n", + "# set per-cube learnable weights to zeros\n", + "weight = torch.zeros((cube_fx8.shape[0], 21), dtype=torch.float, device='cuda') \n", + "weight = torch.nn.Parameter(weight.clone().detach(), requires_grad=True)\n", + "\n", + "# Retrieve all the edges of the voxel grid; these edges will be utilized to \n", + "# compute the regularization loss in subsequent steps of the process.\n", + "all_edges = cube_fx8[:, fc.cube_edges].reshape(-1, 2) \n", + "grid_edges = torch.unique(all_edges, dim=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now do random initiation for the optimization" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "deform = torch.nn.Parameter(torch.zeros_like(x_nx3), requires_grad=True)\n", + "grid_verts = x_nx3 + (2-1e-8) / (voxel_grid_res * 2) * torch.tanh(deform) # apply deformation to the grid vertices\n", + "vertices, faces, L_dev = fc(\n", + " grid_verts, sdf, cube_fx8, voxel_grid_res, beta=weight[:,:12], alpha=weight[:,12:20],\n", + " gamma_f=weight[:,20], training=False) # run isosurfacing to extract the mesh\n", + "init_mesh = kal.rep.SurfaceMesh(vertices=vertices, faces=faces)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's extract the meshes from the initial FlexiCubes grid to see what it looks like. The initial mesh topology (on the left) is very different from our reference (on the right). Don't worry, it will converge to the reference in the end! " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "camera = render.get_rotate_camera(0)\n", + "f, ax = plt.subplots(1, 2)\n", + "output = render.render_mesh(init_mesh, camera, [512, 512], return_types=['normals'])\n", + "ax[0].imshow(((output['normals'][0] + 1) / 2.).cpu().detach())\n", + "output = render.render_mesh(gt_mesh, camera, [512, 512], return_types=['normals'])\n", + "ax[1].imshow(((output['normals'][0] + 1) / 2.).cpu().detach())\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also visualize interactively with [kaolin's interactive visualizer](https://kaolin.readthedocs.io/en/latest/modules/kaolin.visualize.html), by moving around the camera and adjusting a wireframe to see the topology of the meshes." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d8848758a62646579a83b9512be4164f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Canvas(height=512, width=1024), interactive(children=(FloatLogSlider(value=0.3981071705534972, …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8045d576349a4b9485522790f58ad90d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "render.SplitVisualizer(init_mesh, gt_mesh, 512, 512).show(camera)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The last thing before we start the optimization is to set up the optimizers and a differentiable renderer." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def lr_schedule(iter):\n", + " return max(0.0, 10 ** (-(iter) * 0.0002)) # Exponential falloff from [1.0, 0.1] over 5k epochs. \n", + "optimizer = torch.optim.Adam([sdf, weight, deform], lr=learning_rate)\n", + "scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda x: lr_schedule(x)) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's execute the actual optimization loop. At every iteration, we perform the following steps:\n", + "\n", + "* Sample random camera poses to render both the reference and ground truth images.\n", + "* Extract the mesh with FlexiCubes, as we did above.\n", + "* Render the meshes and evaluate the reconstruction and regularization losses (please see inline comments for more details)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████| 1000/1000 [01:21<00:00, 12.34it/s]\n" + ] + } + ], + "source": [ + "intermediate_results = [init_mesh]\n", + "for it in tqdm.tqdm(range(iter)): \n", + " optimizer.zero_grad()\n", + " # sample random camera poses\n", + " cameras = render.get_random_camera_batch(batch, iter_res=train_res, device=device)\n", + " \n", + " # render gt mesh at sampled views\n", + " target = render.render_mesh(gt_mesh, cameras, train_res)\n", + "\n", + " # extract and render FlexiCubes mesh\n", + " grid_verts = x_nx3 + (2-1e-8) / (voxel_grid_res * 2) * torch.tanh(deform)\n", + " vertices, faces, L_dev = fc(\n", + " grid_verts, sdf, cube_fx8, voxel_grid_res, beta=weight[:,:12], alpha=weight[:,12:20],\n", + " gamma_f=weight[:,20], training=True)\n", + " flexicubes_mesh = kal.rep.SurfaceMesh(vertices=vertices, faces=faces)\n", + " buffers = render.render_mesh(flexicubes_mesh, cameras, train_res)\n", + "\n", + " # evaluate reconstruction loss\n", + " mask_loss = (buffers['mask'] - target['mask']).abs().mean() # mask loss\n", + " depth_loss = (((((buffers['depth'] - (target['depth']))* target['mask'])**2).sum(-1)+1e-8)).sqrt().mean() * 10 # depth loss\n", + " # evaluate regularization losses\n", + " t_iter = it / iter\n", + " # this is the regularization loss described in Equation 2 of the nvdiffrec paper by Munkberg et al., which serves to remove internal floating elements that are not visible to the user.\n", + " sdf_weight = sdf_regularizer - (sdf_regularizer - sdf_regularizer/20)*min(1.0, 4.0 * t_iter)\n", + " reg_loss = loss.sdf_reg_loss(sdf, grid_edges).mean() * sdf_weight \n", + "\n", + " reg_loss += L_dev.mean() * 0.5 # L_dev as in Equation 8 of our paper\n", + " reg_loss += (weight[:,:20]).abs().mean() * 0.1 # regularize weights to be zeros to improve the stability of the optimization process\n", + " total_loss = mask_loss + depth_loss + reg_loss\n", + " total_loss.backward()\n", + " optimizer.step()\n", + " scheduler.step()\n", + " if (it + 1) % 20 == 0: # save intermediate results every 100 iters\n", + " with torch.no_grad():\n", + " # run the mesh extraction again with the parameter 'training=False' so that each quadrilateral face is divided into two triangles, as opposed to the four triangles during the training phase.\n", + " vertices, faces, L_dev = fc(\n", + " grid_verts, sdf, cube_fx8, voxel_grid_res, beta=weight[:,:12], alpha=weight[:,12:20], gamma_f=weight[:,20], training=False)\n", + " intermediate_results.append(kal.rep.SurfaceMesh(vertices, faces))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now visualize how the isosurface of FlexiCubes evolves during optimization. As you can see, it converges smoothly to the reference mesh, successfully recovering all sharp features." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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a/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/optimize.py b/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/optimize.py new file mode 100644 index 00000000..332fa2cd --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/optimize.py @@ -0,0 +1,150 @@ +# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited. +import argparse +import numpy as np +import torch +import nvdiffrast.torch as dr +import trimesh +import os +from util import * +import render +import loss +import imageio + +import sys +sys.path.append('..') +from flexicubes import FlexiCubes + +############################################################################### +# Functions adapted from https://github.com/NVlabs/nvdiffrec +############################################################################### + +def lr_schedule(iter): + return max(0.0, 10**(-(iter)*0.0002)) # Exponential falloff from [1.0, 0.1] over 5k epochs. + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description='flexicubes optimization') + parser.add_argument('-o', '--out_dir', type=str, default=None) + parser.add_argument('-rm', '--ref_mesh', type=str) + + parser.add_argument('-i', '--iter', type=int, default=1000) + parser.add_argument('-b', '--batch', type=int, default=8) + parser.add_argument('-r', '--train_res', nargs=2, type=int, default=[2048, 2048]) + parser.add_argument('-lr', '--learning_rate', type=float, default=0.01) + parser.add_argument('--voxel_grid_res', type=int, default=64) + + parser.add_argument('--sdf_loss', type=bool, default=True) + parser.add_argument('--develop_reg', type=bool, default=False) + parser.add_argument('--sdf_regularizer', type=float, default=0.2) + + parser.add_argument('-dr', '--display_res', nargs=2, type=int, default=[512, 512]) + parser.add_argument('-si', '--save_interval', type=int, default=20) + FLAGS = parser.parse_args() + device = 'cuda' + + os.makedirs(FLAGS.out_dir, exist_ok=True) + glctx = dr.RasterizeGLContext() + + # Load GT mesh + gt_mesh = load_mesh(FLAGS.ref_mesh, device) + gt_mesh.auto_normals() # compute face normals for visualization + + # ============================================================================================== + # Create and initialize FlexiCubes + # ============================================================================================== + fc = FlexiCubes(device) + x_nx3, cube_fx8 = fc.construct_voxel_grid(FLAGS.voxel_grid_res) + x_nx3 *= 2 # scale up the grid so that it's larger than the target object + + sdf = torch.rand_like(x_nx3[:,0]) - 0.1 # randomly init SDF + sdf = torch.nn.Parameter(sdf.clone().detach(), requires_grad=True) + # set per-cube learnable weights to zeros + weight = torch.zeros((cube_fx8.shape[0], 21), dtype=torch.float, device='cuda') + weight = torch.nn.Parameter(weight.clone().detach(), requires_grad=True) + deform = torch.nn.Parameter(torch.zeros_like(x_nx3), requires_grad=True) + + # Retrieve all the edges of the voxel grid; these edges will be utilized to + # compute the regularization loss in subsequent steps of the process. + all_edges = cube_fx8[:, fc.cube_edges].reshape(-1, 2) + grid_edges = torch.unique(all_edges, dim=0) + + # ============================================================================================== + # Setup optimizer + # ============================================================================================== + optimizer = torch.optim.Adam([sdf, weight,deform], lr=FLAGS.learning_rate) + scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda x: lr_schedule(x)) + + # ============================================================================================== + # Train loop + # ============================================================================================== + for it in range(FLAGS.iter): + optimizer.zero_grad() + # sample random camera poses + mv, mvp = render.get_random_camera_batch(FLAGS.batch, iter_res=FLAGS.train_res, device=device, use_kaolin=False) + # render gt mesh + target = render.render_mesh_paper(gt_mesh, mv, mvp, FLAGS.train_res) + # extract and render FlexiCubes mesh + grid_verts = x_nx3 + (2-1e-8) / (FLAGS.voxel_grid_res * 2) * torch.tanh(deform) + vertices, faces, L_dev = fc(grid_verts, sdf, cube_fx8, FLAGS.voxel_grid_res, beta_fx12=weight[:,:12], alpha_fx8=weight[:,12:20], + gamma_f=weight[:,20], training=True) + flexicubes_mesh = Mesh(vertices, faces) + buffers = render.render_mesh_paper(flexicubes_mesh, mv, mvp, FLAGS.train_res) + + # evaluate reconstruction loss + mask_loss = (buffers['mask'] - target['mask']).abs().mean() + depth_loss = (((((buffers['depth'] - (target['depth']))* target['mask'])**2).sum(-1)+1e-8)).sqrt().mean() * 10 + + t_iter = it / FLAGS.iter + sdf_weight = FLAGS.sdf_regularizer - (FLAGS.sdf_regularizer - FLAGS.sdf_regularizer/20)*min(1.0, 4.0 * t_iter) + reg_loss = loss.sdf_reg_loss(sdf, grid_edges).mean() * sdf_weight # Loss to eliminate internal floaters that are not visible + reg_loss += L_dev.mean() * 0.5 + reg_loss += (weight[:,:20]).abs().mean() * 0.1 + total_loss = mask_loss + depth_loss + reg_loss + + if FLAGS.sdf_loss: # optionally add SDF loss to eliminate internal structures + with torch.no_grad(): + pts = sample_random_points(1000, gt_mesh) + gt_sdf = compute_sdf(pts, gt_mesh.vertices, gt_mesh.faces) + pred_sdf = compute_sdf(pts, flexicubes_mesh.vertices, flexicubes_mesh.faces) + total_loss += torch.nn.functional.mse_loss(pred_sdf, gt_sdf) * 2e3 + + # optionally add developability regularizer, as described in paper section 5.2 + if FLAGS.develop_reg: + reg_weight = max(0, t_iter - 0.8) * 5 + if reg_weight > 0: # only applied after shape converges + reg_loss = loss.mesh_developable_reg(flexicubes_mesh).mean() * 10 + reg_loss += (deform).abs().mean() + reg_loss += (weight[:,:20]).abs().mean() + total_loss = mask_loss + depth_loss + reg_loss + + total_loss.backward() + optimizer.step() + scheduler.step() + + if (it % FLAGS.save_interval == 0 or it == (FLAGS.iter-1)): # save normal image for visualization + with torch.no_grad(): + # extract mesh with training=False + vertices, faces, L_dev = fc(grid_verts, sdf, cube_fx8, FLAGS.voxel_grid_res, beta_fx12=weight[:,:12], alpha_fx8=weight[:,12:20], + gamma_f=weight[:,20], training=False) + flexicubes_mesh = Mesh(vertices, faces) + + flexicubes_mesh.auto_normals() # compute face normals for visualization + mv, mvp = render.get_rotate_camera(it//FLAGS.save_interval, iter_res=FLAGS.display_res, device=device,use_kaolin=False) + val_buffers = render.render_mesh_paper(flexicubes_mesh, mv.unsqueeze(0), mvp.unsqueeze(0), FLAGS.display_res, return_types=["normal"], white_bg=True) + val_image = ((val_buffers["normal"][0].detach().cpu().numpy()+1)/2*255).astype(np.uint8) + + gt_buffers = render.render_mesh_paper(gt_mesh, mv.unsqueeze(0), mvp.unsqueeze(0), FLAGS.display_res, return_types=["normal"], white_bg=True) + gt_image = ((gt_buffers["normal"][0].detach().cpu().numpy()+1)/2*255).astype(np.uint8) + imageio.imwrite(os.path.join(FLAGS.out_dir, '{:04d}.png'.format(it)), np.concatenate([val_image, gt_image], 1)) + print(f"Optimization Step [{it}/{FLAGS.iter}], Loss: {total_loss.item():.4f}") + + # ============================================================================================== + # Save ouput + # ============================================================================================== + mesh_np = trimesh.Trimesh(vertices = vertices.detach().cpu().numpy(), faces=faces.detach().cpu().numpy(), process=False) + mesh_np.export(os.path.join(FLAGS.out_dir, 'output_mesh.obj')) \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/render.py b/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/render.py new file mode 100644 index 00000000..034f9613 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/render.py @@ -0,0 +1,267 @@ +# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited. +import numpy as np +import copy +import math +from ipywidgets import interactive, HBox, VBox, FloatLogSlider, IntSlider + +import torch +import nvdiffrast.torch as dr +import kaolin as kal +import util + +############################################################################### +# Functions adapted from https://github.com/NVlabs/nvdiffrec +############################################################################### + +def get_random_camera_batch(batch_size, fovy = np.deg2rad(45), iter_res=[512,512], cam_near_far=[0.1, 1000.0], cam_radius=3.0, device="cuda", use_kaolin=True): + if use_kaolin: + camera_pos = torch.stack(kal.ops.coords.spherical2cartesian( + *kal.ops.random.sample_spherical_coords((batch_size,), azimuth_low=0., azimuth_high=math.pi * 2, + elevation_low=-math.pi / 2., elevation_high=math.pi / 2., device='cuda'), + cam_radius + ), dim=-1) + return kal.render.camera.Camera.from_args( + eye=camera_pos + torch.rand((batch_size, 1), device='cuda') * 0.5 - 0.25, + at=torch.zeros(batch_size, 3), + up=torch.tensor([[0., 1., 0.]]), + fov=fovy, + near=cam_near_far[0], far=cam_near_far[1], + height=iter_res[0], width=iter_res[1], + device='cuda' + ) + else: + def get_random_camera(): + proj_mtx = util.perspective(fovy, iter_res[1] / iter_res[0], cam_near_far[0], cam_near_far[1]) + mv = util.translate(0, 0, -cam_radius) @ util.random_rotation_translation(0.25) + mvp = proj_mtx @ mv + return mv, mvp + mv_batch = [] + mvp_batch = [] + for i in range(batch_size): + mv, mvp = get_random_camera() + mv_batch.append(mv) + mvp_batch.append(mvp) + return torch.stack(mv_batch).to(device), torch.stack(mvp_batch).to(device) + +def get_rotate_camera(itr, fovy = np.deg2rad(45), iter_res=[512,512], cam_near_far=[0.1, 1000.0], cam_radius=3.0, device="cuda", use_kaolin=True): + if use_kaolin: + ang = (itr / 10) * np.pi * 2 + camera_pos = torch.stack(kal.ops.coords.spherical2cartesian(torch.tensor(ang), torch.tensor(0.4), -torch.tensor(cam_radius))) + return kal.render.camera.Camera.from_args( + eye=camera_pos, + at=torch.zeros(3), + up=torch.tensor([0., 1., 0.]), + fov=fovy, + near=cam_near_far[0], far=cam_near_far[1], + height=iter_res[0], width=iter_res[1], + device='cuda' + ) + else: + proj_mtx = util.perspective(fovy, iter_res[1] / iter_res[0], cam_near_far[0], cam_near_far[1]) + + # Smooth rotation for display. + ang = (itr / 10) * np.pi * 2 + mv = util.translate(0, 0, -cam_radius) @ (util.rotate_x(-0.4) @ util.rotate_y(ang)) + mvp = proj_mtx @ mv + return mv.to(device), mvp.to(device) + +glctx = dr.RasterizeGLContext() +def render_mesh(mesh, camera, iter_res, return_types = ["mask", "depth"], white_bg=False, wireframe_thickness=0.4): + vertices_camera = camera.extrinsics.transform(mesh.vertices) + face_vertices_camera = kal.ops.mesh.index_vertices_by_faces( + vertices_camera, mesh.faces + ) + + # Projection: nvdiffrast take clip coordinates as input to apply barycentric perspective correction. + # Using `camera.intrinsics.transform(vertices_camera) would return the normalized device coordinates. + proj = camera.projection_matrix().unsqueeze(1) + proj[:, :, 1, 1] = -proj[:, :, 1, 1] + homogeneous_vecs = kal.render.camera.up_to_homogeneous( + vertices_camera + ) + vertices_clip = (proj @ homogeneous_vecs.unsqueeze(-1)).squeeze(-1) + faces_int = mesh.faces.int() + + rast, _ = dr.rasterize( + glctx, vertices_clip, faces_int, iter_res) + + out_dict = {} + for type in return_types: + if type == "mask" : + img = dr.antialias((rast[..., -1:] > 0).float(), rast, vertices_clip, faces_int) + elif type == "depth": + img = dr.interpolate(homogeneous_vecs, rast, faces_int)[0] + elif type == "wireframe": + img = torch.logical_or( + torch.logical_or(rast[..., 0] < wireframe_thickness, rast[..., 1] < wireframe_thickness), + (rast[..., 0] + rast[..., 1]) > (1. - wireframe_thickness) + ).unsqueeze(-1) + elif type == "normals" : + img = dr.interpolate( + mesh.face_normals.reshape(len(mesh), -1, 3), rast, + torch.arange(mesh.faces.shape[0] * 3, device='cuda', dtype=torch.int).reshape(-1, 3) + )[0] + if white_bg: + bg = torch.ones_like(img) + alpha = (rast[..., -1:] > 0).float() + img = torch.lerp(bg, img, alpha) + out_dict[type] = img + + + return out_dict + +def render_mesh_paper(mesh, mv, mvp, iter_res, return_types = ["mask", "depth"], white_bg=False): + ''' + The rendering function used to produce the results in the paper. + ''' + v_pos_clip = util.xfm_points(mesh.vertices.unsqueeze(0), mvp) # Rotate it to camera coordinates + rast, db = dr.rasterize( + dr.RasterizeGLContext(), v_pos_clip, mesh.faces.int(), iter_res) + + out_dict = {} + for type in return_types: + if type == "mask" : + img = dr.antialias((rast[..., -1:] > 0).float(), rast, v_pos_clip, mesh.faces.int()) + elif type == "depth": + v_pos_cam = util.xfm_points(mesh.vertices.unsqueeze(0), mv) + img, _ = util.interpolate(v_pos_cam, rast, mesh.faces.int()) + elif type == "normal" : + normal_indices = (torch.arange(0, mesh.nrm.shape[0], dtype=torch.int64, device='cuda')[:, None]).repeat(1, 3) + img, _ = util.interpolate(mesh.nrm.unsqueeze(0).contiguous(), rast, normal_indices.int()) + elif type == "vertex_normal": + img, _ = util.interpolate(mesh.v_nrm.unsqueeze(0).contiguous(), rast, mesh.faces.int()) + img = dr.antialias((img + 1) * 0.5, rast, v_pos_clip, mesh.faces.int()) + if white_bg: + bg = torch.ones_like(img) + alpha = (rast[..., -1:] > 0).float() + img = torch.lerp(bg, img, alpha) + out_dict[type] = img + return out_dict + +class SplitVisualizer(): + def __init__(self, lh_mesh, rh_mesh, height, width): + self.lh_mesh = lh_mesh + self.rh_mesh = rh_mesh + self.height = height + self.width = width + self.wireframe_thickness = 0.4 + + + def render(self, camera): + lh_outputs = render_mesh( + self.lh_mesh, camera, (self.height, self.width), + return_types=["normals", "wireframe"], wireframe_thickness=self.wireframe_thickness + ) + rh_outputs = render_mesh( + self.rh_mesh, camera, (self.height, self.width), + return_types=["normals", "wireframe"], wireframe_thickness=self.wireframe_thickness + ) + outputs = { + k: torch.cat( + [lh_outputs[k][0].permute(1, 0, 2), rh_outputs[k][0].permute(1, 0, 2)], + dim=0 + ).permute(1, 0, 2) for k in ["normals", "wireframe"] + } + return { + 'img': (outputs['wireframe'] * ((outputs['normals'] + 1.) / 2.) * 255).to(torch.uint8), + 'normals': outputs['normals'] + } + + def show(self, init_camera): + visualizer = kal.visualize.IpyTurntableVisualizer( + self.height, self.width * 2, copy.deepcopy(init_camera), self.render, + max_fps=24, world_up_axis=1) + + def slider_callback(new_wireframe_thickness): + """ipywidgets sliders callback""" + with visualizer.out: # This is in case of bug + self.wireframe_thickness = new_wireframe_thickness + # this is how we request a new update + visualizer.render_update() + + wireframe_thickness_slider = FloatLogSlider( + value=self.wireframe_thickness, + base=10, + min=-3, + max=-0.4, + step=0.1, + description='wireframe_thickness', + continuous_update=True, + readout=True, + readout_format='.3f', + ) + + interactive_slider = interactive( + slider_callback, + new_wireframe_thickness=wireframe_thickness_slider, + ) + + full_output = VBox([visualizer.canvas, interactive_slider]) + display(full_output, visualizer.out) + +class TimelineVisualizer(): + def __init__(self, meshes, height, width): + self.meshes = meshes + self.height = height + self.width = width + self.wireframe_thickness = 0.4 + self.idx = len(meshes) - 1 + + def render(self, camera): + outputs = render_mesh( + self.meshes[self.idx], camera, (self.height, self.width), + return_types=["normals", "wireframe"], wireframe_thickness=self.wireframe_thickness + ) + + return { + 'img': (outputs['wireframe'] * ((outputs['normals'] + 1.) / 2.) * 255).to(torch.uint8)[0], + 'normals': outputs['normals'][0] + } + + def show(self, init_camera): + visualizer = kal.visualize.IpyTurntableVisualizer( + self.height, self.width, copy.deepcopy(init_camera), self.render, + max_fps=24, world_up_axis=1) + + def slider_callback(new_wireframe_thickness, new_idx): + """ipywidgets sliders callback""" + with visualizer.out: # This is in case of bug + self.wireframe_thickness = new_wireframe_thickness + self.idx = new_idx + # this is how we request a new update + visualizer.render_update() + + wireframe_thickness_slider = FloatLogSlider( + value=self.wireframe_thickness, + base=10, + min=-3, + max=-0.4, + step=0.1, + description='wireframe_thickness', + continuous_update=True, + readout=True, + readout_format='.3f', + ) + + idx_slider = IntSlider( + value=self.idx, + min=0, + max=len(self.meshes) - 1, + description='idx', + continuous_update=True, + readout=True + ) + + interactive_slider = interactive( + slider_callback, + new_wireframe_thickness=wireframe_thickness_slider, + new_idx=idx_slider + ) + full_output = HBox([visualizer.canvas, interactive_slider]) + display(full_output, visualizer.out) diff --git a/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/util.py b/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/util.py new file mode 100644 index 00000000..f39ea1c7 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/examples/util.py @@ -0,0 +1,122 @@ +# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited. +import numpy as np +import torch +import trimesh +import kaolin +import nvdiffrast.torch as dr + +############################################################################### +# Functions adapted from https://github.com/NVlabs/nvdiffrec +############################################################################### + +def dot(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: + return torch.sum(x*y, -1, keepdim=True) + +def length(x: torch.Tensor, eps: float =1e-8) -> torch.Tensor: + return torch.sqrt(torch.clamp(dot(x,x), min=eps)) # Clamp to avoid nan gradients because grad(sqrt(0)) = NaN + +def safe_normalize(x: torch.Tensor, eps: float =1e-8) -> torch.Tensor: + return x / length(x, eps) + +def perspective(fovy=0.7854, aspect=1.0, n=0.1, f=1000.0, device=None): + y = np.tan(fovy / 2) + return torch.tensor([[1/(y*aspect), 0, 0, 0], + [ 0, 1/-y, 0, 0], + [ 0, 0, -(f+n)/(f-n), -(2*f*n)/(f-n)], + [ 0, 0, -1, 0]], dtype=torch.float32, device=device) + +def translate(x, y, z, device=None): + return torch.tensor([[1, 0, 0, x], + [0, 1, 0, y], + [0, 0, 1, z], + [0, 0, 0, 1]], dtype=torch.float32, device=device) + +@torch.no_grad() +def random_rotation_translation(t, device=None): + m = np.random.normal(size=[3, 3]) + m[1] = np.cross(m[0], m[2]) + m[2] = np.cross(m[0], m[1]) + m = m / np.linalg.norm(m, axis=1, keepdims=True) + m = np.pad(m, [[0, 1], [0, 1]], mode='constant') + m[3, 3] = 1.0 + m[:3, 3] = np.random.uniform(-t, t, size=[3]) + return torch.tensor(m, dtype=torch.float32, device=device) + +def rotate_x(a, device=None): + s, c = np.sin(a), np.cos(a) + return torch.tensor([[1, 0, 0, 0], + [0, c, s, 0], + [0, -s, c, 0], + [0, 0, 0, 1]], dtype=torch.float32, device=device) + +def rotate_y(a, device=None): + s, c = np.sin(a), np.cos(a) + return torch.tensor([[ c, 0, s, 0], + [ 0, 1, 0, 0], + [-s, 0, c, 0], + [ 0, 0, 0, 1]], dtype=torch.float32, device=device) + +class Mesh: + def __init__(self, vertices, faces): + self.vertices = vertices + self.faces = faces + + def auto_normals(self): + v0 = self.vertices[self.faces[:, 0], :] + v1 = self.vertices[self.faces[:, 1], :] + v2 = self.vertices[self.faces[:, 2], :] + nrm = safe_normalize(torch.cross(v1 - v0, v2 - v0)) + self.nrm = nrm + +def load_mesh(path, device): + mesh_np = trimesh.load(path) + vertices = torch.tensor(mesh_np.vertices, device=device, dtype=torch.float) + faces = torch.tensor(mesh_np.faces, device=device, dtype=torch.long) + + # Normalize + vmin, vmax = vertices.min(dim=0)[0], vertices.max(dim=0)[0] + scale = 1.8 / torch.max(vmax - vmin).item() + vertices = vertices - (vmax + vmin) / 2 # Center mesh on origin + vertices = vertices * scale # Rescale to [-0.9, 0.9] + return Mesh(vertices, faces) + +def compute_sdf(points, vertices, faces): + face_vertices = kaolin.ops.mesh.index_vertices_by_faces(vertices.clone().unsqueeze(0), faces) + distance = kaolin.metrics.trianglemesh.point_to_mesh_distance(points.unsqueeze(0), face_vertices)[0] + with torch.no_grad(): + sign = (kaolin.ops.mesh.check_sign(vertices.unsqueeze(0), faces, points.unsqueeze(0))<1).float() * 2 - 1 + sdf = (sign*distance).squeeze(0) + return sdf + +def sample_random_points(n, mesh): + pts_random = (torch.rand((n//2,3),device='cuda') - 0.5) * 2 + pts_surface = kaolin.ops.mesh.sample_points(mesh.vertices.unsqueeze(0), mesh.faces, 500)[0].squeeze(0) + pts_surface += torch.randn_like(pts_surface) * 0.05 + pts = torch.cat([pts_random, pts_surface]) + return pts + +def xfm_points(points, matrix): + '''Transform points. + Args: + points: Tensor containing 3D points with shape [minibatch_size, num_vertices, 3] or [1, num_vertices, 3] + matrix: A 4x4 transform matrix with shape [minibatch_size, 4, 4] + use_python: Use PyTorch's torch.matmul (for validation) + Returns: + Transformed points in homogeneous 4D with shape [minibatch_size, num_vertices, 4]. + ''' + out = torch.matmul( + torch.nn.functional.pad(points, pad=(0, 1), mode='constant', value=1.0), torch.transpose(matrix, 1, 2)) + if torch.is_anomaly_enabled(): + assert torch.all(torch.isfinite(out)), "Output of xfm_points contains inf or NaN" + return out + +def interpolate(attr, rast, attr_idx, rast_db=None): + return dr.interpolate( + attr, rast, attr_idx, rast_db=rast_db, + diff_attrs=None if rast_db is None else 'all') \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/flexicubes.py b/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/flexicubes.py index 297c5137..15a5960b 100644 --- a/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/flexicubes.py +++ b/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/flexicubes.py @@ -8,45 +8,12 @@ import torch from .tables import * +from kaolin.utils.testing import check_tensor __all__ = [ 'FlexiCubes' ] -def check_tensor(tensor, shape=None, dtype=None, device=None, throw=True): - """Check if :class:`torch.Tensor` is valid given set of criteria. - - Args: - tensor (torch.Tensor): the tensor to be tested. - shape (list or tuple of int, optional): the expected shape, - if a dimension is set at ``None`` then it's not verified. - dtype (torch.dtype, optional): the expected dtype. - device (torch.device, optional): the expected device. - throw (bool): if true (default), will throw if checks fail - - Return: - (bool) True if checks pass - """ - if shape is not None: - if len(shape) != tensor.ndim: - if throw: - raise ValueError(f"tensor have {tensor.ndim} ndim, should have {len(shape)}") - return False - for i, dim in enumerate(shape): - if dim is not None and tensor.shape[i] != dim: - if throw: - raise ValueError(f"tensor shape is {tensor.shape}, should be {shape}") - return False - if dtype is not None and dtype != tensor.dtype: - if throw: - raise TypeError(f"tensor dtype is {tensor.dtype}, should be {dtype}") - return False - if device is not None and device != tensor.device.type: - if throw: - raise TypeError(f"tensor device is {tensor.device.type}, should be {device}") - return False - return True - class FlexiCubes: def __init__(self, device="cuda"): diff --git a/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/images/ablate_L_dev.jpg b/Gen_3D_Modules/TRELLIS/trellis/representations/mesh/flexicubes/images/ablate_L_dev.jpg new file mode 100644 index 0000000000000000000000000000000000000000..461bd1ce2a73d6b6e0ee61648af7746c2254bc53 GIT binary patch literal 73011 zcmeEtXH-*Rw`KqV3q=$VkfMN8LFv6H(rZEwMWlx+AT4we(9pYt4haydKzdH1>gc|8xftEQx?1Rx+F z09fE3fa__10)U8+@b449-M~MIZxR#VxIuiIl$7Kq*=;hiJGbxLA*Z-^mz;u%;?AAB zw0EiQQ`6AUkWoIMqot<1M@>Wh_eThb@ZY&Xe2bX)7B%@Da_axb(3(Kc$SEO z6+lQuKtx4w-40*@00@ZjXZ!br|JO!9h(AXX(wnz#-@zYHa~D8JKtx1%gXr&BDgbu2mnO?)2)B??BDrCh4+i_#totyq<{S)AoRu?5!DUi#{wkxUI0noxZP(J 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sphere_hammersley_sequence from .render_utils import render_multiview +from ..renderers import GaussianRenderer from ..representations import Strivec, Gaussian, MeshExtractResult @@ -311,14 +314,11 @@ def bake_texture( views = [utils3d.torch.extrinsics_to_view(torch.tensor(extr).cuda()) for extr in extrinsics] projections = [utils3d.torch.intrinsics_to_perspective(torch.tensor(intr).cuda(), near, far) for intr in intrinsics] - steps = len(views) - comfy_pbar = comfy.utils.ProgressBar(steps) - if mode == 'fast': texture = torch.zeros((texture_size * texture_size, 3), dtype=torch.float32).cuda() texture_weights = torch.zeros((texture_size * texture_size), dtype=torch.float32).cuda() rastctx = utils3d.torch.RastContext(backend='cuda') - for i, (observation, view, projection) in enumerate(tqdm(zip(observations, views, projections), total=steps, disable=not verbose, desc='Texture baking (fast)')): + for observation, view, projection in tqdm(zip(observations, views, projections), total=len(observations), disable=not verbose, desc='Texture baking (fast)'): with torch.no_grad(): rast = utils3d.torch.rasterize_triangle_faces( rastctx, vertices[None], faces, observation.shape[1], observation.shape[0], uv=uvs[None], view=view, projection=projection @@ -334,8 +334,6 @@ def bake_texture( texture = texture.scatter_add(0, idx.view(-1, 1).expand(-1, 3), obs) texture_weights = texture_weights.scatter_add(0, idx, torch.ones((obs.shape[0]), dtype=torch.float32, device=texture.device)) - comfy_pbar.update_absolute(i + 1) - mask = texture_weights > 0 texture[mask] /= texture_weights[mask][:, None] texture = np.clip(texture.reshape(texture_size, texture_size, 3).cpu().numpy() * 255, 0, 255).astype(np.uint8) @@ -350,7 +348,7 @@ def bake_texture( masks = [m.flip(0) for m in masks] _uv = [] _uv_dr = [] - for i, (observation, view, projection) in enumerate(tqdm(zip(observations, views, projections), total=steps, disable=not verbose, desc='Texture baking (opt): UV')): + for observation, view, projection in tqdm(zip(observations, views, projections), total=len(views), disable=not verbose, desc='Texture baking (opt): UV'): with torch.no_grad(): rast = utils3d.torch.rasterize_triangle_faces( rastctx, vertices[None], faces, observation.shape[1], observation.shape[0], uv=uvs[None], view=view, projection=projection @@ -358,8 +356,6 @@ def bake_texture( _uv.append(rast['uv'].detach()) _uv_dr.append(rast['uv_dr'].detach()) - comfy_pbar.update_absolute(i + 1) - texture = torch.nn.Parameter(torch.zeros((1, texture_size, texture_size, 3), dtype=torch.float32).cuda()) optimizer = torch.optim.Adam([texture], betas=(0.5, 0.9), lr=1e-2) @@ -374,7 +370,6 @@ def tv_loss(texture): torch.nn.functional.l1_loss(texture[:, :, :-1, :], texture[:, :, 1:, :]) total_steps = 2500 - comfy_pbar = comfy.utils.ProgressBar(total_steps) with tqdm(total=total_steps, disable=not verbose, desc='Texture baking (opt): optimizing') as pbar: for step in range(total_steps): optimizer.zero_grad() @@ -390,9 +385,6 @@ def tv_loss(texture): optimizer.param_groups[0]['lr'] = cosine_anealing(optimizer, step, total_steps, 1e-2, 1e-5) pbar.set_postfix({'loss': loss.item()}) pbar.update() - - comfy_pbar.update_absolute(step + 1) - texture = np.clip(texture[0].flip(0).detach().cpu().numpy() * 255, 0, 255).astype(np.uint8) mask = 1 - utils3d.torch.rasterize_triangle_faces( rastctx, (uvs * 2 - 1)[None], faces, texture_size, texture_size @@ -404,7 +396,7 @@ def tv_loss(texture): return texture -def finalize_mesh( +def to_glb( app_rep: Union[Strivec, Gaussian], mesh: MeshExtractResult, simplify: float = 0.95, @@ -413,7 +405,7 @@ def finalize_mesh( texture_size: int = 1024, debug: bool = False, verbose: bool = True, -): +) -> trimesh.Trimesh: """ Convert a generated asset to a glb file. @@ -459,9 +451,137 @@ def finalize_mesh( lambda_tv=0.01, verbose=verbose ) - texture = texture.astype(np.float32) / 255 - uvs[:, 1] = 1 - uvs[:, 1] + texture = Image.fromarray(texture) # rotate mesh (from z-up to y-up) vertices = vertices @ np.array([[1, 0, 0], [0, 0, -1], [0, 1, 0]]) - return vertices, faces, uvs, texture + material = trimesh.visual.material.PBRMaterial( + roughnessFactor=1.0, + baseColorTexture=texture, + baseColorFactor=np.array([255, 255, 255, 255], dtype=np.uint8) + ) + mesh = trimesh.Trimesh(vertices, faces, visual=trimesh.visual.TextureVisuals(uv=uvs, material=material)) + return mesh + + +def simplify_gs( + gs: Gaussian, + simplify: float = 0.95, + verbose: bool = True, +): + """ + Simplify 3D Gaussians + NOTE: this function is not used in the current implementation for the unsatisfactory performance. + + Args: + gs (Gaussian): 3D Gaussian. + simplify (float): Ratio of Gaussians to remove in simplification. + """ + if simplify <= 0: + return gs + + # simplify + observations, extrinsics, intrinsics = render_multiview(gs, resolution=1024, nviews=100) + observations = [torch.tensor(obs / 255.0).float().cuda().permute(2, 0, 1) for obs in observations] + + # Following https://arxiv.org/pdf/2411.06019 + renderer = GaussianRenderer({ + "resolution": 1024, + "near": 0.8, + "far": 1.6, + "ssaa": 1, + "bg_color": (0,0,0), + }) + new_gs = Gaussian(**gs.init_params) + new_gs._features_dc = gs._features_dc.clone() + new_gs._features_rest = gs._features_rest.clone() if gs._features_rest is not None else None + new_gs._opacity = torch.nn.Parameter(gs._opacity.clone()) + new_gs._rotation = torch.nn.Parameter(gs._rotation.clone()) + new_gs._scaling = torch.nn.Parameter(gs._scaling.clone()) + new_gs._xyz = torch.nn.Parameter(gs._xyz.clone()) + + start_lr = [1e-4, 1e-3, 5e-3, 0.025] + end_lr = [1e-6, 1e-5, 5e-5, 0.00025] + optimizer = torch.optim.Adam([ + {"params": new_gs._xyz, "lr": start_lr[0]}, + {"params": new_gs._rotation, "lr": start_lr[1]}, + {"params": new_gs._scaling, "lr": start_lr[2]}, + {"params": new_gs._opacity, "lr": start_lr[3]}, + ], lr=start_lr[0]) + + def exp_anealing(optimizer, step, total_steps, start_lr, end_lr): + return start_lr * (end_lr / start_lr) ** (step / total_steps) + + def cosine_anealing(optimizer, step, total_steps, start_lr, end_lr): + return end_lr + 0.5 * (start_lr - end_lr) * (1 + np.cos(np.pi * step / total_steps)) + + _zeta = new_gs.get_opacity.clone().detach().squeeze() + _lambda = torch.zeros_like(_zeta) + _delta = 1e-7 + _interval = 10 + num_target = int((1 - simplify) * _zeta.shape[0]) + + with tqdm(total=2500, disable=not verbose, desc='Simplifying Gaussian') as pbar: + for i in range(2500): + # prune + if i % 100 == 0: + mask = new_gs.get_opacity.squeeze() > 0.05 + mask = torch.nonzero(mask).squeeze() + new_gs._xyz = torch.nn.Parameter(new_gs._xyz[mask]) + new_gs._rotation = torch.nn.Parameter(new_gs._rotation[mask]) + new_gs._scaling = torch.nn.Parameter(new_gs._scaling[mask]) + new_gs._opacity = torch.nn.Parameter(new_gs._opacity[mask]) + new_gs._features_dc = new_gs._features_dc[mask] + new_gs._features_rest = new_gs._features_rest[mask] if new_gs._features_rest is not None else None + _zeta = _zeta[mask] + _lambda = _lambda[mask] + # update optimizer state + for param_group, new_param in zip(optimizer.param_groups, [new_gs._xyz, new_gs._rotation, new_gs._scaling, new_gs._opacity]): + stored_state = optimizer.state[param_group['params'][0]] + if 'exp_avg' in stored_state: + stored_state['exp_avg'] = stored_state['exp_avg'][mask] + stored_state['exp_avg_sq'] = stored_state['exp_avg_sq'][mask] + del optimizer.state[param_group['params'][0]] + param_group['params'][0] = new_param + optimizer.state[param_group['params'][0]] = stored_state + + opacity = new_gs.get_opacity.squeeze() + + # sparisfy + if i % _interval == 0: + _zeta = _lambda + opacity.detach() + if opacity.shape[0] > num_target: + index = _zeta.topk(num_target)[1] + _m = torch.ones_like(_zeta, dtype=torch.bool) + _m[index] = 0 + _zeta[_m] = 0 + _lambda = _lambda + opacity.detach() - _zeta + + # sample a random view + view_idx = np.random.randint(len(observations)) + observation = observations[view_idx] + extrinsic = extrinsics[view_idx] + intrinsic = intrinsics[view_idx] + + color = renderer.render(new_gs, extrinsic, intrinsic)['color'] + rgb_loss = torch.nn.functional.l1_loss(color, observation) + loss = rgb_loss + \ + _delta * torch.sum(torch.pow(_lambda + opacity - _zeta, 2)) + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + # update lr + for j in range(len(optimizer.param_groups)): + optimizer.param_groups[j]['lr'] = cosine_anealing(optimizer, i, 2500, start_lr[j], end_lr[j]) + + pbar.set_postfix({'loss': rgb_loss.item(), 'num': opacity.shape[0], 'lambda': _lambda.mean().item()}) + pbar.update() + + new_gs._xyz = new_gs._xyz.data + new_gs._rotation = new_gs._rotation.data + new_gs._scaling = new_gs._scaling.data + new_gs._opacity = new_gs._opacity.data + + return new_gs diff --git a/Gen_3D_Modules/TRELLIS/trellis_/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/__init__.py new file mode 100644 index 00000000..20d240af --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/__init__.py @@ -0,0 +1,6 @@ +from . import models +from . import modules +from . import pipelines +from . import renderers +from . import representations +from . import utils diff --git a/Gen_3D_Modules/TRELLIS/trellis_/models/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/models/__init__.py new file mode 100644 index 00000000..d90e9f9a --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/models/__init__.py @@ -0,0 +1,70 @@ +import importlib + +__attributes = { + 'SparseStructureEncoder': 'sparse_structure_vae', + 'SparseStructureDecoder': 'sparse_structure_vae', + 'SparseStructureFlowModel': 'sparse_structure_flow', + 'SLatEncoder': 'structured_latent_vae', + 'SLatGaussianDecoder': 'structured_latent_vae', + 'SLatRadianceFieldDecoder': 'structured_latent_vae', + 'SLatMeshDecoder': 'structured_latent_vae', + 'SLatFlowModel': 'structured_latent_flow', +} + +__submodules = [] + +__all__ = list(__attributes.keys()) + __submodules + +def __getattr__(name): + if name not in globals(): + if name in __attributes: + module_name = __attributes[name] + module = importlib.import_module(f".{module_name}", __name__) + globals()[name] = getattr(module, name) + elif name in __submodules: + module = importlib.import_module(f".{name}", __name__) + globals()[name] = module + else: + raise AttributeError(f"module {__name__} has no attribute {name}") + return globals()[name] + + +def from_pretrained(path: str, **kwargs): + """ + Load a model from a pretrained checkpoint. + + Args: + path: The path to the checkpoint. Can be either local path or a Hugging Face model name. + NOTE: config file and model file should take the name f'{path}.json' and f'{path}.safetensors' respectively. + **kwargs: Additional arguments for the model constructor. + """ + import os + import json + from safetensors.torch import load_file + is_local = os.path.exists(f"{path}.json") and os.path.exists(f"{path}.safetensors") + + if is_local: + config_file = f"{path}.json" + model_file = f"{path}.safetensors" + else: + from huggingface_hub import hf_hub_download + path_parts = path.split('/') + repo_id = f'{path_parts[0]}/{path_parts[1]}' + model_name = '/'.join(path_parts[2:]) + config_file = hf_hub_download(repo_id, f"{model_name}.json") + model_file = hf_hub_download(repo_id, f"{model_name}.safetensors") + + with open(config_file, 'r') as f: + config = json.load(f) + model = __getattr__(config['name'])(**config['args'], **kwargs) + model.load_state_dict(load_file(model_file)) + + return model + + +# For Pylance +if __name__ == '__main__': + from .sparse_structure_vae import SparseStructureEncoder, SparseStructureDecoder + from .sparse_structure_flow import SparseStructureFlowModel + from .structured_latent_vae import SLatEncoder, SLatGaussianDecoder, SLatRadianceFieldDecoder, SLatMeshDecoder + from .structured_latent_flow import SLatFlowModel diff --git a/Gen_3D_Modules/TRELLIS/trellis_/models/sparse_structure_flow.py b/Gen_3D_Modules/TRELLIS/trellis_/models/sparse_structure_flow.py new file mode 100644 index 00000000..aee71a96 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/models/sparse_structure_flow.py @@ -0,0 +1,200 @@ +from typing import * +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +from ..modules.utils import convert_module_to_f16, convert_module_to_f32 +from ..modules.transformer import AbsolutePositionEmbedder, ModulatedTransformerCrossBlock +from ..modules.spatial import patchify, unpatchify + + +class TimestepEmbedder(nn.Module): + """ + Embeds scalar timesteps into vector representations. + """ + def __init__(self, hidden_size, frequency_embedding_size=256): + super().__init__() + self.mlp = nn.Sequential( + nn.Linear(frequency_embedding_size, hidden_size, bias=True), + nn.SiLU(), + nn.Linear(hidden_size, hidden_size, bias=True), + ) + self.frequency_embedding_size = frequency_embedding_size + + @staticmethod + def timestep_embedding(t, dim, max_period=10000): + """ + Create sinusoidal timestep embeddings. + + Args: + t: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + dim: the dimension of the output. + max_period: controls the minimum frequency of the embeddings. + + Returns: + an (N, D) Tensor of positional embeddings. + """ + # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py + half = dim // 2 + freqs = torch.exp( + -np.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half + ).to(device=t.device) + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + return embedding + + def forward(self, t): + t_freq = self.timestep_embedding(t, self.frequency_embedding_size) + t_emb = self.mlp(t_freq) + return t_emb + + +class SparseStructureFlowModel(nn.Module): + def __init__( + self, + resolution: int, + in_channels: int, + model_channels: int, + cond_channels: int, + out_channels: int, + num_blocks: int, + num_heads: Optional[int] = None, + num_head_channels: Optional[int] = 64, + mlp_ratio: float = 4, + patch_size: int = 2, + pe_mode: Literal["ape", "rope"] = "ape", + use_fp16: bool = False, + use_checkpoint: bool = False, + share_mod: bool = False, + qk_rms_norm: bool = False, + qk_rms_norm_cross: bool = False, + ): + super().__init__() + self.resolution = resolution + self.in_channels = in_channels + self.model_channels = model_channels + self.cond_channels = cond_channels + self.out_channels = out_channels + self.num_blocks = num_blocks + self.num_heads = num_heads or model_channels // num_head_channels + self.mlp_ratio = mlp_ratio + self.patch_size = patch_size + self.pe_mode = pe_mode + self.use_fp16 = use_fp16 + self.use_checkpoint = use_checkpoint + self.share_mod = share_mod + self.qk_rms_norm = qk_rms_norm + self.qk_rms_norm_cross = qk_rms_norm_cross + self.dtype = torch.float16 if use_fp16 else torch.float32 + + self.t_embedder = TimestepEmbedder(model_channels) + if share_mod: + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(model_channels, 6 * model_channels, bias=True) + ) + + if pe_mode == "ape": + pos_embedder = AbsolutePositionEmbedder(model_channels, 3) + coords = torch.meshgrid(*[torch.arange(res, device=self.device) for res in [resolution // patch_size] * 3], indexing='ij') + coords = torch.stack(coords, dim=-1).reshape(-1, 3) + pos_emb = pos_embedder(coords) + self.register_buffer("pos_emb", pos_emb) + + self.input_layer = nn.Linear(in_channels * patch_size**3, model_channels) + + self.blocks = nn.ModuleList([ + ModulatedTransformerCrossBlock( + model_channels, + cond_channels, + num_heads=self.num_heads, + mlp_ratio=self.mlp_ratio, + attn_mode='full', + use_checkpoint=self.use_checkpoint, + use_rope=(pe_mode == "rope"), + share_mod=share_mod, + qk_rms_norm=self.qk_rms_norm, + qk_rms_norm_cross=self.qk_rms_norm_cross, + ) + for _ in range(num_blocks) + ]) + + self.out_layer = nn.Linear(model_channels, out_channels * patch_size**3) + + self.initialize_weights() + if use_fp16: + self.convert_to_fp16() + + @property + def device(self) -> torch.device: + """ + Return the device of the model. + """ + return next(self.parameters()).device + + def convert_to_fp16(self) -> None: + """ + Convert the torso of the model to float16. + """ + self.blocks.apply(convert_module_to_f16) + + def convert_to_fp32(self) -> None: + """ + Convert the torso of the model to float32. + """ + self.blocks.apply(convert_module_to_f32) + + def initialize_weights(self) -> None: + # Initialize transformer layers: + def _basic_init(module): + if isinstance(module, nn.Linear): + torch.nn.init.xavier_uniform_(module.weight) + if module.bias is not None: + nn.init.constant_(module.bias, 0) + self.apply(_basic_init) + + # Initialize timestep embedding MLP: + nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02) + nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02) + + # Zero-out adaLN modulation layers in DiT blocks: + if self.share_mod: + nn.init.constant_(self.adaLN_modulation[-1].weight, 0) + nn.init.constant_(self.adaLN_modulation[-1].bias, 0) + else: + for block in self.blocks: + nn.init.constant_(block.adaLN_modulation[-1].weight, 0) + nn.init.constant_(block.adaLN_modulation[-1].bias, 0) + + # Zero-out output layers: + nn.init.constant_(self.out_layer.weight, 0) + nn.init.constant_(self.out_layer.bias, 0) + + def forward(self, x: torch.Tensor, t: torch.Tensor, cond: torch.Tensor) -> torch.Tensor: + assert [*x.shape] == [x.shape[0], self.in_channels, *[self.resolution] * 3], \ + f"Input shape mismatch, got {x.shape}, expected {[x.shape[0], self.in_channels, *[self.resolution] * 3]}" + + h = patchify(x, self.patch_size) + h = h.view(*h.shape[:2], -1).permute(0, 2, 1).contiguous() + + h = self.input_layer(h) + h = h + self.pos_emb[None] + t_emb = self.t_embedder(t) + if self.share_mod: + t_emb = self.adaLN_modulation(t_emb) + t_emb = t_emb.type(self.dtype) + h = h.type(self.dtype) + cond = cond.type(self.dtype) + for block in self.blocks: + h = block(h, t_emb, cond) + h = h.type(x.dtype) + h = F.layer_norm(h, h.shape[-1:]) + h = self.out_layer(h) + + h = h.permute(0, 2, 1).view(h.shape[0], h.shape[2], *[self.resolution // self.patch_size] * 3) + h = unpatchify(h, self.patch_size).contiguous() + + return h diff --git a/Gen_3D_Modules/TRELLIS/trellis_/models/sparse_structure_vae.py b/Gen_3D_Modules/TRELLIS/trellis_/models/sparse_structure_vae.py new file mode 100644 index 00000000..c3e09136 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/models/sparse_structure_vae.py @@ -0,0 +1,306 @@ +from typing import * +import torch +import torch.nn as nn +import torch.nn.functional as F +from ..modules.norm import GroupNorm32, ChannelLayerNorm32 +from ..modules.spatial import pixel_shuffle_3d +from ..modules.utils import zero_module, convert_module_to_f16, convert_module_to_f32 + + +def norm_layer(norm_type: str, *args, **kwargs) -> nn.Module: + """ + Return a normalization layer. + """ + if norm_type == "group": + return GroupNorm32(32, *args, **kwargs) + elif norm_type == "layer": + return ChannelLayerNorm32(*args, **kwargs) + else: + raise ValueError(f"Invalid norm type {norm_type}") + + +class ResBlock3d(nn.Module): + def __init__( + self, + channels: int, + out_channels: Optional[int] = None, + norm_type: Literal["group", "layer"] = "layer", + ): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + + self.norm1 = norm_layer(norm_type, channels) + self.norm2 = norm_layer(norm_type, self.out_channels) + self.conv1 = nn.Conv3d(channels, self.out_channels, 3, padding=1) + self.conv2 = zero_module(nn.Conv3d(self.out_channels, self.out_channels, 3, padding=1)) + self.skip_connection = nn.Conv3d(channels, self.out_channels, 1) if channels != self.out_channels else nn.Identity() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + h = self.norm1(x) + h = F.silu(h) + h = self.conv1(h) + h = self.norm2(h) + h = F.silu(h) + h = self.conv2(h) + h = h + self.skip_connection(x) + return h + + +class DownsampleBlock3d(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + mode: Literal["conv", "avgpool"] = "conv", + ): + assert mode in ["conv", "avgpool"], f"Invalid mode {mode}" + + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + + if mode == "conv": + self.conv = nn.Conv3d(in_channels, out_channels, 2, stride=2) + elif mode == "avgpool": + assert in_channels == out_channels, "Pooling mode requires in_channels to be equal to out_channels" + + def forward(self, x: torch.Tensor) -> torch.Tensor: + if hasattr(self, "conv"): + return self.conv(x) + else: + return F.avg_pool3d(x, 2) + + +class UpsampleBlock3d(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + mode: Literal["conv", "nearest"] = "conv", + ): + assert mode in ["conv", "nearest"], f"Invalid mode {mode}" + + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + + if mode == "conv": + self.conv = nn.Conv3d(in_channels, out_channels*8, 3, padding=1) + elif mode == "nearest": + assert in_channels == out_channels, "Nearest mode requires in_channels to be equal to out_channels" + + def forward(self, x: torch.Tensor) -> torch.Tensor: + if hasattr(self, "conv"): + x = self.conv(x) + return pixel_shuffle_3d(x, 2) + else: + return F.interpolate(x, scale_factor=2, mode="nearest") + + +class SparseStructureEncoder(nn.Module): + """ + Encoder for Sparse Structure (\mathcal{E}_S in the paper Sec. 3.3). + + Args: + in_channels (int): Channels of the input. + latent_channels (int): Channels of the latent representation. + num_res_blocks (int): Number of residual blocks at each resolution. + channels (List[int]): Channels of the encoder blocks. + num_res_blocks_middle (int): Number of residual blocks in the middle. + norm_type (Literal["group", "layer"]): Type of normalization layer. + use_fp16 (bool): Whether to use FP16. + """ + def __init__( + self, + in_channels: int, + latent_channels: int, + num_res_blocks: int, + channels: List[int], + num_res_blocks_middle: int = 2, + norm_type: Literal["group", "layer"] = "layer", + use_fp16: bool = False, + ): + super().__init__() + self.in_channels = in_channels + self.latent_channels = latent_channels + self.num_res_blocks = num_res_blocks + self.channels = channels + self.num_res_blocks_middle = num_res_blocks_middle + self.norm_type = norm_type + self.use_fp16 = use_fp16 + self.dtype = torch.float16 if use_fp16 else torch.float32 + + self.input_layer = nn.Conv3d(in_channels, channels[0], 3, padding=1) + + self.blocks = nn.ModuleList([]) + for i, ch in enumerate(channels): + self.blocks.extend([ + ResBlock3d(ch, ch) + for _ in range(num_res_blocks) + ]) + if i < len(channels) - 1: + self.blocks.append( + DownsampleBlock3d(ch, channels[i+1]) + ) + + self.middle_block = nn.Sequential(*[ + ResBlock3d(channels[-1], channels[-1]) + for _ in range(num_res_blocks_middle) + ]) + + self.out_layer = nn.Sequential( + norm_layer(norm_type, channels[-1]), + nn.SiLU(), + nn.Conv3d(channels[-1], latent_channels*2, 3, padding=1) + ) + + if use_fp16: + self.convert_to_fp16() + + @property + def device(self) -> torch.device: + """ + Return the device of the model. + """ + return next(self.parameters()).device + + def convert_to_fp16(self) -> None: + """ + Convert the torso of the model to float16. + """ + self.use_fp16 = True + self.dtype = torch.float16 + self.blocks.apply(convert_module_to_f16) + self.middle_block.apply(convert_module_to_f16) + + def convert_to_fp32(self) -> None: + """ + Convert the torso of the model to float32. + """ + self.use_fp16 = False + self.dtype = torch.float32 + self.blocks.apply(convert_module_to_f32) + self.middle_block.apply(convert_module_to_f32) + + def forward(self, x: torch.Tensor, sample_posterior: bool = False, return_raw: bool = False) -> torch.Tensor: + h = self.input_layer(x) + h = h.type(self.dtype) + + for block in self.blocks: + h = block(h) + h = self.middle_block(h) + + h = h.type(x.dtype) + h = self.out_layer(h) + + mean, logvar = h.chunk(2, dim=1) + + if sample_posterior: + std = torch.exp(0.5 * logvar) + z = mean + std * torch.randn_like(std) + else: + z = mean + + if return_raw: + return z, mean, logvar + return z + + +class SparseStructureDecoder(nn.Module): + """ + Decoder for Sparse Structure (\mathcal{D}_S in the paper Sec. 3.3). + + Args: + out_channels (int): Channels of the output. + latent_channels (int): Channels of the latent representation. + num_res_blocks (int): Number of residual blocks at each resolution. + channels (List[int]): Channels of the decoder blocks. + num_res_blocks_middle (int): Number of residual blocks in the middle. + norm_type (Literal["group", "layer"]): Type of normalization layer. + use_fp16 (bool): Whether to use FP16. + """ + def __init__( + self, + out_channels: int, + latent_channels: int, + num_res_blocks: int, + channels: List[int], + num_res_blocks_middle: int = 2, + norm_type: Literal["group", "layer"] = "layer", + use_fp16: bool = False, + ): + super().__init__() + self.out_channels = out_channels + self.latent_channels = latent_channels + self.num_res_blocks = num_res_blocks + self.channels = channels + self.num_res_blocks_middle = num_res_blocks_middle + self.norm_type = norm_type + self.use_fp16 = use_fp16 + self.dtype = torch.float16 if use_fp16 else torch.float32 + + self.input_layer = nn.Conv3d(latent_channels, channels[0], 3, padding=1) + + self.middle_block = nn.Sequential(*[ + ResBlock3d(channels[0], channels[0]) + for _ in range(num_res_blocks_middle) + ]) + + self.blocks = nn.ModuleList([]) + for i, ch in enumerate(channels): + self.blocks.extend([ + ResBlock3d(ch, ch) + for _ in range(num_res_blocks) + ]) + if i < len(channels) - 1: + self.blocks.append( + UpsampleBlock3d(ch, channels[i+1]) + ) + + self.out_layer = nn.Sequential( + norm_layer(norm_type, channels[-1]), + nn.SiLU(), + nn.Conv3d(channels[-1], out_channels, 3, padding=1) + ) + + if use_fp16: + self.convert_to_fp16() + + @property + def device(self) -> torch.device: + """ + Return the device of the model. + """ + return next(self.parameters()).device + + def convert_to_fp16(self) -> None: + """ + Convert the torso of the model to float16. + """ + self.use_fp16 = True + self.dtype = torch.float16 + self.blocks.apply(convert_module_to_f16) + self.middle_block.apply(convert_module_to_f16) + + def convert_to_fp32(self) -> None: + """ + Convert the torso of the model to float32. + """ + self.use_fp16 = False + self.dtype = torch.float32 + self.blocks.apply(convert_module_to_f32) + self.middle_block.apply(convert_module_to_f32) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + h = self.input_layer(x) + + h = h.type(self.dtype) + + h = self.middle_block(h) + for block in self.blocks: + h = block(h) + + h = h.type(x.dtype) + h = self.out_layer(h) + return h diff --git a/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_flow.py b/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_flow.py new file mode 100644 index 00000000..f1463d79 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_flow.py @@ -0,0 +1,262 @@ +from typing import * +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +from ..modules.utils import zero_module, convert_module_to_f16, convert_module_to_f32 +from ..modules.transformer import AbsolutePositionEmbedder +from ..modules.norm import LayerNorm32 +from ..modules import sparse as sp +from ..modules.sparse.transformer import ModulatedSparseTransformerCrossBlock +from .sparse_structure_flow import TimestepEmbedder + + +class SparseResBlock3d(nn.Module): + def __init__( + self, + channels: int, + emb_channels: int, + out_channels: Optional[int] = None, + downsample: bool = False, + upsample: bool = False, + ): + super().__init__() + self.channels = channels + self.emb_channels = emb_channels + self.out_channels = out_channels or channels + self.downsample = downsample + self.upsample = upsample + + assert not (downsample and upsample), "Cannot downsample and upsample at the same time" + + self.norm1 = LayerNorm32(channels, elementwise_affine=True, eps=1e-6) + self.norm2 = LayerNorm32(self.out_channels, elementwise_affine=False, eps=1e-6) + self.conv1 = sp.SparseConv3d(channels, self.out_channels, 3) + self.conv2 = zero_module(sp.SparseConv3d(self.out_channels, self.out_channels, 3)) + self.emb_layers = nn.Sequential( + nn.SiLU(), + nn.Linear(emb_channels, 2 * self.out_channels, bias=True), + ) + self.skip_connection = sp.SparseLinear(channels, self.out_channels) if channels != self.out_channels else nn.Identity() + self.updown = None + if self.downsample: + self.updown = sp.SparseDownsample(2) + elif self.upsample: + self.updown = sp.SparseUpsample(2) + + def _updown(self, x: sp.SparseTensor) -> sp.SparseTensor: + if self.updown is not None: + x = self.updown(x) + return x + + def forward(self, x: sp.SparseTensor, emb: torch.Tensor) -> sp.SparseTensor: + emb_out = self.emb_layers(emb).type(x.dtype) + scale, shift = torch.chunk(emb_out, 2, dim=1) + + x = self._updown(x) + h = x.replace(self.norm1(x.feats)) + h = h.replace(F.silu(h.feats)) + h = self.conv1(h) + h = h.replace(self.norm2(h.feats)) * (1 + scale) + shift + h = h.replace(F.silu(h.feats)) + h = self.conv2(h) + h = h + self.skip_connection(x) + + return h + + +class SLatFlowModel(nn.Module): + def __init__( + self, + resolution: int, + in_channels: int, + model_channels: int, + cond_channels: int, + out_channels: int, + num_blocks: int, + num_heads: Optional[int] = None, + num_head_channels: Optional[int] = 64, + mlp_ratio: float = 4, + patch_size: int = 2, + num_io_res_blocks: int = 2, + io_block_channels: List[int] = None, + pe_mode: Literal["ape", "rope"] = "ape", + use_fp16: bool = False, + use_checkpoint: bool = False, + use_skip_connection: bool = True, + share_mod: bool = False, + qk_rms_norm: bool = False, + qk_rms_norm_cross: bool = False, + ): + super().__init__() + self.resolution = resolution + self.in_channels = in_channels + self.model_channels = model_channels + self.cond_channels = cond_channels + self.out_channels = out_channels + self.num_blocks = num_blocks + self.num_heads = num_heads or model_channels // num_head_channels + self.mlp_ratio = mlp_ratio + self.patch_size = patch_size + self.num_io_res_blocks = num_io_res_blocks + self.io_block_channels = io_block_channels + self.pe_mode = pe_mode + self.use_fp16 = use_fp16 + self.use_checkpoint = use_checkpoint + self.use_skip_connection = use_skip_connection + self.share_mod = share_mod + self.qk_rms_norm = qk_rms_norm + self.qk_rms_norm_cross = qk_rms_norm_cross + self.dtype = torch.float16 if use_fp16 else torch.float32 + + assert int(np.log2(patch_size)) == np.log2(patch_size), "Patch size must be a power of 2" + assert np.log2(patch_size) == len(io_block_channels), "Number of IO ResBlocks must match the number of stages" + + self.t_embedder = TimestepEmbedder(model_channels) + if share_mod: + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(model_channels, 6 * model_channels, bias=True) + ) + + if pe_mode == "ape": + self.pos_embedder = AbsolutePositionEmbedder(model_channels) + + self.input_layer = sp.SparseLinear(in_channels, io_block_channels[0]) + self.input_blocks = nn.ModuleList([]) + for chs, next_chs in zip(io_block_channels, io_block_channels[1:] + [model_channels]): + self.input_blocks.extend([ + SparseResBlock3d( + chs, + model_channels, + out_channels=chs, + ) + for _ in range(num_io_res_blocks-1) + ]) + self.input_blocks.append( + SparseResBlock3d( + chs, + model_channels, + out_channels=next_chs, + downsample=True, + ) + ) + + self.blocks = nn.ModuleList([ + ModulatedSparseTransformerCrossBlock( + model_channels, + cond_channels, + num_heads=self.num_heads, + mlp_ratio=self.mlp_ratio, + attn_mode='full', + use_checkpoint=self.use_checkpoint, + use_rope=(pe_mode == "rope"), + share_mod=self.share_mod, + qk_rms_norm=self.qk_rms_norm, + qk_rms_norm_cross=self.qk_rms_norm_cross, + ) + for _ in range(num_blocks) + ]) + + self.out_blocks = nn.ModuleList([]) + for chs, prev_chs in zip(reversed(io_block_channels), [model_channels] + list(reversed(io_block_channels[1:]))): + self.out_blocks.append( + SparseResBlock3d( + prev_chs * 2 if self.use_skip_connection else prev_chs, + model_channels, + out_channels=chs, + upsample=True, + ) + ) + self.out_blocks.extend([ + SparseResBlock3d( + chs * 2 if self.use_skip_connection else chs, + model_channels, + out_channels=chs, + ) + for _ in range(num_io_res_blocks-1) + ]) + self.out_layer = sp.SparseLinear(io_block_channels[0], out_channels) + + self.initialize_weights() + if use_fp16: + self.convert_to_fp16() + + @property + def device(self) -> torch.device: + """ + Return the device of the model. + """ + return next(self.parameters()).device + + def convert_to_fp16(self) -> None: + """ + Convert the torso of the model to float16. + """ + self.input_blocks.apply(convert_module_to_f16) + self.blocks.apply(convert_module_to_f16) + self.out_blocks.apply(convert_module_to_f16) + + def convert_to_fp32(self) -> None: + """ + Convert the torso of the model to float32. + """ + self.input_blocks.apply(convert_module_to_f32) + self.blocks.apply(convert_module_to_f32) + self.out_blocks.apply(convert_module_to_f32) + + def initialize_weights(self) -> None: + # Initialize transformer layers: + def _basic_init(module): + if isinstance(module, nn.Linear): + torch.nn.init.xavier_uniform_(module.weight) + if module.bias is not None: + nn.init.constant_(module.bias, 0) + self.apply(_basic_init) + + # Initialize timestep embedding MLP: + nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02) + nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02) + + # Zero-out adaLN modulation layers in DiT blocks: + if self.share_mod: + nn.init.constant_(self.adaLN_modulation[-1].weight, 0) + nn.init.constant_(self.adaLN_modulation[-1].bias, 0) + else: + for block in self.blocks: + nn.init.constant_(block.adaLN_modulation[-1].weight, 0) + nn.init.constant_(block.adaLN_modulation[-1].bias, 0) + + # Zero-out output layers: + nn.init.constant_(self.out_layer.weight, 0) + nn.init.constant_(self.out_layer.bias, 0) + + def forward(self, x: sp.SparseTensor, t: torch.Tensor, cond: torch.Tensor) -> sp.SparseTensor: + h = self.input_layer(x).type(self.dtype) + t_emb = self.t_embedder(t) + if self.share_mod: + t_emb = self.adaLN_modulation(t_emb) + t_emb = t_emb.type(self.dtype) + cond = cond.type(self.dtype) + + skips = [] + # pack with input blocks + for block in self.input_blocks: + h = block(h, t_emb) + skips.append(h.feats) + + if self.pe_mode == "ape": + h = h + self.pos_embedder(h.coords[:, 1:]).type(self.dtype) + for block in self.blocks: + h = block(h, t_emb, cond) + + # unpack with output blocks + for block, skip in zip(self.out_blocks, reversed(skips)): + if self.use_skip_connection: + h = block(h.replace(torch.cat([h.feats, skip], dim=1)), t_emb) + else: + h = block(h, t_emb) + + h = h.replace(F.layer_norm(h.feats, h.feats.shape[-1:])) + h = self.out_layer(h.type(x.dtype)) + return h diff --git a/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/__init__.py new file mode 100644 index 00000000..75603bc1 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/__init__.py @@ -0,0 +1,4 @@ +from .encoder import SLatEncoder +from .decoder_gs import SLatGaussianDecoder +from .decoder_rf import SLatRadianceFieldDecoder +from .decoder_mesh import SLatMeshDecoder diff --git a/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/base.py b/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/base.py new file mode 100644 index 00000000..ab0bf6a8 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/base.py @@ -0,0 +1,117 @@ +from typing import * +import torch +import torch.nn as nn +from ...modules.utils import convert_module_to_f16, convert_module_to_f32 +from ...modules import sparse as sp +from ...modules.transformer import AbsolutePositionEmbedder +from ...modules.sparse.transformer import SparseTransformerBlock + + +def block_attn_config(self): + """ + Return the attention configuration of the model. + """ + for i in range(self.num_blocks): + if self.attn_mode == "shift_window": + yield "serialized", self.window_size, 0, (16 * (i % 2),) * 3, sp.SerializeMode.Z_ORDER + elif self.attn_mode == "shift_sequence": + yield "serialized", self.window_size, self.window_size // 2 * (i % 2), (0, 0, 0), sp.SerializeMode.Z_ORDER + elif self.attn_mode == "shift_order": + yield "serialized", self.window_size, 0, (0, 0, 0), sp.SerializeModes[i % 4] + elif self.attn_mode == "full": + yield "full", None, None, None, None + elif self.attn_mode == "swin": + yield "windowed", self.window_size, None, self.window_size // 2 * (i % 2), None + + +class SparseTransformerBase(nn.Module): + """ + Sparse Transformer without output layers. + Serve as the base class for encoder and decoder. + """ + def __init__( + self, + in_channels: int, + model_channels: int, + num_blocks: int, + num_heads: Optional[int] = None, + num_head_channels: Optional[int] = 64, + mlp_ratio: float = 4.0, + attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "full", + window_size: Optional[int] = None, + pe_mode: Literal["ape", "rope"] = "ape", + use_fp16: bool = False, + use_checkpoint: bool = False, + qk_rms_norm: bool = False, + ): + super().__init__() + self.in_channels = in_channels + self.model_channels = model_channels + self.num_blocks = num_blocks + self.window_size = window_size + self.num_heads = num_heads or model_channels // num_head_channels + self.mlp_ratio = mlp_ratio + self.attn_mode = attn_mode + self.pe_mode = pe_mode + self.use_fp16 = use_fp16 + self.use_checkpoint = use_checkpoint + self.qk_rms_norm = qk_rms_norm + self.dtype = torch.float16 if use_fp16 else torch.float32 + + if pe_mode == "ape": + self.pos_embedder = AbsolutePositionEmbedder(model_channels) + + self.input_layer = sp.SparseLinear(in_channels, model_channels) + self.blocks = nn.ModuleList([ + SparseTransformerBlock( + model_channels, + num_heads=self.num_heads, + mlp_ratio=self.mlp_ratio, + attn_mode=attn_mode, + window_size=window_size, + shift_sequence=shift_sequence, + shift_window=shift_window, + serialize_mode=serialize_mode, + use_checkpoint=self.use_checkpoint, + use_rope=(pe_mode == "rope"), + qk_rms_norm=self.qk_rms_norm, + ) + for attn_mode, window_size, shift_sequence, shift_window, serialize_mode in block_attn_config(self) + ]) + + @property + def device(self) -> torch.device: + """ + Return the device of the model. + """ + return next(self.parameters()).device + + def convert_to_fp16(self) -> None: + """ + Convert the torso of the model to float16. + """ + self.blocks.apply(convert_module_to_f16) + + def convert_to_fp32(self) -> None: + """ + Convert the torso of the model to float32. + """ + self.blocks.apply(convert_module_to_f32) + + def initialize_weights(self) -> None: + # Initialize transformer layers: + def _basic_init(module): + if isinstance(module, nn.Linear): + torch.nn.init.xavier_uniform_(module.weight) + if module.bias is not None: + nn.init.constant_(module.bias, 0) + self.apply(_basic_init) + + def forward(self, x: sp.SparseTensor) -> sp.SparseTensor: + h = self.input_layer(x) + if self.pe_mode == "ape": + h = h + self.pos_embedder(x.coords[:, 1:]) + h = h.type(self.dtype) + for block in self.blocks: + h = block(h) + return h diff --git a/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/decoder_gs.py b/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/decoder_gs.py new file mode 100644 index 00000000..b893cfcf --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/decoder_gs.py @@ -0,0 +1,122 @@ +from typing import * +import torch +import torch.nn as nn +import torch.nn.functional as F +from ...modules import sparse as sp +from ...utils.random_utils import hammersley_sequence +from .base import SparseTransformerBase +from ...representations import Gaussian + + +class SLatGaussianDecoder(SparseTransformerBase): + def __init__( + self, + resolution: int, + model_channels: int, + latent_channels: int, + num_blocks: int, + num_heads: Optional[int] = None, + num_head_channels: Optional[int] = 64, + mlp_ratio: float = 4, + attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "swin", + window_size: int = 8, + pe_mode: Literal["ape", "rope"] = "ape", + use_fp16: bool = False, + use_checkpoint: bool = False, + qk_rms_norm: bool = False, + representation_config: dict = None, + ): + super().__init__( + in_channels=latent_channels, + model_channels=model_channels, + num_blocks=num_blocks, + num_heads=num_heads, + num_head_channels=num_head_channels, + mlp_ratio=mlp_ratio, + attn_mode=attn_mode, + window_size=window_size, + pe_mode=pe_mode, + use_fp16=use_fp16, + use_checkpoint=use_checkpoint, + qk_rms_norm=qk_rms_norm, + ) + self.resolution = resolution + self.rep_config = representation_config + self._calc_layout() + self.out_layer = sp.SparseLinear(model_channels, self.out_channels) + self._build_perturbation() + + self.initialize_weights() + if use_fp16: + self.convert_to_fp16() + + def initialize_weights(self) -> None: + super().initialize_weights() + # Zero-out output layers: + nn.init.constant_(self.out_layer.weight, 0) + nn.init.constant_(self.out_layer.bias, 0) + + def _build_perturbation(self) -> None: + perturbation = [hammersley_sequence(3, i, self.rep_config['num_gaussians']) for i in range(self.rep_config['num_gaussians'])] + perturbation = torch.tensor(perturbation).float() * 2 - 1 + perturbation = perturbation / self.rep_config['voxel_size'] + perturbation = torch.atanh(perturbation).to(self.device) + self.register_buffer('offset_perturbation', perturbation) + + def _calc_layout(self) -> None: + self.layout = { + '_xyz' : {'shape': (self.rep_config['num_gaussians'], 3), 'size': self.rep_config['num_gaussians'] * 3}, + '_features_dc' : {'shape': (self.rep_config['num_gaussians'], 1, 3), 'size': self.rep_config['num_gaussians'] * 3}, + '_scaling' : {'shape': (self.rep_config['num_gaussians'], 3), 'size': self.rep_config['num_gaussians'] * 3}, + '_rotation' : {'shape': (self.rep_config['num_gaussians'], 4), 'size': self.rep_config['num_gaussians'] * 4}, + '_opacity' : {'shape': (self.rep_config['num_gaussians'], 1), 'size': self.rep_config['num_gaussians']}, + } + start = 0 + for k, v in self.layout.items(): + v['range'] = (start, start + v['size']) + start += v['size'] + self.out_channels = start + + def to_representation(self, x: sp.SparseTensor) -> List[Gaussian]: + """ + Convert a batch of network outputs to 3D representations. + + Args: + x: The [N x * x C] sparse tensor output by the network. + + Returns: + list of representations + """ + ret = [] + for i in range(x.shape[0]): + representation = Gaussian( + sh_degree=0, + aabb=[-0.5, -0.5, -0.5, 1.0, 1.0, 1.0], + mininum_kernel_size = self.rep_config['3d_filter_kernel_size'], + scaling_bias = self.rep_config['scaling_bias'], + opacity_bias = self.rep_config['opacity_bias'], + scaling_activation = self.rep_config['scaling_activation'] + ) + xyz = (x.coords[x.layout[i]][:, 1:].float() + 0.5) / self.resolution + for k, v in self.layout.items(): + if k == '_xyz': + offset = x.feats[x.layout[i]][:, v['range'][0]:v['range'][1]].reshape(-1, *v['shape']) + offset = offset * self.rep_config['lr'][k] + if self.rep_config['perturb_offset']: + offset = offset + self.offset_perturbation + offset = torch.tanh(offset) / self.resolution * 0.5 * self.rep_config['voxel_size'] + _xyz = xyz.unsqueeze(1) + offset + setattr(representation, k, _xyz.flatten(0, 1)) + else: + feats = x.feats[x.layout[i]][:, v['range'][0]:v['range'][1]].reshape(-1, *v['shape']).flatten(0, 1) + feats = feats * self.rep_config['lr'][k] + setattr(representation, k, feats) + ret.append(representation) + return ret + + def forward(self, x: sp.SparseTensor) -> List[Gaussian]: + h = super().forward(x) + h = h.type(x.dtype) + h = h.replace(F.layer_norm(h.feats, h.feats.shape[-1:])) + h = self.out_layer(h) + return self.to_representation(h) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/decoder_mesh.py b/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/decoder_mesh.py new file mode 100644 index 00000000..75c1b1ec --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/decoder_mesh.py @@ -0,0 +1,167 @@ +from typing import * +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +from ...modules.utils import zero_module, convert_module_to_f16, convert_module_to_f32 +from ...modules import sparse as sp +from .base import SparseTransformerBase +from ...representations import MeshExtractResult +from ...representations.mesh import SparseFeatures2Mesh + + +class SparseSubdivideBlock3d(nn.Module): + """ + A 3D subdivide block that can subdivide the sparse tensor. + + Args: + channels: channels in the inputs and outputs. + out_channels: if specified, the number of output channels. + num_groups: the number of groups for the group norm. + """ + def __init__( + self, + channels: int, + resolution: int, + out_channels: Optional[int] = None, + num_groups: int = 32 + ): + super().__init__() + self.channels = channels + self.resolution = resolution + self.out_resolution = resolution * 2 + self.out_channels = out_channels or channels + + self.act_layers = nn.Sequential( + sp.SparseGroupNorm32(num_groups, channels), + sp.SparseSiLU() + ) + + self.sub = sp.SparseSubdivide() + + self.out_layers = nn.Sequential( + sp.SparseConv3d(channels, self.out_channels, 3, indice_key=f"res_{self.out_resolution}"), + sp.SparseGroupNorm32(num_groups, self.out_channels), + sp.SparseSiLU(), + zero_module(sp.SparseConv3d(self.out_channels, self.out_channels, 3, indice_key=f"res_{self.out_resolution}")), + ) + + if self.out_channels == channels: + self.skip_connection = nn.Identity() + else: + self.skip_connection = sp.SparseConv3d(channels, self.out_channels, 1, indice_key=f"res_{self.out_resolution}") + + def forward(self, x: sp.SparseTensor) -> sp.SparseTensor: + """ + Apply the block to a Tensor, conditioned on a timestep embedding. + + Args: + x: an [N x C x ...] Tensor of features. + Returns: + an [N x C x ...] Tensor of outputs. + """ + h = self.act_layers(x) + h = self.sub(h) + x = self.sub(x) + h = self.out_layers(h) + h = h + self.skip_connection(x) + return h + + +class SLatMeshDecoder(SparseTransformerBase): + def __init__( + self, + resolution: int, + model_channels: int, + latent_channels: int, + num_blocks: int, + num_heads: Optional[int] = None, + num_head_channels: Optional[int] = 64, + mlp_ratio: float = 4, + attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "swin", + window_size: int = 8, + pe_mode: Literal["ape", "rope"] = "ape", + use_fp16: bool = False, + use_checkpoint: bool = False, + qk_rms_norm: bool = False, + representation_config: dict = None, + ): + super().__init__( + in_channels=latent_channels, + model_channels=model_channels, + num_blocks=num_blocks, + num_heads=num_heads, + num_head_channels=num_head_channels, + mlp_ratio=mlp_ratio, + attn_mode=attn_mode, + window_size=window_size, + pe_mode=pe_mode, + use_fp16=use_fp16, + use_checkpoint=use_checkpoint, + qk_rms_norm=qk_rms_norm, + ) + self.resolution = resolution + self.rep_config = representation_config + self.mesh_extractor = SparseFeatures2Mesh(res=self.resolution*4, use_color=self.rep_config.get('use_color', False)) + self.out_channels = self.mesh_extractor.feats_channels + self.upsample = nn.ModuleList([ + SparseSubdivideBlock3d( + channels=model_channels, + resolution=resolution, + out_channels=model_channels // 4 + ), + SparseSubdivideBlock3d( + channels=model_channels // 4, + resolution=resolution * 2, + out_channels=model_channels // 8 + ) + ]) + self.out_layer = sp.SparseLinear(model_channels // 8, self.out_channels) + + self.initialize_weights() + if use_fp16: + self.convert_to_fp16() + + def initialize_weights(self) -> None: + super().initialize_weights() + # Zero-out output layers: + nn.init.constant_(self.out_layer.weight, 0) + nn.init.constant_(self.out_layer.bias, 0) + + def convert_to_fp16(self) -> None: + """ + Convert the torso of the model to float16. + """ + super().convert_to_fp16() + self.upsample.apply(convert_module_to_f16) + + def convert_to_fp32(self) -> None: + """ + Convert the torso of the model to float32. + """ + super().convert_to_fp32() + self.upsample.apply(convert_module_to_f32) + + def to_representation(self, x: sp.SparseTensor) -> List[MeshExtractResult]: + """ + Convert a batch of network outputs to 3D representations. + + Args: + x: The [N x * x C] sparse tensor output by the network. + + Returns: + list of representations + """ + ret = [] + for i in range(x.shape[0]): + mesh = self.mesh_extractor(x[i], training=self.training) + ret.append(mesh) + return ret + + def forward(self, x: sp.SparseTensor) -> List[MeshExtractResult]: + h = super().forward(x) + for block in self.upsample: + h = block(h) + h = h.type(x.dtype) + h = self.out_layer(h) + return self.to_representation(h) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/decoder_rf.py b/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/decoder_rf.py new file mode 100644 index 00000000..968bb305 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/decoder_rf.py @@ -0,0 +1,104 @@ +from typing import * +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +from ...modules import sparse as sp +from .base import SparseTransformerBase +from ...representations import Strivec + + +class SLatRadianceFieldDecoder(SparseTransformerBase): + def __init__( + self, + resolution: int, + model_channels: int, + latent_channels: int, + num_blocks: int, + num_heads: Optional[int] = None, + num_head_channels: Optional[int] = 64, + mlp_ratio: float = 4, + attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "swin", + window_size: int = 8, + pe_mode: Literal["ape", "rope"] = "ape", + use_fp16: bool = False, + use_checkpoint: bool = False, + qk_rms_norm: bool = False, + representation_config: dict = None, + ): + super().__init__( + in_channels=latent_channels, + model_channels=model_channels, + num_blocks=num_blocks, + num_heads=num_heads, + num_head_channels=num_head_channels, + mlp_ratio=mlp_ratio, + attn_mode=attn_mode, + window_size=window_size, + pe_mode=pe_mode, + use_fp16=use_fp16, + use_checkpoint=use_checkpoint, + qk_rms_norm=qk_rms_norm, + ) + self.resolution = resolution + self.rep_config = representation_config + self._calc_layout() + self.out_layer = sp.SparseLinear(model_channels, self.out_channels) + + self.initialize_weights() + if use_fp16: + self.convert_to_fp16() + + def initialize_weights(self) -> None: + super().initialize_weights() + # Zero-out output layers: + nn.init.constant_(self.out_layer.weight, 0) + nn.init.constant_(self.out_layer.bias, 0) + + def _calc_layout(self) -> None: + self.layout = { + 'trivec': {'shape': (self.rep_config['rank'], 3, self.rep_config['dim']), 'size': self.rep_config['rank'] * 3 * self.rep_config['dim']}, + 'density': {'shape': (self.rep_config['rank'],), 'size': self.rep_config['rank']}, + 'features_dc': {'shape': (self.rep_config['rank'], 1, 3), 'size': self.rep_config['rank'] * 3}, + } + start = 0 + for k, v in self.layout.items(): + v['range'] = (start, start + v['size']) + start += v['size'] + self.out_channels = start + + def to_representation(self, x: sp.SparseTensor) -> List[Strivec]: + """ + Convert a batch of network outputs to 3D representations. + + Args: + x: The [N x * x C] sparse tensor output by the network. + + Returns: + list of representations + """ + ret = [] + for i in range(x.shape[0]): + representation = Strivec( + sh_degree=0, + resolution=self.resolution, + aabb=[-0.5, -0.5, -0.5, 1, 1, 1], + rank=self.rep_config['rank'], + dim=self.rep_config['dim'], + device='cuda', + ) + representation.density_shift = 0.0 + representation.position = (x.coords[x.layout[i]][:, 1:].float() + 0.5) / self.resolution + representation.depth = torch.full((representation.position.shape[0], 1), int(np.log2(self.resolution)), dtype=torch.uint8, device='cuda') + for k, v in self.layout.items(): + setattr(representation, k, x.feats[x.layout[i]][:, v['range'][0]:v['range'][1]].reshape(-1, *v['shape'])) + representation.trivec = representation.trivec + 1 + ret.append(representation) + return ret + + def forward(self, x: sp.SparseTensor) -> List[Strivec]: + h = super().forward(x) + h = h.type(x.dtype) + h = h.replace(F.layer_norm(h.feats, h.feats.shape[-1:])) + h = self.out_layer(h) + return self.to_representation(h) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/encoder.py b/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/encoder.py new file mode 100644 index 00000000..8370921d --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/encoder.py @@ -0,0 +1,72 @@ +from typing import * +import torch +import torch.nn as nn +import torch.nn.functional as F +from ...modules import sparse as sp +from .base import SparseTransformerBase + + +class SLatEncoder(SparseTransformerBase): + def __init__( + self, + resolution: int, + in_channels: int, + model_channels: int, + latent_channels: int, + num_blocks: int, + num_heads: Optional[int] = None, + num_head_channels: Optional[int] = 64, + mlp_ratio: float = 4, + attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "swin", + window_size: int = 8, + pe_mode: Literal["ape", "rope"] = "ape", + use_fp16: bool = False, + use_checkpoint: bool = False, + qk_rms_norm: bool = False, + ): + super().__init__( + in_channels=in_channels, + model_channels=model_channels, + num_blocks=num_blocks, + num_heads=num_heads, + num_head_channels=num_head_channels, + mlp_ratio=mlp_ratio, + attn_mode=attn_mode, + window_size=window_size, + pe_mode=pe_mode, + use_fp16=use_fp16, + use_checkpoint=use_checkpoint, + qk_rms_norm=qk_rms_norm, + ) + self.resolution = resolution + self.out_layer = sp.SparseLinear(model_channels, 2 * latent_channels) + + self.initialize_weights() + if use_fp16: + self.convert_to_fp16() + + def initialize_weights(self) -> None: + super().initialize_weights() + # Zero-out output layers: + nn.init.constant_(self.out_layer.weight, 0) + nn.init.constant_(self.out_layer.bias, 0) + + def forward(self, x: sp.SparseTensor, sample_posterior=True, return_raw=False): + h = super().forward(x) + h = h.type(x.dtype) + h = h.replace(F.layer_norm(h.feats, h.feats.shape[-1:])) + h = self.out_layer(h) + + # Sample from the posterior distribution + mean, logvar = h.feats.chunk(2, dim=-1) + if sample_posterior: + std = torch.exp(0.5 * logvar) + z = mean + std * torch.randn_like(std) + else: + z = mean + z = h.replace(z) + + if return_raw: + return z, mean, logvar + else: + return z diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/attention/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/attention/__init__.py new file mode 100644 index 00000000..b77197d6 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/modules/attention/__init__.py @@ -0,0 +1,36 @@ +from typing import * + +BACKEND = 'xformers' +DEBUG = False + +def __from_env(): + import os + + global BACKEND + global DEBUG + + env_attn_backend = os.environ.get('ATTN_BACKEND') + env_sttn_debug = os.environ.get('ATTN_DEBUG') + + if env_attn_backend is not None and env_attn_backend in ['xformers', 'flash_attn', 'sdpa', 'naive']: + BACKEND = env_attn_backend + if env_sttn_debug is not None: + DEBUG = env_sttn_debug == '1' + + print(f"[ATTENTION] Using backend: {BACKEND}") + + +__from_env() + + +def set_backend(backend: Literal['xformers', 'flash_attn']): + global BACKEND + BACKEND = backend + +def set_debug(debug: bool): + global DEBUG + DEBUG = debug + + +from .full_attn import * +from .modules import * diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/attention/full_attn.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/attention/full_attn.py new file mode 100644 index 00000000..d9ebf638 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/modules/attention/full_attn.py @@ -0,0 +1,140 @@ +from typing import * +import torch +import math +from . import DEBUG, BACKEND + +if BACKEND == 'xformers': + import xformers.ops as xops +elif BACKEND == 'flash_attn': + import flash_attn +elif BACKEND == 'sdpa': + from torch.nn.functional import scaled_dot_product_attention as sdpa +elif BACKEND == 'naive': + pass +else: + raise ValueError(f"Unknown attention backend: {BACKEND}") + + +__all__ = [ + 'scaled_dot_product_attention', +] + + +def _naive_sdpa(q, k, v): + """ + Naive implementation of scaled dot product attention. + """ + q = q.permute(0, 2, 1, 3) # [N, H, L, C] + k = k.permute(0, 2, 1, 3) # [N, H, L, C] + v = v.permute(0, 2, 1, 3) # [N, H, L, C] + scale_factor = 1 / math.sqrt(q.size(-1)) + attn_weight = q @ k.transpose(-2, -1) * scale_factor + attn_weight = torch.softmax(attn_weight, dim=-1) + out = attn_weight @ v + out = out.permute(0, 2, 1, 3) # [N, L, H, C] + return out + + +@overload +def scaled_dot_product_attention(qkv: torch.Tensor) -> torch.Tensor: + """ + Apply scaled dot product attention. + + Args: + qkv (torch.Tensor): A [N, L, 3, H, C] tensor containing Qs, Ks, and Vs. + """ + ... + +@overload +def scaled_dot_product_attention(q: torch.Tensor, kv: torch.Tensor) -> torch.Tensor: + """ + Apply scaled dot product attention. + + Args: + q (torch.Tensor): A [N, L, H, C] tensor containing Qs. + kv (torch.Tensor): A [N, L, 2, H, C] tensor containing Ks and Vs. + """ + ... + +@overload +def scaled_dot_product_attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> torch.Tensor: + """ + Apply scaled dot product attention. + + Args: + q (torch.Tensor): A [N, L, H, Ci] tensor containing Qs. + k (torch.Tensor): A [N, L, H, Ci] tensor containing Ks. + v (torch.Tensor): A [N, L, H, Co] tensor containing Vs. + + Note: + k and v are assumed to have the same coordinate map. + """ + ... + +def scaled_dot_product_attention(*args, **kwargs): + arg_names_dict = { + 1: ['qkv'], + 2: ['q', 'kv'], + 3: ['q', 'k', 'v'] + } + num_all_args = len(args) + len(kwargs) + assert num_all_args in arg_names_dict, f"Invalid number of arguments, got {num_all_args}, expected 1, 2, or 3" + for key in arg_names_dict[num_all_args][len(args):]: + assert key in kwargs, f"Missing argument {key}" + + if num_all_args == 1: + qkv = args[0] if len(args) > 0 else kwargs['qkv'] + assert len(qkv.shape) == 5 and qkv.shape[2] == 3, f"Invalid shape for qkv, got {qkv.shape}, expected [N, L, 3, H, C]" + device = qkv.device + + elif num_all_args == 2: + q = args[0] if len(args) > 0 else kwargs['q'] + kv = args[1] if len(args) > 1 else kwargs['kv'] + assert q.shape[0] == kv.shape[0], f"Batch size mismatch, got {q.shape[0]} and {kv.shape[0]}" + assert len(q.shape) == 4, f"Invalid shape for q, got {q.shape}, expected [N, L, H, C]" + assert len(kv.shape) == 5, f"Invalid shape for kv, got {kv.shape}, expected [N, L, 2, H, C]" + device = q.device + + elif num_all_args == 3: + q = args[0] if len(args) > 0 else kwargs['q'] + k = args[1] if len(args) > 1 else kwargs['k'] + v = args[2] if len(args) > 2 else kwargs['v'] + assert q.shape[0] == k.shape[0] == v.shape[0], f"Batch size mismatch, got {q.shape[0]}, {k.shape[0]}, and {v.shape[0]}" + assert len(q.shape) == 4, f"Invalid shape for q, got {q.shape}, expected [N, L, H, Ci]" + assert len(k.shape) == 4, f"Invalid shape for k, got {k.shape}, expected [N, L, H, Ci]" + assert len(v.shape) == 4, f"Invalid shape for v, got {v.shape}, expected [N, L, H, Co]" + device = q.device + + if BACKEND == 'xformers': + if num_all_args == 1: + q, k, v = qkv.unbind(dim=2) + elif num_all_args == 2: + k, v = kv.unbind(dim=2) + out = xops.memory_efficient_attention(q, k, v) + elif BACKEND == 'flash_attn': + if num_all_args == 1: + out = flash_attn.flash_attn_qkvpacked_func(qkv) + elif num_all_args == 2: + out = flash_attn.flash_attn_kvpacked_func(q, kv) + elif num_all_args == 3: + out = flash_attn.flash_attn_func(q, k, v) + elif BACKEND == 'sdpa': + if num_all_args == 1: + q, k, v = qkv.unbind(dim=2) + elif num_all_args == 2: + k, v = kv.unbind(dim=2) + q = q.permute(0, 2, 1, 3) # [N, H, L, C] + k = k.permute(0, 2, 1, 3) # [N, H, L, C] + v = v.permute(0, 2, 1, 3) # [N, H, L, C] + out = sdpa(q, k, v) # [N, H, L, C] + out = out.permute(0, 2, 1, 3) # [N, L, H, C] + elif BACKEND == 'naive': + if num_all_args == 1: + q, k, v = qkv.unbind(dim=2) + elif num_all_args == 2: + k, v = kv.unbind(dim=2) + out = _naive_sdpa(q, k, v) + else: + raise ValueError(f"Unknown attention module: {BACKEND}") + + return out diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/attention/modules.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/attention/modules.py new file mode 100644 index 00000000..dbe6235c --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/modules/attention/modules.py @@ -0,0 +1,146 @@ +from typing import * +import torch +import torch.nn as nn +import torch.nn.functional as F +from .full_attn import scaled_dot_product_attention + + +class MultiHeadRMSNorm(nn.Module): + def __init__(self, dim: int, heads: int): + super().__init__() + self.scale = dim ** 0.5 + self.gamma = nn.Parameter(torch.ones(heads, dim)) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return (F.normalize(x.float(), dim = -1) * self.gamma * self.scale).to(x.dtype) + + +class RotaryPositionEmbedder(nn.Module): + def __init__(self, hidden_size: int, in_channels: int = 3): + super().__init__() + assert hidden_size % 2 == 0, "Hidden size must be divisible by 2" + self.hidden_size = hidden_size + self.in_channels = in_channels + self.freq_dim = hidden_size // in_channels // 2 + self.freqs = torch.arange(self.freq_dim, dtype=torch.float32) / self.freq_dim + self.freqs = 1.0 / (10000 ** self.freqs) + + def _get_phases(self, indices: torch.Tensor) -> torch.Tensor: + self.freqs = self.freqs.to(indices.device) + phases = torch.outer(indices, self.freqs) + phases = torch.polar(torch.ones_like(phases), phases) + return phases + + def _rotary_embedding(self, x: torch.Tensor, phases: torch.Tensor) -> torch.Tensor: + x_complex = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2)) + x_rotated = x_complex * phases + x_embed = torch.view_as_real(x_rotated).reshape(*x_rotated.shape[:-1], -1).to(x.dtype) + return x_embed + + def forward(self, q: torch.Tensor, k: torch.Tensor, indices: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Args: + q (sp.SparseTensor): [..., N, D] tensor of queries + k (sp.SparseTensor): [..., N, D] tensor of keys + indices (torch.Tensor): [..., N, C] tensor of spatial positions + """ + if indices is None: + indices = torch.arange(q.shape[-2], device=q.device) + if len(q.shape) > 2: + indices = indices.unsqueeze(0).expand(q.shape[:-2] + (-1,)) + + phases = self._get_phases(indices.reshape(-1)).reshape(*indices.shape[:-1], -1) + if phases.shape[1] < self.hidden_size // 2: + phases = torch.cat([phases, torch.polar( + torch.ones(*phases.shape[:-1], self.hidden_size // 2 - phases.shape[1], device=phases.device), + torch.zeros(*phases.shape[:-1], self.hidden_size // 2 - phases.shape[1], device=phases.device) + )], dim=-1) + q_embed = self._rotary_embedding(q, phases) + k_embed = self._rotary_embedding(k, phases) + return q_embed, k_embed + + +class MultiHeadAttention(nn.Module): + def __init__( + self, + channels: int, + num_heads: int, + ctx_channels: Optional[int]=None, + type: Literal["self", "cross"] = "self", + attn_mode: Literal["full", "windowed"] = "full", + window_size: Optional[int] = None, + shift_window: Optional[Tuple[int, int, int]] = None, + qkv_bias: bool = True, + use_rope: bool = False, + qk_rms_norm: bool = False, + ): + super().__init__() + assert channels % num_heads == 0 + assert type in ["self", "cross"], f"Invalid attention type: {type}" + assert attn_mode in ["full", "windowed"], f"Invalid attention mode: {attn_mode}" + assert type == "self" or attn_mode == "full", "Cross-attention only supports full attention" + + if attn_mode == "windowed": + raise NotImplementedError("Windowed attention is not yet implemented") + + self.channels = channels + self.head_dim = channels // num_heads + self.ctx_channels = ctx_channels if ctx_channels is not None else channels + self.num_heads = num_heads + self._type = type + self.attn_mode = attn_mode + self.window_size = window_size + self.shift_window = shift_window + self.use_rope = use_rope + self.qk_rms_norm = qk_rms_norm + + if self._type == "self": + self.to_qkv = nn.Linear(channels, channels * 3, bias=qkv_bias) + else: + self.to_q = nn.Linear(channels, channels, bias=qkv_bias) + self.to_kv = nn.Linear(self.ctx_channels, channels * 2, bias=qkv_bias) + + if self.qk_rms_norm: + self.q_rms_norm = MultiHeadRMSNorm(self.head_dim, num_heads) + self.k_rms_norm = MultiHeadRMSNorm(self.head_dim, num_heads) + + self.to_out = nn.Linear(channels, channels) + + if use_rope: + self.rope = RotaryPositionEmbedder(channels) + + def forward(self, x: torch.Tensor, context: Optional[torch.Tensor] = None, indices: Optional[torch.Tensor] = None) -> torch.Tensor: + B, L, C = x.shape + if self._type == "self": + qkv = self.to_qkv(x) + qkv = qkv.reshape(B, L, 3, self.num_heads, -1) + if self.use_rope: + q, k, v = qkv.unbind(dim=2) + q, k = self.rope(q, k, indices) + qkv = torch.stack([q, k, v], dim=2) + if self.attn_mode == "full": + if self.qk_rms_norm: + q, k, v = qkv.unbind(dim=2) + q = self.q_rms_norm(q) + k = self.k_rms_norm(k) + h = scaled_dot_product_attention(q, k, v) + else: + h = scaled_dot_product_attention(qkv) + elif self.attn_mode == "windowed": + raise NotImplementedError("Windowed attention is not yet implemented") + else: + Lkv = context.shape[1] + q = self.to_q(x) + kv = self.to_kv(context) + q = q.reshape(B, L, self.num_heads, -1) + kv = kv.reshape(B, Lkv, 2, self.num_heads, -1) + if self.qk_rms_norm: + q = self.q_rms_norm(q) + k, v = kv.unbind(dim=2) + k = self.k_rms_norm(k) + h = scaled_dot_product_attention(q, k, v) + else: + h = scaled_dot_product_attention(q, kv) + h = h.reshape(B, L, -1) + h = self.to_out(h) + return h diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/norm.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/norm.py new file mode 100644 index 00000000..09035726 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/modules/norm.py @@ -0,0 +1,25 @@ +import torch +import torch.nn as nn + + +class LayerNorm32(nn.LayerNorm): + def forward(self, x: torch.Tensor) -> torch.Tensor: + return super().forward(x.float()).type(x.dtype) + + +class GroupNorm32(nn.GroupNorm): + """ + A GroupNorm layer that converts to float32 before the forward pass. + """ + def forward(self, x: torch.Tensor) -> torch.Tensor: + return super().forward(x.float()).type(x.dtype) + + +class ChannelLayerNorm32(LayerNorm32): + def forward(self, x: torch.Tensor) -> torch.Tensor: + DIM = x.dim() + x = x.permute(0, *range(2, DIM), 1).contiguous() + x = super().forward(x) + x = x.permute(0, DIM-1, *range(1, DIM-1)).contiguous() + return x + \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/__init__.py new file mode 100644 index 00000000..77108a63 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/__init__.py @@ -0,0 +1,102 @@ +from typing import * + +BACKEND = 'spconv' +DEBUG = False +ATTN = 'xformers' + +def __from_env(): + import os + + global BACKEND + global DEBUG + global ATTN + + env_sparse_backend = os.environ.get('SPARSE_BACKEND') + env_sparse_debug = os.environ.get('SPARSE_DEBUG') + env_sparse_attn = os.environ.get('SPARSE_ATTN_BACKEND') + if env_sparse_attn is None: + env_sparse_attn = os.environ.get('ATTN_BACKEND') + + if env_sparse_backend is not None and env_sparse_backend in ['spconv', 'torchsparse']: + BACKEND = env_sparse_backend + if env_sparse_debug is not None: + DEBUG = env_sparse_debug == '1' + if env_sparse_attn is not None and env_sparse_attn in ['xformers', 'flash_attn']: + ATTN = env_sparse_attn + + print(f"[SPARSE] Backend: {BACKEND}, Attention: {ATTN}") + + +__from_env() + + +def set_backend(backend: Literal['spconv', 'torchsparse']): + global BACKEND + BACKEND = backend + +def set_debug(debug: bool): + global DEBUG + DEBUG = debug + +def set_attn(attn: Literal['xformers', 'flash_attn']): + global ATTN + ATTN = attn + + +import importlib + +__attributes = { + 'SparseTensor': 'basic', + 'sparse_batch_broadcast': 'basic', + 'sparse_batch_op': 'basic', + 'sparse_cat': 'basic', + 'sparse_unbind': 'basic', + 'SparseGroupNorm': 'norm', + 'SparseLayerNorm': 'norm', + 'SparseGroupNorm32': 'norm', + 'SparseLayerNorm32': 'norm', + 'SparseReLU': 'nonlinearity', + 'SparseSiLU': 'nonlinearity', + 'SparseGELU': 'nonlinearity', + 'SparseActivation': 'nonlinearity', + 'SparseLinear': 'linear', + 'sparse_scaled_dot_product_attention': 'attention', + 'SerializeMode': 'attention', + 'sparse_serialized_scaled_dot_product_self_attention': 'attention', + 'sparse_windowed_scaled_dot_product_self_attention': 'attention', + 'SparseMultiHeadAttention': 'attention', + 'SparseConv3d': 'conv', + 'SparseInverseConv3d': 'conv', + 'SparseDownsample': 'spatial', + 'SparseUpsample': 'spatial', + 'SparseSubdivide' : 'spatial' +} + +__submodules = ['transformer'] + +__all__ = list(__attributes.keys()) + __submodules + +def __getattr__(name): + if name not in globals(): + if name in __attributes: + module_name = __attributes[name] + module = importlib.import_module(f".{module_name}", __name__) + globals()[name] = getattr(module, name) + elif name in __submodules: + module = importlib.import_module(f".{name}", __name__) + globals()[name] = module + else: + raise AttributeError(f"module {__name__} has no attribute {name}") + return globals()[name] + + +# For Pylance +if __name__ == '__main__': + from .basic import * + from .norm import * + from .nonlinearity import * + from .linear import * + from .attention import * + from .conv import * + from .spatial import * + import transformer diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/__init__.py new file mode 100644 index 00000000..32b3c2c8 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/__init__.py @@ -0,0 +1,4 @@ +from .full_attn import * +from .serialized_attn import * +from .windowed_attn import * +from .modules import * diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/full_attn.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/full_attn.py new file mode 100644 index 00000000..e9e27aeb --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/full_attn.py @@ -0,0 +1,215 @@ +from typing import * +import torch +from .. import SparseTensor +from .. import DEBUG, ATTN + +if ATTN == 'xformers': + import xformers.ops as xops +elif ATTN == 'flash_attn': + import flash_attn +else: + raise ValueError(f"Unknown attention module: {ATTN}") + + +__all__ = [ + 'sparse_scaled_dot_product_attention', +] + + +@overload +def sparse_scaled_dot_product_attention(qkv: SparseTensor) -> SparseTensor: + """ + Apply scaled dot product attention to a sparse tensor. + + Args: + qkv (SparseTensor): A [N, *, 3, H, C] sparse tensor containing Qs, Ks, and Vs. + """ + ... + +@overload +def sparse_scaled_dot_product_attention(q: SparseTensor, kv: Union[SparseTensor, torch.Tensor]) -> SparseTensor: + """ + Apply scaled dot product attention to a sparse tensor. + + Args: + q (SparseTensor): A [N, *, H, C] sparse tensor containing Qs. + kv (SparseTensor or torch.Tensor): A [N, *, 2, H, C] sparse tensor or a [N, L, 2, H, C] dense tensor containing Ks and Vs. + """ + ... + +@overload +def sparse_scaled_dot_product_attention(q: torch.Tensor, kv: SparseTensor) -> torch.Tensor: + """ + Apply scaled dot product attention to a sparse tensor. + + Args: + q (SparseTensor): A [N, L, H, C] dense tensor containing Qs. + kv (SparseTensor or torch.Tensor): A [N, *, 2, H, C] sparse tensor containing Ks and Vs. + """ + ... + +@overload +def sparse_scaled_dot_product_attention(q: SparseTensor, k: SparseTensor, v: SparseTensor) -> SparseTensor: + """ + Apply scaled dot product attention to a sparse tensor. + + Args: + q (SparseTensor): A [N, *, H, Ci] sparse tensor containing Qs. + k (SparseTensor): A [N, *, H, Ci] sparse tensor containing Ks. + v (SparseTensor): A [N, *, H, Co] sparse tensor containing Vs. + + Note: + k and v are assumed to have the same coordinate map. + """ + ... + +@overload +def sparse_scaled_dot_product_attention(q: SparseTensor, k: torch.Tensor, v: torch.Tensor) -> SparseTensor: + """ + Apply scaled dot product attention to a sparse tensor. + + Args: + q (SparseTensor): A [N, *, H, Ci] sparse tensor containing Qs. + k (torch.Tensor): A [N, L, H, Ci] dense tensor containing Ks. + v (torch.Tensor): A [N, L, H, Co] dense tensor containing Vs. + """ + ... + +@overload +def sparse_scaled_dot_product_attention(q: torch.Tensor, k: SparseTensor, v: SparseTensor) -> torch.Tensor: + """ + Apply scaled dot product attention to a sparse tensor. + + Args: + q (torch.Tensor): A [N, L, H, Ci] dense tensor containing Qs. + k (SparseTensor): A [N, *, H, Ci] sparse tensor containing Ks. + v (SparseTensor): A [N, *, H, Co] sparse tensor containing Vs. + """ + ... + +def sparse_scaled_dot_product_attention(*args, **kwargs): + arg_names_dict = { + 1: ['qkv'], + 2: ['q', 'kv'], + 3: ['q', 'k', 'v'] + } + num_all_args = len(args) + len(kwargs) + assert num_all_args in arg_names_dict, f"Invalid number of arguments, got {num_all_args}, expected 1, 2, or 3" + for key in arg_names_dict[num_all_args][len(args):]: + assert key in kwargs, f"Missing argument {key}" + + if num_all_args == 1: + qkv = args[0] if len(args) > 0 else kwargs['qkv'] + assert isinstance(qkv, SparseTensor), f"qkv must be a SparseTensor, got {type(qkv)}" + assert len(qkv.shape) == 4 and qkv.shape[1] == 3, f"Invalid shape for qkv, got {qkv.shape}, expected [N, *, 3, H, C]" + device = qkv.device + + s = qkv + q_seqlen = [qkv.layout[i].stop - qkv.layout[i].start for i in range(qkv.shape[0])] + kv_seqlen = q_seqlen + qkv = qkv.feats # [T, 3, H, C] + + elif num_all_args == 2: + q = args[0] if len(args) > 0 else kwargs['q'] + kv = args[1] if len(args) > 1 else kwargs['kv'] + assert isinstance(q, SparseTensor) and isinstance(kv, (SparseTensor, torch.Tensor)) or \ + isinstance(q, torch.Tensor) and isinstance(kv, SparseTensor), \ + f"Invalid types, got {type(q)} and {type(kv)}" + assert q.shape[0] == kv.shape[0], f"Batch size mismatch, got {q.shape[0]} and {kv.shape[0]}" + device = q.device + + if isinstance(q, SparseTensor): + assert len(q.shape) == 3, f"Invalid shape for q, got {q.shape}, expected [N, *, H, C]" + s = q + q_seqlen = [q.layout[i].stop - q.layout[i].start for i in range(q.shape[0])] + q = q.feats # [T_Q, H, C] + else: + assert len(q.shape) == 4, f"Invalid shape for q, got {q.shape}, expected [N, L, H, C]" + s = None + N, L, H, C = q.shape + q_seqlen = [L] * N + q = q.reshape(N * L, H, C) # [T_Q, H, C] + + if isinstance(kv, SparseTensor): + assert len(kv.shape) == 4 and kv.shape[1] == 2, f"Invalid shape for kv, got {kv.shape}, expected [N, *, 2, H, C]" + kv_seqlen = [kv.layout[i].stop - kv.layout[i].start for i in range(kv.shape[0])] + kv = kv.feats # [T_KV, 2, H, C] + else: + assert len(kv.shape) == 5, f"Invalid shape for kv, got {kv.shape}, expected [N, L, 2, H, C]" + N, L, _, H, C = kv.shape + kv_seqlen = [L] * N + kv = kv.reshape(N * L, 2, H, C) # [T_KV, 2, H, C] + + elif num_all_args == 3: + q = args[0] if len(args) > 0 else kwargs['q'] + k = args[1] if len(args) > 1 else kwargs['k'] + v = args[2] if len(args) > 2 else kwargs['v'] + assert isinstance(q, SparseTensor) and isinstance(k, (SparseTensor, torch.Tensor)) and type(k) == type(v) or \ + isinstance(q, torch.Tensor) and isinstance(k, SparseTensor) and isinstance(v, SparseTensor), \ + f"Invalid types, got {type(q)}, {type(k)}, and {type(v)}" + assert q.shape[0] == k.shape[0] == v.shape[0], f"Batch size mismatch, got {q.shape[0]}, {k.shape[0]}, and {v.shape[0]}" + device = q.device + + if isinstance(q, SparseTensor): + assert len(q.shape) == 3, f"Invalid shape for q, got {q.shape}, expected [N, *, H, Ci]" + s = q + q_seqlen = [q.layout[i].stop - q.layout[i].start for i in range(q.shape[0])] + q = q.feats # [T_Q, H, Ci] + else: + assert len(q.shape) == 4, f"Invalid shape for q, got {q.shape}, expected [N, L, H, Ci]" + s = None + N, L, H, CI = q.shape + q_seqlen = [L] * N + q = q.reshape(N * L, H, CI) # [T_Q, H, Ci] + + if isinstance(k, SparseTensor): + assert len(k.shape) == 3, f"Invalid shape for k, got {k.shape}, expected [N, *, H, Ci]" + assert len(v.shape) == 3, f"Invalid shape for v, got {v.shape}, expected [N, *, H, Co]" + kv_seqlen = [k.layout[i].stop - k.layout[i].start for i in range(k.shape[0])] + k = k.feats # [T_KV, H, Ci] + v = v.feats # [T_KV, H, Co] + else: + assert len(k.shape) == 4, f"Invalid shape for k, got {k.shape}, expected [N, L, H, Ci]" + assert len(v.shape) == 4, f"Invalid shape for v, got {v.shape}, expected [N, L, H, Co]" + N, L, H, CI, CO = *k.shape, v.shape[-1] + kv_seqlen = [L] * N + k = k.reshape(N * L, H, CI) # [T_KV, H, Ci] + v = v.reshape(N * L, H, CO) # [T_KV, H, Co] + + if DEBUG: + if s is not None: + for i in range(s.shape[0]): + assert (s.coords[s.layout[i]] == i).all(), f"SparseScaledDotProductSelfAttention: batch index mismatch" + if num_all_args in [2, 3]: + assert q.shape[:2] == [1, sum(q_seqlen)], f"SparseScaledDotProductSelfAttention: q shape mismatch" + if num_all_args == 3: + assert k.shape[:2] == [1, sum(kv_seqlen)], f"SparseScaledDotProductSelfAttention: k shape mismatch" + assert v.shape[:2] == [1, sum(kv_seqlen)], f"SparseScaledDotProductSelfAttention: v shape mismatch" + + if ATTN == 'xformers': + if num_all_args == 1: + q, k, v = qkv.unbind(dim=1) + elif num_all_args == 2: + k, v = kv.unbind(dim=1) + q = q.unsqueeze(0) + k = k.unsqueeze(0) + v = v.unsqueeze(0) + mask = xops.fmha.BlockDiagonalMask.from_seqlens(q_seqlen, kv_seqlen) + out = xops.memory_efficient_attention(q, k, v, mask)[0] + elif ATTN == 'flash_attn': + cu_seqlens_q = torch.cat([torch.tensor([0]), torch.cumsum(torch.tensor(q_seqlen), dim=0)]).int().to(device) + if num_all_args in [2, 3]: + cu_seqlens_kv = torch.cat([torch.tensor([0]), torch.cumsum(torch.tensor(kv_seqlen), dim=0)]).int().to(device) + if num_all_args == 1: + out = flash_attn.flash_attn_varlen_qkvpacked_func(qkv, cu_seqlens_q, max(q_seqlen)) + elif num_all_args == 2: + out = flash_attn.flash_attn_varlen_kvpacked_func(q, kv, cu_seqlens_q, cu_seqlens_kv, max(q_seqlen), max(kv_seqlen)) + elif num_all_args == 3: + out = flash_attn.flash_attn_varlen_func(q, k, v, cu_seqlens_q, cu_seqlens_kv, max(q_seqlen), max(kv_seqlen)) + else: + raise ValueError(f"Unknown attention module: {ATTN}") + + if s is not None: + return s.replace(out) + else: + return out.reshape(N, L, H, -1) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/modules.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/modules.py new file mode 100644 index 00000000..5d2fe782 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/modules.py @@ -0,0 +1,139 @@ +from typing import * +import torch +import torch.nn as nn +import torch.nn.functional as F +from .. import SparseTensor +from .full_attn import sparse_scaled_dot_product_attention +from .serialized_attn import SerializeMode, sparse_serialized_scaled_dot_product_self_attention +from .windowed_attn import sparse_windowed_scaled_dot_product_self_attention +from ...attention import RotaryPositionEmbedder + + +class SparseMultiHeadRMSNorm(nn.Module): + def __init__(self, dim: int, heads: int): + super().__init__() + self.scale = dim ** 0.5 + self.gamma = nn.Parameter(torch.ones(heads, dim)) + + def forward(self, x: Union[SparseTensor, torch.Tensor]) -> Union[SparseTensor, torch.Tensor]: + x_type = x.dtype + x = x.float() + if isinstance(x, SparseTensor): + x = x.replace(F.normalize(x.feats, dim=-1)) + else: + x = F.normalize(x, dim=-1) + return (x * self.gamma * self.scale).to(x_type) + + +class SparseMultiHeadAttention(nn.Module): + def __init__( + self, + channels: int, + num_heads: int, + ctx_channels: Optional[int] = None, + type: Literal["self", "cross"] = "self", + attn_mode: Literal["full", "serialized", "windowed"] = "full", + window_size: Optional[int] = None, + shift_sequence: Optional[int] = None, + shift_window: Optional[Tuple[int, int, int]] = None, + serialize_mode: Optional[SerializeMode] = None, + qkv_bias: bool = True, + use_rope: bool = False, + qk_rms_norm: bool = False, + ): + super().__init__() + assert channels % num_heads == 0 + assert type in ["self", "cross"], f"Invalid attention type: {type}" + assert attn_mode in ["full", "serialized", "windowed"], f"Invalid attention mode: {attn_mode}" + assert type == "self" or attn_mode == "full", "Cross-attention only supports full attention" + assert type == "self" or use_rope is False, "Rotary position embeddings only supported for self-attention" + self.channels = channels + self.ctx_channels = ctx_channels if ctx_channels is not None else channels + self.num_heads = num_heads + self._type = type + self.attn_mode = attn_mode + self.window_size = window_size + self.shift_sequence = shift_sequence + self.shift_window = shift_window + self.serialize_mode = serialize_mode + self.use_rope = use_rope + self.qk_rms_norm = qk_rms_norm + + if self._type == "self": + self.to_qkv = nn.Linear(channels, channels * 3, bias=qkv_bias) + else: + self.to_q = nn.Linear(channels, channels, bias=qkv_bias) + self.to_kv = nn.Linear(self.ctx_channels, channels * 2, bias=qkv_bias) + + if self.qk_rms_norm: + self.q_rms_norm = SparseMultiHeadRMSNorm(channels // num_heads, num_heads) + self.k_rms_norm = SparseMultiHeadRMSNorm(channels // num_heads, num_heads) + + self.to_out = nn.Linear(channels, channels) + + if use_rope: + self.rope = RotaryPositionEmbedder(channels) + + @staticmethod + def _linear(module: nn.Linear, x: Union[SparseTensor, torch.Tensor]) -> Union[SparseTensor, torch.Tensor]: + if isinstance(x, SparseTensor): + return x.replace(module(x.feats)) + else: + return module(x) + + @staticmethod + def _reshape_chs(x: Union[SparseTensor, torch.Tensor], shape: Tuple[int, ...]) -> Union[SparseTensor, torch.Tensor]: + if isinstance(x, SparseTensor): + return x.reshape(*shape) + else: + return x.reshape(*x.shape[:2], *shape) + + def _fused_pre(self, x: Union[SparseTensor, torch.Tensor], num_fused: int) -> Union[SparseTensor, torch.Tensor]: + if isinstance(x, SparseTensor): + x_feats = x.feats.unsqueeze(0) + else: + x_feats = x + x_feats = x_feats.reshape(*x_feats.shape[:2], num_fused, self.num_heads, -1) + return x.replace(x_feats.squeeze(0)) if isinstance(x, SparseTensor) else x_feats + + def _rope(self, qkv: SparseTensor) -> SparseTensor: + q, k, v = qkv.feats.unbind(dim=1) # [T, H, C] + q, k = self.rope(q, k, qkv.coords[:, 1:]) + qkv = qkv.replace(torch.stack([q, k, v], dim=1)) + return qkv + + def forward(self, x: Union[SparseTensor, torch.Tensor], context: Optional[Union[SparseTensor, torch.Tensor]] = None) -> Union[SparseTensor, torch.Tensor]: + if self._type == "self": + qkv = self._linear(self.to_qkv, x) + qkv = self._fused_pre(qkv, num_fused=3) + if self.use_rope: + qkv = self._rope(qkv) + if self.qk_rms_norm: + q, k, v = qkv.unbind(dim=1) + q = self.q_rms_norm(q) + k = self.k_rms_norm(k) + qkv = qkv.replace(torch.stack([q.feats, k.feats, v.feats], dim=1)) + if self.attn_mode == "full": + h = sparse_scaled_dot_product_attention(qkv) + elif self.attn_mode == "serialized": + h = sparse_serialized_scaled_dot_product_self_attention( + qkv, self.window_size, serialize_mode=self.serialize_mode, shift_sequence=self.shift_sequence, shift_window=self.shift_window + ) + elif self.attn_mode == "windowed": + h = sparse_windowed_scaled_dot_product_self_attention( + qkv, self.window_size, shift_window=self.shift_window + ) + else: + q = self._linear(self.to_q, x) + q = self._reshape_chs(q, (self.num_heads, -1)) + kv = self._linear(self.to_kv, context) + kv = self._fused_pre(kv, num_fused=2) + if self.qk_rms_norm: + q = self.q_rms_norm(q) + k, v = kv.unbind(dim=1) + k = self.k_rms_norm(k) + kv = kv.replace(torch.stack([k.feats, v.feats], dim=1)) + h = sparse_scaled_dot_product_attention(q, kv) + h = self._reshape_chs(h, (-1,)) + h = self._linear(self.to_out, h) + return h diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/serialized_attn.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/serialized_attn.py new file mode 100644 index 00000000..5950b75b --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/serialized_attn.py @@ -0,0 +1,193 @@ +from typing import * +from enum import Enum +import torch +import math +from .. import SparseTensor +from .. import DEBUG, ATTN + +if ATTN == 'xformers': + import xformers.ops as xops +elif ATTN == 'flash_attn': + import flash_attn +else: + raise ValueError(f"Unknown attention module: {ATTN}") + + +__all__ = [ + 'sparse_serialized_scaled_dot_product_self_attention', +] + + +class SerializeMode(Enum): + Z_ORDER = 0 + Z_ORDER_TRANSPOSED = 1 + HILBERT = 2 + HILBERT_TRANSPOSED = 3 + + +SerializeModes = [ + SerializeMode.Z_ORDER, + SerializeMode.Z_ORDER_TRANSPOSED, + SerializeMode.HILBERT, + SerializeMode.HILBERT_TRANSPOSED +] + + +def calc_serialization( + tensor: SparseTensor, + window_size: int, + serialize_mode: SerializeMode = SerializeMode.Z_ORDER, + shift_sequence: int = 0, + shift_window: Tuple[int, int, int] = (0, 0, 0) +) -> Tuple[torch.Tensor, torch.Tensor, List[int]]: + """ + Calculate serialization and partitioning for a set of coordinates. + + Args: + tensor (SparseTensor): The input tensor. + window_size (int): The window size to use. + serialize_mode (SerializeMode): The serialization mode to use. + shift_sequence (int): The shift of serialized sequence. + shift_window (Tuple[int, int, int]): The shift of serialized coordinates. + + Returns: + (torch.Tensor, torch.Tensor): Forwards and backwards indices. + """ + fwd_indices = [] + bwd_indices = [] + seq_lens = [] + seq_batch_indices = [] + offsets = [0] + + if 'vox2seq' not in globals(): + import vox2seq + + # Serialize the input + serialize_coords = tensor.coords[:, 1:].clone() + serialize_coords += torch.tensor(shift_window, dtype=torch.int32, device=tensor.device).reshape(1, 3) + if serialize_mode == SerializeMode.Z_ORDER: + code = vox2seq.encode(serialize_coords, mode='z_order', permute=[0, 1, 2]) + elif serialize_mode == SerializeMode.Z_ORDER_TRANSPOSED: + code = vox2seq.encode(serialize_coords, mode='z_order', permute=[1, 0, 2]) + elif serialize_mode == SerializeMode.HILBERT: + code = vox2seq.encode(serialize_coords, mode='hilbert', permute=[0, 1, 2]) + elif serialize_mode == SerializeMode.HILBERT_TRANSPOSED: + code = vox2seq.encode(serialize_coords, mode='hilbert', permute=[1, 0, 2]) + else: + raise ValueError(f"Unknown serialize mode: {serialize_mode}") + + for bi, s in enumerate(tensor.layout): + num_points = s.stop - s.start + num_windows = (num_points + window_size - 1) // window_size + valid_window_size = num_points / num_windows + to_ordered = torch.argsort(code[s.start:s.stop]) + if num_windows == 1: + fwd_indices.append(to_ordered) + bwd_indices.append(torch.zeros_like(to_ordered).scatter_(0, to_ordered, torch.arange(num_points, device=tensor.device))) + fwd_indices[-1] += s.start + bwd_indices[-1] += offsets[-1] + seq_lens.append(num_points) + seq_batch_indices.append(bi) + offsets.append(offsets[-1] + seq_lens[-1]) + else: + # Partition the input + offset = 0 + mids = [(i + 0.5) * valid_window_size + shift_sequence for i in range(num_windows)] + split = [math.floor(i * valid_window_size + shift_sequence) for i in range(num_windows + 1)] + bwd_index = torch.zeros((num_points,), dtype=torch.int64, device=tensor.device) + for i in range(num_windows): + mid = mids[i] + valid_start = split[i] + valid_end = split[i + 1] + padded_start = math.floor(mid - 0.5 * window_size) + padded_end = padded_start + window_size + fwd_indices.append(to_ordered[torch.arange(padded_start, padded_end, device=tensor.device) % num_points]) + offset += valid_start - padded_start + bwd_index.scatter_(0, fwd_indices[-1][valid_start-padded_start:valid_end-padded_start], torch.arange(offset, offset + valid_end - valid_start, device=tensor.device)) + offset += padded_end - valid_start + fwd_indices[-1] += s.start + seq_lens.extend([window_size] * num_windows) + seq_batch_indices.extend([bi] * num_windows) + bwd_indices.append(bwd_index + offsets[-1]) + offsets.append(offsets[-1] + num_windows * window_size) + + fwd_indices = torch.cat(fwd_indices) + bwd_indices = torch.cat(bwd_indices) + + return fwd_indices, bwd_indices, seq_lens, seq_batch_indices + + +def sparse_serialized_scaled_dot_product_self_attention( + qkv: SparseTensor, + window_size: int, + serialize_mode: SerializeMode = SerializeMode.Z_ORDER, + shift_sequence: int = 0, + shift_window: Tuple[int, int, int] = (0, 0, 0) +) -> SparseTensor: + """ + Apply serialized scaled dot product self attention to a sparse tensor. + + Args: + qkv (SparseTensor): [N, *, 3, H, C] sparse tensor containing Qs, Ks, and Vs. + window_size (int): The window size to use. + serialize_mode (SerializeMode): The serialization mode to use. + shift_sequence (int): The shift of serialized sequence. + shift_window (Tuple[int, int, int]): The shift of serialized coordinates. + shift (int): The shift to use. + """ + assert len(qkv.shape) == 4 and qkv.shape[1] == 3, f"Invalid shape for qkv, got {qkv.shape}, expected [N, *, 3, H, C]" + + serialization_spatial_cache_name = f'serialization_{serialize_mode}_{window_size}_{shift_sequence}_{shift_window}' + serialization_spatial_cache = qkv.get_spatial_cache(serialization_spatial_cache_name) + if serialization_spatial_cache is None: + fwd_indices, bwd_indices, seq_lens, seq_batch_indices = calc_serialization(qkv, window_size, serialize_mode, shift_sequence, shift_window) + qkv.register_spatial_cache(serialization_spatial_cache_name, (fwd_indices, bwd_indices, seq_lens, seq_batch_indices)) + else: + fwd_indices, bwd_indices, seq_lens, seq_batch_indices = serialization_spatial_cache + + M = fwd_indices.shape[0] + T = qkv.feats.shape[0] + H = qkv.feats.shape[2] + C = qkv.feats.shape[3] + + qkv_feats = qkv.feats[fwd_indices] # [M, 3, H, C] + + if DEBUG: + start = 0 + qkv_coords = qkv.coords[fwd_indices] + for i in range(len(seq_lens)): + assert (qkv_coords[start:start+seq_lens[i], 0] == seq_batch_indices[i]).all(), f"SparseWindowedScaledDotProductSelfAttention: batch index mismatch" + start += seq_lens[i] + + if all([seq_len == window_size for seq_len in seq_lens]): + B = len(seq_lens) + N = window_size + qkv_feats = qkv_feats.reshape(B, N, 3, H, C) + if ATTN == 'xformers': + q, k, v = qkv_feats.unbind(dim=2) # [B, N, H, C] + out = xops.memory_efficient_attention(q, k, v) # [B, N, H, C] + elif ATTN == 'flash_attn': + out = flash_attn.flash_attn_qkvpacked_func(qkv_feats) # [B, N, H, C] + else: + raise ValueError(f"Unknown attention module: {ATTN}") + out = out.reshape(B * N, H, C) # [M, H, C] + else: + if ATTN == 'xformers': + q, k, v = qkv_feats.unbind(dim=1) # [M, H, C] + q = q.unsqueeze(0) # [1, M, H, C] + k = k.unsqueeze(0) # [1, M, H, C] + v = v.unsqueeze(0) # [1, M, H, C] + mask = xops.fmha.BlockDiagonalMask.from_seqlens(seq_lens) + out = xops.memory_efficient_attention(q, k, v, mask)[0] # [M, H, C] + elif ATTN == 'flash_attn': + cu_seqlens = torch.cat([torch.tensor([0]), torch.cumsum(torch.tensor(seq_lens), dim=0)], dim=0) \ + .to(qkv.device).int() + out = flash_attn.flash_attn_varlen_qkvpacked_func(qkv_feats, cu_seqlens, max(seq_lens)) # [M, H, C] + + out = out[bwd_indices] # [T, H, C] + + if DEBUG: + qkv_coords = qkv_coords[bwd_indices] + assert torch.equal(qkv_coords, qkv.coords), "SparseWindowedScaledDotProductSelfAttention: coordinate mismatch" + + return qkv.replace(out) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/windowed_attn.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/windowed_attn.py new file mode 100644 index 00000000..cd642c52 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/windowed_attn.py @@ -0,0 +1,135 @@ +from typing import * +import torch +import math +from .. import SparseTensor +from .. import DEBUG, ATTN + +if ATTN == 'xformers': + import xformers.ops as xops +elif ATTN == 'flash_attn': + import flash_attn +else: + raise ValueError(f"Unknown attention module: {ATTN}") + + +__all__ = [ + 'sparse_windowed_scaled_dot_product_self_attention', +] + + +def calc_window_partition( + tensor: SparseTensor, + window_size: Union[int, Tuple[int, ...]], + shift_window: Union[int, Tuple[int, ...]] = 0 +) -> Tuple[torch.Tensor, torch.Tensor, List[int], List[int]]: + """ + Calculate serialization and partitioning for a set of coordinates. + + Args: + tensor (SparseTensor): The input tensor. + window_size (int): The window size to use. + shift_window (Tuple[int, ...]): The shift of serialized coordinates. + + Returns: + (torch.Tensor): Forwards indices. + (torch.Tensor): Backwards indices. + (List[int]): Sequence lengths. + (List[int]): Sequence batch indices. + """ + DIM = tensor.coords.shape[1] - 1 + shift_window = (shift_window,) * DIM if isinstance(shift_window, int) else shift_window + window_size = (window_size,) * DIM if isinstance(window_size, int) else window_size + shifted_coords = tensor.coords.clone().detach() + shifted_coords[:, 1:] += torch.tensor(shift_window, device=tensor.device, dtype=torch.int32).unsqueeze(0) + + MAX_COORDS = shifted_coords[:, 1:].max(dim=0).values.tolist() + NUM_WINDOWS = [math.ceil((mc + 1) / ws) for mc, ws in zip(MAX_COORDS, window_size)] + OFFSET = torch.cumprod(torch.tensor([1] + NUM_WINDOWS[::-1]), dim=0).tolist()[::-1] + + shifted_coords[:, 1:] //= torch.tensor(window_size, device=tensor.device, dtype=torch.int32).unsqueeze(0) + shifted_indices = (shifted_coords * torch.tensor(OFFSET, device=tensor.device, dtype=torch.int32).unsqueeze(0)).sum(dim=1) + fwd_indices = torch.argsort(shifted_indices) + bwd_indices = torch.empty_like(fwd_indices) + bwd_indices[fwd_indices] = torch.arange(fwd_indices.shape[0], device=tensor.device) + seq_lens = torch.bincount(shifted_indices) + seq_batch_indices = torch.arange(seq_lens.shape[0], device=tensor.device, dtype=torch.int32) // OFFSET[0] + mask = seq_lens != 0 + seq_lens = seq_lens[mask].tolist() + seq_batch_indices = seq_batch_indices[mask].tolist() + + return fwd_indices, bwd_indices, seq_lens, seq_batch_indices + + +def sparse_windowed_scaled_dot_product_self_attention( + qkv: SparseTensor, + window_size: int, + shift_window: Tuple[int, int, int] = (0, 0, 0) +) -> SparseTensor: + """ + Apply windowed scaled dot product self attention to a sparse tensor. + + Args: + qkv (SparseTensor): [N, *, 3, H, C] sparse tensor containing Qs, Ks, and Vs. + window_size (int): The window size to use. + shift_window (Tuple[int, int, int]): The shift of serialized coordinates. + shift (int): The shift to use. + """ + assert len(qkv.shape) == 4 and qkv.shape[1] == 3, f"Invalid shape for qkv, got {qkv.shape}, expected [N, *, 3, H, C]" + + serialization_spatial_cache_name = f'window_partition_{window_size}_{shift_window}' + serialization_spatial_cache = qkv.get_spatial_cache(serialization_spatial_cache_name) + if serialization_spatial_cache is None: + fwd_indices, bwd_indices, seq_lens, seq_batch_indices = calc_window_partition(qkv, window_size, shift_window) + qkv.register_spatial_cache(serialization_spatial_cache_name, (fwd_indices, bwd_indices, seq_lens, seq_batch_indices)) + else: + fwd_indices, bwd_indices, seq_lens, seq_batch_indices = serialization_spatial_cache + + M = fwd_indices.shape[0] + T = qkv.feats.shape[0] + H = qkv.feats.shape[2] + C = qkv.feats.shape[3] + + qkv_feats = qkv.feats[fwd_indices] # [M, 3, H, C] + + if DEBUG: + start = 0 + qkv_coords = qkv.coords[fwd_indices] + for i in range(len(seq_lens)): + seq_coords = qkv_coords[start:start+seq_lens[i]] + assert (seq_coords[:, 0] == seq_batch_indices[i]).all(), f"SparseWindowedScaledDotProductSelfAttention: batch index mismatch" + assert (seq_coords[:, 1:].max(dim=0).values - seq_coords[:, 1:].min(dim=0).values < window_size).all(), \ + f"SparseWindowedScaledDotProductSelfAttention: window size exceeded" + start += seq_lens[i] + + if all([seq_len == window_size for seq_len in seq_lens]): + B = len(seq_lens) + N = window_size + qkv_feats = qkv_feats.reshape(B, N, 3, H, C) + if ATTN == 'xformers': + q, k, v = qkv_feats.unbind(dim=2) # [B, N, H, C] + out = xops.memory_efficient_attention(q, k, v) # [B, N, H, C] + elif ATTN == 'flash_attn': + out = flash_attn.flash_attn_qkvpacked_func(qkv_feats) # [B, N, H, C] + else: + raise ValueError(f"Unknown attention module: {ATTN}") + out = out.reshape(B * N, H, C) # [M, H, C] + else: + if ATTN == 'xformers': + q, k, v = qkv_feats.unbind(dim=1) # [M, H, C] + q = q.unsqueeze(0) # [1, M, H, C] + k = k.unsqueeze(0) # [1, M, H, C] + v = v.unsqueeze(0) # [1, M, H, C] + mask = xops.fmha.BlockDiagonalMask.from_seqlens(seq_lens) + out = xops.memory_efficient_attention(q, k, v, mask)[0] # [M, H, C] + elif ATTN == 'flash_attn': + cu_seqlens = torch.cat([torch.tensor([0]), torch.cumsum(torch.tensor(seq_lens), dim=0)], dim=0) \ + .to(qkv.device).int() + out = flash_attn.flash_attn_varlen_qkvpacked_func(qkv_feats, cu_seqlens, max(seq_lens)) # [M, H, C] + + out = out[bwd_indices] # [T, H, C] + + if DEBUG: + qkv_coords = qkv_coords[bwd_indices] + assert torch.equal(qkv_coords, qkv.coords), "SparseWindowedScaledDotProductSelfAttention: coordinate mismatch" + + return qkv.replace(out) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/basic.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/basic.py new file mode 100644 index 00000000..8837f440 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/basic.py @@ -0,0 +1,459 @@ +from typing import * +import torch +import torch.nn as nn +from . import BACKEND, DEBUG +SparseTensorData = None # Lazy import + + +__all__ = [ + 'SparseTensor', + 'sparse_batch_broadcast', + 'sparse_batch_op', + 'sparse_cat', + 'sparse_unbind', +] + + +class SparseTensor: + """ + Sparse tensor with support for both torchsparse and spconv backends. + + Parameters: + - feats (torch.Tensor): Features of the sparse tensor. + - coords (torch.Tensor): Coordinates of the sparse tensor. + - shape (torch.Size): Shape of the sparse tensor. + - layout (List[slice]): Layout of the sparse tensor for each batch + - data (SparseTensorData): Sparse tensor data used for convolusion + + NOTE: + - Data corresponding to a same batch should be contiguous. + - Coords should be in [0, 1023] + """ + @overload + def __init__(self, feats: torch.Tensor, coords: torch.Tensor, shape: Optional[torch.Size] = None, layout: Optional[List[slice]] = None, **kwargs): ... + + @overload + def __init__(self, data, shape: Optional[torch.Size] = None, layout: Optional[List[slice]] = None, **kwargs): ... + + def __init__(self, *args, **kwargs): + # Lazy import of sparse tensor backend + global SparseTensorData + if SparseTensorData is None: + import importlib + if BACKEND == 'torchsparse': + SparseTensorData = importlib.import_module('torchsparse').SparseTensor + elif BACKEND == 'spconv': + SparseTensorData = importlib.import_module('spconv.pytorch').SparseConvTensor + + method_id = 0 + if len(args) != 0: + method_id = 0 if isinstance(args[0], torch.Tensor) else 1 + else: + method_id = 1 if 'data' in kwargs else 0 + + if method_id == 0: + feats, coords, shape, layout = args + (None,) * (4 - len(args)) + if 'feats' in kwargs: + feats = kwargs['feats'] + del kwargs['feats'] + if 'coords' in kwargs: + coords = kwargs['coords'] + del kwargs['coords'] + if 'shape' in kwargs: + shape = kwargs['shape'] + del kwargs['shape'] + if 'layout' in kwargs: + layout = kwargs['layout'] + del kwargs['layout'] + + if shape is None: + shape = self.__cal_shape(feats, coords) + if layout is None: + layout = self.__cal_layout(coords, shape[0]) + if BACKEND == 'torchsparse': + self.data = SparseTensorData(feats, coords, **kwargs) + elif BACKEND == 'spconv': + spatial_shape = list(coords.max(0)[0] + 1)[1:] + self.data = SparseTensorData(feats.reshape(feats.shape[0], -1), coords, spatial_shape, shape[0], **kwargs) + self.data._features = feats + elif method_id == 1: + data, shape, layout = args + (None,) * (3 - len(args)) + if 'data' in kwargs: + data = kwargs['data'] + del kwargs['data'] + if 'shape' in kwargs: + shape = kwargs['shape'] + del kwargs['shape'] + if 'layout' in kwargs: + layout = kwargs['layout'] + del kwargs['layout'] + + self.data = data + if shape is None: + shape = self.__cal_shape(self.feats, self.coords) + if layout is None: + layout = self.__cal_layout(self.coords, shape[0]) + + self._shape = shape + self._layout = layout + self._scale = kwargs.get('scale', (1, 1, 1)) + self._spatial_cache = kwargs.get('spatial_cache', {}) + + if DEBUG: + try: + assert self.feats.shape[0] == self.coords.shape[0], f"Invalid feats shape: {self.feats.shape}, coords shape: {self.coords.shape}" + assert self.shape == self.__cal_shape(self.feats, self.coords), f"Invalid shape: {self.shape}" + assert self.layout == self.__cal_layout(self.coords, self.shape[0]), f"Invalid layout: {self.layout}" + for i in range(self.shape[0]): + assert torch.all(self.coords[self.layout[i], 0] == i), f"The data of batch {i} is not contiguous" + except Exception as e: + print('Debugging information:') + print(f"- Shape: {self.shape}") + print(f"- Layout: {self.layout}") + print(f"- Scale: {self._scale}") + print(f"- Coords: {self.coords}") + raise e + + def __cal_shape(self, feats, coords): + shape = [] + shape.append(coords[:, 0].max().item() + 1) + shape.extend([*feats.shape[1:]]) + return torch.Size(shape) + + def __cal_layout(self, coords, batch_size): + seq_len = torch.bincount(coords[:, 0], minlength=batch_size) + offset = torch.cumsum(seq_len, dim=0) + layout = [slice((offset[i] - seq_len[i]).item(), offset[i].item()) for i in range(batch_size)] + return layout + + @property + def shape(self) -> torch.Size: + return self._shape + + def dim(self) -> int: + return len(self.shape) + + @property + def layout(self) -> List[slice]: + return self._layout + + @property + def feats(self) -> torch.Tensor: + if BACKEND == 'torchsparse': + return self.data.F + elif BACKEND == 'spconv': + return self.data.features + + @feats.setter + def feats(self, value: torch.Tensor): + if BACKEND == 'torchsparse': + self.data.F = value + elif BACKEND == 'spconv': + self.data.features = value + + @property + def coords(self) -> torch.Tensor: + if BACKEND == 'torchsparse': + return self.data.C + elif BACKEND == 'spconv': + return self.data.indices + + @coords.setter + def coords(self, value: torch.Tensor): + if BACKEND == 'torchsparse': + self.data.C = value + elif BACKEND == 'spconv': + self.data.indices = value + + @property + def dtype(self): + return self.feats.dtype + + @property + def device(self): + return self.feats.device + + @overload + def to(self, dtype: torch.dtype) -> 'SparseTensor': ... + + @overload + def to(self, device: Optional[Union[str, torch.device]] = None, dtype: Optional[torch.dtype] = None) -> 'SparseTensor': ... + + def to(self, *args, **kwargs) -> 'SparseTensor': + device = None + dtype = None + if len(args) == 2: + device, dtype = args + elif len(args) == 1: + if isinstance(args[0], torch.dtype): + dtype = args[0] + else: + device = args[0] + if 'dtype' in kwargs: + assert dtype is None, "to() received multiple values for argument 'dtype'" + dtype = kwargs['dtype'] + if 'device' in kwargs: + assert device is None, "to() received multiple values for argument 'device'" + device = kwargs['device'] + + new_feats = self.feats.to(device=device, dtype=dtype) + new_coords = self.coords.to(device=device) + return self.replace(new_feats, new_coords) + + def type(self, dtype): + new_feats = self.feats.type(dtype) + return self.replace(new_feats) + + def cpu(self) -> 'SparseTensor': + new_feats = self.feats.cpu() + new_coords = self.coords.cpu() + return self.replace(new_feats, new_coords) + + def cuda(self) -> 'SparseTensor': + new_feats = self.feats.cuda() + new_coords = self.coords.cuda() + return self.replace(new_feats, new_coords) + + def half(self) -> 'SparseTensor': + new_feats = self.feats.half() + return self.replace(new_feats) + + def float(self) -> 'SparseTensor': + new_feats = self.feats.float() + return self.replace(new_feats) + + def detach(self) -> 'SparseTensor': + new_coords = self.coords.detach() + new_feats = self.feats.detach() + return self.replace(new_feats, new_coords) + + def dense(self) -> torch.Tensor: + if BACKEND == 'torchsparse': + return self.data.dense() + elif BACKEND == 'spconv': + return self.data.dense() + + def reshape(self, *shape) -> 'SparseTensor': + new_feats = self.feats.reshape(self.feats.shape[0], *shape) + return self.replace(new_feats) + + def unbind(self, dim: int) -> List['SparseTensor']: + return sparse_unbind(self, dim) + + def replace(self, feats: torch.Tensor, coords: Optional[torch.Tensor] = None) -> 'SparseTensor': + new_shape = [self.shape[0]] + new_shape.extend(feats.shape[1:]) + if BACKEND == 'torchsparse': + new_data = SparseTensorData( + feats=feats, + coords=self.data.coords if coords is None else coords, + stride=self.data.stride, + spatial_range=self.data.spatial_range, + ) + new_data._caches = self.data._caches + elif BACKEND == 'spconv': + new_data = SparseTensorData( + self.data.features.reshape(self.data.features.shape[0], -1), + self.data.indices, + self.data.spatial_shape, + self.data.batch_size, + self.data.grid, + self.data.voxel_num, + self.data.indice_dict + ) + new_data._features = feats + new_data.benchmark = self.data.benchmark + new_data.benchmark_record = self.data.benchmark_record + new_data.thrust_allocator = self.data.thrust_allocator + new_data._timer = self.data._timer + new_data.force_algo = self.data.force_algo + new_data.int8_scale = self.data.int8_scale + if coords is not None: + new_data.indices = coords + new_tensor = SparseTensor(new_data, shape=torch.Size(new_shape), layout=self.layout, scale=self._scale, spatial_cache=self._spatial_cache) + return new_tensor + + @staticmethod + def full(aabb, dim, value, dtype=torch.float32, device=None) -> 'SparseTensor': + N, C = dim + x = torch.arange(aabb[0], aabb[3] + 1) + y = torch.arange(aabb[1], aabb[4] + 1) + z = torch.arange(aabb[2], aabb[5] + 1) + coords = torch.stack(torch.meshgrid(x, y, z, indexing='ij'), dim=-1).reshape(-1, 3) + coords = torch.cat([ + torch.arange(N).view(-1, 1).repeat(1, coords.shape[0]).view(-1, 1), + coords.repeat(N, 1), + ], dim=1).to(dtype=torch.int32, device=device) + feats = torch.full((coords.shape[0], C), value, dtype=dtype, device=device) + return SparseTensor(feats=feats, coords=coords) + + def __merge_sparse_cache(self, other: 'SparseTensor') -> dict: + new_cache = {} + for k in set(list(self._spatial_cache.keys()) + list(other._spatial_cache.keys())): + if k in self._spatial_cache: + new_cache[k] = self._spatial_cache[k] + if k in other._spatial_cache: + if k not in new_cache: + new_cache[k] = other._spatial_cache[k] + else: + new_cache[k].update(other._spatial_cache[k]) + return new_cache + + def __neg__(self) -> 'SparseTensor': + return self.replace(-self.feats) + + def __elemwise__(self, other: Union[torch.Tensor, 'SparseTensor'], op: callable) -> 'SparseTensor': + if isinstance(other, torch.Tensor): + try: + other = torch.broadcast_to(other, self.shape) + other = sparse_batch_broadcast(self, other) + except: + pass + if isinstance(other, SparseTensor): + other = other.feats + new_feats = op(self.feats, other) + new_tensor = self.replace(new_feats) + if isinstance(other, SparseTensor): + new_tensor._spatial_cache = self.__merge_sparse_cache(other) + return new_tensor + + def __add__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor': + return self.__elemwise__(other, torch.add) + + def __radd__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor': + return self.__elemwise__(other, torch.add) + + def __sub__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor': + return self.__elemwise__(other, torch.sub) + + def __rsub__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor': + return self.__elemwise__(other, lambda x, y: torch.sub(y, x)) + + def __mul__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor': + return self.__elemwise__(other, torch.mul) + + def __rmul__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor': + return self.__elemwise__(other, torch.mul) + + def __truediv__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor': + return self.__elemwise__(other, torch.div) + + def __rtruediv__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor': + return self.__elemwise__(other, lambda x, y: torch.div(y, x)) + + def __getitem__(self, idx): + if isinstance(idx, int): + idx = [idx] + elif isinstance(idx, slice): + idx = range(*idx.indices(self.shape[0])) + elif isinstance(idx, torch.Tensor): + if idx.dtype == torch.bool: + assert idx.shape == (self.shape[0],), f"Invalid index shape: {idx.shape}" + idx = idx.nonzero().squeeze(1) + elif idx.dtype in [torch.int32, torch.int64]: + assert len(idx.shape) == 1, f"Invalid index shape: {idx.shape}" + else: + raise ValueError(f"Unknown index type: {idx.dtype}") + else: + raise ValueError(f"Unknown index type: {type(idx)}") + + coords = [] + feats = [] + for new_idx, old_idx in enumerate(idx): + coords.append(self.coords[self.layout[old_idx]].clone()) + coords[-1][:, 0] = new_idx + feats.append(self.feats[self.layout[old_idx]]) + coords = torch.cat(coords, dim=0).contiguous() + feats = torch.cat(feats, dim=0).contiguous() + return SparseTensor(feats=feats, coords=coords) + + def register_spatial_cache(self, key, value) -> None: + """ + Register a spatial cache. + The spatial cache can be any thing you want to cache. + The registery and retrieval of the cache is based on current scale. + """ + scale_key = str(self._scale) + if scale_key not in self._spatial_cache: + self._spatial_cache[scale_key] = {} + self._spatial_cache[scale_key][key] = value + + def get_spatial_cache(self, key=None): + """ + Get a spatial cache. + """ + scale_key = str(self._scale) + cur_scale_cache = self._spatial_cache.get(scale_key, {}) + if key is None: + return cur_scale_cache + return cur_scale_cache.get(key, None) + + +def sparse_batch_broadcast(input: SparseTensor, other: torch.Tensor) -> torch.Tensor: + """ + Broadcast a 1D tensor to a sparse tensor along the batch dimension then perform an operation. + + Args: + input (torch.Tensor): 1D tensor to broadcast. + target (SparseTensor): Sparse tensor to broadcast to. + op (callable): Operation to perform after broadcasting. Defaults to torch.add. + """ + coords, feats = input.coords, input.feats + broadcasted = torch.zeros_like(feats) + for k in range(input.shape[0]): + broadcasted[input.layout[k]] = other[k] + return broadcasted + + +def sparse_batch_op(input: SparseTensor, other: torch.Tensor, op: callable = torch.add) -> SparseTensor: + """ + Broadcast a 1D tensor to a sparse tensor along the batch dimension then perform an operation. + + Args: + input (torch.Tensor): 1D tensor to broadcast. + target (SparseTensor): Sparse tensor to broadcast to. + op (callable): Operation to perform after broadcasting. Defaults to torch.add. + """ + return input.replace(op(input.feats, sparse_batch_broadcast(input, other))) + + +def sparse_cat(inputs: List[SparseTensor], dim: int = 0) -> SparseTensor: + """ + Concatenate a list of sparse tensors. + + Args: + inputs (List[SparseTensor]): List of sparse tensors to concatenate. + """ + if dim == 0: + start = 0 + coords = [] + for input in inputs: + coords.append(input.coords.clone()) + coords[-1][:, 0] += start + start += input.shape[0] + coords = torch.cat(coords, dim=0) + feats = torch.cat([input.feats for input in inputs], dim=0) + output = SparseTensor( + coords=coords, + feats=feats, + ) + else: + feats = torch.cat([input.feats for input in inputs], dim=dim) + output = inputs[0].replace(feats) + + return output + + +def sparse_unbind(input: SparseTensor, dim: int) -> List[SparseTensor]: + """ + Unbind a sparse tensor along a dimension. + + Args: + input (SparseTensor): Sparse tensor to unbind. + dim (int): Dimension to unbind. + """ + if dim == 0: + return [input[i] for i in range(input.shape[0])] + else: + feats = input.feats.unbind(dim) + return [input.replace(f) for f in feats] diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/conv/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/conv/__init__.py new file mode 100644 index 00000000..340a8712 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/conv/__init__.py @@ -0,0 +1,21 @@ +from .. import BACKEND + + +SPCONV_ALGO = 'auto' # 'auto', 'implicit_gemm', 'native' + +def __from_env(): + import os + + global SPCONV_ALGO + env_spconv_algo = os.environ.get('SPCONV_ALGO') + if env_spconv_algo is not None and env_spconv_algo in ['auto', 'implicit_gemm', 'native']: + SPCONV_ALGO = env_spconv_algo + print(f"[SPARSE][CONV] spconv algo: {SPCONV_ALGO}") + + +__from_env() + +if BACKEND == 'torchsparse': + from .conv_torchsparse import * +elif BACKEND == 'spconv': + from .conv_spconv import * diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/conv/conv_spconv.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/conv/conv_spconv.py new file mode 100644 index 00000000..524bcd4a --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/conv/conv_spconv.py @@ -0,0 +1,80 @@ +import torch +import torch.nn as nn +from .. import SparseTensor +from .. import DEBUG +from . import SPCONV_ALGO + +class SparseConv3d(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, padding=None, bias=True, indice_key=None): + super(SparseConv3d, self).__init__() + if 'spconv' not in globals(): + import spconv.pytorch as spconv + algo = None + if SPCONV_ALGO == 'native': + algo = spconv.ConvAlgo.Native + elif SPCONV_ALGO == 'implicit_gemm': + algo = spconv.ConvAlgo.MaskImplicitGemm + if stride == 1 and (padding is None): + self.conv = spconv.SubMConv3d(in_channels, out_channels, kernel_size, dilation=dilation, bias=bias, indice_key=indice_key, algo=algo) + else: + self.conv = spconv.SparseConv3d(in_channels, out_channels, kernel_size, stride=stride, dilation=dilation, padding=padding, bias=bias, indice_key=indice_key, algo=algo) + self.stride = tuple(stride) if isinstance(stride, (list, tuple)) else (stride, stride, stride) + self.padding = padding + + def forward(self, x: SparseTensor) -> SparseTensor: + spatial_changed = any(s != 1 for s in self.stride) or (self.padding is not None) + new_data = self.conv(x.data) + new_shape = [x.shape[0], self.conv.out_channels] + new_layout = None if spatial_changed else x.layout + + if spatial_changed and (x.shape[0] != 1): + # spconv was non-1 stride will break the contiguous of the output tensor, sort by the coords + fwd = new_data.indices[:, 0].argsort() + bwd = torch.zeros_like(fwd).scatter_(0, fwd, torch.arange(fwd.shape[0], device=fwd.device)) + sorted_feats = new_data.features[fwd] + sorted_coords = new_data.indices[fwd] + unsorted_data = new_data + new_data = spconv.SparseConvTensor(sorted_feats, sorted_coords, unsorted_data.spatial_shape, unsorted_data.batch_size) # type: ignore + + out = SparseTensor( + new_data, shape=torch.Size(new_shape), layout=new_layout, + scale=tuple([s * stride for s, stride in zip(x._scale, self.stride)]), + spatial_cache=x._spatial_cache, + ) + + if spatial_changed and (x.shape[0] != 1): + out.register_spatial_cache(f'conv_{self.stride}_unsorted_data', unsorted_data) + out.register_spatial_cache(f'conv_{self.stride}_sort_bwd', bwd) + + return out + + +class SparseInverseConv3d(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, bias=True, indice_key=None): + super(SparseInverseConv3d, self).__init__() + if 'spconv' not in globals(): + import spconv.pytorch as spconv + self.conv = spconv.SparseInverseConv3d(in_channels, out_channels, kernel_size, bias=bias, indice_key=indice_key) + self.stride = tuple(stride) if isinstance(stride, (list, tuple)) else (stride, stride, stride) + + def forward(self, x: SparseTensor) -> SparseTensor: + spatial_changed = any(s != 1 for s in self.stride) + if spatial_changed: + # recover the original spconv order + data = x.get_spatial_cache(f'conv_{self.stride}_unsorted_data') + bwd = x.get_spatial_cache(f'conv_{self.stride}_sort_bwd') + data = data.replace_feature(x.feats[bwd]) + if DEBUG: + assert torch.equal(data.indices, x.coords[bwd]), 'Recover the original order failed' + else: + data = x.data + + new_data = self.conv(data) + new_shape = [x.shape[0], self.conv.out_channels] + new_layout = None if spatial_changed else x.layout + out = SparseTensor( + new_data, shape=torch.Size(new_shape), layout=new_layout, + scale=tuple([s // stride for s, stride in zip(x._scale, self.stride)]), + spatial_cache=x._spatial_cache, + ) + return out diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/conv/conv_torchsparse.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/conv/conv_torchsparse.py new file mode 100644 index 00000000..1d612582 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/conv/conv_torchsparse.py @@ -0,0 +1,38 @@ +import torch +import torch.nn as nn +from .. import SparseTensor + + +class SparseConv3d(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, bias=True, indice_key=None): + super(SparseConv3d, self).__init__() + if 'torchsparse' not in globals(): + import torchsparse + self.conv = torchsparse.nn.Conv3d(in_channels, out_channels, kernel_size, stride, 0, dilation, bias) + + def forward(self, x: SparseTensor) -> SparseTensor: + out = self.conv(x.data) + new_shape = [x.shape[0], self.conv.out_channels] + out = SparseTensor(out, shape=torch.Size(new_shape), layout=x.layout if all(s == 1 for s in self.conv.stride) else None) + out._spatial_cache = x._spatial_cache + out._scale = tuple([s * stride for s, stride in zip(x._scale, self.conv.stride)]) + return out + + +class SparseInverseConv3d(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, bias=True, indice_key=None): + super(SparseInverseConv3d, self).__init__() + if 'torchsparse' not in globals(): + import torchsparse + self.conv = torchsparse.nn.Conv3d(in_channels, out_channels, kernel_size, stride, 0, dilation, bias, transposed=True) + + def forward(self, x: SparseTensor) -> SparseTensor: + out = self.conv(x.data) + new_shape = [x.shape[0], self.conv.out_channels] + out = SparseTensor(out, shape=torch.Size(new_shape), layout=x.layout if all(s == 1 for s in self.conv.stride) else None) + out._spatial_cache = x._spatial_cache + out._scale = tuple([s // stride for s, stride in zip(x._scale, self.conv.stride)]) + return out + + + diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/linear.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/linear.py new file mode 100644 index 00000000..a854e77c --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/linear.py @@ -0,0 +1,15 @@ +import torch +import torch.nn as nn +from . import SparseTensor + +__all__ = [ + 'SparseLinear' +] + + +class SparseLinear(nn.Linear): + def __init__(self, in_features, out_features, bias=True): + super(SparseLinear, self).__init__(in_features, out_features, bias) + + def forward(self, input: SparseTensor) -> SparseTensor: + return input.replace(super().forward(input.feats)) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/nonlinearity.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/nonlinearity.py new file mode 100644 index 00000000..f200098d --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/nonlinearity.py @@ -0,0 +1,35 @@ +import torch +import torch.nn as nn +from . import SparseTensor + +__all__ = [ + 'SparseReLU', + 'SparseSiLU', + 'SparseGELU', + 'SparseActivation' +] + + +class SparseReLU(nn.ReLU): + def forward(self, input: SparseTensor) -> SparseTensor: + return input.replace(super().forward(input.feats)) + + +class SparseSiLU(nn.SiLU): + def forward(self, input: SparseTensor) -> SparseTensor: + return input.replace(super().forward(input.feats)) + + +class SparseGELU(nn.GELU): + def forward(self, input: SparseTensor) -> SparseTensor: + return input.replace(super().forward(input.feats)) + + +class SparseActivation(nn.Module): + def __init__(self, activation: nn.Module): + super().__init__() + self.activation = activation + + def forward(self, input: SparseTensor) -> SparseTensor: + return input.replace(self.activation(input.feats)) + diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/norm.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/norm.py new file mode 100644 index 00000000..6b38a366 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/norm.py @@ -0,0 +1,58 @@ +import torch +import torch.nn as nn +from . import SparseTensor +from . import DEBUG + +__all__ = [ + 'SparseGroupNorm', + 'SparseLayerNorm', + 'SparseGroupNorm32', + 'SparseLayerNorm32', +] + + +class SparseGroupNorm(nn.GroupNorm): + def __init__(self, num_groups, num_channels, eps=1e-5, affine=True): + super(SparseGroupNorm, self).__init__(num_groups, num_channels, eps, affine) + + def forward(self, input: SparseTensor) -> SparseTensor: + nfeats = torch.zeros_like(input.feats) + for k in range(input.shape[0]): + if DEBUG: + assert (input.coords[input.layout[k], 0] == k).all(), f"SparseGroupNorm: batch index mismatch" + bfeats = input.feats[input.layout[k]] + bfeats = bfeats.permute(1, 0).reshape(1, input.shape[1], -1) + bfeats = super().forward(bfeats) + bfeats = bfeats.reshape(input.shape[1], -1).permute(1, 0) + nfeats[input.layout[k]] = bfeats + return input.replace(nfeats) + + +class SparseLayerNorm(nn.LayerNorm): + def __init__(self, normalized_shape, eps=1e-5, elementwise_affine=True): + super(SparseLayerNorm, self).__init__(normalized_shape, eps, elementwise_affine) + + def forward(self, input: SparseTensor) -> SparseTensor: + nfeats = torch.zeros_like(input.feats) + for k in range(input.shape[0]): + bfeats = input.feats[input.layout[k]] + bfeats = bfeats.permute(1, 0).reshape(1, input.shape[1], -1) + bfeats = super().forward(bfeats) + bfeats = bfeats.reshape(input.shape[1], -1).permute(1, 0) + nfeats[input.layout[k]] = bfeats + return input.replace(nfeats) + + +class SparseGroupNorm32(SparseGroupNorm): + """ + A GroupNorm layer that converts to float32 before the forward pass. + """ + def forward(self, x: SparseTensor) -> SparseTensor: + return super().forward(x.float()).type(x.dtype) + +class SparseLayerNorm32(SparseLayerNorm): + """ + A LayerNorm layer that converts to float32 before the forward pass. + """ + def forward(self, x: SparseTensor) -> SparseTensor: + return super().forward(x.float()).type(x.dtype) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/spatial.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/spatial.py new file mode 100644 index 00000000..ad712147 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/spatial.py @@ -0,0 +1,110 @@ +from typing import * +import torch +import torch.nn as nn +from . import SparseTensor + +__all__ = [ + 'SparseDownsample', + 'SparseUpsample', + 'SparseSubdivide' +] + + +class SparseDownsample(nn.Module): + """ + Downsample a sparse tensor by a factor of `factor`. + Implemented as average pooling. + """ + def __init__(self, factor: Union[int, Tuple[int, ...], List[int]]): + super(SparseDownsample, self).__init__() + self.factor = tuple(factor) if isinstance(factor, (list, tuple)) else factor + + def forward(self, input: SparseTensor) -> SparseTensor: + DIM = input.coords.shape[-1] - 1 + factor = self.factor if isinstance(self.factor, tuple) else (self.factor,) * DIM + assert DIM == len(factor), 'Input coordinates must have the same dimension as the downsample factor.' + + coord = list(input.coords.unbind(dim=-1)) + for i, f in enumerate(factor): + coord[i+1] = coord[i+1] // f + + MAX = [coord[i+1].max().item() + 1 for i in range(DIM)] + OFFSET = torch.cumprod(torch.tensor(MAX[::-1]), 0).tolist()[::-1] + [1] + code = sum([c * o for c, o in zip(coord, OFFSET)]) + code, idx = code.unique(return_inverse=True) + + new_feats = torch.scatter_reduce( + torch.zeros(code.shape[0], input.feats.shape[1], device=input.feats.device, dtype=input.feats.dtype), + dim=0, + index=idx.unsqueeze(1).expand(-1, input.feats.shape[1]), + src=input.feats, + reduce='mean' + ) + new_coords = torch.stack( + [code // OFFSET[0]] + + [(code // OFFSET[i+1]) % MAX[i] for i in range(DIM)], + dim=-1 + ) + out = SparseTensor(new_feats, new_coords, input.shape,) + out._scale = tuple([s // f for s, f in zip(input._scale, factor)]) + out._spatial_cache = input._spatial_cache + + out.register_spatial_cache(f'upsample_{factor}_coords', input.coords) + out.register_spatial_cache(f'upsample_{factor}_layout', input.layout) + out.register_spatial_cache(f'upsample_{factor}_idx', idx) + + return out + + +class SparseUpsample(nn.Module): + """ + Upsample a sparse tensor by a factor of `factor`. + Implemented as nearest neighbor interpolation. + """ + def __init__(self, factor: Union[int, Tuple[int, int, int], List[int]]): + super(SparseUpsample, self).__init__() + self.factor = tuple(factor) if isinstance(factor, (list, tuple)) else factor + + def forward(self, input: SparseTensor) -> SparseTensor: + DIM = input.coords.shape[-1] - 1 + factor = self.factor if isinstance(self.factor, tuple) else (self.factor,) * DIM + assert DIM == len(factor), 'Input coordinates must have the same dimension as the upsample factor.' + + new_coords = input.get_spatial_cache(f'upsample_{factor}_coords') + new_layout = input.get_spatial_cache(f'upsample_{factor}_layout') + idx = input.get_spatial_cache(f'upsample_{factor}_idx') + if any([x is None for x in [new_coords, new_layout, idx]]): + raise ValueError('Upsample cache not found. SparseUpsample must be paired with SparseDownsample.') + new_feats = input.feats[idx] + out = SparseTensor(new_feats, new_coords, input.shape, new_layout) + out._scale = tuple([s * f for s, f in zip(input._scale, factor)]) + out._spatial_cache = input._spatial_cache + return out + +class SparseSubdivide(nn.Module): + """ + Upsample a sparse tensor by a factor of `factor`. + Implemented as nearest neighbor interpolation. + """ + def __init__(self): + super(SparseSubdivide, self).__init__() + + def forward(self, input: SparseTensor) -> SparseTensor: + DIM = input.coords.shape[-1] - 1 + # upsample scale=2^DIM + n_cube = torch.ones([2] * DIM, device=input.device, dtype=torch.int) + n_coords = torch.nonzero(n_cube) + n_coords = torch.cat([torch.zeros_like(n_coords[:, :1]), n_coords], dim=-1) + factor = n_coords.shape[0] + assert factor == 2 ** DIM + # print(n_coords.shape) + new_coords = input.coords.clone() + new_coords[:, 1:] *= 2 + new_coords = new_coords.unsqueeze(1) + n_coords.unsqueeze(0).to(new_coords.dtype) + + new_feats = input.feats.unsqueeze(1).expand(input.feats.shape[0], factor, *input.feats.shape[1:]) + out = SparseTensor(new_feats.flatten(0, 1), new_coords.flatten(0, 1), input.shape) + out._scale = input._scale * 2 + out._spatial_cache = input._spatial_cache + return out + diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/transformer/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/transformer/__init__.py new file mode 100644 index 00000000..b08b0d4e --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/transformer/__init__.py @@ -0,0 +1,2 @@ +from .blocks import * +from .modulated import * \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/transformer/blocks.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/transformer/blocks.py new file mode 100644 index 00000000..9d037a49 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/transformer/blocks.py @@ -0,0 +1,151 @@ +from typing import * +import torch +import torch.nn as nn +from ..basic import SparseTensor +from ..linear import SparseLinear +from ..nonlinearity import SparseGELU +from ..attention import SparseMultiHeadAttention, SerializeMode +from ...norm import LayerNorm32 + + +class SparseFeedForwardNet(nn.Module): + def __init__(self, channels: int, mlp_ratio: float = 4.0): + super().__init__() + self.mlp = nn.Sequential( + SparseLinear(channels, int(channels * mlp_ratio)), + SparseGELU(approximate="tanh"), + SparseLinear(int(channels * mlp_ratio), channels), + ) + + def forward(self, x: SparseTensor) -> SparseTensor: + return self.mlp(x) + + +class SparseTransformerBlock(nn.Module): + """ + Sparse Transformer block (MSA + FFN). + """ + def __init__( + self, + channels: int, + num_heads: int, + mlp_ratio: float = 4.0, + attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "full", + window_size: Optional[int] = None, + shift_sequence: Optional[int] = None, + shift_window: Optional[Tuple[int, int, int]] = None, + serialize_mode: Optional[SerializeMode] = None, + use_checkpoint: bool = False, + use_rope: bool = False, + qk_rms_norm: bool = False, + qkv_bias: bool = True, + ln_affine: bool = False, + ): + super().__init__() + self.use_checkpoint = use_checkpoint + self.norm1 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6) + self.norm2 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6) + self.attn = SparseMultiHeadAttention( + channels, + num_heads=num_heads, + attn_mode=attn_mode, + window_size=window_size, + shift_sequence=shift_sequence, + shift_window=shift_window, + serialize_mode=serialize_mode, + qkv_bias=qkv_bias, + use_rope=use_rope, + qk_rms_norm=qk_rms_norm, + ) + self.mlp = SparseFeedForwardNet( + channels, + mlp_ratio=mlp_ratio, + ) + + def _forward(self, x: SparseTensor) -> SparseTensor: + h = x.replace(self.norm1(x.feats)) + h = self.attn(h) + x = x + h + h = x.replace(self.norm2(x.feats)) + h = self.mlp(h) + x = x + h + return x + + def forward(self, x: SparseTensor) -> SparseTensor: + if self.use_checkpoint: + return torch.utils.checkpoint.checkpoint(self._forward, x, use_reentrant=False) + else: + return self._forward(x) + + +class SparseTransformerCrossBlock(nn.Module): + """ + Sparse Transformer cross-attention block (MSA + MCA + FFN). + """ + def __init__( + self, + channels: int, + ctx_channels: int, + num_heads: int, + mlp_ratio: float = 4.0, + attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "full", + window_size: Optional[int] = None, + shift_sequence: Optional[int] = None, + shift_window: Optional[Tuple[int, int, int]] = None, + serialize_mode: Optional[SerializeMode] = None, + use_checkpoint: bool = False, + use_rope: bool = False, + qk_rms_norm: bool = False, + qk_rms_norm_cross: bool = False, + qkv_bias: bool = True, + ln_affine: bool = False, + ): + super().__init__() + self.use_checkpoint = use_checkpoint + self.norm1 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6) + self.norm2 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6) + self.norm3 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6) + self.self_attn = SparseMultiHeadAttention( + channels, + num_heads=num_heads, + type="self", + attn_mode=attn_mode, + window_size=window_size, + shift_sequence=shift_sequence, + shift_window=shift_window, + serialize_mode=serialize_mode, + qkv_bias=qkv_bias, + use_rope=use_rope, + qk_rms_norm=qk_rms_norm, + ) + self.cross_attn = SparseMultiHeadAttention( + channels, + ctx_channels=ctx_channels, + num_heads=num_heads, + type="cross", + attn_mode="full", + qkv_bias=qkv_bias, + qk_rms_norm=qk_rms_norm_cross, + ) + self.mlp = SparseFeedForwardNet( + channels, + mlp_ratio=mlp_ratio, + ) + + def _forward(self, x: SparseTensor, mod: torch.Tensor, context: torch.Tensor): + h = x.replace(self.norm1(x.feats)) + h = self.self_attn(h) + x = x + h + h = x.replace(self.norm2(x.feats)) + h = self.cross_attn(h, context) + x = x + h + h = x.replace(self.norm3(x.feats)) + h = self.mlp(h) + x = x + h + return x + + def forward(self, x: SparseTensor, context: torch.Tensor): + if self.use_checkpoint: + return torch.utils.checkpoint.checkpoint(self._forward, x, context, use_reentrant=False) + else: + return self._forward(x, context) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/transformer/modulated.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/transformer/modulated.py new file mode 100644 index 00000000..4a841655 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/transformer/modulated.py @@ -0,0 +1,166 @@ +from typing import * +import torch +import torch.nn as nn +from ..basic import SparseTensor +from ..attention import SparseMultiHeadAttention, SerializeMode +from ...norm import LayerNorm32 +from .blocks import SparseFeedForwardNet + + +class ModulatedSparseTransformerBlock(nn.Module): + """ + Sparse Transformer block (MSA + FFN) with adaptive layer norm conditioning. + """ + def __init__( + self, + channels: int, + num_heads: int, + mlp_ratio: float = 4.0, + attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "full", + window_size: Optional[int] = None, + shift_sequence: Optional[int] = None, + shift_window: Optional[Tuple[int, int, int]] = None, + serialize_mode: Optional[SerializeMode] = None, + use_checkpoint: bool = False, + use_rope: bool = False, + qk_rms_norm: bool = False, + qkv_bias: bool = True, + share_mod: bool = False, + ): + super().__init__() + self.use_checkpoint = use_checkpoint + self.share_mod = share_mod + self.norm1 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6) + self.norm2 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6) + self.attn = SparseMultiHeadAttention( + channels, + num_heads=num_heads, + attn_mode=attn_mode, + window_size=window_size, + shift_sequence=shift_sequence, + shift_window=shift_window, + serialize_mode=serialize_mode, + qkv_bias=qkv_bias, + use_rope=use_rope, + qk_rms_norm=qk_rms_norm, + ) + self.mlp = SparseFeedForwardNet( + channels, + mlp_ratio=mlp_ratio, + ) + if not share_mod: + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(channels, 6 * channels, bias=True) + ) + + def _forward(self, x: SparseTensor, mod: torch.Tensor) -> SparseTensor: + if self.share_mod: + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = mod.chunk(6, dim=1) + else: + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(mod).chunk(6, dim=1) + h = x.replace(self.norm1(x.feats)) + h = h * (1 + scale_msa) + shift_msa + h = self.attn(h) + h = h * gate_msa + x = x + h + h = x.replace(self.norm2(x.feats)) + h = h * (1 + scale_mlp) + shift_mlp + h = self.mlp(h) + h = h * gate_mlp + x = x + h + return x + + def forward(self, x: SparseTensor, mod: torch.Tensor) -> SparseTensor: + if self.use_checkpoint: + return torch.utils.checkpoint.checkpoint(self._forward, x, mod, use_reentrant=False) + else: + return self._forward(x, mod) + + +class ModulatedSparseTransformerCrossBlock(nn.Module): + """ + Sparse Transformer cross-attention block (MSA + MCA + FFN) with adaptive layer norm conditioning. + """ + def __init__( + self, + channels: int, + ctx_channels: int, + num_heads: int, + mlp_ratio: float = 4.0, + attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "full", + window_size: Optional[int] = None, + shift_sequence: Optional[int] = None, + shift_window: Optional[Tuple[int, int, int]] = None, + serialize_mode: Optional[SerializeMode] = None, + use_checkpoint: bool = False, + use_rope: bool = False, + qk_rms_norm: bool = False, + qk_rms_norm_cross: bool = False, + qkv_bias: bool = True, + share_mod: bool = False, + + ): + super().__init__() + self.use_checkpoint = use_checkpoint + self.share_mod = share_mod + self.norm1 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6) + self.norm2 = LayerNorm32(channels, elementwise_affine=True, eps=1e-6) + self.norm3 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6) + self.self_attn = SparseMultiHeadAttention( + channels, + num_heads=num_heads, + type="self", + attn_mode=attn_mode, + window_size=window_size, + shift_sequence=shift_sequence, + shift_window=shift_window, + serialize_mode=serialize_mode, + qkv_bias=qkv_bias, + use_rope=use_rope, + qk_rms_norm=qk_rms_norm, + ) + self.cross_attn = SparseMultiHeadAttention( + channels, + ctx_channels=ctx_channels, + num_heads=num_heads, + type="cross", + attn_mode="full", + qkv_bias=qkv_bias, + qk_rms_norm=qk_rms_norm_cross, + ) + self.mlp = SparseFeedForwardNet( + channels, + mlp_ratio=mlp_ratio, + ) + if not share_mod: + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(channels, 6 * channels, bias=True) + ) + + def _forward(self, x: SparseTensor, mod: torch.Tensor, context: torch.Tensor) -> SparseTensor: + if self.share_mod: + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = mod.chunk(6, dim=1) + else: + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(mod).chunk(6, dim=1) + h = x.replace(self.norm1(x.feats)) + h = h * (1 + scale_msa) + shift_msa + h = self.self_attn(h) + h = h * gate_msa + x = x + h + h = x.replace(self.norm2(x.feats)) + h = self.cross_attn(h, context) + x = x + h + h = x.replace(self.norm3(x.feats)) + h = h * (1 + scale_mlp) + shift_mlp + h = self.mlp(h) + h = h * gate_mlp + x = x + h + return x + + def forward(self, x: SparseTensor, mod: torch.Tensor, context: torch.Tensor) -> SparseTensor: + if self.use_checkpoint: + return torch.utils.checkpoint.checkpoint(self._forward, x, mod, context, use_reentrant=False) + else: + return self._forward(x, mod, context) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/spatial.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/spatial.py new file mode 100644 index 00000000..79e268d3 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/modules/spatial.py @@ -0,0 +1,48 @@ +import torch + + +def pixel_shuffle_3d(x: torch.Tensor, scale_factor: int) -> torch.Tensor: + """ + 3D pixel shuffle. + """ + B, C, H, W, D = x.shape + C_ = C // scale_factor**3 + x = x.reshape(B, C_, scale_factor, scale_factor, scale_factor, H, W, D) + x = x.permute(0, 1, 5, 2, 6, 3, 7, 4) + x = x.reshape(B, C_, H*scale_factor, W*scale_factor, D*scale_factor) + return x + + +def patchify(x: torch.Tensor, patch_size: int): + """ + Patchify a tensor. + + Args: + x (torch.Tensor): (N, C, *spatial) tensor + patch_size (int): Patch size + """ + DIM = x.dim() - 2 + for d in range(2, DIM + 2): + assert x.shape[d] % patch_size == 0, f"Dimension {d} of input tensor must be divisible by patch size, got {x.shape[d]} and {patch_size}" + + x = x.reshape(*x.shape[:2], *sum([[x.shape[d] // patch_size, patch_size] for d in range(2, DIM + 2)], [])) + x = x.permute(0, 1, *([2 * i + 3 for i in range(DIM)] + [2 * i + 2 for i in range(DIM)])) + x = x.reshape(x.shape[0], x.shape[1] * (patch_size ** DIM), *(x.shape[-DIM:])) + return x + + +def unpatchify(x: torch.Tensor, patch_size: int): + """ + Unpatchify a tensor. + + Args: + x (torch.Tensor): (N, C, *spatial) tensor + patch_size (int): Patch size + """ + DIM = x.dim() - 2 + assert x.shape[1] % (patch_size ** DIM) == 0, f"Second dimension of input tensor must be divisible by patch size to unpatchify, got {x.shape[1]} and {patch_size ** DIM}" + + x = x.reshape(x.shape[0], x.shape[1] // (patch_size ** DIM), *([patch_size] * DIM), *(x.shape[-DIM:])) + x = x.permute(0, 1, *(sum([[2 + DIM + i, 2 + i] for i in range(DIM)], []))) + x = x.reshape(x.shape[0], x.shape[1], *[x.shape[2 + 2 * i] * patch_size for i in range(DIM)]) + return x diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/transformer/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/transformer/__init__.py new file mode 100644 index 00000000..b08b0d4e --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/modules/transformer/__init__.py @@ -0,0 +1,2 @@ +from .blocks import * +from .modulated import * \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/transformer/blocks.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/transformer/blocks.py new file mode 100644 index 00000000..c37eb7ed --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/modules/transformer/blocks.py @@ -0,0 +1,182 @@ +from typing import * +import torch +import torch.nn as nn +from ..attention import MultiHeadAttention +from ..norm import LayerNorm32 + + +class AbsolutePositionEmbedder(nn.Module): + """ + Embeds spatial positions into vector representations. + """ + def __init__(self, channels: int, in_channels: int = 3): + super().__init__() + self.channels = channels + self.in_channels = in_channels + self.freq_dim = channels // in_channels // 2 + self.freqs = torch.arange(self.freq_dim, dtype=torch.float32) / self.freq_dim + self.freqs = 1.0 / (10000 ** self.freqs) + + def _sin_cos_embedding(self, x: torch.Tensor) -> torch.Tensor: + """ + Create sinusoidal position embeddings. + + Args: + x: a 1-D Tensor of N indices + + Returns: + an (N, D) Tensor of positional embeddings. + """ + self.freqs = self.freqs.to(x.device) + out = torch.outer(x, self.freqs) + out = torch.cat([torch.sin(out), torch.cos(out)], dim=-1) + return out + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Args: + x (torch.Tensor): (N, D) tensor of spatial positions + """ + N, D = x.shape + assert D == self.in_channels, "Input dimension must match number of input channels" + embed = self._sin_cos_embedding(x.reshape(-1)) + embed = embed.reshape(N, -1) + if embed.shape[1] < self.channels: + embed = torch.cat([embed, torch.zeros(N, self.channels - embed.shape[1], device=embed.device)], dim=-1) + return embed + + +class FeedForwardNet(nn.Module): + def __init__(self, channels: int, mlp_ratio: float = 4.0): + super().__init__() + self.mlp = nn.Sequential( + nn.Linear(channels, int(channels * mlp_ratio)), + nn.GELU(approximate="tanh"), + nn.Linear(int(channels * mlp_ratio), channels), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.mlp(x) + + +class TransformerBlock(nn.Module): + """ + Transformer block (MSA + FFN). + """ + def __init__( + self, + channels: int, + num_heads: int, + mlp_ratio: float = 4.0, + attn_mode: Literal["full", "windowed"] = "full", + window_size: Optional[int] = None, + shift_window: Optional[int] = None, + use_checkpoint: bool = False, + use_rope: bool = False, + qk_rms_norm: bool = False, + qkv_bias: bool = True, + ln_affine: bool = False, + ): + super().__init__() + self.use_checkpoint = use_checkpoint + self.norm1 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6) + self.norm2 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6) + self.attn = MultiHeadAttention( + channels, + num_heads=num_heads, + attn_mode=attn_mode, + window_size=window_size, + shift_window=shift_window, + qkv_bias=qkv_bias, + use_rope=use_rope, + qk_rms_norm=qk_rms_norm, + ) + self.mlp = FeedForwardNet( + channels, + mlp_ratio=mlp_ratio, + ) + + def _forward(self, x: torch.Tensor) -> torch.Tensor: + h = self.norm1(x) + h = self.attn(h) + x = x + h + h = self.norm2(x) + h = self.mlp(h) + x = x + h + return x + + def forward(self, x: torch.Tensor) -> torch.Tensor: + if self.use_checkpoint: + return torch.utils.checkpoint.checkpoint(self._forward, x, use_reentrant=False) + else: + return self._forward(x) + + +class TransformerCrossBlock(nn.Module): + """ + Transformer cross-attention block (MSA + MCA + FFN). + """ + def __init__( + self, + channels: int, + ctx_channels: int, + num_heads: int, + mlp_ratio: float = 4.0, + attn_mode: Literal["full", "windowed"] = "full", + window_size: Optional[int] = None, + shift_window: Optional[Tuple[int, int, int]] = None, + use_checkpoint: bool = False, + use_rope: bool = False, + qk_rms_norm: bool = False, + qk_rms_norm_cross: bool = False, + qkv_bias: bool = True, + ln_affine: bool = False, + ): + super().__init__() + self.use_checkpoint = use_checkpoint + self.norm1 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6) + self.norm2 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6) + self.norm3 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6) + self.self_attn = MultiHeadAttention( + channels, + num_heads=num_heads, + type="self", + attn_mode=attn_mode, + window_size=window_size, + shift_window=shift_window, + qkv_bias=qkv_bias, + use_rope=use_rope, + qk_rms_norm=qk_rms_norm, + ) + self.cross_attn = MultiHeadAttention( + channels, + ctx_channels=ctx_channels, + num_heads=num_heads, + type="cross", + attn_mode="full", + qkv_bias=qkv_bias, + qk_rms_norm=qk_rms_norm_cross, + ) + self.mlp = FeedForwardNet( + channels, + mlp_ratio=mlp_ratio, + ) + + def _forward(self, x: torch.Tensor, context: torch.Tensor): + h = self.norm1(x) + h = self.self_attn(h) + x = x + h + h = self.norm2(x) + h = self.cross_attn(h, context) + x = x + h + h = self.norm3(x) + h = self.mlp(h) + x = x + h + return x + + def forward(self, x: torch.Tensor, context: torch.Tensor): + if self.use_checkpoint: + return torch.utils.checkpoint.checkpoint(self._forward, x, context, use_reentrant=False) + else: + return self._forward(x, context) + \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/transformer/modulated.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/transformer/modulated.py new file mode 100644 index 00000000..d4aeca06 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/modules/transformer/modulated.py @@ -0,0 +1,157 @@ +from typing import * +import torch +import torch.nn as nn +from ..attention import MultiHeadAttention +from ..norm import LayerNorm32 +from .blocks import FeedForwardNet + + +class ModulatedTransformerBlock(nn.Module): + """ + Transformer block (MSA + FFN) with adaptive layer norm conditioning. + """ + def __init__( + self, + channels: int, + num_heads: int, + mlp_ratio: float = 4.0, + attn_mode: Literal["full", "windowed"] = "full", + window_size: Optional[int] = None, + shift_window: Optional[Tuple[int, int, int]] = None, + use_checkpoint: bool = False, + use_rope: bool = False, + qk_rms_norm: bool = False, + qkv_bias: bool = True, + share_mod: bool = False, + ): + super().__init__() + self.use_checkpoint = use_checkpoint + self.share_mod = share_mod + self.norm1 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6) + self.norm2 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6) + self.attn = MultiHeadAttention( + channels, + num_heads=num_heads, + attn_mode=attn_mode, + window_size=window_size, + shift_window=shift_window, + qkv_bias=qkv_bias, + use_rope=use_rope, + qk_rms_norm=qk_rms_norm, + ) + self.mlp = FeedForwardNet( + channels, + mlp_ratio=mlp_ratio, + ) + if not share_mod: + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(channels, 6 * channels, bias=True) + ) + + def _forward(self, x: torch.Tensor, mod: torch.Tensor) -> torch.Tensor: + if self.share_mod: + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = mod.chunk(6, dim=1) + else: + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(mod).chunk(6, dim=1) + h = self.norm1(x) + h = h * (1 + scale_msa.unsqueeze(1)) + shift_msa.unsqueeze(1) + h = self.attn(h) + h = h * gate_msa.unsqueeze(1) + x = x + h + h = self.norm2(x) + h = h * (1 + scale_mlp.unsqueeze(1)) + shift_mlp.unsqueeze(1) + h = self.mlp(h) + h = h * gate_mlp.unsqueeze(1) + x = x + h + return x + + def forward(self, x: torch.Tensor, mod: torch.Tensor) -> torch.Tensor: + if self.use_checkpoint: + return torch.utils.checkpoint.checkpoint(self._forward, x, mod, use_reentrant=False) + else: + return self._forward(x, mod) + + +class ModulatedTransformerCrossBlock(nn.Module): + """ + Transformer cross-attention block (MSA + MCA + FFN) with adaptive layer norm conditioning. + """ + def __init__( + self, + channels: int, + ctx_channels: int, + num_heads: int, + mlp_ratio: float = 4.0, + attn_mode: Literal["full", "windowed"] = "full", + window_size: Optional[int] = None, + shift_window: Optional[Tuple[int, int, int]] = None, + use_checkpoint: bool = False, + use_rope: bool = False, + qk_rms_norm: bool = False, + qk_rms_norm_cross: bool = False, + qkv_bias: bool = True, + share_mod: bool = False, + ): + super().__init__() + self.use_checkpoint = use_checkpoint + self.share_mod = share_mod + self.norm1 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6) + self.norm2 = LayerNorm32(channels, elementwise_affine=True, eps=1e-6) + self.norm3 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6) + self.self_attn = MultiHeadAttention( + channels, + num_heads=num_heads, + type="self", + attn_mode=attn_mode, + window_size=window_size, + shift_window=shift_window, + qkv_bias=qkv_bias, + use_rope=use_rope, + qk_rms_norm=qk_rms_norm, + ) + self.cross_attn = MultiHeadAttention( + channels, + ctx_channels=ctx_channels, + num_heads=num_heads, + type="cross", + attn_mode="full", + qkv_bias=qkv_bias, + qk_rms_norm=qk_rms_norm_cross, + ) + self.mlp = FeedForwardNet( + channels, + mlp_ratio=mlp_ratio, + ) + if not share_mod: + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(channels, 6 * channels, bias=True) + ) + + def _forward(self, x: torch.Tensor, mod: torch.Tensor, context: torch.Tensor): + if self.share_mod: + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = mod.chunk(6, dim=1) + else: + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(mod).chunk(6, dim=1) + h = self.norm1(x) + h = h * (1 + scale_msa.unsqueeze(1)) + shift_msa.unsqueeze(1) + h = self.self_attn(h) + h = h * gate_msa.unsqueeze(1) + x = x + h + h = self.norm2(x) + h = self.cross_attn(h, context) + x = x + h + h = self.norm3(x) + h = h * (1 + scale_mlp.unsqueeze(1)) + shift_mlp.unsqueeze(1) + h = self.mlp(h) + h = h * gate_mlp.unsqueeze(1) + x = x + h + return x + + def forward(self, x: torch.Tensor, mod: torch.Tensor, context: torch.Tensor): + if self.use_checkpoint: + return torch.utils.checkpoint.checkpoint(self._forward, x, mod, context, use_reentrant=False) + else: + return self._forward(x, mod, context) + \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/utils.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/utils.py new file mode 100644 index 00000000..f0afb1b6 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/modules/utils.py @@ -0,0 +1,54 @@ +import torch.nn as nn +from ..modules import sparse as sp + +FP16_MODULES = ( + nn.Conv1d, + nn.Conv2d, + nn.Conv3d, + nn.ConvTranspose1d, + nn.ConvTranspose2d, + nn.ConvTranspose3d, + nn.Linear, + sp.SparseConv3d, + sp.SparseInverseConv3d, + sp.SparseLinear, +) + +def convert_module_to_f16(l): + """ + Convert primitive modules to float16. + """ + if isinstance(l, FP16_MODULES): + for p in l.parameters(): + p.data = p.data.half() + + +def convert_module_to_f32(l): + """ + Convert primitive modules to float32, undoing convert_module_to_f16(). + """ + if isinstance(l, FP16_MODULES): + for p in l.parameters(): + p.data = p.data.float() + + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + +def scale_module(module, scale): + """ + Scale the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().mul_(scale) + return module + + +def modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/pipelines/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/pipelines/__init__.py new file mode 100644 index 00000000..f9e8548b --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/pipelines/__init__.py @@ -0,0 +1,24 @@ +from . import samplers +from .trellis_image_to_3d import TrellisImageTo3DPipeline + + +def from_pretrained(path: str): + """ + Load a pipeline from a model folder or a Hugging Face model hub. + + Args: + path: The path to the model. Can be either local path or a Hugging Face model name. + """ + import os + import json + is_local = os.path.exists(f"{path}/pipeline.json") + + if is_local: + config_file = f"{path}/pipeline.json" + else: + from huggingface_hub import hf_hub_download + config_file = hf_hub_download(path, "pipeline.json") + + with open(config_file, 'r') as f: + config = json.load(f) + return globals()[config['name']].from_pretrained(path) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/pipelines/base.py b/Gen_3D_Modules/TRELLIS/trellis_/pipelines/base.py new file mode 100644 index 00000000..3a9e0df4 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/pipelines/base.py @@ -0,0 +1,66 @@ +from typing import * +import torch +import torch.nn as nn +from .. import models + + +class Pipeline: + """ + A base class for pipelines. + """ + def __init__( + self, + models: dict[str, nn.Module] = None, + ): + if models is None: + return + self.models = models + for model in self.models.values(): + model.eval() + + @staticmethod + def from_pretrained(path: str) -> "Pipeline": + """ + Load a pretrained model. + """ + import os + import json + is_local = os.path.exists(f"{path}/pipeline.json") + + if is_local: + config_file = f"{path}/pipeline.json" + else: + from huggingface_hub import hf_hub_download + config_file = hf_hub_download(path, "pipeline.json") + + with open(config_file, 'r') as f: + args = json.load(f)['args'] + + _models = { + k: models.from_pretrained(f"{path}/{v}") + for k, v in args['models'].items() + } + + new_pipeline = Pipeline(_models) + new_pipeline._pretrained_args = args + return new_pipeline + + @property + def device(self) -> torch.device: + for model in self.models.values(): + if hasattr(model, 'device'): + return model.device + for model in self.models.values(): + if hasattr(model, 'parameters'): + return next(model.parameters()).device + raise RuntimeError("No device found.") + + def to(self, device: torch.device) -> None: + for model in self.models.values(): + model.to(device) + + def cuda(self) -> None: + self.to(torch.device("cuda")) + + def cpu(self) -> None: + self.to(torch.device("cpu")) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/__init__.py new file mode 100644 index 00000000..54d412fc --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/__init__.py @@ -0,0 +1,2 @@ +from .base import Sampler +from .flow_euler import FlowEulerSampler, FlowEulerCfgSampler, FlowEulerGuidanceIntervalSampler \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/base.py b/Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/base.py new file mode 100644 index 00000000..1966ce78 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/base.py @@ -0,0 +1,20 @@ +from typing import * +from abc import ABC, abstractmethod + + +class Sampler(ABC): + """ + A base class for samplers. + """ + + @abstractmethod + def sample( + self, + model, + **kwargs + ): + """ + Sample from a model. + """ + pass + \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/classifier_free_guidance_mixin.py b/Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/classifier_free_guidance_mixin.py new file mode 100644 index 00000000..5701b25f --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/classifier_free_guidance_mixin.py @@ -0,0 +1,12 @@ +from typing import * + + +class ClassifierFreeGuidanceSamplerMixin: + """ + A mixin class for samplers that apply classifier-free guidance. + """ + + def _inference_model(self, model, x_t, t, cond, neg_cond, cfg_strength, **kwargs): + pred = super()._inference_model(model, x_t, t, cond, **kwargs) + neg_pred = super()._inference_model(model, x_t, t, neg_cond, **kwargs) + return (1 + cfg_strength) * pred - cfg_strength * neg_pred diff --git a/Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/flow_euler.py b/Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/flow_euler.py new file mode 100644 index 00000000..b2d48607 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/flow_euler.py @@ -0,0 +1,202 @@ +from typing import * +import torch +import numpy as np +from tqdm import tqdm +import comfy.utils +from easydict import EasyDict as edict +from .base import Sampler +from .classifier_free_guidance_mixin import ClassifierFreeGuidanceSamplerMixin +from .guidance_interval_mixin import GuidanceIntervalSamplerMixin + + +class FlowEulerSampler(Sampler): + """ + Generate samples from a flow-matching model using Euler sampling. + + Args: + sigma_min: The minimum scale of noise in flow. + """ + def __init__( + self, + sigma_min: float, + ): + self.sigma_min = sigma_min + + def _eps_to_xstart(self, x_t, t, eps): + assert x_t.shape == eps.shape + return (x_t - (self.sigma_min + (1 - self.sigma_min) * t) * eps) / (1 - t) + + def _xstart_to_eps(self, x_t, t, x_0): + assert x_t.shape == x_0.shape + return (x_t - (1 - t) * x_0) / (self.sigma_min + (1 - self.sigma_min) * t) + + def _v_to_xstart_eps(self, x_t, t, v): + assert x_t.shape == v.shape + eps = (1 - t) * v + x_t + x_0 = (1 - self.sigma_min) * x_t - (self.sigma_min + (1 - self.sigma_min) * t) * v + return x_0, eps + + def _inference_model(self, model, x_t, t, cond=None, **kwargs): + t = torch.tensor([1000 * t] * x_t.shape[0], device=x_t.device, dtype=torch.float32) + return model(x_t, t, cond, **kwargs) + + def _get_model_prediction(self, model, x_t, t, cond=None, **kwargs): + pred_v = self._inference_model(model, x_t, t, cond, **kwargs) + pred_x_0, pred_eps = self._v_to_xstart_eps(x_t=x_t, t=t, v=pred_v) + return pred_x_0, pred_eps, pred_v + + @torch.no_grad() + def sample_once( + self, + model, + x_t, + t: float, + t_prev: float, + cond: Optional[Any] = None, + **kwargs + ): + """ + Sample x_{t-1} from the model using Euler method. + + Args: + model: The model to sample from. + x_t: The [N x C x ...] tensor of noisy inputs at time t. + t: The current timestep. + t_prev: The previous timestep. + cond: conditional information. + **kwargs: Additional arguments for model inference. + + Returns: + a dict containing the following + - 'pred_x_prev': x_{t-1}. + - 'pred_x_0': a prediction of x_0. + """ + pred_x_0, pred_eps, pred_v = self._get_model_prediction(model, x_t, t, cond, **kwargs) + pred_x_prev = x_t - (t - t_prev) * pred_v + return edict({"pred_x_prev": pred_x_prev, "pred_x_0": pred_x_0}) + + @torch.no_grad() + def sample( + self, + model, + noise, + cond: Optional[Any] = None, + steps: int = 50, + rescale_t: float = 1.0, + verbose: bool = True, + **kwargs + ): + """ + Generate samples from the model using Euler method. + + Args: + model: The model to sample from. + noise: The initial noise tensor. + cond: conditional information. + steps: The number of steps to sample. + rescale_t: The rescale factor for t. + verbose: If True, show a progress bar. + **kwargs: Additional arguments for model_inference. + + Returns: + a dict containing the following + - 'samples': the model samples. + - 'pred_x_t': a list of prediction of x_t. + - 'pred_x_0': a list of prediction of x_0. + """ + sample = noise + t_seq = np.linspace(1, 0, steps + 1) + t_seq = rescale_t * t_seq / (1 + (rescale_t - 1) * t_seq) + t_pairs = list((t_seq[i], t_seq[i + 1]) for i in range(steps)) + ret = edict({"samples": None, "pred_x_t": [], "pred_x_0": []}) + comfy_pbar = comfy.utils.ProgressBar(steps) + for i, (t, t_prev) in enumerate(tqdm(t_pairs, desc="Sampling", disable=not verbose)): + out = self.sample_once(model, sample, t, t_prev, cond, **kwargs) + sample = out.pred_x_prev + ret.pred_x_t.append(out.pred_x_prev) + ret.pred_x_0.append(out.pred_x_0) + comfy_pbar.update_absolute(i + 1) + ret.samples = sample + return ret + + +class FlowEulerCfgSampler(ClassifierFreeGuidanceSamplerMixin, FlowEulerSampler): + """ + Generate samples from a flow-matching model using Euler sampling with classifier-free guidance. + """ + @torch.no_grad() + def sample( + self, + model, + noise, + cond, + neg_cond, + steps: int = 50, + rescale_t: float = 1.0, + cfg_strength: float = 3.0, + verbose: bool = True, + **kwargs + ): + """ + Generate samples from the model using Euler method. + + Args: + model: The model to sample from. + noise: The initial noise tensor. + cond: conditional information. + neg_cond: negative conditional information. + steps: The number of steps to sample. + rescale_t: The rescale factor for t. + cfg_strength: The strength of classifier-free guidance. + verbose: If True, show a progress bar. + **kwargs: Additional arguments for model_inference. + + Returns: + a dict containing the following + - 'samples': the model samples. + - 'pred_x_t': a list of prediction of x_t. + - 'pred_x_0': a list of prediction of x_0. + """ + return super().sample(model, noise, cond, steps, rescale_t, verbose, neg_cond=neg_cond, cfg_strength=cfg_strength, **kwargs) + + +class FlowEulerGuidanceIntervalSampler(GuidanceIntervalSamplerMixin, FlowEulerSampler): + """ + Generate samples from a flow-matching model using Euler sampling with classifier-free guidance and interval. + """ + @torch.no_grad() + def sample( + self, + model, + noise, + cond, + neg_cond, + steps: int = 50, + rescale_t: float = 1.0, + cfg_strength: float = 3.0, + cfg_interval: Tuple[float, float] = (0.0, 1.0), + verbose: bool = True, + **kwargs + ): + """ + Generate samples from the model using Euler method. + + Args: + model: The model to sample from. + noise: The initial noise tensor. + cond: conditional information. + neg_cond: negative conditional information. + steps: The number of steps to sample. + rescale_t: The rescale factor for t. + cfg_strength: The strength of classifier-free guidance. + cfg_interval: The interval for classifier-free guidance. + verbose: If True, show a progress bar. + **kwargs: Additional arguments for model_inference. + + Returns: + a dict containing the following + - 'samples': the model samples. + - 'pred_x_t': a list of prediction of x_t. + - 'pred_x_0': a list of prediction of x_0. + """ + return super().sample(model, noise, cond, steps, rescale_t, verbose, neg_cond=neg_cond, cfg_strength=cfg_strength, cfg_interval=cfg_interval, **kwargs) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/guidance_interval_mixin.py b/Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/guidance_interval_mixin.py new file mode 100644 index 00000000..7074a4d5 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/guidance_interval_mixin.py @@ -0,0 +1,15 @@ +from typing import * + + +class GuidanceIntervalSamplerMixin: + """ + A mixin class for samplers that apply classifier-free guidance with interval. + """ + + def _inference_model(self, model, x_t, t, cond, neg_cond, cfg_strength, cfg_interval, **kwargs): + if cfg_interval[0] <= t <= cfg_interval[1]: + pred = super()._inference_model(model, x_t, t, cond, **kwargs) + neg_pred = super()._inference_model(model, x_t, t, neg_cond, **kwargs) + return (1 + cfg_strength) * pred - cfg_strength * neg_pred + else: + return super()._inference_model(model, x_t, t, cond, **kwargs) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/pipelines/trellis_image_to_3d.py b/Gen_3D_Modules/TRELLIS/trellis_/pipelines/trellis_image_to_3d.py new file mode 100644 index 00000000..033083e0 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/pipelines/trellis_image_to_3d.py @@ -0,0 +1,283 @@ +from typing import * +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +from tqdm import tqdm +from easydict import EasyDict as edict +from torchvision import transforms +from PIL import Image +from .base import Pipeline +from . import samplers +from ..modules import sparse as sp +from ..representations import Gaussian, Strivec, MeshExtractResult + + +class TrellisImageTo3DPipeline(Pipeline): + """ + Pipeline for inferring Trellis image-to-3D models. + + Args: + models (dict[str, nn.Module]): The models to use in the pipeline. + sparse_structure_sampler (samplers.Sampler): The sampler for the sparse structure. + slat_sampler (samplers.Sampler): The sampler for the structured latent. + slat_normalization (dict): The normalization parameters for the structured latent. + image_cond_model (str): The name of the image conditioning model. + """ + def __init__( + self, + models: dict[str, nn.Module] = None, + sparse_structure_sampler: samplers.Sampler = None, + slat_sampler: samplers.Sampler = None, + slat_normalization: dict = None, + image_cond_model: str = None, + ): + if models is None: + return + super().__init__(models) + self.sparse_structure_sampler = sparse_structure_sampler + self.slat_sampler = slat_sampler + self.sparse_structure_sampler_params = {} + self.slat_sampler_params = {} + self.slat_normalization = slat_normalization + self.rembg_session = None + self._init_image_cond_model(image_cond_model) + + @staticmethod + def from_pretrained(path: str) -> "TrellisImageTo3DPipeline": + """ + Load a pretrained model. + + Args: + path (str): The path to the model. Can be either local path or a Hugging Face repository. + """ + pipeline = super(TrellisImageTo3DPipeline, TrellisImageTo3DPipeline).from_pretrained(path) + new_pipeline = TrellisImageTo3DPipeline() + new_pipeline.__dict__ = pipeline.__dict__ + args = pipeline._pretrained_args + + new_pipeline.sparse_structure_sampler = getattr(samplers, args['sparse_structure_sampler']['name'])(**args['sparse_structure_sampler']['args']) + new_pipeline.sparse_structure_sampler_params = args['sparse_structure_sampler']['params'] + + new_pipeline.slat_sampler = getattr(samplers, args['slat_sampler']['name'])(**args['slat_sampler']['args']) + new_pipeline.slat_sampler_params = args['slat_sampler']['params'] + + new_pipeline.slat_normalization = args['slat_normalization'] + + new_pipeline._init_image_cond_model(args['image_cond_model']) + + return new_pipeline + + def _init_image_cond_model(self, name: str): + """ + Initialize the image conditioning model. + """ + dinov2_model = torch.hub.load('facebookresearch/dinov2', name, pretrained=True) + dinov2_model.eval() + self.models['image_cond_model'] = dinov2_model + transform = transforms.Compose([ + transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), + ]) + self.image_cond_model_transform = transform + + def preprocess_image(self, input: Image.Image) -> Image.Image: + """ + Preprocess the input image. + """ + # if has alpha channel, use it directly; otherwise, remove background + has_alpha = False + if input.mode == 'RGBA': + alpha = np.array(input)[:, :, 3] + if not np.all(alpha == 255): + has_alpha = True + if has_alpha: + output = input + else: + import rembg + input = input.convert('RGB') + max_size = max(input.size) + scale = min(1, 1024 / max_size) + if scale < 1: + input = input.resize((int(input.width * scale), int(input.height * scale)), Image.Resampling.LANCZOS) + if getattr(self, 'rembg_session', None) is None: + self.rembg_session = rembg.new_session('u2net') + output = rembg.remove(input, session=self.rembg_session) + output_np = np.array(output) + alpha = output_np[:, :, 3] + bbox = np.argwhere(alpha > 0.8 * 255) + bbox = np.min(bbox[:, 1]), np.min(bbox[:, 0]), np.max(bbox[:, 1]), np.max(bbox[:, 0]) + center = (bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2 + size = max(bbox[2] - bbox[0], bbox[3] - bbox[1]) + size = int(size * 1.2) + bbox = center[0] - size // 2, center[1] - size // 2, center[0] + size // 2, center[1] + size // 2 + output = output.crop(bbox) # type: ignore + output = output.resize((518, 518), Image.Resampling.LANCZOS) + output = np.array(output).astype(np.float32) / 255 + output = output[:, :, :3] * output[:, :, 3:4] + output = Image.fromarray((output * 255).astype(np.uint8)) + return output + + @torch.no_grad() + def encode_image(self, image: Union[torch.Tensor, list[Image.Image]]) -> torch.Tensor: + """ + Encode the image. + + Args: + image (Union[torch.Tensor, list[Image.Image]]): The image to encode + + Returns: + torch.Tensor: The encoded features. + """ + if isinstance(image, torch.Tensor): + assert image.ndim == 4, "Image tensor should be batched (B, C, H, W)" + elif isinstance(image, list): + assert all(isinstance(i, Image.Image) for i in image), "Image list should be list of PIL images" + image = [i.resize((518, 518), Image.LANCZOS) for i in image] + image = [np.array(i.convert('RGB')).astype(np.float32) / 255 for i in image] + image = [torch.from_numpy(i).permute(2, 0, 1).float() for i in image] + image = torch.stack(image).to(self.device) + else: + raise ValueError(f"Unsupported type of image: {type(image)}") + + image = self.image_cond_model_transform(image).to(self.device) + features = self.models['image_cond_model'](image, is_training=True)['x_prenorm'] + patchtokens = F.layer_norm(features, features.shape[-1:]) + return patchtokens + + def get_cond(self, image: Union[torch.Tensor, list[Image.Image]]) -> dict: + """ + Get the conditioning information for the model. + + Args: + image (Union[torch.Tensor, list[Image.Image]]): The image prompts. + + Returns: + dict: The conditioning information + """ + cond = self.encode_image(image) + neg_cond = torch.zeros_like(cond) + return { + 'cond': cond, + 'neg_cond': neg_cond, + } + + def sample_sparse_structure( + self, + cond: dict, + num_samples: int = 1, + sampler_params: dict = {}, + ) -> torch.Tensor: + """ + Sample sparse structures with the given conditioning. + + Args: + cond (dict): The conditioning information. + num_samples (int): The number of samples to generate. + sampler_params (dict): Additional parameters for the sampler. + """ + # Sample occupancy latent + flow_model = self.models['sparse_structure_flow_model'] + reso = flow_model.resolution + noise = torch.randn(num_samples, flow_model.in_channels, reso, reso, reso).to(self.device) + sampler_params = {**self.sparse_structure_sampler_params, **sampler_params} + z_s = self.sparse_structure_sampler.sample( + flow_model, + noise, + **cond, + **sampler_params, + verbose=True + ).samples + + # Decode occupancy latent + decoder = self.models['sparse_structure_decoder'] + coords = torch.argwhere(decoder(z_s)>0)[:, [0, 2, 3, 4]].int() + + return coords + + def decode_slat( + self, + slat: sp.SparseTensor, + formats: List[str] = ['mesh', 'gaussian', 'radiance_field'], + ) -> dict: + """ + Decode the structured latent. + + Args: + slat (sp.SparseTensor): The structured latent. + formats (List[str]): The formats to decode the structured latent to. + + Returns: + dict: The decoded structured latent. + """ + ret = {} + if 'mesh' in formats: + ret['mesh'] = self.models['slat_decoder_mesh'](slat) + if 'gaussian' in formats: + ret['gaussian'] = self.models['slat_decoder_gs'](slat) + if 'radiance_field' in formats: + ret['radiance_field'] = self.models['slat_decoder_rf'](slat) + return ret + + def sample_slat( + self, + cond: dict, + coords: torch.Tensor, + sampler_params: dict = {}, + ) -> sp.SparseTensor: + """ + Sample structured latent with the given conditioning. + + Args: + cond (dict): The conditioning information. + coords (torch.Tensor): The coordinates of the sparse structure. + sampler_params (dict): Additional parameters for the sampler. + """ + # Sample structured latent + flow_model = self.models['slat_flow_model'] + noise = sp.SparseTensor( + feats=torch.randn(coords.shape[0], flow_model.in_channels).to(self.device), + coords=coords, + ) + sampler_params = {**self.slat_sampler_params, **sampler_params} + slat = self.slat_sampler.sample( + flow_model, + noise, + **cond, + **sampler_params, + verbose=True + ).samples + + std = torch.tensor(self.slat_normalization['std'])[None].to(slat.device) + mean = torch.tensor(self.slat_normalization['mean'])[None].to(slat.device) + slat = slat * std + mean + + return slat + + @torch.no_grad() + def run( + self, + image: Image.Image, + num_samples: int = 1, + seed: int = 42, + sparse_structure_sampler_params: dict = {}, + slat_sampler_params: dict = {}, + formats: List[str] = ['mesh', 'gaussian', 'radiance_field'], + preprocess_image: bool = True, + ) -> dict: + """ + Run the pipeline. + + Args: + image (Image.Image): The image prompt. + num_samples (int): The number of samples to generate. + sparse_structure_sampler_params (dict): Additional parameters for the sparse structure sampler. + slat_sampler_params (dict): Additional parameters for the structured latent sampler. + preprocess_image (bool): Whether to preprocess the image. + """ + if preprocess_image: + image = self.preprocess_image(image) + cond = self.get_cond([image]) + torch.manual_seed(seed) + coords = self.sample_sparse_structure(cond, num_samples, sparse_structure_sampler_params) + slat = self.sample_slat(cond, coords, slat_sampler_params) + return self.decode_slat(slat, formats) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/renderers/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/renderers/__init__.py new file mode 100644 index 00000000..0339355c --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/renderers/__init__.py @@ -0,0 +1,31 @@ +import importlib + +__attributes = { + 'OctreeRenderer': 'octree_renderer', + 'GaussianRenderer': 'gaussian_render', + 'MeshRenderer': 'mesh_renderer', +} + +__submodules = [] + +__all__ = list(__attributes.keys()) + __submodules + +def __getattr__(name): + if name not in globals(): + if name in __attributes: + module_name = __attributes[name] + module = importlib.import_module(f".{module_name}", __name__) + globals()[name] = getattr(module, name) + elif name in __submodules: + module = importlib.import_module(f".{name}", __name__) + globals()[name] = module + else: + raise AttributeError(f"module {__name__} has no attribute {name}") + return globals()[name] + + +# For Pylance +if __name__ == '__main__': + from .octree_renderer import OctreeRenderer + from .gaussian_render import GaussianRenderer + from .mesh_renderer import MeshRenderer \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/renderers/gaussian_render.py b/Gen_3D_Modules/TRELLIS/trellis_/renderers/gaussian_render.py new file mode 100644 index 00000000..ef3ef8c4 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/renderers/gaussian_render.py @@ -0,0 +1,235 @@ +# +# Copyright (C) 2023, Inria +# GRAPHDECO research group, https://team.inria.fr/graphdeco +# All rights reserved. +# +# This software is free for non-commercial, research and evaluation use +# under the terms of the LICENSE.md file. +# +# For inquiries contact george.drettakis@inria.fr +# + +import torch +import math +from easydict import EasyDict as edict +import numpy as np +from ..representations.gaussian import Gaussian +from .sh_utils import eval_sh +import torch.nn.functional as F +from easydict import EasyDict as edict + + +def intrinsics_to_projection( + intrinsics: torch.Tensor, + near: float, + far: float, + ) -> torch.Tensor: + """ + OpenCV intrinsics to OpenGL perspective matrix + + Args: + intrinsics (torch.Tensor): [3, 3] OpenCV intrinsics matrix + near (float): near plane to clip + far (float): far plane to clip + Returns: + (torch.Tensor): [4, 4] OpenGL perspective matrix + """ + fx, fy = intrinsics[0, 0], intrinsics[1, 1] + cx, cy = intrinsics[0, 2], intrinsics[1, 2] + ret = torch.zeros((4, 4), dtype=intrinsics.dtype, device=intrinsics.device) + ret[0, 0] = 2 * fx + ret[1, 1] = 2 * fy + ret[0, 2] = 2 * cx - 1 + ret[1, 2] = - 2 * cy + 1 + ret[2, 2] = far / (far - near) + ret[2, 3] = near * far / (near - far) + ret[3, 2] = 1. + return ret + + +def render(viewpoint_camera, pc : Gaussian, pipe, bg_color : torch.Tensor, scaling_modifier = 1.0, override_color = None): + """ + Render the scene. + + Background tensor (bg_color) must be on GPU! + + Original code use the Differential Gaussian Rasterization from https://github.com/autonomousvision/mip-splatting/tree/main/submodules/diff-gaussian-rasterization + Modified to use the GaussianRasterizer from https://github.com/ashawkey/diff-gaussian-rasterization + Only changes are the inputs to GaussianRasterizationSettings: kernel_size and subpixel_offset are commented out. + """ + # lazy import + if 'GaussianRasterizer' not in globals(): + from diff_gaussian_rasterization import GaussianRasterizer, GaussianRasterizationSettings + + # Create zero tensor. We will use it to make pytorch return gradients of the 2D (screen-space) means + screenspace_points = torch.zeros_like(pc.get_xyz, dtype=pc.get_xyz.dtype, requires_grad=True, device="cuda") + 0 + try: + screenspace_points.retain_grad() + except: + pass + # Set up rasterization configuration + tanfovx = math.tan(viewpoint_camera.FoVx * 0.5) + tanfovy = math.tan(viewpoint_camera.FoVy * 0.5) + + #kernel_size = pipe.kernel_size + #subpixel_offset = torch.zeros((int(viewpoint_camera.image_height), int(viewpoint_camera.image_width), 2), dtype=torch.float32, device="cuda") + + raster_settings = GaussianRasterizationSettings( + image_height=int(viewpoint_camera.image_height), + image_width=int(viewpoint_camera.image_width), + tanfovx=tanfovx, + tanfovy=tanfovy, + #kernel_size=kernel_size, + #subpixel_offset=subpixel_offset, + bg=bg_color, + scale_modifier=scaling_modifier, + viewmatrix=viewpoint_camera.world_view_transform, + projmatrix=viewpoint_camera.full_proj_transform, + sh_degree=pc.active_sh_degree, + campos=viewpoint_camera.camera_center, + prefiltered=False, + debug=pipe.debug + ) + + rasterizer = GaussianRasterizer(raster_settings=raster_settings) + + means3D = pc.get_xyz + means2D = screenspace_points + opacity = pc.get_opacity + + # If precomputed 3d covariance is provided, use it. If not, then it will be computed from + # scaling / rotation by the rasterizer. + scales = None + rotations = None + cov3D_precomp = None + if pipe.compute_cov3D_python: + cov3D_precomp = pc.get_covariance(scaling_modifier) + else: + scales = pc.get_scaling + rotations = pc.get_rotation + + # If precomputed colors are provided, use them. Otherwise, if it is desired to precompute colors + # from SHs in Python, do it. If not, then SH -> RGB conversion will be done by rasterizer. + shs = None + colors_precomp = None + if override_color is None: + if pipe.convert_SHs_python: + shs_view = pc.get_features.transpose(1, 2).view(-1, 3, (pc.max_sh_degree+1)**2) + dir_pp = (pc.get_xyz - viewpoint_camera.camera_center.repeat(pc.get_features.shape[0], 1)) + dir_pp_normalized = dir_pp/dir_pp.norm(dim=1, keepdim=True) + sh2rgb = eval_sh(pc.active_sh_degree, shs_view, dir_pp_normalized) + colors_precomp = torch.clamp_min(sh2rgb + 0.5, 0.0) + else: + shs = pc.get_features + else: + colors_precomp = override_color + + # Rasterize visible Gaussians to image, obtain their radii (on screen). + rendered_image, radii, rendered_depth, rendered_alpha = rasterizer( + means3D = means3D, + means2D = means2D, + shs = shs, + colors_precomp = colors_precomp, + opacities = opacity, + scales = scales, + rotations = rotations, + cov3D_precomp = cov3D_precomp + ) + + # Those Gaussians that were frustum culled or had a radius of 0 were not visible. + # They will be excluded from value updates used in the splitting criteria. + return edict({"render": rendered_image, + "viewspace_points": screenspace_points, + "visibility_filter" : radii > 0, + "radii": radii}) + + +class GaussianRenderer: + """ + Renderer for the Voxel representation. + + Args: + rendering_options (dict): Rendering options. + """ + + def __init__(self, rendering_options={}) -> None: + self.pipe = edict({ + "kernel_size": 0.1, + "convert_SHs_python": False, + "compute_cov3D_python": False, + "scale_modifier": 1.0, + "debug": False + }) + self.rendering_options = edict({ + "resolution": None, + "near": None, + "far": None, + "ssaa": 1, + "bg_color": 'random', + }) + self.rendering_options.update(rendering_options) + self.bg_color = None + + def render( + self, + gausssian: Gaussian, + extrinsics: torch.Tensor, + intrinsics: torch.Tensor, + colors_overwrite: torch.Tensor = None + ) -> edict: + """ + Render the gausssian. + + Args: + gaussian : gaussianmodule + extrinsics (torch.Tensor): (4, 4) camera extrinsics + intrinsics (torch.Tensor): (3, 3) camera intrinsics + colors_overwrite (torch.Tensor): (N, 3) override color + + Returns: + edict containing: + color (torch.Tensor): (3, H, W) rendered color image + """ + resolution = self.rendering_options["resolution"] + near = self.rendering_options["near"] + far = self.rendering_options["far"] + ssaa = self.rendering_options["ssaa"] + + if self.rendering_options["bg_color"] == 'random': + self.bg_color = torch.zeros(3, dtype=torch.float32, device="cuda") + if np.random.rand() < 0.5: + self.bg_color += 1 + else: + self.bg_color = torch.tensor(self.rendering_options["bg_color"], dtype=torch.float32, device="cuda") + + view = extrinsics + perspective = intrinsics_to_projection(intrinsics, near, far) + camera = torch.inverse(view)[:3, 3] + focalx = intrinsics[0, 0] + focaly = intrinsics[1, 1] + fovx = 2 * torch.atan(0.5 / focalx) + fovy = 2 * torch.atan(0.5 / focaly) + + camera_dict = edict({ + "image_height": resolution * ssaa, + "image_width": resolution * ssaa, + "FoVx": fovx, + "FoVy": fovy, + "znear": near, + "zfar": far, + "world_view_transform": view.T.contiguous(), + "projection_matrix": perspective.T.contiguous(), + "full_proj_transform": (perspective @ view).T.contiguous(), + "camera_center": camera + }) + + # Render + render_ret = render(camera_dict, gausssian, self.pipe, self.bg_color, override_color=colors_overwrite, scaling_modifier=self.pipe.scale_modifier) + + if ssaa > 1: + render_ret.render = F.interpolate(render_ret.render[None], size=(resolution, resolution), mode='bilinear', align_corners=False, antialias=True).squeeze() + + ret = edict({ + 'color': render_ret['render'] + }) + return ret diff --git a/Gen_3D_Modules/TRELLIS/trellis_/renderers/mesh_renderer.py b/Gen_3D_Modules/TRELLIS/trellis_/renderers/mesh_renderer.py new file mode 100644 index 00000000..b504fa4d --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/renderers/mesh_renderer.py @@ -0,0 +1,133 @@ +import torch +import nvdiffrast.torch as dr +from easydict import EasyDict as edict +from ..representations.mesh import MeshExtractResult +import torch.nn.functional as F + + +def intrinsics_to_projection( + intrinsics: torch.Tensor, + near: float, + far: float, + ) -> torch.Tensor: + """ + OpenCV intrinsics to OpenGL perspective matrix + + Args: + intrinsics (torch.Tensor): [3, 3] OpenCV intrinsics matrix + near (float): near plane to clip + far (float): far plane to clip + Returns: + (torch.Tensor): [4, 4] OpenGL perspective matrix + """ + fx, fy = intrinsics[0, 0], intrinsics[1, 1] + cx, cy = intrinsics[0, 2], intrinsics[1, 2] + ret = torch.zeros((4, 4), dtype=intrinsics.dtype, device=intrinsics.device) + ret[0, 0] = 2 * fx + ret[1, 1] = 2 * fy + ret[0, 2] = 2 * cx - 1 + ret[1, 2] = - 2 * cy + 1 + ret[2, 2] = far / (far - near) + ret[2, 3] = near * far / (near - far) + ret[3, 2] = 1. + return ret + + +class MeshRenderer: + """ + Renderer for the Mesh representation. + + Args: + rendering_options (dict): Rendering options. + glctx (nvdiffrast.torch.RasterizeGLContext): RasterizeGLContext object for CUDA/OpenGL interop. + """ + def __init__(self, rendering_options={}, device='cuda'): + self.rendering_options = edict({ + "resolution": None, + "near": None, + "far": None, + "ssaa": 1 + }) + self.rendering_options.update(rendering_options) + self.glctx = dr.RasterizeCudaContext(device=device) + self.device=device + + def render( + self, + mesh : MeshExtractResult, + extrinsics: torch.Tensor, + intrinsics: torch.Tensor, + return_types = ["mask", "normal", "depth"] + ) -> edict: + """ + Render the mesh. + + Args: + mesh : meshmodel + extrinsics (torch.Tensor): (4, 4) camera extrinsics + intrinsics (torch.Tensor): (3, 3) camera intrinsics + return_types (list): list of return types, can be "mask", "depth", "normal_map", "normal", "color" + + Returns: + edict based on return_types containing: + color (torch.Tensor): [3, H, W] rendered color image + depth (torch.Tensor): [H, W] rendered depth image + normal (torch.Tensor): [3, H, W] rendered normal image + normal_map (torch.Tensor): [3, H, W] rendered normal map image + mask (torch.Tensor): [H, W] rendered mask image + """ + resolution = self.rendering_options["resolution"] + near = self.rendering_options["near"] + far = self.rendering_options["far"] + ssaa = self.rendering_options["ssaa"] + + if mesh.vertices.shape[0] == 0 or mesh.faces.shape[0] == 0: + default_img = torch.zeros((1, resolution, resolution, 3), dtype=torch.float32, device=self.device) + ret_dict = {k : default_img if k in ['normal', 'normal_map', 'color'] else default_img[..., :1] for k in return_types} + return ret_dict + + perspective = intrinsics_to_projection(intrinsics, near, far) + + RT = extrinsics.unsqueeze(0) + full_proj = (perspective @ extrinsics).unsqueeze(0) + + vertices = mesh.vertices.unsqueeze(0) + + vertices_homo = torch.cat([vertices, torch.ones_like(vertices[..., :1])], dim=-1) + vertices_camera = torch.bmm(vertices_homo, RT.transpose(-1, -2)) + vertices_clip = torch.bmm(vertices_homo, full_proj.transpose(-1, -2)) + faces_int = mesh.faces.int() + rast, _ = dr.rasterize( + self.glctx, vertices_clip, faces_int, (resolution * ssaa, resolution * ssaa)) + + out_dict = edict() + for type in return_types: + img = None + if type == "mask" : + img = dr.antialias((rast[..., -1:] > 0).float(), rast, vertices_clip, faces_int) + elif type == "depth": + img = dr.interpolate(vertices_camera[..., 2:3].contiguous(), rast, faces_int)[0] + img = dr.antialias(img, rast, vertices_clip, faces_int) + elif type == "normal" : + img = dr.interpolate( + mesh.face_normal.reshape(1, -1, 3), rast, + torch.arange(mesh.faces.shape[0] * 3, device=self.device, dtype=torch.int).reshape(-1, 3) + )[0] + img = dr.antialias(img, rast, vertices_clip, faces_int) + # normalize norm pictures + img = (img + 1) / 2 + elif type == "normal_map" : + img = dr.interpolate(mesh.vertex_attrs[:, 3:].contiguous(), rast, faces_int)[0] + img = dr.antialias(img, rast, vertices_clip, faces_int) + elif type == "color" : + img = dr.interpolate(mesh.vertex_attrs[:, :3].contiguous(), rast, faces_int)[0] + img = dr.antialias(img, rast, vertices_clip, faces_int) + + if ssaa > 1: + img = F.interpolate(img.permute(0, 3, 1, 2), (resolution, resolution), mode='bilinear', align_corners=False, antialias=True) + img = img.squeeze() + else: + img = img.permute(0, 3, 1, 2).squeeze() + out_dict[type] = img + + return out_dict diff --git a/Gen_3D_Modules/TRELLIS/trellis_/renderers/octree_renderer.py b/Gen_3D_Modules/TRELLIS/trellis_/renderers/octree_renderer.py new file mode 100644 index 00000000..136069cd --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/renderers/octree_renderer.py @@ -0,0 +1,300 @@ +import numpy as np +import torch +import torch.nn.functional as F +import math +import cv2 +from scipy.stats import qmc +from easydict import EasyDict as edict +from ..representations.octree import DfsOctree + + +def intrinsics_to_projection( + intrinsics: torch.Tensor, + near: float, + far: float, + ) -> torch.Tensor: + """ + OpenCV intrinsics to OpenGL perspective matrix + + Args: + intrinsics (torch.Tensor): [3, 3] OpenCV intrinsics matrix + near (float): near plane to clip + far (float): far plane to clip + Returns: + (torch.Tensor): [4, 4] OpenGL perspective matrix + """ + fx, fy = intrinsics[0, 0], intrinsics[1, 1] + cx, cy = intrinsics[0, 2], intrinsics[1, 2] + ret = torch.zeros((4, 4), dtype=intrinsics.dtype, device=intrinsics.device) + ret[0, 0] = 2 * fx + ret[1, 1] = 2 * fy + ret[0, 2] = 2 * cx - 1 + ret[1, 2] = - 2 * cy + 1 + ret[2, 2] = far / (far - near) + ret[2, 3] = near * far / (near - far) + ret[3, 2] = 1. + return ret + + +def render(viewpoint_camera, octree : DfsOctree, pipe, bg_color : torch.Tensor, scaling_modifier = 1.0, used_rank = None, colors_overwrite = None, aux=None, halton_sampler=None): + """ + Render the scene. + + Background tensor (bg_color) must be on GPU! + """ + # lazy import + if 'OctreeTrivecRasterizer' not in globals(): + from diffoctreerast import OctreeVoxelRasterizer, OctreeGaussianRasterizer, OctreeTrivecRasterizer, OctreeDecoupolyRasterizer + + # Set up rasterization configuration + tanfovx = math.tan(viewpoint_camera.FoVx * 0.5) + tanfovy = math.tan(viewpoint_camera.FoVy * 0.5) + + raster_settings = edict( + image_height=int(viewpoint_camera.image_height), + image_width=int(viewpoint_camera.image_width), + tanfovx=tanfovx, + tanfovy=tanfovy, + bg=bg_color, + scale_modifier=scaling_modifier, + viewmatrix=viewpoint_camera.world_view_transform, + projmatrix=viewpoint_camera.full_proj_transform, + sh_degree=octree.active_sh_degree, + campos=viewpoint_camera.camera_center, + with_distloss=pipe.with_distloss, + jitter=pipe.jitter, + debug=pipe.debug, + ) + + positions = octree.get_xyz + if octree.primitive == "voxel": + densities = octree.get_density + elif octree.primitive == "gaussian": + opacities = octree.get_opacity + elif octree.primitive == "trivec": + trivecs = octree.get_trivec + densities = octree.get_density + raster_settings.density_shift = octree.density_shift + elif octree.primitive == "decoupoly": + decoupolys_V, decoupolys_g = octree.get_decoupoly + densities = octree.get_density + raster_settings.density_shift = octree.density_shift + else: + raise ValueError(f"Unknown primitive {octree.primitive}") + depths = octree.get_depth + + # If precomputed colors are provided, use them. Otherwise, if it is desired to precompute colors + # from SHs in Python, do it. If not, then SH -> RGB conversion will be done by rasterizer. + colors_precomp = None + shs = octree.get_features + if octree.primitive in ["voxel", "gaussian"] and colors_overwrite is not None: + colors_precomp = colors_overwrite + shs = None + + ret = edict() + + if octree.primitive == "voxel": + renderer = OctreeVoxelRasterizer(raster_settings=raster_settings) + rgb, depth, alpha, distloss = renderer( + positions = positions, + densities = densities, + shs = shs, + colors_precomp = colors_precomp, + depths = depths, + aabb = octree.aabb, + aux = aux, + ) + ret['rgb'] = rgb + ret['depth'] = depth + ret['alpha'] = alpha + ret['distloss'] = distloss + elif octree.primitive == "gaussian": + renderer = OctreeGaussianRasterizer(raster_settings=raster_settings) + rgb, depth, alpha = renderer( + positions = positions, + opacities = opacities, + shs = shs, + colors_precomp = colors_precomp, + depths = depths, + aabb = octree.aabb, + aux = aux, + ) + ret['rgb'] = rgb + ret['depth'] = depth + ret['alpha'] = alpha + elif octree.primitive == "trivec": + raster_settings.used_rank = used_rank if used_rank is not None else trivecs.shape[1] + renderer = OctreeTrivecRasterizer(raster_settings=raster_settings) + rgb, depth, alpha, percent_depth = renderer( + positions = positions, + trivecs = trivecs, + densities = densities, + shs = shs, + colors_precomp = colors_precomp, + colors_overwrite = colors_overwrite, + depths = depths, + aabb = octree.aabb, + aux = aux, + halton_sampler = halton_sampler, + ) + ret['percent_depth'] = percent_depth + ret['rgb'] = rgb + ret['depth'] = depth + ret['alpha'] = alpha + elif octree.primitive == "decoupoly": + raster_settings.used_rank = used_rank if used_rank is not None else decoupolys_V.shape[1] + renderer = OctreeDecoupolyRasterizer(raster_settings=raster_settings) + rgb, depth, alpha = renderer( + positions = positions, + decoupolys_V = decoupolys_V, + decoupolys_g = decoupolys_g, + densities = densities, + shs = shs, + colors_precomp = colors_precomp, + depths = depths, + aabb = octree.aabb, + aux = aux, + ) + ret['rgb'] = rgb + ret['depth'] = depth + ret['alpha'] = alpha + + return ret + + +class OctreeRenderer: + """ + Renderer for the Voxel representation. + + Args: + rendering_options (dict): Rendering options. + """ + + def __init__(self, rendering_options={}) -> None: + try: + import diffoctreerast + except ImportError: + print("\033[93m[WARNING] diffoctreerast is not installed. The renderer will be disabled.\033[0m") + self.unsupported = True + else: + self.unsupported = False + + self.pipe = edict({ + "with_distloss": False, + "with_aux": False, + "scale_modifier": 1.0, + "used_rank": None, + "jitter": False, + "debug": False, + }) + self.rendering_options = edict({ + "resolution": None, + "near": None, + "far": None, + "ssaa": 1, + "bg_color": 'random', + }) + self.halton_sampler = qmc.Halton(2, scramble=False) + self.rendering_options.update(rendering_options) + self.bg_color = None + + def render( + self, + octree: DfsOctree, + extrinsics: torch.Tensor, + intrinsics: torch.Tensor, + colors_overwrite: torch.Tensor = None, + ) -> edict: + """ + Render the octree. + + Args: + octree (Octree): octree + extrinsics (torch.Tensor): (4, 4) camera extrinsics + intrinsics (torch.Tensor): (3, 3) camera intrinsics + colors_overwrite (torch.Tensor): (N, 3) override color + + Returns: + edict containing: + color (torch.Tensor): (3, H, W) rendered color + depth (torch.Tensor): (H, W) rendered depth + alpha (torch.Tensor): (H, W) rendered alpha + distloss (Optional[torch.Tensor]): (H, W) rendered distance loss + percent_depth (Optional[torch.Tensor]): (H, W) rendered percent depth + aux (Optional[edict]): auxiliary tensors + """ + resolution = self.rendering_options["resolution"] + near = self.rendering_options["near"] + far = self.rendering_options["far"] + ssaa = self.rendering_options["ssaa"] + + if self.unsupported: + image = np.zeros((512, 512, 3), dtype=np.uint8) + text_bbox = cv2.getTextSize("Unsupported", cv2.FONT_HERSHEY_SIMPLEX, 2, 3)[0] + origin = (512 - text_bbox[0]) // 2, (512 - text_bbox[1]) // 2 + image = cv2.putText(image, "Unsupported", origin, cv2.FONT_HERSHEY_SIMPLEX, 2, (255, 255, 255), 3, cv2.LINE_AA) + return { + 'color': torch.tensor(image, dtype=torch.float32).permute(2, 0, 1) / 255, + } + + if self.rendering_options["bg_color"] == 'random': + self.bg_color = torch.zeros(3, dtype=torch.float32, device="cuda") + if np.random.rand() < 0.5: + self.bg_color += 1 + else: + self.bg_color = torch.tensor(self.rendering_options["bg_color"], dtype=torch.float32, device="cuda") + + if self.pipe["with_aux"]: + aux = { + 'grad_color2': torch.zeros((octree.num_leaf_nodes, 3), dtype=torch.float32, requires_grad=True, device="cuda") + 0, + 'contributions': torch.zeros((octree.num_leaf_nodes, 1), dtype=torch.float32, requires_grad=True, device="cuda") + 0, + } + for k in aux.keys(): + aux[k].requires_grad_() + aux[k].retain_grad() + else: + aux = None + + view = extrinsics + perspective = intrinsics_to_projection(intrinsics, near, far) + camera = torch.inverse(view)[:3, 3] + focalx = intrinsics[0, 0] + focaly = intrinsics[1, 1] + fovx = 2 * torch.atan(0.5 / focalx) + fovy = 2 * torch.atan(0.5 / focaly) + + camera_dict = edict({ + "image_height": resolution * ssaa, + "image_width": resolution * ssaa, + "FoVx": fovx, + "FoVy": fovy, + "znear": near, + "zfar": far, + "world_view_transform": view.T.contiguous(), + "projection_matrix": perspective.T.contiguous(), + "full_proj_transform": (perspective @ view).T.contiguous(), + "camera_center": camera + }) + + # Render + render_ret = render(camera_dict, octree, self.pipe, self.bg_color, aux=aux, colors_overwrite=colors_overwrite, scaling_modifier=self.pipe.scale_modifier, used_rank=self.pipe.used_rank, halton_sampler=self.halton_sampler) + + if ssaa > 1: + render_ret.rgb = F.interpolate(render_ret.rgb[None], size=(resolution, resolution), mode='bilinear', align_corners=False, antialias=True).squeeze() + render_ret.depth = F.interpolate(render_ret.depth[None, None], size=(resolution, resolution), mode='bilinear', align_corners=False, antialias=True).squeeze() + render_ret.alpha = F.interpolate(render_ret.alpha[None, None], size=(resolution, resolution), mode='bilinear', align_corners=False, antialias=True).squeeze() + if hasattr(render_ret, 'percent_depth'): + render_ret.percent_depth = F.interpolate(render_ret.percent_depth[None, None], size=(resolution, resolution), mode='bilinear', align_corners=False, antialias=True).squeeze() + + ret = edict({ + 'color': render_ret.rgb, + 'depth': render_ret.depth, + 'alpha': render_ret.alpha, + }) + if self.pipe["with_distloss"] and 'distloss' in render_ret: + ret['distloss'] = render_ret.distloss + if self.pipe["with_aux"]: + ret['aux'] = aux + if hasattr(render_ret, 'percent_depth'): + ret['percent_depth'] = render_ret.percent_depth + return ret diff --git a/Gen_3D_Modules/TRELLIS/trellis_/renderers/sh_utils.py b/Gen_3D_Modules/TRELLIS/trellis_/renderers/sh_utils.py new file mode 100644 index 00000000..bbca7d19 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/renderers/sh_utils.py @@ -0,0 +1,118 @@ +# Copyright 2021 The PlenOctree Authors. +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, +# this list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. + +import torch + +C0 = 0.28209479177387814 +C1 = 0.4886025119029199 +C2 = [ + 1.0925484305920792, + -1.0925484305920792, + 0.31539156525252005, + -1.0925484305920792, + 0.5462742152960396 +] +C3 = [ + -0.5900435899266435, + 2.890611442640554, + -0.4570457994644658, + 0.3731763325901154, + -0.4570457994644658, + 1.445305721320277, + -0.5900435899266435 +] +C4 = [ + 2.5033429417967046, + -1.7701307697799304, + 0.9461746957575601, + -0.6690465435572892, + 0.10578554691520431, + -0.6690465435572892, + 0.47308734787878004, + -1.7701307697799304, + 0.6258357354491761, +] + + +def eval_sh(deg, sh, dirs): + """ + Evaluate spherical harmonics at unit directions + using hardcoded SH polynomials. + Works with torch/np/jnp. + ... Can be 0 or more batch dimensions. + Args: + deg: int SH deg. Currently, 0-3 supported + sh: jnp.ndarray SH coeffs [..., C, (deg + 1) ** 2] + dirs: jnp.ndarray unit directions [..., 3] + Returns: + [..., C] + """ + assert deg <= 4 and deg >= 0 + coeff = (deg + 1) ** 2 + assert sh.shape[-1] >= coeff + + result = C0 * sh[..., 0] + if deg > 0: + x, y, z = dirs[..., 0:1], dirs[..., 1:2], dirs[..., 2:3] + result = (result - + C1 * y * sh[..., 1] + + C1 * z * sh[..., 2] - + C1 * x * sh[..., 3]) + + if deg > 1: + xx, yy, zz = x * x, y * y, z * z + xy, yz, xz = x * y, y * z, x * z + result = (result + + C2[0] * xy * sh[..., 4] + + C2[1] * yz * sh[..., 5] + + C2[2] * (2.0 * zz - xx - yy) * sh[..., 6] + + C2[3] * xz * sh[..., 7] + + C2[4] * (xx - yy) * sh[..., 8]) + + if deg > 2: + result = (result + + C3[0] * y * (3 * xx - yy) * sh[..., 9] + + C3[1] * xy * z * sh[..., 10] + + C3[2] * y * (4 * zz - xx - yy)* sh[..., 11] + + C3[3] * z * (2 * zz - 3 * xx - 3 * yy) * sh[..., 12] + + C3[4] * x * (4 * zz - xx - yy) * sh[..., 13] + + C3[5] * z * (xx - yy) * sh[..., 14] + + C3[6] * x * (xx - 3 * yy) * sh[..., 15]) + + if deg > 3: + result = (result + C4[0] * xy * (xx - yy) * sh[..., 16] + + C4[1] * yz * (3 * xx - yy) * sh[..., 17] + + C4[2] * xy * (7 * zz - 1) * sh[..., 18] + + C4[3] * yz * (7 * zz - 3) * sh[..., 19] + + C4[4] * (zz * (35 * zz - 30) + 3) * sh[..., 20] + + C4[5] * xz * (7 * zz - 3) * sh[..., 21] + + C4[6] * (xx - yy) * (7 * zz - 1) * sh[..., 22] + + C4[7] * xz * (xx - 3 * yy) * sh[..., 23] + + C4[8] * (xx * (xx - 3 * yy) - yy * (3 * xx - yy)) * sh[..., 24]) + return result + +def RGB2SH(rgb): + return (rgb - 0.5) / C0 + +def SH2RGB(sh): + return sh * C0 + 0.5 \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/representations/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/representations/__init__.py new file mode 100644 index 00000000..549ffdb9 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/representations/__init__.py @@ -0,0 +1,4 @@ +from .radiance_field import Strivec +from .octree import DfsOctree as Octree +from .gaussian import Gaussian +from .mesh import MeshExtractResult diff --git a/Gen_3D_Modules/TRELLIS/trellis_/representations/gaussian/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/representations/gaussian/__init__.py new file mode 100644 index 00000000..e3de6e18 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/representations/gaussian/__init__.py @@ -0,0 +1 @@ +from .gaussian_model import Gaussian \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/representations/gaussian/gaussian_model.py b/Gen_3D_Modules/TRELLIS/trellis_/representations/gaussian/gaussian_model.py new file mode 100644 index 00000000..2dc70552 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/representations/gaussian/gaussian_model.py @@ -0,0 +1,194 @@ +import torch +import numpy as np +from plyfile import PlyData, PlyElement +from .general_utils import inverse_sigmoid, strip_symmetric, build_scaling_rotation + + +class Gaussian: + def __init__( + self, + aabb : list, + sh_degree : int = 0, + mininum_kernel_size : float = 0.0, + scaling_bias : float = 0.01, + opacity_bias : float = 0.1, + scaling_activation : str = "exp", + device='cuda' + ): + self.init_params = { + 'aabb': aabb, + 'sh_degree': sh_degree, + 'mininum_kernel_size': mininum_kernel_size, + 'scaling_bias': scaling_bias, + 'opacity_bias': opacity_bias, + 'scaling_activation': scaling_activation, + } + + self.sh_degree = sh_degree + self.active_sh_degree = sh_degree + self.mininum_kernel_size = mininum_kernel_size + self.scaling_bias = scaling_bias + self.opacity_bias = opacity_bias + self.scaling_activation_type = scaling_activation + self.device = device + self.aabb = torch.tensor(aabb, dtype=torch.float32, device=device) + self.setup_functions() + + self._xyz = None + self._features_dc = None + self._features_rest = None + self._scaling = None + self._rotation = None + self._opacity = None + + def setup_functions(self): + def build_covariance_from_scaling_rotation(scaling, scaling_modifier, rotation): + L = build_scaling_rotation(scaling_modifier * scaling, rotation) + actual_covariance = L @ L.transpose(1, 2) + symm = strip_symmetric(actual_covariance) + return symm + + if self.scaling_activation_type == "exp": + self.scaling_activation = torch.exp + self.inverse_scaling_activation = torch.log + elif self.scaling_activation_type == "softplus": + self.scaling_activation = torch.nn.functional.softplus + self.inverse_scaling_activation = lambda x: x + torch.log(-torch.expm1(-x)) + + self.covariance_activation = build_covariance_from_scaling_rotation + + self.opacity_activation = torch.sigmoid + self.inverse_opacity_activation = inverse_sigmoid + + self.rotation_activation = torch.nn.functional.normalize + + self.scale_bias = self.inverse_scaling_activation(torch.tensor(self.scaling_bias)).cuda() + self.rots_bias = torch.zeros((4)).cuda() + self.rots_bias[0] = 1 + self.opacity_bias = self.inverse_opacity_activation(torch.tensor(self.opacity_bias)).cuda() + + @property + def get_scaling(self): + scales = self.scaling_activation(self._scaling + self.scale_bias) + scales = torch.square(scales) + self.mininum_kernel_size ** 2 + scales = torch.sqrt(scales) + return scales + + @property + def get_rotation(self): + return self.rotation_activation(self._rotation + self.rots_bias[None, :]) + + @property + def get_xyz(self): + return self._xyz * self.aabb[None, 3:] + self.aabb[None, :3] + + @property + def get_features(self): + return torch.cat((self._features_dc, self._features_rest), dim=2) if self._features_rest is not None else self._features_dc + + @property + def get_opacity(self): + return self.opacity_activation(self._opacity + self.opacity_bias) + + def get_covariance(self, scaling_modifier = 1): + return self.covariance_activation(self.get_scaling, scaling_modifier, self._rotation + self.rots_bias[None, :]) + + def from_scaling(self, scales): + scales = torch.sqrt(torch.square(scales) - self.mininum_kernel_size ** 2) + self._scaling = self.inverse_scaling_activation(scales) - self.scale_bias + + def from_rotation(self, rots): + self._rotation = rots - self.rots_bias[None, :] + + def from_xyz(self, xyz): + self._xyz = (xyz - self.aabb[None, :3]) / self.aabb[None, 3:] + + def from_features(self, features): + self._features_dc = features + + def from_opacity(self, opacities): + self._opacity = self.inverse_opacity_activation(opacities) - self.opacity_bias + + def construct_list_of_attributes(self): + l = ['x', 'y', 'z', 'nx', 'ny', 'nz'] + # All channels except the 3 DC + for i in range(self._features_dc.shape[1]*self._features_dc.shape[2]): + l.append('f_dc_{}'.format(i)) + l.append('opacity') + for i in range(self._scaling.shape[1]): + l.append('scale_{}'.format(i)) + for i in range(self._rotation.shape[1]): + l.append('rot_{}'.format(i)) + return l + + def save_ply(self, path): + xyz = self.get_xyz.detach().cpu().numpy() + normals = np.zeros_like(xyz) + f_dc = self._features_dc.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy() + opacities = inverse_sigmoid(self.get_opacity).detach().cpu().numpy() + scale = torch.log(self.get_scaling).detach().cpu().numpy() + rotation = (self._rotation + self.rots_bias[None, :]).detach().cpu().numpy() + + dtype_full = [(attribute, 'f4') for attribute in self.construct_list_of_attributes()] + + elements = np.empty(xyz.shape[0], dtype=dtype_full) + attributes = np.concatenate((xyz, normals, f_dc, opacities, scale, rotation), axis=1) + elements[:] = list(map(tuple, attributes)) + el = PlyElement.describe(elements, 'vertex') + PlyData([el]).write(path) + + def load_ply(self, path): + plydata = PlyData.read(path) + + xyz = np.stack((np.asarray(plydata.elements[0]["x"]), + np.asarray(plydata.elements[0]["y"]), + np.asarray(plydata.elements[0]["z"])), axis=1) + opacities = np.asarray(plydata.elements[0]["opacity"])[..., np.newaxis] + + features_dc = np.zeros((xyz.shape[0], 3, 1)) + features_dc[:, 0, 0] = np.asarray(plydata.elements[0]["f_dc_0"]) + features_dc[:, 1, 0] = np.asarray(plydata.elements[0]["f_dc_1"]) + features_dc[:, 2, 0] = np.asarray(plydata.elements[0]["f_dc_2"]) + + if self.sh_degree > 0: + extra_f_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("f_rest_")] + extra_f_names = sorted(extra_f_names, key = lambda x: int(x.split('_')[-1])) + assert len(extra_f_names)==3*(self.sh_degree + 1) ** 2 - 3 + features_extra = np.zeros((xyz.shape[0], len(extra_f_names))) + for idx, attr_name in enumerate(extra_f_names): + features_extra[:, idx] = np.asarray(plydata.elements[0][attr_name]) + # Reshape (P,F*SH_coeffs) to (P, F, SH_coeffs except DC) + features_extra = features_extra.reshape((features_extra.shape[0], 3, (self.max_sh_degree + 1) ** 2 - 1)) + + scale_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("scale_")] + scale_names = sorted(scale_names, key = lambda x: int(x.split('_')[-1])) + scales = np.zeros((xyz.shape[0], len(scale_names))) + for idx, attr_name in enumerate(scale_names): + scales[:, idx] = np.asarray(plydata.elements[0][attr_name]) + + rot_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("rot")] + rot_names = sorted(rot_names, key = lambda x: int(x.split('_')[-1])) + rots = np.zeros((xyz.shape[0], len(rot_names))) + for idx, attr_name in enumerate(rot_names): + rots[:, idx] = np.asarray(plydata.elements[0][attr_name]) + + # convert to actual gaussian attributes + xyz = torch.tensor(xyz, dtype=torch.float, device=self.device) + features_dc = torch.tensor(features_dc, dtype=torch.float, device=self.device).transpose(1, 2).contiguous() + if self.sh_degree > 0: + features_extra = torch.tensor(features_extra, dtype=torch.float, device=self.device).transpose(1, 2).contiguous() + opacities = torch.sigmoid(torch.tensor(opacities, dtype=torch.float, device=self.device)) + scales = torch.exp(torch.tensor(scales, dtype=torch.float, device=self.device)) + rots = torch.tensor(rots, dtype=torch.float, device=self.device) + + # convert to _hidden attributes + self._xyz = (xyz - self.aabb[None, :3]) / self.aabb[None, 3:] + self._features_dc = features_dc + if self.sh_degree > 0: + self._features_rest = features_extra + else: + self._features_rest = None + self._opacity = self.inverse_opacity_activation(opacities) - self.opacity_bias + self._scaling = self.inverse_scaling_activation(torch.sqrt(torch.square(scales) - self.mininum_kernel_size ** 2)) - self.scale_bias + self._rotation = rots - self.rots_bias[None, :] + \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/representations/gaussian/general_utils.py b/Gen_3D_Modules/TRELLIS/trellis_/representations/gaussian/general_utils.py new file mode 100644 index 00000000..541c0825 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/representations/gaussian/general_utils.py @@ -0,0 +1,133 @@ +# +# Copyright (C) 2023, Inria +# GRAPHDECO research group, https://team.inria.fr/graphdeco +# All rights reserved. +# +# This software is free for non-commercial, research and evaluation use +# under the terms of the LICENSE.md file. +# +# For inquiries contact george.drettakis@inria.fr +# + +import torch +import sys +from datetime import datetime +import numpy as np +import random + +def inverse_sigmoid(x): + return torch.log(x/(1-x)) + +def PILtoTorch(pil_image, resolution): + resized_image_PIL = pil_image.resize(resolution) + resized_image = torch.from_numpy(np.array(resized_image_PIL)) / 255.0 + if len(resized_image.shape) == 3: + return resized_image.permute(2, 0, 1) + else: + return resized_image.unsqueeze(dim=-1).permute(2, 0, 1) + +def get_expon_lr_func( + lr_init, lr_final, lr_delay_steps=0, lr_delay_mult=1.0, max_steps=1000000 +): + """ + Copied from Plenoxels + + Continuous learning rate decay function. Adapted from JaxNeRF + The returned rate is lr_init when step=0 and lr_final when step=max_steps, and + is log-linearly interpolated elsewhere (equivalent to exponential decay). + If lr_delay_steps>0 then the learning rate will be scaled by some smooth + function of lr_delay_mult, such that the initial learning rate is + lr_init*lr_delay_mult at the beginning of optimization but will be eased back + to the normal learning rate when steps>lr_delay_steps. + :param conf: config subtree 'lr' or similar + :param max_steps: int, the number of steps during optimization. + :return HoF which takes step as input + """ + + def helper(step): + if step < 0 or (lr_init == 0.0 and lr_final == 0.0): + # Disable this parameter + return 0.0 + if lr_delay_steps > 0: + # A kind of reverse cosine decay. + delay_rate = lr_delay_mult + (1 - lr_delay_mult) * np.sin( + 0.5 * np.pi * np.clip(step / lr_delay_steps, 0, 1) + ) + else: + delay_rate = 1.0 + t = np.clip(step / max_steps, 0, 1) + log_lerp = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t) + return delay_rate * log_lerp + + return helper + +def strip_lowerdiag(L): + uncertainty = torch.zeros((L.shape[0], 6), dtype=torch.float, device="cuda") + + uncertainty[:, 0] = L[:, 0, 0] + uncertainty[:, 1] = L[:, 0, 1] + uncertainty[:, 2] = L[:, 0, 2] + uncertainty[:, 3] = L[:, 1, 1] + uncertainty[:, 4] = L[:, 1, 2] + uncertainty[:, 5] = L[:, 2, 2] + return uncertainty + +def strip_symmetric(sym): + return strip_lowerdiag(sym) + +def build_rotation(r): + norm = torch.sqrt(r[:,0]*r[:,0] + r[:,1]*r[:,1] + r[:,2]*r[:,2] + r[:,3]*r[:,3]) + + q = r / norm[:, None] + + R = torch.zeros((q.size(0), 3, 3), device='cuda') + + r = q[:, 0] + x = q[:, 1] + y = q[:, 2] + z = q[:, 3] + + R[:, 0, 0] = 1 - 2 * (y*y + z*z) + R[:, 0, 1] = 2 * (x*y - r*z) + R[:, 0, 2] = 2 * (x*z + r*y) + R[:, 1, 0] = 2 * (x*y + r*z) + R[:, 1, 1] = 1 - 2 * (x*x + z*z) + R[:, 1, 2] = 2 * (y*z - r*x) + R[:, 2, 0] = 2 * (x*z - r*y) + R[:, 2, 1] = 2 * (y*z + r*x) + R[:, 2, 2] = 1 - 2 * (x*x + y*y) + return R + +def build_scaling_rotation(s, r): + L = torch.zeros((s.shape[0], 3, 3), dtype=torch.float, device="cuda") + R = build_rotation(r) + + L[:,0,0] = s[:,0] + L[:,1,1] = s[:,1] + L[:,2,2] = s[:,2] + + L = R @ L + return L + +def safe_state(silent): + old_f = sys.stdout + class F: + def __init__(self, silent): + self.silent = silent + + def write(self, x): + if not self.silent: + if x.endswith("\n"): + old_f.write(x.replace("\n", " [{}]\n".format(str(datetime.now().strftime("%d/%m %H:%M:%S"))))) + else: + old_f.write(x) + + def flush(self): + old_f.flush() + + sys.stdout = F(silent) + + random.seed(0) + np.random.seed(0) + torch.manual_seed(0) + torch.cuda.set_device(torch.device("cuda:0")) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/__init__.py new file mode 100644 index 00000000..38cf35c0 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/__init__.py @@ -0,0 +1 @@ +from .cube2mesh import SparseFeatures2Mesh, MeshExtractResult diff --git a/Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/cube2mesh.py b/Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/cube2mesh.py new file mode 100644 index 00000000..fe2cca76 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/cube2mesh.py @@ -0,0 +1,146 @@ +import torch +from ...modules.sparse import SparseTensor +from easydict import EasyDict as edict +from .utils_cube import * +try: + from .flexicubes.flexicubes import FlexiCubes +except: + print("Please install kaolin and diso to use the mesh extractor.") + + +class MeshExtractResult: + def __init__(self, + vertices, + faces, + vertex_attrs=None, + res=64 + ): + self.vertices = vertices + self.faces = faces.long() + self.vertex_attrs = vertex_attrs + self.face_normal = self.comput_face_normals(vertices, faces) + self.res = res + self.success = (vertices.shape[0] != 0 and faces.shape[0] != 0) + + # training only + self.tsdf_v = None + self.tsdf_s = None + self.reg_loss = None + + def comput_face_normals(self, verts, faces): + i0 = faces[..., 0].long() + i1 = faces[..., 1].long() + i2 = faces[..., 2].long() + + v0 = verts[i0, :] + v1 = verts[i1, :] + v2 = verts[i2, :] + face_normals = torch.cross(v1 - v0, v2 - v0, dim=-1) + face_normals = torch.nn.functional.normalize(face_normals, dim=1) + # print(face_normals.min(), face_normals.max(), face_normals.shape) + return face_normals[:, None, :].repeat(1, 3, 1) + + def comput_v_normals(self, verts, faces): + i0 = faces[..., 0].long() + i1 = faces[..., 1].long() + i2 = faces[..., 2].long() + + v0 = verts[i0, :] + v1 = verts[i1, :] + v2 = verts[i2, :] + face_normals = torch.cross(v1 - v0, v2 - v0, dim=-1) + v_normals = torch.zeros_like(verts) + v_normals.scatter_add_(0, i0[..., None].repeat(1, 3), face_normals) + v_normals.scatter_add_(0, i1[..., None].repeat(1, 3), face_normals) + v_normals.scatter_add_(0, i2[..., None].repeat(1, 3), face_normals) + + v_normals = torch.nn.functional.normalize(v_normals, dim=1) + return v_normals + + +class SparseFeatures2Mesh: + def __init__(self, device="cuda", res=64, use_color=True): + ''' + a model to generate a mesh from sparse features structures using flexicube + ''' + super().__init__() + self.device=device + self.res = res + self.mesh_extractor = FlexiCubes(device=device) + self.sdf_bias = -1.0 / res + verts, cube = construct_dense_grid(self.res, self.device) + self.reg_c = cube.to(self.device) + self.reg_v = verts.to(self.device) + self.use_color = use_color + self._calc_layout() + + def _calc_layout(self): + LAYOUTS = { + 'sdf': {'shape': (8, 1), 'size': 8}, + 'deform': {'shape': (8, 3), 'size': 8 * 3}, + 'weights': {'shape': (21,), 'size': 21} + } + if self.use_color: + ''' + 6 channel color including normal map + ''' + LAYOUTS['color'] = {'shape': (8, 6,), 'size': 8 * 6} + self.layouts = edict(LAYOUTS) + start = 0 + for k, v in self.layouts.items(): + v['range'] = (start, start + v['size']) + start += v['size'] + self.feats_channels = start + + def get_layout(self, feats : torch.Tensor, name : str): + if name not in self.layouts: + return None + return feats[:, self.layouts[name]['range'][0]:self.layouts[name]['range'][1]].reshape(-1, *self.layouts[name]['shape']) + + def __call__(self, cubefeats : SparseTensor, training=False): + """ + Generates a mesh based on the specified sparse voxel structures. + Args: + cube_attrs [Nx21] : Sparse Tensor attrs about cube weights + verts_attrs [Nx10] : [0:1] SDF [1:4] deform [4:7] color [7:10] normal + Returns: + return the success tag and ni you loss, + """ + # add sdf bias to verts_attrs + coords = cubefeats.coords[:, 1:] + feats = cubefeats.feats + + sdf, deform, color, weights = [self.get_layout(feats, name) for name in ['sdf', 'deform', 'color', 'weights']] + sdf += self.sdf_bias + v_attrs = [sdf, deform, color] if self.use_color else [sdf, deform] + v_pos, v_attrs, reg_loss = sparse_cube2verts(coords, torch.cat(v_attrs, dim=-1), training=training) + v_attrs_d = get_dense_attrs(v_pos, v_attrs, res=self.res+1, sdf_init=True) + weights_d = get_dense_attrs(coords, weights, res=self.res, sdf_init=False) + if self.use_color: + sdf_d, deform_d, colors_d = v_attrs_d[..., 0], v_attrs_d[..., 1:4], v_attrs_d[..., 4:] + else: + sdf_d, deform_d = v_attrs_d[..., 0], v_attrs_d[..., 1:4] + colors_d = None + + x_nx3 = get_defomed_verts(self.reg_v, deform_d, self.res) + + vertices, faces, L_dev, colors = self.mesh_extractor( + voxelgrid_vertices=x_nx3, + scalar_field=sdf_d, + cube_idx=self.reg_c, + resolution=self.res, + beta=weights_d[:, :12], + alpha=weights_d[:, 12:20], + gamma_f=weights_d[:, 20], + voxelgrid_colors=colors_d, + training=training) + + mesh = MeshExtractResult(vertices=vertices, faces=faces, vertex_attrs=colors, res=self.res) + if training: + if mesh.success: + reg_loss += L_dev.mean() * 0.5 + reg_loss += (weights[:,:20]).abs().mean() * 0.2 + mesh.reg_loss = reg_loss + mesh.tsdf_v = get_defomed_verts(v_pos, v_attrs[:, 1:4], self.res) + mesh.tsdf_s = v_attrs[:, 0] + return mesh diff --git a/Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/flexicubes/flexicubes.py b/Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/flexicubes/flexicubes.py new file mode 100644 index 00000000..297c5137 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/flexicubes/flexicubes.py @@ -0,0 +1,417 @@ +# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited. + +import torch +from .tables import * + +__all__ = [ + 'FlexiCubes' +] + +def check_tensor(tensor, shape=None, dtype=None, device=None, throw=True): + """Check if :class:`torch.Tensor` is valid given set of criteria. + + Args: + tensor (torch.Tensor): the tensor to be tested. + shape (list or tuple of int, optional): the expected shape, + if a dimension is set at ``None`` then it's not verified. + dtype (torch.dtype, optional): the expected dtype. + device (torch.device, optional): the expected device. + throw (bool): if true (default), will throw if checks fail + + Return: + (bool) True if checks pass + """ + if shape is not None: + if len(shape) != tensor.ndim: + if throw: + raise ValueError(f"tensor have {tensor.ndim} ndim, should have {len(shape)}") + return False + for i, dim in enumerate(shape): + if dim is not None and tensor.shape[i] != dim: + if throw: + raise ValueError(f"tensor shape is {tensor.shape}, should be {shape}") + return False + if dtype is not None and dtype != tensor.dtype: + if throw: + raise TypeError(f"tensor dtype is {tensor.dtype}, should be {dtype}") + return False + if device is not None and device != tensor.device.type: + if throw: + raise TypeError(f"tensor device is {tensor.device.type}, should be {device}") + return False + return True + + +class FlexiCubes: + def __init__(self, device="cuda"): + + self.device = device + self.dmc_table = torch.tensor(dmc_table, dtype=torch.long, device=device, requires_grad=False) + self.num_vd_table = torch.tensor(num_vd_table, + dtype=torch.long, device=device, requires_grad=False) + self.check_table = torch.tensor( + check_table, + dtype=torch.long, device=device, requires_grad=False) + + self.tet_table = torch.tensor(tet_table, dtype=torch.long, device=device, requires_grad=False) + self.quad_split_1 = torch.tensor([0, 1, 2, 0, 2, 3], dtype=torch.long, device=device, requires_grad=False) + self.quad_split_2 = torch.tensor([0, 1, 3, 3, 1, 2], dtype=torch.long, device=device, requires_grad=False) + self.quad_split_train = torch.tensor( + [0, 1, 1, 2, 2, 3, 3, 0], dtype=torch.long, device=device, requires_grad=False) + + self.cube_corners = torch.tensor([[0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0], [0, 0, 1], [ + 1, 0, 1], [0, 1, 1], [1, 1, 1]], dtype=torch.float, device=device) + self.cube_corners_idx = torch.pow(2, torch.arange(8, requires_grad=False)) + self.cube_edges = torch.tensor([0, 1, 1, 5, 4, 5, 0, 4, 2, 3, 3, 7, 6, 7, 2, 6, + 2, 0, 3, 1, 7, 5, 6, 4], dtype=torch.long, device=device, requires_grad=False) + + self.edge_dir_table = torch.tensor([0, 2, 0, 2, 0, 2, 0, 2, 1, 1, 1, 1], + dtype=torch.long, device=device) + self.dir_faces_table = torch.tensor([ + [[5, 4], [3, 2], [4, 5], [2, 3]], + [[5, 4], [1, 0], [4, 5], [0, 1]], + [[3, 2], [1, 0], [2, 3], [0, 1]] + ], dtype=torch.long, device=device) + self.adj_pairs = torch.tensor([0, 1, 1, 3, 3, 2, 2, 0], dtype=torch.long, device=device) + + def __call__(self, voxelgrid_vertices, scalar_field, cube_idx, resolution, qef_reg_scale=1e-3, + weight_scale=0.99, beta=None, alpha=None, gamma_f=None, voxelgrid_colors=None, training=False): + assert torch.is_tensor(voxelgrid_vertices) and \ + check_tensor(voxelgrid_vertices, (None, 3), throw=False), \ + "'voxelgrid_vertices' should be a tensor of shape (num_vertices, 3)" + num_vertices = voxelgrid_vertices.shape[0] + assert torch.is_tensor(scalar_field) and \ + check_tensor(scalar_field, (num_vertices,), throw=False), \ + "'scalar_field' should be a tensor of shape (num_vertices,)" + assert torch.is_tensor(cube_idx) and \ + check_tensor(cube_idx, (None, 8), throw=False), \ + "'cube_idx' should be a tensor of shape (num_cubes, 8)" + num_cubes = cube_idx.shape[0] + assert beta is None or ( + torch.is_tensor(beta) and + check_tensor(beta, (num_cubes, 12), throw=False) + ), "'beta' should be a tensor of shape (num_cubes, 12)" + assert alpha is None or ( + torch.is_tensor(alpha) and + check_tensor(alpha, (num_cubes, 8), throw=False) + ), "'alpha' should be a tensor of shape (num_cubes, 8)" + assert gamma_f is None or ( + torch.is_tensor(gamma_f) and + check_tensor(gamma_f, (num_cubes,), throw=False) + ), "'gamma_f' should be a tensor of shape (num_cubes,)" + + surf_cubes, occ_fx8 = self._identify_surf_cubes(scalar_field, cube_idx) + if surf_cubes.sum() == 0: + return ( + torch.zeros((0, 3), device=self.device), + torch.zeros((0, 3), dtype=torch.long, device=self.device), + torch.zeros((0), device=self.device), + torch.zeros((0, voxelgrid_colors.shape[-1]), device=self.device) if voxelgrid_colors is not None else None + ) + beta, alpha, gamma_f = self._normalize_weights( + beta, alpha, gamma_f, surf_cubes, weight_scale) + + if voxelgrid_colors is not None: + voxelgrid_colors = torch.sigmoid(voxelgrid_colors) + + case_ids = self._get_case_id(occ_fx8, surf_cubes, resolution) + + surf_edges, idx_map, edge_counts, surf_edges_mask = self._identify_surf_edges( + scalar_field, cube_idx, surf_cubes + ) + + vd, L_dev, vd_gamma, vd_idx_map, vd_color = self._compute_vd( + voxelgrid_vertices, cube_idx[surf_cubes], surf_edges, scalar_field, + case_ids, beta, alpha, gamma_f, idx_map, qef_reg_scale, voxelgrid_colors) + vertices, faces, s_edges, edge_indices, vertices_color = self._triangulate( + scalar_field, surf_edges, vd, vd_gamma, edge_counts, idx_map, + vd_idx_map, surf_edges_mask, training, vd_color) + return vertices, faces, L_dev, vertices_color + + def _compute_reg_loss(self, vd, ue, edge_group_to_vd, vd_num_edges): + """ + Regularizer L_dev as in Equation 8 + """ + dist = torch.norm(ue - torch.index_select(input=vd, index=edge_group_to_vd, dim=0), dim=-1) + mean_l2 = torch.zeros_like(vd[:, 0]) + mean_l2 = (mean_l2).index_add_(0, edge_group_to_vd, dist) / vd_num_edges.squeeze(1).float() + mad = (dist - torch.index_select(input=mean_l2, index=edge_group_to_vd, dim=0)).abs() + return mad + + def _normalize_weights(self, beta, alpha, gamma_f, surf_cubes, weight_scale): + """ + Normalizes the given weights to be non-negative. If input weights are None, it creates and returns a set of weights of ones. + """ + n_cubes = surf_cubes.shape[0] + + if beta is not None: + beta = (torch.tanh(beta) * weight_scale + 1) + else: + beta = torch.ones((n_cubes, 12), dtype=torch.float, device=self.device) + + if alpha is not None: + alpha = (torch.tanh(alpha) * weight_scale + 1) + else: + alpha = torch.ones((n_cubes, 8), dtype=torch.float, device=self.device) + + if gamma_f is not None: + gamma_f = torch.sigmoid(gamma_f) * weight_scale + (1 - weight_scale) / 2 + else: + gamma_f = torch.ones((n_cubes), dtype=torch.float, device=self.device) + + return beta[surf_cubes], alpha[surf_cubes], gamma_f[surf_cubes] + + @torch.no_grad() + def _get_case_id(self, occ_fx8, surf_cubes, res): + """ + Obtains the ID of topology cases based on cell corner occupancy. This function resolves the + ambiguity in the Dual Marching Cubes (DMC) configurations as described in Section 1.3 of the + supplementary material. It should be noted that this function assumes a regular grid. + """ + case_ids = (occ_fx8[surf_cubes] * self.cube_corners_idx.to(self.device).unsqueeze(0)).sum(-1) + + problem_config = self.check_table.to(self.device)[case_ids] + to_check = problem_config[..., 0] == 1 + problem_config = problem_config[to_check] + if not isinstance(res, (list, tuple)): + res = [res, res, res] + + # The 'problematic_configs' only contain configurations for surface cubes. Next, we construct a 3D array, + # 'problem_config_full', to store configurations for all cubes (with default config for non-surface cubes). + # This allows efficient checking on adjacent cubes. + problem_config_full = torch.zeros(list(res) + [5], device=self.device, dtype=torch.long) + vol_idx = torch.nonzero(problem_config_full[..., 0] == 0) # N, 3 + vol_idx_problem = vol_idx[surf_cubes][to_check] + problem_config_full[vol_idx_problem[..., 0], vol_idx_problem[..., 1], vol_idx_problem[..., 2]] = problem_config + vol_idx_problem_adj = vol_idx_problem + problem_config[..., 1:4] + + within_range = ( + vol_idx_problem_adj[..., 0] >= 0) & ( + vol_idx_problem_adj[..., 0] < res[0]) & ( + vol_idx_problem_adj[..., 1] >= 0) & ( + vol_idx_problem_adj[..., 1] < res[1]) & ( + vol_idx_problem_adj[..., 2] >= 0) & ( + vol_idx_problem_adj[..., 2] < res[2]) + + vol_idx_problem = vol_idx_problem[within_range] + vol_idx_problem_adj = vol_idx_problem_adj[within_range] + problem_config = problem_config[within_range] + problem_config_adj = problem_config_full[vol_idx_problem_adj[..., 0], + vol_idx_problem_adj[..., 1], vol_idx_problem_adj[..., 2]] + # If two cubes with cases C16 and C19 share an ambiguous face, both cases are inverted. + to_invert = (problem_config_adj[..., 0] == 1) + idx = torch.arange(case_ids.shape[0], device=self.device)[to_check][within_range][to_invert] + case_ids.index_put_((idx,), problem_config[to_invert][..., -1]) + return case_ids + + @torch.no_grad() + def _identify_surf_edges(self, scalar_field, cube_idx, surf_cubes): + """ + Identifies grid edges that intersect with the underlying surface by checking for opposite signs. As each edge + can be shared by multiple cubes, this function also assigns a unique index to each surface-intersecting edge + and marks the cube edges with this index. + """ + occ_n = scalar_field < 0 + all_edges = cube_idx[surf_cubes][:, self.cube_edges].reshape(-1, 2) + unique_edges, _idx_map, counts = torch.unique(all_edges, dim=0, return_inverse=True, return_counts=True) + + unique_edges = unique_edges.long() + mask_edges = occ_n[unique_edges.reshape(-1)].reshape(-1, 2).sum(-1) == 1 + + surf_edges_mask = mask_edges[_idx_map] + counts = counts[_idx_map] + + mapping = torch.ones((unique_edges.shape[0]), dtype=torch.long, device=cube_idx.device) * -1 + mapping[mask_edges] = torch.arange(mask_edges.sum(), device=cube_idx.device) + # Shaped as [number of cubes x 12 edges per cube]. This is later used to map a cube edge to the unique index + # for a surface-intersecting edge. Non-surface-intersecting edges are marked with -1. + idx_map = mapping[_idx_map] + surf_edges = unique_edges[mask_edges] + return surf_edges, idx_map, counts, surf_edges_mask + + @torch.no_grad() + def _identify_surf_cubes(self, scalar_field, cube_idx): + """ + Identifies grid cubes that intersect with the underlying surface by checking if the signs at + all corners are not identical. + """ + occ_n = scalar_field < 0 + occ_fx8 = occ_n[cube_idx.reshape(-1)].reshape(-1, 8) + _occ_sum = torch.sum(occ_fx8, -1) + surf_cubes = (_occ_sum > 0) & (_occ_sum < 8) + return surf_cubes, occ_fx8 + + def _linear_interp(self, edges_weight, edges_x): + """ + Computes the location of zero-crossings on 'edges_x' using linear interpolation with 'edges_weight'. + """ + edge_dim = edges_weight.dim() - 2 + assert edges_weight.shape[edge_dim] == 2 + edges_weight = torch.cat([torch.index_select(input=edges_weight, index=torch.tensor(1, device=self.device), dim=edge_dim), - + torch.index_select(input=edges_weight, index=torch.tensor(0, device=self.device), dim=edge_dim)] + , edge_dim) + denominator = edges_weight.sum(edge_dim) + ue = (edges_x * edges_weight).sum(edge_dim) / denominator + return ue + + def _solve_vd_QEF(self, p_bxnx3, norm_bxnx3, c_bx3, qef_reg_scale): + p_bxnx3 = p_bxnx3.reshape(-1, 7, 3) + norm_bxnx3 = norm_bxnx3.reshape(-1, 7, 3) + c_bx3 = c_bx3.reshape(-1, 3) + A = norm_bxnx3 + B = ((p_bxnx3) * norm_bxnx3).sum(-1, keepdims=True) + + A_reg = (torch.eye(3, device=p_bxnx3.device) * qef_reg_scale).unsqueeze(0).repeat(p_bxnx3.shape[0], 1, 1) + B_reg = (qef_reg_scale * c_bx3).unsqueeze(-1) + A = torch.cat([A, A_reg], 1) + B = torch.cat([B, B_reg], 1) + dual_verts = torch.linalg.lstsq(A, B).solution.squeeze(-1) + return dual_verts + + def _compute_vd(self, voxelgrid_vertices, surf_cubes_fx8, surf_edges, scalar_field, + case_ids, beta, alpha, gamma_f, idx_map, qef_reg_scale, voxelgrid_colors): + """ + Computes the location of dual vertices as described in Section 4.2 + """ + alpha_nx12x2 = torch.index_select(input=alpha, index=self.cube_edges, dim=1).reshape(-1, 12, 2) + surf_edges_x = torch.index_select(input=voxelgrid_vertices, index=surf_edges.reshape(-1), dim=0).reshape(-1, 2, 3) + surf_edges_s = torch.index_select(input=scalar_field, index=surf_edges.reshape(-1), dim=0).reshape(-1, 2, 1) + zero_crossing = self._linear_interp(surf_edges_s, surf_edges_x) + + if voxelgrid_colors is not None: + C = voxelgrid_colors.shape[-1] + surf_edges_c = torch.index_select(input=voxelgrid_colors, index=surf_edges.reshape(-1), dim=0).reshape(-1, 2, C) + + idx_map = idx_map.reshape(-1, 12) + num_vd = torch.index_select(input=self.num_vd_table, index=case_ids, dim=0) + edge_group, edge_group_to_vd, edge_group_to_cube, vd_num_edges, vd_gamma = [], [], [], [], [] + + # if color is not None: + # vd_color = [] + + total_num_vd = 0 + vd_idx_map = torch.zeros((case_ids.shape[0], 12), dtype=torch.long, device=self.device, requires_grad=False) + + for num in torch.unique(num_vd): + cur_cubes = (num_vd == num) # consider cubes with the same numbers of vd emitted (for batching) + curr_num_vd = cur_cubes.sum() * num + curr_edge_group = self.dmc_table[case_ids[cur_cubes], :num].reshape(-1, num * 7) + curr_edge_group_to_vd = torch.arange( + curr_num_vd, device=self.device).unsqueeze(-1).repeat(1, 7) + total_num_vd + total_num_vd += curr_num_vd + curr_edge_group_to_cube = torch.arange(idx_map.shape[0], device=self.device)[ + cur_cubes].unsqueeze(-1).repeat(1, num * 7).reshape_as(curr_edge_group) + + curr_mask = (curr_edge_group != -1) + edge_group.append(torch.masked_select(curr_edge_group, curr_mask)) + edge_group_to_vd.append(torch.masked_select(curr_edge_group_to_vd.reshape_as(curr_edge_group), curr_mask)) + edge_group_to_cube.append(torch.masked_select(curr_edge_group_to_cube, curr_mask)) + vd_num_edges.append(curr_mask.reshape(-1, 7).sum(-1, keepdims=True)) + vd_gamma.append(torch.masked_select(gamma_f, cur_cubes).unsqueeze(-1).repeat(1, num).reshape(-1)) + # if color is not None: + # vd_color.append(color[cur_cubes].unsqueeze(1).repeat(1, num, 1).reshape(-1, 3)) + + edge_group = torch.cat(edge_group) + edge_group_to_vd = torch.cat(edge_group_to_vd) + edge_group_to_cube = torch.cat(edge_group_to_cube) + vd_num_edges = torch.cat(vd_num_edges) + vd_gamma = torch.cat(vd_gamma) + # if color is not None: + # vd_color = torch.cat(vd_color) + # else: + # vd_color = None + + vd = torch.zeros((total_num_vd, 3), device=self.device) + beta_sum = torch.zeros((total_num_vd, 1), device=self.device) + + idx_group = torch.gather(input=idx_map.reshape(-1), dim=0, index=edge_group_to_cube * 12 + edge_group) + + x_group = torch.index_select(input=surf_edges_x, index=idx_group.reshape(-1), dim=0).reshape(-1, 2, 3) + s_group = torch.index_select(input=surf_edges_s, index=idx_group.reshape(-1), dim=0).reshape(-1, 2, 1) + + + zero_crossing_group = torch.index_select( + input=zero_crossing, index=idx_group.reshape(-1), dim=0).reshape(-1, 3) + + alpha_group = torch.index_select(input=alpha_nx12x2.reshape(-1, 2), dim=0, + index=edge_group_to_cube * 12 + edge_group).reshape(-1, 2, 1) + ue_group = self._linear_interp(s_group * alpha_group, x_group) + + beta_group = torch.gather(input=beta.reshape(-1), dim=0, + index=edge_group_to_cube * 12 + edge_group).reshape(-1, 1) + beta_sum = beta_sum.index_add_(0, index=edge_group_to_vd, source=beta_group) + vd = vd.index_add_(0, index=edge_group_to_vd, source=ue_group * beta_group) / beta_sum + + ''' + interpolate colors use the same method as dual vertices + ''' + if voxelgrid_colors is not None: + vd_color = torch.zeros((total_num_vd, C), device=self.device) + c_group = torch.index_select(input=surf_edges_c, index=idx_group.reshape(-1), dim=0).reshape(-1, 2, C) + uc_group = self._linear_interp(s_group * alpha_group, c_group) + vd_color = vd_color.index_add_(0, index=edge_group_to_vd, source=uc_group * beta_group) / beta_sum + else: + vd_color = None + + L_dev = self._compute_reg_loss(vd, zero_crossing_group, edge_group_to_vd, vd_num_edges) + + v_idx = torch.arange(vd.shape[0], device=self.device) # + total_num_vd + + vd_idx_map = (vd_idx_map.reshape(-1)).scatter(dim=0, index=edge_group_to_cube * + 12 + edge_group, src=v_idx[edge_group_to_vd]) + + return vd, L_dev, vd_gamma, vd_idx_map, vd_color + + def _triangulate(self, scalar_field, surf_edges, vd, vd_gamma, edge_counts, idx_map, vd_idx_map, surf_edges_mask, training, vd_color): + """ + Connects four neighboring dual vertices to form a quadrilateral. The quadrilaterals are then split into + triangles based on the gamma parameter, as described in Section 4.3. + """ + with torch.no_grad(): + group_mask = (edge_counts == 4) & surf_edges_mask # surface edges shared by 4 cubes. + group = idx_map.reshape(-1)[group_mask] + vd_idx = vd_idx_map[group_mask] + edge_indices, indices = torch.sort(group, stable=True) + quad_vd_idx = vd_idx[indices].reshape(-1, 4) + + # Ensure all face directions point towards the positive SDF to maintain consistent winding. + s_edges = scalar_field[surf_edges[edge_indices.reshape(-1, 4)[:, 0]].reshape(-1)].reshape(-1, 2) + flip_mask = s_edges[:, 0] > 0 + quad_vd_idx = torch.cat((quad_vd_idx[flip_mask][:, [0, 1, 3, 2]], + quad_vd_idx[~flip_mask][:, [2, 3, 1, 0]])) + + quad_gamma = torch.index_select(input=vd_gamma, index=quad_vd_idx.reshape(-1), dim=0).reshape(-1, 4) + gamma_02 = quad_gamma[:, 0] * quad_gamma[:, 2] + gamma_13 = quad_gamma[:, 1] * quad_gamma[:, 3] + if not training: + mask = (gamma_02 > gamma_13) + faces = torch.zeros((quad_gamma.shape[0], 6), dtype=torch.long, device=quad_vd_idx.device) + faces[mask] = quad_vd_idx[mask][:, self.quad_split_1] + faces[~mask] = quad_vd_idx[~mask][:, self.quad_split_2] + faces = faces.reshape(-1, 3) + else: + vd_quad = torch.index_select(input=vd, index=quad_vd_idx.reshape(-1), dim=0).reshape(-1, 4, 3) + vd_02 = (vd_quad[:, 0] + vd_quad[:, 2]) / 2 + vd_13 = (vd_quad[:, 1] + vd_quad[:, 3]) / 2 + weight_sum = (gamma_02 + gamma_13) + 1e-8 + vd_center = (vd_02 * gamma_02.unsqueeze(-1) + vd_13 * gamma_13.unsqueeze(-1)) / weight_sum.unsqueeze(-1) + + if vd_color is not None: + color_quad = torch.index_select(input=vd_color, index=quad_vd_idx.reshape(-1), dim=0).reshape(-1, 4, vd_color.shape[-1]) + color_02 = (color_quad[:, 0] + color_quad[:, 2]) / 2 + color_13 = (color_quad[:, 1] + color_quad[:, 3]) / 2 + color_center = (color_02 * gamma_02.unsqueeze(-1) + color_13 * gamma_13.unsqueeze(-1)) / weight_sum.unsqueeze(-1) + vd_color = torch.cat([vd_color, color_center]) + + + vd_center_idx = torch.arange(vd_center.shape[0], device=self.device) + vd.shape[0] + vd = torch.cat([vd, vd_center]) + faces = quad_vd_idx[:, self.quad_split_train].reshape(-1, 4, 2) + faces = torch.cat([faces, vd_center_idx.reshape(-1, 1, 1).repeat(1, 4, 1)], -1).reshape(-1, 3) + return vd, faces, s_edges, edge_indices, vd_color \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/flexicubes/tables.py b/Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/flexicubes/tables.py new file mode 100644 index 00000000..5873e772 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/flexicubes/tables.py @@ -0,0 +1,791 @@ +# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited. +dmc_table = [ +[[-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[0, 3, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[0, 1, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[1, 3, 8, 9, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[4, 7, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[0, 3, 4, 7, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[0, 1, 9, -1, -1, -1, -1], [4, 7, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[1, 3, 4, 7, 9, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[4, 5, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[0, 3, 8, -1, -1, -1, -1], [4, 5, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[0, 1, 4, 5, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[1, 3, 4, 5, 8, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[5, 7, 8, 9, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[0, 3, 5, 7, 9, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[0, 1, 5, 7, 8, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[1, 3, 5, 7, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[0, 2, 8, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[0, 1, 9, -1, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[1, 2, 8, 9, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[4, 7, 8, -1, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[0, 2, 4, 7, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[0, 1, 9, -1, -1, -1, -1], [4, 7, 8, -1, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[1, 2, 4, 7, 9, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[4, 5, 9, -1, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[0, 2, 8, 11, -1, -1, -1], [4, 5, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[0, 1, 4, 5, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[1, 2, 4, 5, 8, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[5, 7, 8, 9, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[0, 2, 5, 7, 9, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[0, 1, 5, 7, 8, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[1, 2, 5, 7, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[0, 3, 8, -1, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[0, 2, 9, 10, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[2, 3, 8, 9, 10, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[4, 7, 8, -1, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[0, 3, 4, 7, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[0, 2, 9, 10, -1, -1, -1], [4, 7, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[2, 3, 4, 7, 9, 10, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[4, 5, 9, -1, -1, -1, -1], [1, 2, 10, -1, 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0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[1, 1, 0, 0, 44], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[1, 1, 0, 0, 40], +[0, 0, 0, 0, 0], +[1, 0, 0, -1, 38], +[1, 0, -1, 0, 37], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[1, 0, -1, 0, 33], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[1, -1, 0, 0, 28], +[0, 0, 0, 0, 0], +[1, 0, -1, 0, 26], +[1, 0, 0, -1, 25], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[1, -1, 0, 0, 20], +[0, 0, 0, 0, 0], +[1, 0, -1, 0, 18], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[1, 0, 0, -1, 9], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[1, 0, 0, -1, 6], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0] +] +tet_table = [ +[-1, -1, -1, -1, -1, -1], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[1, 1, 1, 1, 1, 1], +[4, 4, 4, 4, 4, 4], +[0, 0, 0, 0, 0, 0], +[4, 0, 0, 4, 4, -1], +[1, 1, 1, 1, 1, 1], +[4, 4, 4, 4, 4, 4], +[0, 4, 0, 4, 4, -1], +[0, 0, 0, 0, 0, 0], +[1, 1, 1, 1, 1, 1], +[5, 5, 5, 5, 5, 5], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[1, 1, 1, 1, 1, 1], +[2, 2, 2, 2, 2, 2], +[0, 0, 0, 0, 0, 0], +[2, 0, 2, -1, 0, 2], +[1, 1, 1, 1, 1, 1], +[2, -1, 2, 4, 4, 2], +[0, 0, 0, 0, 0, 0], +[2, 0, 2, 4, 4, 2], +[1, 1, 1, 1, 1, 1], +[2, 4, 2, 4, 4, 2], +[0, 4, 0, 4, 4, 0], +[2, 0, 2, 0, 0, 2], +[1, 1, 1, 1, 1, 1], +[2, 5, 2, 5, 5, 2], +[0, 0, 0, 0, 0, 0], +[2, 0, 2, 0, 0, 2], +[1, 1, 1, 1, 1, 1], +[1, 1, 1, 1, 1, 1], +[0, 1, 1, -1, 0, 1], +[0, 0, 0, 0, 0, 0], +[2, 2, 2, 2, 2, 2], +[4, 1, 1, 4, 4, 1], +[0, 1, 1, 0, 0, 1], +[4, 0, 0, 4, 4, 0], +[2, 2, 2, 2, 2, 2], +[-1, 1, 1, 4, 4, 1], +[0, 1, 1, 4, 4, 1], +[0, 0, 0, 0, 0, 0], +[2, 2, 2, 2, 2, 2], +[5, 1, 1, 5, 5, 1], +[0, 1, 1, 0, 0, 1], +[0, 0, 0, 0, 0, 0], +[2, 2, 2, 2, 2, 2], +[1, 1, 1, 1, 1, 1], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[8, 8, 8, 8, 8, 8], +[1, 1, 1, 4, 4, 1], +[0, 0, 0, 0, 0, 0], +[4, 0, 0, 4, 4, 0], +[4, 4, 4, 4, 4, 4], +[1, 1, 1, 4, 4, 1], +[0, 4, 0, 4, 4, 0], +[0, 0, 0, 0, 0, 0], +[4, 4, 4, 4, 4, 4], +[1, 1, 1, 5, 5, 1], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[5, 5, 5, 5, 5, 5], +[6, 6, 6, 6, 6, 6], +[6, -1, 0, 6, 0, 6], +[6, 0, 0, 6, 0, 6], +[6, 1, 1, 6, 1, 6], +[4, 4, 4, 4, 4, 4], +[0, 0, 0, 0, 0, 0], +[4, 0, 0, 4, 4, 4], +[1, 1, 1, 1, 1, 1], +[6, 4, -1, 6, 4, 6], +[6, 4, 0, 6, 4, 6], +[6, 0, 0, 6, 0, 6], +[6, 1, 1, 6, 1, 6], +[5, 5, 5, 5, 5, 5], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[1, 1, 1, 1, 1, 1], +[2, 2, 2, 2, 2, 2], +[0, 0, 0, 0, 0, 0], +[2, 0, 2, 2, 0, 2], +[1, 1, 1, 1, 1, 1], +[2, 2, 2, 2, 2, 2], +[0, 0, 0, 0, 0, 0], +[2, 0, 2, 2, 2, 2], +[1, 1, 1, 1, 1, 1], +[2, 4, 2, 2, 4, 2], +[0, 4, 0, 4, 4, 0], +[2, 0, 2, 2, 0, 2], +[1, 1, 1, 1, 1, 1], +[2, 2, 2, 2, 2, 2], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[1, 1, 1, 1, 1, 1], +[6, 1, 1, 6, -1, 6], +[6, 1, 1, 6, 0, 6], +[6, 0, 0, 6, 0, 6], +[6, 2, 2, 6, 2, 6], +[4, 1, 1, 4, 4, 1], +[0, 1, 1, 0, 0, 1], +[4, 0, 0, 4, 4, 4], +[2, 2, 2, 2, 2, 2], +[6, 1, 1, 6, 4, 6], +[6, 1, 1, 6, 4, 6], +[6, 0, 0, 6, 0, 6], +[6, 2, 2, 6, 2, 6], +[5, 1, 1, 5, 5, 1], +[0, 1, 1, 0, 0, 1], +[0, 0, 0, 0, 0, 0], +[2, 2, 2, 2, 2, 2], +[1, 1, 1, 1, 1, 1], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[6, 6, 6, 6, 6, 6], +[1, 1, 1, 1, 1, 1], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[4, 4, 4, 4, 4, 4], +[1, 1, 1, 1, 4, 1], +[0, 4, 0, 4, 4, 0], +[0, 0, 0, 0, 0, 0], +[4, 4, 4, 4, 4, 4], +[1, 1, 1, 1, 1, 1], +[0, 0, 0, 0, 0, 0], +[0, 5, 0, 5, 0, 5], +[5, 5, 5, 5, 5, 5], +[5, 5, 5, 5, 5, 5], +[0, 5, 0, 5, 0, 5], +[-1, 5, 0, 5, 0, 5], +[1, 5, 1, 5, 1, 5], +[4, 5, -1, 5, 4, 5], +[0, 5, 0, 5, 0, 5], +[4, 5, 0, 5, 4, 5], +[1, 5, 1, 5, 1, 5], +[4, 4, 4, 4, 4, 4], +[0, 4, 0, 4, 4, 4], +[0, 0, 0, 0, 0, 0], +[1, 1, 1, 1, 1, 1], +[6, 6, 6, 6, 6, 6], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[1, 1, 1, 1, 1, 1], +[2, 5, 2, 5, -1, 5], +[0, 5, 0, 5, 0, 5], +[2, 5, 2, 5, 0, 5], +[1, 5, 1, 5, 1, 5], +[2, 5, 2, 5, 4, 5], +[0, 5, 0, 5, 0, 5], +[2, 5, 2, 5, 4, 5], +[1, 5, 1, 5, 1, 5], +[2, 4, 2, 4, 4, 2], +[0, 4, 0, 4, 4, 4], +[2, 0, 2, 0, 0, 2], +[1, 1, 1, 1, 1, 1], +[2, 6, 2, 6, 6, 2], +[0, 0, 0, 0, 0, 0], +[2, 0, 2, 0, 0, 2], +[1, 1, 1, 1, 1, 1], +[1, 1, 1, 1, 1, 1], +[0, 1, 1, 1, 0, 1], +[0, 0, 0, 0, 0, 0], +[2, 2, 2, 2, 2, 2], +[4, 1, 1, 1, 4, 1], +[0, 1, 1, 1, 0, 1], +[4, 0, 0, 4, 4, 0], +[2, 2, 2, 2, 2, 2], +[1, 1, 1, 1, 1, 1], +[0, 1, 1, 1, 1, 1], +[0, 0, 0, 0, 0, 0], +[2, 2, 2, 2, 2, 2], +[1, 1, 1, 1, 1, 1], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[2, 2, 2, 2, 2, 2], +[1, 1, 1, 1, 1, 1], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[5, 5, 5, 5, 5, 5], +[1, 1, 1, 1, 4, 1], +[0, 0, 0, 0, 0, 0], +[4, 0, 0, 4, 4, 0], +[4, 4, 4, 4, 4, 4], +[1, 1, 1, 1, 1, 1], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[4, 4, 4, 4, 4, 4], +[1, 1, 1, 1, 1, 1], +[6, 0, 0, 6, 0, 6], +[0, 0, 0, 0, 0, 0], +[6, 6, 6, 6, 6, 6], +[5, 5, 5, 5, 5, 5], +[5, 5, 0, 5, 0, 5], +[5, 5, 0, 5, 0, 5], +[5, 5, 1, 5, 1, 5], +[4, 4, 4, 4, 4, 4], +[0, 0, 0, 0, 0, 0], +[4, 4, 0, 4, 4, 4], +[1, 1, 1, 1, 1, 1], +[4, 4, 4, 4, 4, 4], +[4, 4, 0, 4, 4, 4], +[0, 0, 0, 0, 0, 0], +[1, 1, 1, 1, 1, 1], +[8, 8, 8, 8, 8, 8], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[1, 1, 1, 1, 1, 1], +[2, 2, 2, 2, 2, 2], +[0, 0, 0, 0, 0, 0], +[2, 2, 2, 2, 0, 2], +[1, 1, 1, 1, 1, 1], +[2, 2, 2, 2, 2, 2], +[0, 0, 0, 0, 0, 0], +[2, 2, 2, 2, 2, 2], +[1, 1, 1, 1, 1, 1], +[2, 2, 2, 2, 2, 2], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[4, 1, 1, 4, 4, 1], +[2, 2, 2, 2, 2, 2], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[1, 1, 1, 1, 1, 1], +[1, 1, 1, 1, 1, 1], +[1, 1, 1, 1, 0, 1], +[0, 0, 0, 0, 0, 0], +[2, 2, 2, 2, 2, 2], +[1, 1, 1, 1, 1, 1], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[2, 4, 2, 4, 4, 2], +[1, 1, 1, 1, 1, 1], +[1, 1, 1, 1, 1, 1], +[0, 0, 0, 0, 0, 0], +[2, 2, 2, 2, 2, 2], +[1, 1, 1, 1, 1, 1], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[2, 2, 2, 2, 2, 2], +[1, 1, 1, 1, 1, 1], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[5, 5, 5, 5, 5, 5], +[1, 1, 1, 1, 1, 1], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[4, 4, 4, 4, 4, 4], +[1, 1, 1, 1, 1, 1], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[4, 4, 4, 4, 4, 4], +[1, 1, 1, 1, 1, 1], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[12, 12, 12, 12, 12, 12] +] diff --git a/Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/utils_cube.py b/Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/utils_cube.py new file mode 100644 index 00000000..23913c97 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/utils_cube.py @@ -0,0 +1,61 @@ +import torch +cube_corners = torch.tensor([[0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0], [0, 0, 1], [ + 1, 0, 1], [0, 1, 1], [1, 1, 1]], dtype=torch.int) +cube_neighbor = torch.tensor([[1, 0, 0], [-1, 0, 0], [0, 1, 0], [0, -1, 0], [0, 0, 1], [0, 0, -1]]) +cube_edges = torch.tensor([0, 1, 1, 5, 4, 5, 0, 4, 2, 3, 3, 7, 6, 7, 2, 6, + 2, 0, 3, 1, 7, 5, 6, 4], dtype=torch.long, requires_grad=False) + +def construct_dense_grid(res, device='cuda'): + '''construct a dense grid based on resolution''' + res_v = res + 1 + vertsid = torch.arange(res_v ** 3, device=device) + coordsid = vertsid.reshape(res_v, res_v, res_v)[:res, :res, :res].flatten() + cube_corners_bias = (cube_corners[:, 0] * res_v + cube_corners[:, 1]) * res_v + cube_corners[:, 2] + cube_fx8 = (coordsid.unsqueeze(1) + cube_corners_bias.unsqueeze(0).to(device)) + verts = torch.stack([vertsid // (res_v ** 2), (vertsid // res_v) % res_v, vertsid % res_v], dim=1) + return verts, cube_fx8 + + +def construct_voxel_grid(coords): + verts = (cube_corners.unsqueeze(0).to(coords) + coords.unsqueeze(1)).reshape(-1, 3) + verts_unique, inverse_indices = torch.unique(verts, dim=0, return_inverse=True) + cubes = inverse_indices.reshape(-1, 8) + return verts_unique, cubes + + +def cubes_to_verts(num_verts, cubes, value, reduce='mean'): + """ + Args: + cubes [Vx8] verts index for each cube + value [Vx8xM] value to be scattered + Operation: + reduced[cubes[i][j]][k] += value[i][k] + """ + M = value.shape[2] # number of channels + reduced = torch.zeros(num_verts, M, device=cubes.device) + return torch.scatter_reduce(reduced, 0, + cubes.unsqueeze(-1).expand(-1, -1, M).flatten(0, 1), + value.flatten(0, 1), reduce=reduce, include_self=False) + +def sparse_cube2verts(coords, feats, training=True): + new_coords, cubes = construct_voxel_grid(coords) + new_feats = cubes_to_verts(new_coords.shape[0], cubes, feats) + if training: + con_loss = torch.mean((feats - new_feats[cubes]) ** 2) + else: + con_loss = 0.0 + return new_coords, new_feats, con_loss + + +def get_dense_attrs(coords : torch.Tensor, feats : torch.Tensor, res : int, sdf_init=True): + F = feats.shape[-1] + dense_attrs = torch.zeros([res] * 3 + [F], device=feats.device) + if sdf_init: + dense_attrs[..., 0] = 1 # initial outside sdf value + dense_attrs[coords[:, 0], coords[:, 1], coords[:, 2], :] = feats + return dense_attrs.reshape(-1, F) + + +def get_defomed_verts(v_pos : torch.Tensor, deform : torch.Tensor, res): + return v_pos / res - 0.5 + (1 - 1e-8) / (res * 2) * torch.tanh(deform) + \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/representations/octree/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/representations/octree/__init__.py new file mode 100644 index 00000000..f66a39a5 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/representations/octree/__init__.py @@ -0,0 +1 @@ +from .octree_dfs import DfsOctree \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/representations/octree/octree_dfs.py b/Gen_3D_Modules/TRELLIS/trellis_/representations/octree/octree_dfs.py new file mode 100644 index 00000000..9d1f7898 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/representations/octree/octree_dfs.py @@ -0,0 +1,362 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +DEFAULT_TRIVEC_CONFIG = { + 'dim': 8, + 'rank': 8, +} + +DEFAULT_VOXEL_CONFIG = { + 'solid': False, +} + +DEFAULT_DECOPOLY_CONFIG = { + 'degree': 8, + 'rank': 16, +} + + +class DfsOctree: + """ + Sparse Voxel Octree (SVO) implementation for PyTorch. + Using Depth-First Search (DFS) order to store the octree. + DFS order suits rendering and ray tracing. + + The structure and data are separatedly stored. + Structure is stored as a continuous array, each element is a 3*32 bits descriptor. + |-----------------------------------------| + | 0:3 bits | 4:31 bits | + | leaf num | unused | + |-----------------------------------------| + | 0:31 bits | + | child ptr | + |-----------------------------------------| + | 0:31 bits | + | data ptr | + |-----------------------------------------| + Each element represents a non-leaf node in the octree. + The valid mask is used to indicate whether the children are valid. + The leaf mask is used to indicate whether the children are leaf nodes. + The child ptr is used to point to the first non-leaf child. Non-leaf children descriptors are stored continuously from the child ptr. + The data ptr is used to point to the data of leaf children. Leaf children data are stored continuously from the data ptr. + + There are also auxiliary arrays to store the additional structural information to facilitate parallel processing. + - Position: the position of the octree nodes. + - Depth: the depth of the octree nodes. + + Args: + depth (int): the depth of the octree. + """ + + def __init__( + self, + depth, + aabb=[0,0,0,1,1,1], + sh_degree=2, + primitive='voxel', + primitive_config={}, + device='cuda', + ): + self.max_depth = depth + self.aabb = torch.tensor(aabb, dtype=torch.float32, device=device) + self.device = device + self.sh_degree = sh_degree + self.active_sh_degree = sh_degree + self.primitive = primitive + self.primitive_config = primitive_config + + self.structure = torch.tensor([[8, 1, 0]], dtype=torch.int32, device=self.device) + self.position = torch.zeros((8, 3), dtype=torch.float32, device=self.device) + self.depth = torch.zeros((8, 1), dtype=torch.uint8, device=self.device) + self.position[:, 0] = torch.tensor([0.25, 0.75, 0.25, 0.75, 0.25, 0.75, 0.25, 0.75], device=self.device) + self.position[:, 1] = torch.tensor([0.25, 0.25, 0.75, 0.75, 0.25, 0.25, 0.75, 0.75], device=self.device) + self.position[:, 2] = torch.tensor([0.25, 0.25, 0.25, 0.25, 0.75, 0.75, 0.75, 0.75], device=self.device) + self.depth[:, 0] = 1 + + self.data = ['position', 'depth'] + self.param_names = [] + + if primitive == 'voxel': + self.features_dc = torch.zeros((8, 1, 3), dtype=torch.float32, device=self.device) + self.features_ac = torch.zeros((8, (sh_degree+1)**2-1, 3), dtype=torch.float32, device=self.device) + self.data += ['features_dc', 'features_ac'] + self.param_names += ['features_dc', 'features_ac'] + if not primitive_config.get('solid', False): + self.density = torch.zeros((8, 1), dtype=torch.float32, device=self.device) + self.data.append('density') + self.param_names.append('density') + elif primitive == 'gaussian': + self.features_dc = torch.zeros((8, 1, 3), dtype=torch.float32, device=self.device) + self.features_ac = torch.zeros((8, (sh_degree+1)**2-1, 3), dtype=torch.float32, device=self.device) + self.opacity = torch.zeros((8, 1), dtype=torch.float32, device=self.device) + self.data += ['features_dc', 'features_ac', 'opacity'] + self.param_names += ['features_dc', 'features_ac', 'opacity'] + elif primitive == 'trivec': + self.trivec = torch.zeros((8, primitive_config['rank'], 3, primitive_config['dim']), dtype=torch.float32, device=self.device) + self.density = torch.zeros((8, primitive_config['rank']), dtype=torch.float32, device=self.device) + self.features_dc = torch.zeros((8, primitive_config['rank'], 1, 3), dtype=torch.float32, device=self.device) + self.features_ac = torch.zeros((8, primitive_config['rank'], (sh_degree+1)**2-1, 3), dtype=torch.float32, device=self.device) + self.density_shift = 0 + self.data += ['trivec', 'density', 'features_dc', 'features_ac'] + self.param_names += ['trivec', 'density', 'features_dc', 'features_ac'] + elif primitive == 'decoupoly': + self.decoupoly_V = torch.zeros((8, primitive_config['rank'], 3), dtype=torch.float32, device=self.device) + self.decoupoly_g = torch.zeros((8, primitive_config['rank'], primitive_config['degree']), dtype=torch.float32, device=self.device) + self.density = torch.zeros((8, primitive_config['rank']), dtype=torch.float32, device=self.device) + self.features_dc = torch.zeros((8, primitive_config['rank'], 1, 3), dtype=torch.float32, device=self.device) + self.features_ac = torch.zeros((8, primitive_config['rank'], (sh_degree+1)**2-1, 3), dtype=torch.float32, device=self.device) + self.density_shift = 0 + self.data += ['decoupoly_V', 'decoupoly_g', 'density', 'features_dc', 'features_ac'] + self.param_names += ['decoupoly_V', 'decoupoly_g', 'density', 'features_dc', 'features_ac'] + + self.setup_functions() + + def setup_functions(self): + self.density_activation = (lambda x: torch.exp(x - 2)) if self.primitive != 'trivec' else (lambda x: x) + self.opacity_activation = lambda x: torch.sigmoid(x - 6) + self.inverse_opacity_activation = lambda x: torch.log(x / (1 - x)) + 6 + self.color_activation = lambda x: torch.sigmoid(x) + + @property + def num_non_leaf_nodes(self): + return self.structure.shape[0] + + @property + def num_leaf_nodes(self): + return self.depth.shape[0] + + @property + def cur_depth(self): + return self.depth.max().item() + + @property + def occupancy(self): + return self.num_leaf_nodes / 8 ** self.cur_depth + + @property + def get_xyz(self): + return self.position + + @property + def get_depth(self): + return self.depth + + @property + def get_density(self): + if self.primitive == 'voxel' and self.voxel_config['solid']: + return torch.full((self.position.shape[0], 1), 1000, dtype=torch.float32, device=self.device) + return self.density_activation(self.density) + + @property + def get_opacity(self): + return self.opacity_activation(self.density) + + @property + def get_trivec(self): + return self.trivec + + @property + def get_decoupoly(self): + return F.normalize(self.decoupoly_V, dim=-1), self.decoupoly_g + + @property + def get_color(self): + return self.color_activation(self.colors) + + @property + def get_features(self): + if self.sh_degree == 0: + return self.features_dc + return torch.cat([self.features_dc, self.features_ac], dim=-2) + + def state_dict(self): + ret = {'structure': self.structure, 'position': self.position, 'depth': self.depth, 'sh_degree': self.sh_degree, 'active_sh_degree': self.active_sh_degree, 'trivec_config': self.trivec_config, 'voxel_config': self.voxel_config, 'primitive': self.primitive} + if hasattr(self, 'density_shift'): + ret['density_shift'] = self.density_shift + for data in set(self.data + self.param_names): + if not isinstance(getattr(self, data), nn.Module): + ret[data] = getattr(self, data) + else: + ret[data] = getattr(self, data).state_dict() + return ret + + def load_state_dict(self, state_dict): + keys = list(set(self.data + self.param_names + list(state_dict.keys()) + ['structure', 'position', 'depth'])) + for key in keys: + if key not in state_dict: + print(f"Warning: key {key} not found in the state_dict.") + continue + try: + if not isinstance(getattr(self, key), nn.Module): + setattr(self, key, state_dict[key]) + else: + getattr(self, key).load_state_dict(state_dict[key]) + except Exception as e: + print(e) + raise ValueError(f"Error loading key {key}.") + + def gather_from_leaf_children(self, data): + """ + Gather the data from the leaf children. + + Args: + data (torch.Tensor): the data to gather. The first dimension should be the number of leaf nodes. + """ + leaf_cnt = self.structure[:, 0] + leaf_cnt_masks = [leaf_cnt == i for i in range(1, 9)] + ret = torch.zeros((self.num_non_leaf_nodes,), dtype=data.dtype, device=self.device) + for i in range(8): + if leaf_cnt_masks[i].sum() == 0: + continue + start = self.structure[leaf_cnt_masks[i], 2] + for j in range(i+1): + ret[leaf_cnt_masks[i]] += data[start + j] + return ret + + def gather_from_non_leaf_children(self, data): + """ + Gather the data from the non-leaf children. + + Args: + data (torch.Tensor): the data to gather. The first dimension should be the number of leaf nodes. + """ + non_leaf_cnt = 8 - self.structure[:, 0] + non_leaf_cnt_masks = [non_leaf_cnt == i for i in range(1, 9)] + ret = torch.zeros_like(data, device=self.device) + for i in range(8): + if non_leaf_cnt_masks[i].sum() == 0: + continue + start = self.structure[non_leaf_cnt_masks[i], 1] + for j in range(i+1): + ret[non_leaf_cnt_masks[i]] += data[start + j] + return ret + + def structure_control(self, mask): + """ + Control the structure of the octree. + + Args: + mask (torch.Tensor): the mask to control the structure. 1 for subdivide, -1 for merge, 0 for keep. + """ + # Dont subdivide when the depth is the maximum. + mask[self.depth.squeeze() == self.max_depth] = torch.clamp_max(mask[self.depth.squeeze() == self.max_depth], 0) + # Dont merge when the depth is the minimum. + mask[self.depth.squeeze() == 1] = torch.clamp_min(mask[self.depth.squeeze() == 1], 0) + + # Gather control mask + structre_ctrl = self.gather_from_leaf_children(mask) + structre_ctrl[structre_ctrl==-8] = -1 + + new_leaf_num = self.structure[:, 0].clone() + # Modify the leaf num. + structre_valid = structre_ctrl >= 0 + new_leaf_num[structre_valid] -= structre_ctrl[structre_valid] # Add the new nodes. + structre_delete = structre_ctrl < 0 + merged_nodes = self.gather_from_non_leaf_children(structre_delete.int()) + new_leaf_num += merged_nodes # Delete the merged nodes. + + # Update the structure array to allocate new nodes. + mem_offset = torch.zeros((self.num_non_leaf_nodes + 1,), dtype=torch.int32, device=self.device) + mem_offset.index_add_(0, self.structure[structre_valid, 1], structre_ctrl[structre_valid]) # Add the new nodes. + mem_offset[:-1] -= structre_delete.int() # Delete the merged nodes. + new_structre_idx = torch.arange(0, self.num_non_leaf_nodes + 1, dtype=torch.int32, device=self.device) + mem_offset.cumsum(0) + new_structure_length = new_structre_idx[-1].item() + new_structre_idx = new_structre_idx[:-1] + new_structure = torch.empty((new_structure_length, 3), dtype=torch.int32, device=self.device) + new_structure[new_structre_idx[structre_valid], 0] = new_leaf_num[structre_valid] + + # Initialize the new nodes. + new_node_mask = torch.ones((new_structure_length,), dtype=torch.bool, device=self.device) + new_node_mask[new_structre_idx[structre_valid]] = False + new_structure[new_node_mask, 0] = 8 # Initialize to all leaf nodes. + new_node_num = new_node_mask.sum().item() + + # Rebuild child ptr. + non_leaf_cnt = 8 - new_structure[:, 0] + new_child_ptr = torch.cat([torch.zeros((1,), dtype=torch.int32, device=self.device), non_leaf_cnt.cumsum(0)[:-1]]) + new_structure[:, 1] = new_child_ptr + 1 + + # Rebuild data ptr with old data. + leaf_cnt = torch.zeros((new_structure_length,), dtype=torch.int32, device=self.device) + leaf_cnt.index_add_(0, new_structre_idx, self.structure[:, 0]) + old_data_ptr = torch.cat([torch.zeros((1,), dtype=torch.int32, device=self.device), leaf_cnt.cumsum(0)[:-1]]) + + # Update the data array + subdivide_mask = mask == 1 + merge_mask = mask == -1 + data_valid = ~(subdivide_mask | merge_mask) + mem_offset = torch.zeros((self.num_leaf_nodes + 1,), dtype=torch.int32, device=self.device) + mem_offset.index_add_(0, old_data_ptr[new_node_mask], torch.full((new_node_num,), 8, dtype=torch.int32, device=self.device)) # Add data array for new nodes + mem_offset[:-1] -= subdivide_mask.int() # Delete data elements for subdivide nodes + mem_offset[:-1] -= merge_mask.int() # Delete data elements for merge nodes + mem_offset.index_add_(0, self.structure[structre_valid, 2], merged_nodes[structre_valid]) # Add data elements for merge nodes + new_data_idx = torch.arange(0, self.num_leaf_nodes + 1, dtype=torch.int32, device=self.device) + mem_offset.cumsum(0) + new_data_length = new_data_idx[-1].item() + new_data_idx = new_data_idx[:-1] + new_data = {data: torch.empty((new_data_length,) + getattr(self, data).shape[1:], dtype=getattr(self, data).dtype, device=self.device) for data in self.data} + for data in self.data: + new_data[data][new_data_idx[data_valid]] = getattr(self, data)[data_valid] + + # Rebuild data ptr + leaf_cnt = new_structure[:, 0] + new_data_ptr = torch.cat([torch.zeros((1,), dtype=torch.int32, device=self.device), leaf_cnt.cumsum(0)[:-1]]) + new_structure[:, 2] = new_data_ptr + + # Initialize the new data array + ## For subdivide nodes + if subdivide_mask.sum() > 0: + subdivide_data_ptr = new_structure[new_node_mask, 2] + for data in self.data: + for i in range(8): + if data == 'position': + offset = torch.tensor([i // 4, (i // 2) % 2, i % 2], dtype=torch.float32, device=self.device) - 0.5 + scale = 2 ** (-1.0 - self.depth[subdivide_mask]) + new_data['position'][subdivide_data_ptr + i] = self.position[subdivide_mask] + offset * scale + elif data == 'depth': + new_data['depth'][subdivide_data_ptr + i] = self.depth[subdivide_mask] + 1 + elif data == 'opacity': + new_data['opacity'][subdivide_data_ptr + i] = self.inverse_opacity_activation(torch.sqrt(self.opacity_activation(self.opacity[subdivide_mask]))) + elif data == 'trivec': + offset = torch.tensor([i // 4, (i // 2) % 2, i % 2], dtype=torch.float32, device=self.device) * 0.5 + coord = (torch.linspace(0, 0.5, self.trivec.shape[-1], dtype=torch.float32, device=self.device)[None] + offset[:, None]).reshape(1, 3, self.trivec.shape[-1], 1) + axis = torch.linspace(0, 1, 3, dtype=torch.float32, device=self.device).reshape(1, 3, 1, 1).repeat(1, 1, self.trivec.shape[-1], 1) + coord = torch.stack([coord, axis], dim=3).reshape(1, 3, self.trivec.shape[-1], 2).expand(self.trivec[subdivide_mask].shape[0], -1, -1, -1) * 2 - 1 + new_data['trivec'][subdivide_data_ptr + i] = F.grid_sample(self.trivec[subdivide_mask], coord, align_corners=True) + else: + new_data[data][subdivide_data_ptr + i] = getattr(self, data)[subdivide_mask] + ## For merge nodes + if merge_mask.sum() > 0: + merge_data_ptr = torch.empty((merged_nodes.sum().item(),), dtype=torch.int32, device=self.device) + merge_nodes_cumsum = torch.cat([torch.zeros((1,), dtype=torch.int32, device=self.device), merged_nodes.cumsum(0)[:-1]]) + for i in range(8): + merge_data_ptr[merge_nodes_cumsum[merged_nodes > i] + i] = new_structure[new_structre_idx[merged_nodes > i], 2] + i + old_merge_data_ptr = self.structure[structre_delete, 2] + for data in self.data: + if data == 'position': + scale = 2 ** (1.0 - self.depth[old_merge_data_ptr]) + new_data['position'][merge_data_ptr] = ((self.position[old_merge_data_ptr] + 0.5) / scale).floor() * scale + 0.5 * scale - 0.5 + elif data == 'depth': + new_data['depth'][merge_data_ptr] = self.depth[old_merge_data_ptr] - 1 + elif data == 'opacity': + new_data['opacity'][subdivide_data_ptr + i] = self.inverse_opacity_activation(self.opacity_activation(self.opacity[subdivide_mask])**2) + elif data == 'trivec': + new_data['trivec'][merge_data_ptr] = self.trivec[old_merge_data_ptr] + else: + new_data[data][merge_data_ptr] = getattr(self, data)[old_merge_data_ptr] + + # Update the structure and data array + self.structure = new_structure + for data in self.data: + setattr(self, data, new_data[data]) + + # Save data array control temp variables + self.data_rearrange_buffer = { + 'subdivide_mask': subdivide_mask, + 'merge_mask': merge_mask, + 'data_valid': data_valid, + 'new_data_idx': new_data_idx, + 'new_data_length': new_data_length, + 'new_data': new_data + } diff --git a/Gen_3D_Modules/TRELLIS/trellis_/representations/radiance_field/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/representations/radiance_field/__init__.py new file mode 100644 index 00000000..b72a1b7e --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/representations/radiance_field/__init__.py @@ -0,0 +1 @@ +from .strivec import Strivec \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/representations/radiance_field/strivec.py b/Gen_3D_Modules/TRELLIS/trellis_/representations/radiance_field/strivec.py new file mode 100644 index 00000000..8fc4b749 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/representations/radiance_field/strivec.py @@ -0,0 +1,28 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +from ..octree import DfsOctree as Octree + + +class Strivec(Octree): + def __init__( + self, + resolution: int, + aabb: list, + sh_degree: int = 0, + rank: int = 8, + dim: int = 8, + device: str = "cuda", + ): + assert np.log2(resolution) % 1 == 0, "Resolution must be a power of 2" + self.resolution = resolution + depth = int(np.round(np.log2(resolution))) + super().__init__( + depth=depth, + aabb=aabb, + sh_degree=sh_degree, + primitive="trivec", + primitive_config={"rank": rank, "dim": dim}, + device=device, + ) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/utils/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/utils/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/Gen_3D_Modules/TRELLIS/trellis_/utils/general_utils.py b/Gen_3D_Modules/TRELLIS/trellis_/utils/general_utils.py new file mode 100644 index 00000000..3b454d9c --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/utils/general_utils.py @@ -0,0 +1,187 @@ +import numpy as np +import cv2 +import torch + + +# Dictionary utils +def _dict_merge(dicta, dictb, prefix=''): + """ + Merge two dictionaries. + """ + assert isinstance(dicta, dict), 'input must be a dictionary' + assert isinstance(dictb, dict), 'input must be a dictionary' + dict_ = {} + all_keys = set(dicta.keys()).union(set(dictb.keys())) + for key in all_keys: + if key in dicta.keys() and key in dictb.keys(): + if isinstance(dicta[key], dict) and isinstance(dictb[key], dict): + dict_[key] = _dict_merge(dicta[key], dictb[key], prefix=f'{prefix}.{key}') + else: + raise ValueError(f'Duplicate key {prefix}.{key} found in both dictionaries. Types: {type(dicta[key])}, {type(dictb[key])}') + elif key in dicta.keys(): + dict_[key] = dicta[key] + else: + dict_[key] = dictb[key] + return dict_ + + +def dict_merge(dicta, dictb): + """ + Merge two dictionaries. + """ + return _dict_merge(dicta, dictb, prefix='') + + +def dict_foreach(dic, func, special_func={}): + """ + Recursively apply a function to all non-dictionary leaf values in a dictionary. + """ + assert isinstance(dic, dict), 'input must be a dictionary' + for key in dic.keys(): + if isinstance(dic[key], dict): + dic[key] = dict_foreach(dic[key], func) + else: + if key in special_func.keys(): + dic[key] = special_func[key](dic[key]) + else: + dic[key] = func(dic[key]) + return dic + + +def dict_reduce(dicts, func, special_func={}): + """ + Reduce a list of dictionaries. Leaf values must be scalars. + """ + assert isinstance(dicts, list), 'input must be a list of dictionaries' + assert all([isinstance(d, dict) for d in dicts]), 'input must be a list of dictionaries' + assert len(dicts) > 0, 'input must be a non-empty list of dictionaries' + all_keys = set([key for dict_ in dicts for key in dict_.keys()]) + reduced_dict = {} + for key in all_keys: + vlist = [dict_[key] for dict_ in dicts if key in dict_.keys()] + if isinstance(vlist[0], dict): + reduced_dict[key] = dict_reduce(vlist, func, special_func) + else: + if key in special_func.keys(): + reduced_dict[key] = special_func[key](vlist) + else: + reduced_dict[key] = func(vlist) + return reduced_dict + + +def dict_any(dic, func): + """ + Recursively apply a function to all non-dictionary leaf values in a dictionary. + """ + assert isinstance(dic, dict), 'input must be a dictionary' + for key in dic.keys(): + if isinstance(dic[key], dict): + if dict_any(dic[key], func): + return True + else: + if func(dic[key]): + return True + return False + + +def dict_all(dic, func): + """ + Recursively apply a function to all non-dictionary leaf values in a dictionary. + """ + assert isinstance(dic, dict), 'input must be a dictionary' + for key in dic.keys(): + if isinstance(dic[key], dict): + if not dict_all(dic[key], func): + return False + else: + if not func(dic[key]): + return False + return True + + +def dict_flatten(dic, sep='.'): + """ + Flatten a nested dictionary into a dictionary with no nested dictionaries. + """ + assert isinstance(dic, dict), 'input must be a dictionary' + flat_dict = {} + for key in dic.keys(): + if isinstance(dic[key], dict): + sub_dict = dict_flatten(dic[key], sep=sep) + for sub_key in sub_dict.keys(): + flat_dict[str(key) + sep + str(sub_key)] = sub_dict[sub_key] + else: + flat_dict[key] = dic[key] + return flat_dict + + +def make_grid(images, nrow=None, ncol=None, aspect_ratio=None): + num_images = len(images) + if nrow is None and ncol is None: + if aspect_ratio is not None: + nrow = int(np.round(np.sqrt(num_images / aspect_ratio))) + else: + nrow = int(np.sqrt(num_images)) + ncol = (num_images + nrow - 1) // nrow + elif nrow is None and ncol is not None: + nrow = (num_images + ncol - 1) // ncol + elif nrow is not None and ncol is None: + ncol = (num_images + nrow - 1) // nrow + else: + assert nrow * ncol >= num_images, 'nrow * ncol must be greater than or equal to the number of images' + + grid = np.zeros((nrow * images[0].shape[0], ncol * images[0].shape[1], images[0].shape[2]), dtype=images[0].dtype) + for i, img in enumerate(images): + row = i // ncol + col = i % ncol + grid[row * img.shape[0]:(row + 1) * img.shape[0], col * img.shape[1]:(col + 1) * img.shape[1]] = img + return grid + + +def notes_on_image(img, notes=None): + img = np.pad(img, ((0, 32), (0, 0), (0, 0)), 'constant', constant_values=0) + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + if notes is not None: + img = cv2.putText(img, notes, (0, img.shape[0] - 4), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1) + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + return img + + +def save_image_with_notes(img, path, notes=None): + """ + Save an image with notes. + """ + if isinstance(img, torch.Tensor): + img = img.cpu().numpy().transpose(1, 2, 0) + if img.dtype == np.float32 or img.dtype == np.float64: + img = np.clip(img * 255, 0, 255).astype(np.uint8) + img = notes_on_image(img, notes) + cv2.imwrite(path, cv2.cvtColor(img, cv2.COLOR_RGB2BGR)) + + +# debug utils + +def atol(x, y): + """ + Absolute tolerance. + """ + return torch.abs(x - y) + + +def rtol(x, y): + """ + Relative tolerance. + """ + return torch.abs(x - y) / torch.clamp_min(torch.maximum(torch.abs(x), torch.abs(y)), 1e-12) + + +# print utils +def indent(s, n=4): + """ + Indent a string. + """ + lines = s.split('\n') + for i in range(1, len(lines)): + lines[i] = ' ' * n + lines[i] + return '\n'.join(lines) + diff --git a/Gen_3D_Modules/TRELLIS/trellis_/utils/postprocessing_utils.py b/Gen_3D_Modules/TRELLIS/trellis_/utils/postprocessing_utils.py new file mode 100644 index 00000000..8f5c42b4 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/utils/postprocessing_utils.py @@ -0,0 +1,467 @@ +from typing import * +import numpy as np +import torch +import utils3d +import nvdiffrast.torch as dr +from tqdm import tqdm +import comfy.utils +import xatlas +import pyvista as pv +from pymeshfix import _meshfix +import igraph +import cv2 +from .random_utils import sphere_hammersley_sequence +from .render_utils import render_multiview +from ..representations import Strivec, Gaussian, MeshExtractResult + + +@torch.no_grad() +def _fill_holes( + verts, + faces, + max_hole_size=0.04, + max_hole_nbe=32, + resolution=128, + num_views=500, + debug=False, + verbose=False +): + """ + Rasterize a mesh from multiple views and remove invisible faces. + Also includes postprocessing to: + 1. Remove connected components that are have low visibility. + 2. Mincut to remove faces at the inner side of the mesh connected to the outer side with a small hole. + + Args: + verts (torch.Tensor): Vertices of the mesh. Shape (V, 3). + faces (torch.Tensor): Faces of the mesh. Shape (F, 3). + max_hole_size (float): Maximum area of a hole to fill. + resolution (int): Resolution of the rasterization. + num_views (int): Number of views to rasterize the mesh. + verbose (bool): Whether to print progress. + """ + # Construct cameras + yaws = [] + pitchs = [] + for i in range(num_views): + y, p = sphere_hammersley_sequence(i, num_views) + yaws.append(y) + pitchs.append(p) + yaws = torch.tensor(yaws).cuda() + pitchs = torch.tensor(pitchs).cuda() + radius = 2.0 + fov = torch.deg2rad(torch.tensor(40)).cuda() + projection = utils3d.torch.perspective_from_fov_xy(fov, fov, 1, 3) + views = [] + for (yaw, pitch) in zip(yaws, pitchs): + orig = torch.tensor([ + torch.sin(yaw) * torch.cos(pitch), + torch.cos(yaw) * torch.cos(pitch), + torch.sin(pitch), + ]).cuda().float() * radius + view = utils3d.torch.view_look_at(orig, torch.tensor([0, 0, 0]).float().cuda(), torch.tensor([0, 0, 1]).float().cuda()) + views.append(view) + views = torch.stack(views, dim=0) + + # Rasterize + visblity = torch.zeros(faces.shape[0], dtype=torch.int32, device=verts.device) + rastctx = utils3d.torch.RastContext(backend='cuda') + for i in tqdm(range(views.shape[0]), total=views.shape[0], disable=not verbose, desc='Rasterizing'): + view = views[i] + buffers = utils3d.torch.rasterize_triangle_faces( + rastctx, verts[None], faces, resolution, resolution, view=view, projection=projection + ) + face_id = buffers['face_id'][0][buffers['mask'][0] > 0.95] - 1 + face_id = torch.unique(face_id).long() + visblity[face_id] += 1 + visblity = visblity.float() / num_views + + # Mincut + ## construct outer faces + edges, face2edge, edge_degrees = utils3d.torch.compute_edges(faces) + boundary_edge_indices = torch.nonzero(edge_degrees == 1).reshape(-1) + connected_components = utils3d.torch.compute_connected_components(faces, edges, face2edge) + outer_face_indices = torch.zeros(faces.shape[0], dtype=torch.bool, device=faces.device) + for i in range(len(connected_components)): + outer_face_indices[connected_components[i]] = visblity[connected_components[i]] > min(max(visblity[connected_components[i]].quantile(0.75).item(), 0.25), 0.5) + outer_face_indices = outer_face_indices.nonzero().reshape(-1) + + ## construct inner faces + inner_face_indices = torch.nonzero(visblity == 0).reshape(-1) + if verbose: + tqdm.write(f'Found {inner_face_indices.shape[0]} invisible faces') + if inner_face_indices.shape[0] == 0: + return verts, faces + + ## Construct dual graph (faces as nodes, edges as edges) + dual_edges, dual_edge2edge = utils3d.torch.compute_dual_graph(face2edge) + dual_edge2edge = edges[dual_edge2edge] + dual_edges_weights = torch.norm(verts[dual_edge2edge[:, 0]] - verts[dual_edge2edge[:, 1]], dim=1) + if verbose: + tqdm.write(f'Dual graph: {dual_edges.shape[0]} edges') + + ## solve mincut problem + ### construct main graph + g = igraph.Graph() + g.add_vertices(faces.shape[0]) + g.add_edges(dual_edges.cpu().numpy()) + g.es['weight'] = dual_edges_weights.cpu().numpy() + + ### source and target + g.add_vertex('s') + g.add_vertex('t') + + ### connect invisible faces to source + g.add_edges([(f, 's') for f in inner_face_indices], attributes={'weight': torch.ones(inner_face_indices.shape[0], dtype=torch.float32).cpu().numpy()}) + + ### connect outer faces to target + g.add_edges([(f, 't') for f in outer_face_indices], attributes={'weight': torch.ones(outer_face_indices.shape[0], dtype=torch.float32).cpu().numpy()}) + + ### solve mincut + cut = g.mincut('s', 't', (np.array(g.es['weight']) * 1000).tolist()) + remove_face_indices = torch.tensor([v for v in cut.partition[0] if v < faces.shape[0]], dtype=torch.long, device=faces.device) + if verbose: + tqdm.write(f'Mincut solved, start checking the cut') + + ### check if the cut is valid with each connected component + to_remove_cc = utils3d.torch.compute_connected_components(faces[remove_face_indices]) + if debug: + tqdm.write(f'Number of connected components of the cut: {len(to_remove_cc)}') + valid_remove_cc = [] + cutting_edges = [] + for cc in to_remove_cc: + #### check if the connected component has low visibility + visblity_median = visblity[remove_face_indices[cc]].median() + if debug: + tqdm.write(f'visblity_median: {visblity_median}') + if visblity_median > 0.25: + continue + + #### check if the cuting loop is small enough + cc_edge_indices, cc_edges_degree = torch.unique(face2edge[remove_face_indices[cc]], return_counts=True) + cc_boundary_edge_indices = cc_edge_indices[cc_edges_degree == 1] + cc_new_boundary_edge_indices = cc_boundary_edge_indices[~torch.isin(cc_boundary_edge_indices, boundary_edge_indices)] + if len(cc_new_boundary_edge_indices) > 0: + cc_new_boundary_edge_cc = utils3d.torch.compute_edge_connected_components(edges[cc_new_boundary_edge_indices]) + cc_new_boundary_edges_cc_center = [verts[edges[cc_new_boundary_edge_indices[edge_cc]]].mean(dim=1).mean(dim=0) for edge_cc in cc_new_boundary_edge_cc] + cc_new_boundary_edges_cc_area = [] + for i, edge_cc in enumerate(cc_new_boundary_edge_cc): + _e1 = verts[edges[cc_new_boundary_edge_indices[edge_cc]][:, 0]] - cc_new_boundary_edges_cc_center[i] + _e2 = verts[edges[cc_new_boundary_edge_indices[edge_cc]][:, 1]] - cc_new_boundary_edges_cc_center[i] + cc_new_boundary_edges_cc_area.append(torch.norm(torch.cross(_e1, _e2, dim=-1), dim=1).sum() * 0.5) + if debug: + cutting_edges.append(cc_new_boundary_edge_indices) + tqdm.write(f'Area of the cutting loop: {cc_new_boundary_edges_cc_area}') + if any([l > max_hole_size for l in cc_new_boundary_edges_cc_area]): + continue + + valid_remove_cc.append(cc) + + if debug: + face_v = verts[faces].mean(dim=1).cpu().numpy() + vis_dual_edges = dual_edges.cpu().numpy() + vis_colors = np.zeros((faces.shape[0], 3), dtype=np.uint8) + vis_colors[inner_face_indices.cpu().numpy()] = [0, 0, 255] + vis_colors[outer_face_indices.cpu().numpy()] = [0, 255, 0] + vis_colors[remove_face_indices.cpu().numpy()] = [255, 0, 255] + if len(valid_remove_cc) > 0: + vis_colors[remove_face_indices[torch.cat(valid_remove_cc)].cpu().numpy()] = [255, 0, 0] + utils3d.io.write_ply('dbg_dual.ply', face_v, edges=vis_dual_edges, vertex_colors=vis_colors) + + vis_verts = verts.cpu().numpy() + vis_edges = edges[torch.cat(cutting_edges)].cpu().numpy() + utils3d.io.write_ply('dbg_cut.ply', vis_verts, edges=vis_edges) + + + if len(valid_remove_cc) > 0: + remove_face_indices = remove_face_indices[torch.cat(valid_remove_cc)] + mask = torch.ones(faces.shape[0], dtype=torch.bool, device=faces.device) + mask[remove_face_indices] = 0 + faces = faces[mask] + faces, verts = utils3d.torch.remove_unreferenced_vertices(faces, verts) + if verbose: + tqdm.write(f'Removed {(~mask).sum()} faces by mincut') + else: + if verbose: + tqdm.write(f'Removed 0 faces by mincut') + + mesh = _meshfix.PyTMesh() + mesh.load_array(verts.cpu().numpy(), faces.cpu().numpy()) + mesh.fill_small_boundaries(nbe=max_hole_nbe, refine=True) + verts, faces = mesh.return_arrays() + verts, faces = torch.tensor(verts, device='cuda', dtype=torch.float32), torch.tensor(faces, device='cuda', dtype=torch.int32) + + return verts, faces + + +def postprocess_mesh( + vertices: np.array, + faces: np.array, + simplify: bool = True, + simplify_ratio: float = 0.9, + fill_holes: bool = True, + fill_holes_max_hole_size: float = 0.04, + fill_holes_max_hole_nbe: int = 32, + fill_holes_resolution: int = 1024, + fill_holes_num_views: int = 1000, + debug: bool = False, + verbose: bool = False, +): + """ + Postprocess a mesh by simplifying, removing invisible faces, and removing isolated pieces. + + Args: + vertices (np.array): Vertices of the mesh. Shape (V, 3). + faces (np.array): Faces of the mesh. Shape (F, 3). + simplify (bool): Whether to simplify the mesh, using quadric edge collapse. + simplify_ratio (float): Ratio of faces to keep after simplification. + fill_holes (bool): Whether to fill holes in the mesh. + fill_holes_max_hole_size (float): Maximum area of a hole to fill. + fill_holes_max_hole_nbe (int): Maximum number of boundary edges of a hole to fill. + fill_holes_resolution (int): Resolution of the rasterization. + fill_holes_num_views (int): Number of views to rasterize the mesh. + verbose (bool): Whether to print progress. + """ + + if verbose: + tqdm.write(f'Before postprocess: {vertices.shape[0]} vertices, {faces.shape[0]} faces') + + # Simplify + if simplify and simplify_ratio > 0: + mesh = pv.PolyData(vertices, np.concatenate([np.full((faces.shape[0], 1), 3), faces], axis=1)) + mesh = mesh.decimate(simplify_ratio, progress_bar=verbose) + vertices, faces = mesh.points, mesh.faces.reshape(-1, 4)[:, 1:] + if verbose: + tqdm.write(f'After decimate: {vertices.shape[0]} vertices, {faces.shape[0]} faces') + + # Remove invisible faces + if fill_holes: + vertices, faces = torch.tensor(vertices).cuda(), torch.tensor(faces.astype(np.int32)).cuda() + vertices, faces = _fill_holes( + vertices, faces, + max_hole_size=fill_holes_max_hole_size, + max_hole_nbe=fill_holes_max_hole_nbe, + resolution=fill_holes_resolution, + num_views=fill_holes_num_views, + debug=debug, + verbose=verbose, + ) + vertices, faces = vertices.cpu().numpy(), faces.cpu().numpy() + if verbose: + tqdm.write(f'After remove invisible faces: {vertices.shape[0]} vertices, {faces.shape[0]} faces') + + return vertices, faces + + +def parametrize_mesh(vertices: np.array, faces: np.array): + """ + Parametrize a mesh to a texture space, using xatlas. + + Args: + vertices (np.array): Vertices of the mesh. Shape (V, 3). + faces (np.array): Faces of the mesh. Shape (F, 3). + """ + + vmapping, indices, uvs = xatlas.parametrize(vertices, faces) + + vertices = vertices[vmapping] + faces = indices + + return vertices, faces, uvs + + +def bake_texture( + vertices: np.array, + faces: np.array, + uvs: np.array, + observations: List[np.array], + masks: List[np.array], + extrinsics: List[np.array], + intrinsics: List[np.array], + texture_size: int = 2048, + near: float = 0.1, + far: float = 10.0, + mode: Literal['fast', 'opt'] = 'opt', + lambda_tv: float = 1e-2, + verbose: bool = False, +): + """ + Bake texture to a mesh from multiple observations. + + Args: + vertices (np.array): Vertices of the mesh. Shape (V, 3). + faces (np.array): Faces of the mesh. Shape (F, 3). + uvs (np.array): UV coordinates of the mesh. Shape (V, 2). + observations (List[np.array]): List of observations. Each observation is a 2D image. Shape (H, W, 3). + masks (List[np.array]): List of masks. Each mask is a 2D image. Shape (H, W). + extrinsics (List[np.array]): List of extrinsics. Shape (4, 4). + intrinsics (List[np.array]): List of intrinsics. Shape (3, 3). + texture_size (int): Size of the texture. + near (float): Near plane of the camera. + far (float): Far plane of the camera. + mode (Literal['fast', 'opt']): Mode of texture baking. + lambda_tv (float): Weight of total variation loss in optimization. + verbose (bool): Whether to print progress. + """ + vertices = torch.tensor(vertices).cuda() + faces = torch.tensor(faces.astype(np.int32)).cuda() + uvs = torch.tensor(uvs).cuda() + observations = [torch.tensor(obs / 255.0).float().cuda() for obs in observations] + masks = [torch.tensor(m>0).bool().cuda() for m in masks] + views = [utils3d.torch.extrinsics_to_view(torch.tensor(extr).cuda()) for extr in extrinsics] + projections = [utils3d.torch.intrinsics_to_perspective(torch.tensor(intr).cuda(), near, far) for intr in intrinsics] + + steps = len(views) + comfy_pbar = comfy.utils.ProgressBar(steps) + + if mode == 'fast': + texture = torch.zeros((texture_size * texture_size, 3), dtype=torch.float32).cuda() + texture_weights = torch.zeros((texture_size * texture_size), dtype=torch.float32).cuda() + rastctx = utils3d.torch.RastContext(backend='cuda') + for i, (observation, view, projection) in enumerate(tqdm(zip(observations, views, projections), total=steps, disable=not verbose, desc='Texture baking (fast)')): + with torch.no_grad(): + rast = utils3d.torch.rasterize_triangle_faces( + rastctx, vertices[None], faces, observation.shape[1], observation.shape[0], uv=uvs[None], view=view, projection=projection + ) + uv_map = rast['uv'][0].detach().flip(0) + mask = rast['mask'][0].detach().bool() & masks[0] + + # nearest neighbor interpolation + uv_map = (uv_map * texture_size).floor().long() + obs = observation[mask] + uv_map = uv_map[mask] + idx = uv_map[:, 0] + (texture_size - uv_map[:, 1] - 1) * texture_size + texture = texture.scatter_add(0, idx.view(-1, 1).expand(-1, 3), obs) + texture_weights = texture_weights.scatter_add(0, idx, torch.ones((obs.shape[0]), dtype=torch.float32, device=texture.device)) + + comfy_pbar.update_absolute(i + 1) + + mask = texture_weights > 0 + texture[mask] /= texture_weights[mask][:, None] + texture = np.clip(texture.reshape(texture_size, texture_size, 3).cpu().numpy() * 255, 0, 255).astype(np.uint8) + + # inpaint + mask = (texture_weights == 0).cpu().numpy().astype(np.uint8).reshape(texture_size, texture_size) + texture = cv2.inpaint(texture, mask, 3, cv2.INPAINT_TELEA) + + elif mode == 'opt': + rastctx = utils3d.torch.RastContext(backend='cuda') + observations = [observations.flip(0) for observations in observations] + masks = [m.flip(0) for m in masks] + _uv = [] + _uv_dr = [] + for i, (observation, view, projection) in enumerate(tqdm(zip(observations, views, projections), total=steps, disable=not verbose, desc='Texture baking (opt): UV')): + with torch.no_grad(): + rast = utils3d.torch.rasterize_triangle_faces( + rastctx, vertices[None], faces, observation.shape[1], observation.shape[0], uv=uvs[None], view=view, projection=projection + ) + _uv.append(rast['uv'].detach()) + _uv_dr.append(rast['uv_dr'].detach()) + + comfy_pbar.update_absolute(i + 1) + + texture = torch.nn.Parameter(torch.zeros((1, texture_size, texture_size, 3), dtype=torch.float32).cuda()) + optimizer = torch.optim.Adam([texture], betas=(0.5, 0.9), lr=1e-2) + + def exp_anealing(optimizer, step, total_steps, start_lr, end_lr): + return start_lr * (end_lr / start_lr) ** (step / total_steps) + + def cosine_anealing(optimizer, step, total_steps, start_lr, end_lr): + return end_lr + 0.5 * (start_lr - end_lr) * (1 + np.cos(np.pi * step / total_steps)) + + def tv_loss(texture): + return torch.nn.functional.l1_loss(texture[:, :-1, :, :], texture[:, 1:, :, :]) + \ + torch.nn.functional.l1_loss(texture[:, :, :-1, :], texture[:, :, 1:, :]) + + total_steps = 2500 + comfy_pbar = comfy.utils.ProgressBar(total_steps) + with tqdm(total=total_steps, disable=not verbose, desc='Texture baking (opt): optimizing') as pbar: + for step in range(total_steps): + optimizer.zero_grad() + selected = np.random.randint(0, len(views)) + uv, uv_dr, observation, mask = _uv[selected], _uv_dr[selected], observations[selected], masks[selected] + render = dr.texture(texture, uv, uv_dr)[0] + loss = torch.nn.functional.l1_loss(render[mask], observation[mask]) + if lambda_tv > 0: + loss += lambda_tv * tv_loss(texture) + loss.backward() + optimizer.step() + # annealing + optimizer.param_groups[0]['lr'] = cosine_anealing(optimizer, step, total_steps, 1e-2, 1e-5) + pbar.set_postfix({'loss': loss.item()}) + pbar.update() + + comfy_pbar.update_absolute(step + 1) + + texture = np.clip(texture[0].flip(0).detach().cpu().numpy() * 255, 0, 255).astype(np.uint8) + mask = 1 - utils3d.torch.rasterize_triangle_faces( + rastctx, (uvs * 2 - 1)[None], faces, texture_size, texture_size + )['mask'][0].detach().cpu().numpy().astype(np.uint8) + texture = cv2.inpaint(texture, mask, 3, cv2.INPAINT_TELEA) + else: + raise ValueError(f'Unknown mode: {mode}') + + return texture + + +def finalize_mesh( + app_rep: Union[Strivec, Gaussian], + mesh: MeshExtractResult, + simplify: float = 0.95, + fill_holes: bool = True, + fill_holes_max_size: float = 0.04, + texture_size: int = 1024, + debug: bool = False, + verbose: bool = True, +): + """ + Convert a generated asset to a glb file. + + Args: + app_rep (Union[Strivec, Gaussian]): Appearance representation. + mesh (MeshExtractResult): Extracted mesh. + simplify (float): Ratio of faces to remove in simplification. + fill_holes (bool): Whether to fill holes in the mesh. + fill_holes_max_size (float): Maximum area of a hole to fill. + texture_size (int): Size of the texture. + debug (bool): Whether to print debug information. + verbose (bool): Whether to print progress. + """ + vertices = mesh.vertices.cpu().numpy() + faces = mesh.faces.cpu().numpy() + + # mesh postprocess + vertices, faces = postprocess_mesh( + vertices, faces, + simplify=simplify > 0, + simplify_ratio=simplify, + fill_holes=fill_holes, + fill_holes_max_hole_size=fill_holes_max_size, + fill_holes_max_hole_nbe=int(250 * np.sqrt(1-simplify)), + fill_holes_resolution=1024, + fill_holes_num_views=1000, + debug=debug, + verbose=verbose, + ) + + # parametrize mesh + vertices, faces, uvs = parametrize_mesh(vertices, faces) + + # bake texture + observations, extrinsics, intrinsics = render_multiview(app_rep, resolution=1024, nviews=100) + masks = [np.any(observation > 0, axis=-1) for observation in observations] + extrinsics = [extrinsics[i].cpu().numpy() for i in range(len(extrinsics))] + intrinsics = [intrinsics[i].cpu().numpy() for i in range(len(intrinsics))] + texture = bake_texture( + vertices, faces, uvs, + observations, masks, extrinsics, intrinsics, + texture_size=texture_size, mode='opt', + lambda_tv=0.01, + verbose=verbose + ) + texture = texture.astype(np.float32) / 255 + uvs[:, 1] = 1 - uvs[:, 1] + + # rotate mesh (from z-up to y-up) + vertices = vertices @ np.array([[1, 0, 0], [0, 0, -1], [0, 1, 0]]) + return vertices, faces, uvs, texture diff --git a/Gen_3D_Modules/TRELLIS/trellis_/utils/random_utils.py b/Gen_3D_Modules/TRELLIS/trellis_/utils/random_utils.py new file mode 100644 index 00000000..5b668c27 --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/utils/random_utils.py @@ -0,0 +1,30 @@ +import numpy as np + +PRIMES = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53] + +def radical_inverse(base, n): + val = 0 + inv_base = 1.0 / base + inv_base_n = inv_base + while n > 0: + digit = n % base + val += digit * inv_base_n + n //= base + inv_base_n *= inv_base + return val + +def halton_sequence(dim, n): + return [radical_inverse(PRIMES[dim], n) for dim in range(dim)] + +def hammersley_sequence(dim, n, num_samples): + return [n / num_samples] + halton_sequence(dim - 1, n) + +def sphere_hammersley_sequence(n, num_samples, offset=(0, 0), remap=False): + u, v = hammersley_sequence(2, n, num_samples) + u += offset[0] / num_samples + v += offset[1] + if remap: + u = 2 * u if u < 0.25 else 2 / 3 * u + 1 / 3 + theta = np.arccos(1 - 2 * u) - np.pi / 2 + phi = v * 2 * np.pi + return [phi, theta] \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/utils/render_utils.py b/Gen_3D_Modules/TRELLIS/trellis_/utils/render_utils.py new file mode 100644 index 00000000..8187c84f --- /dev/null +++ b/Gen_3D_Modules/TRELLIS/trellis_/utils/render_utils.py @@ -0,0 +1,116 @@ +import torch +import numpy as np +from tqdm import tqdm +import utils3d +from PIL import Image + +from ..renderers import OctreeRenderer, GaussianRenderer, MeshRenderer +from ..representations import Octree, Gaussian, MeshExtractResult +from ..modules import sparse as sp +from .random_utils import sphere_hammersley_sequence + + +def yaw_pitch_r_fov_to_extrinsics_intrinsics(yaws, pitchs, rs, fovs): + is_list = isinstance(yaws, list) + if not is_list: + yaws = [yaws] + pitchs = [pitchs] + if not isinstance(rs, list): + rs = [rs] * len(yaws) + if not isinstance(fovs, list): + fovs = [fovs] * len(yaws) + extrinsics = [] + intrinsics = [] + for yaw, pitch, r, fov in zip(yaws, pitchs, rs, fovs): + fov = torch.deg2rad(torch.tensor(float(fov))).cuda() + yaw = torch.tensor(float(yaw)).cuda() + pitch = torch.tensor(float(pitch)).cuda() + orig = torch.tensor([ + torch.sin(yaw) * torch.cos(pitch), + torch.cos(yaw) * torch.cos(pitch), + torch.sin(pitch), + ]).cuda() * r + extr = utils3d.torch.extrinsics_look_at(orig, torch.tensor([0, 0, 0]).float().cuda(), torch.tensor([0, 0, 1]).float().cuda()) + intr = utils3d.torch.intrinsics_from_fov_xy(fov, fov) + extrinsics.append(extr) + intrinsics.append(intr) + if not is_list: + extrinsics = extrinsics[0] + intrinsics = intrinsics[0] + return extrinsics, intrinsics + + +def render_frames(sample, extrinsics, intrinsics, options={}, colors_overwrite=None, verbose=True, **kwargs): + if isinstance(sample, Octree): + renderer = OctreeRenderer() + renderer.rendering_options.resolution = options.get('resolution', 512) + renderer.rendering_options.near = options.get('near', 0.8) + renderer.rendering_options.far = options.get('far', 1.6) + renderer.rendering_options.bg_color = options.get('bg_color', (0, 0, 0)) + renderer.rendering_options.ssaa = options.get('ssaa', 4) + renderer.pipe.primitive = sample.primitive + elif isinstance(sample, Gaussian): + renderer = GaussianRenderer() + renderer.rendering_options.resolution = options.get('resolution', 512) + renderer.rendering_options.near = options.get('near', 0.8) + renderer.rendering_options.far = options.get('far', 1.6) + renderer.rendering_options.bg_color = options.get('bg_color', (0, 0, 0)) + renderer.rendering_options.ssaa = options.get('ssaa', 1) + renderer.pipe.kernel_size = kwargs.get('kernel_size', 0.1) + renderer.pipe.use_mip_gaussian = True + elif isinstance(sample, MeshExtractResult): + renderer = MeshRenderer() + renderer.rendering_options.resolution = options.get('resolution', 512) + renderer.rendering_options.near = options.get('near', 1) + renderer.rendering_options.far = options.get('far', 100) + renderer.rendering_options.ssaa = options.get('ssaa', 4) + else: + raise ValueError(f'Unsupported sample type: {type(sample)}') + + rets = {} + for j, (extr, intr) in tqdm(enumerate(zip(extrinsics, intrinsics)), desc='Rendering', disable=not verbose): + if not isinstance(sample, MeshExtractResult): + res = renderer.render(sample, extr, intr, colors_overwrite=colors_overwrite) + if 'color' not in rets: rets['color'] = [] + if 'depth' not in rets: rets['depth'] = [] + rets['color'].append(np.clip(res['color'].detach().cpu().numpy().transpose(1, 2, 0) * 255, 0, 255).astype(np.uint8)) + if 'percent_depth' in res: + rets['depth'].append(res['percent_depth'].detach().cpu().numpy()) + elif 'depth' in res: + rets['depth'].append(res['depth'].detach().cpu().numpy()) + else: + rets['depth'].append(None) + else: + res = renderer.render(sample, extr, intr) + if 'normal' not in rets: rets['normal'] = [] + rets['normal'].append(np.clip(res['normal'].detach().cpu().numpy().transpose(1, 2, 0) * 255, 0, 255).astype(np.uint8)) + return rets + + +def render_video(sample, resolution=512, bg_color=(0, 0, 0), num_frames=300, r=2, fov=40, **kwargs): + yaws = torch.linspace(0, 2 * 3.1415, num_frames) + pitch = 0.25 + 0.5 * torch.sin(torch.linspace(0, 2 * 3.1415, num_frames)) + yaws = yaws.tolist() + pitch = pitch.tolist() + extrinsics, intrinsics = yaw_pitch_r_fov_to_extrinsics_intrinsics(yaws, pitch, r, fov) + return render_frames(sample, extrinsics, intrinsics, {'resolution': resolution, 'bg_color': bg_color}, **kwargs) + + +def render_multiview(sample, resolution=512, nviews=30): + r = 2 + fov = 40 + cams = [sphere_hammersley_sequence(i, nviews) for i in range(nviews)] + yaws = [cam[0] for cam in cams] + pitchs = [cam[1] for cam in cams] + extrinsics, intrinsics = yaw_pitch_r_fov_to_extrinsics_intrinsics(yaws, pitchs, r, fov) + res = render_frames(sample, extrinsics, intrinsics, {'resolution': resolution, 'bg_color': (0, 0, 0)}) + return res['color'], extrinsics, intrinsics + + +def render_snapshot(samples, resolution=512, bg_color=(0, 0, 0), offset=(-16 / 180 * np.pi, 20 / 180 * np.pi), r=10, fov=8, **kwargs): + yaw = [0, np.pi/2, np.pi, 3*np.pi/2] + yaw_offset = offset[0] + yaw = [y + yaw_offset for y in yaw] + pitch = [offset[1] for _ in range(4)] + extrinsics, intrinsics = yaw_pitch_r_fov_to_extrinsics_intrinsics(yaw, pitch, r, fov) + return render_frames(samples, extrinsics, intrinsics, {'resolution': resolution, 'bg_color': bg_color}, **kwargs) diff --git a/nodes.py b/nodes.py index d3e44a0c..2b353b25 100644 --- a/nodes.py +++ b/nodes.py @@ -95,6 +95,8 @@ from Hunyuan3D_V1.infer import Views2Mesh from TRELLIS.trellis.pipelines import TrellisImageTo3DPipeline from TRELLIS.trellis.utils import render_utils, postprocessing_utils +from TRELLIS.trellis.representations import Gaussian +from easydict import EasyDict as edict os.environ['SPCONV_ALGO'] = 'native' @@ -3927,14 +3929,19 @@ def INPUT_TYPES(cls): "sparse_structure_sample_steps": ("INT", {"default": 12, "min": 1}), "structured_latent_guidance_scale": ("FLOAT", {"default": 3.0, "min": 0.0, "step": 0.01}), "structured_latent_sample_steps": ("INT", {"default": 12, "min": 1}), + "texture_size": ("INT", {"default": 1024, "min": 256, "max": 4096, "step": 256}), + "render_resolution": ("INT", {"default": 1024, "min": 256, "max": 4096, "step": 256}), + "simplify_ratio": ("FLOAT", {"default": 0.95, "min": 0.80, "max": 0.99, "step": 0.01}), } } RETURN_TYPES = ( "MESH", + "IMAGE", ) RETURN_NAMES = ( "mesh", + "texture", ) FUNCTION = "run_model" CATEGORY = "Comfy3D/Algorithm" @@ -3950,6 +3957,9 @@ def run_model( sparse_structure_sample_steps, structured_latent_guidance_scale, structured_latent_sample_steps, + texture_size, + render_resolution, + simplify_ratio, ): single_image = torch_imgs_to_pils(reference_image, reference_mask)[0] @@ -3969,20 +3979,66 @@ def run_model( }, ) + ## GLB files can be extracted from the outputs + #vertices, faces, uvs, texture = postprocessing_utils.finalize_mesh( + # outputs['gaussian'][0], + # outputs['mesh'][0], + # # Optional parameters + # simplify=0.95, # Ratio of triangles to remove in the simplification process + # texture_size=1024, # Size of the texture used for the GLB + #) # GLB files can be extracted from the outputs - vertices, faces, uvs, texture = postprocessing_utils.finalize_mesh( - outputs['gaussian'][0], - outputs['mesh'][0], + + + gs = outputs['gaussian'][0] + mesh = outputs['mesh'][0] + + vertices = mesh.vertices.cpu().numpy() + faces = mesh.faces.cpu().numpy() + + vertices, faces = postprocessing_utils.postprocess_mesh( + vertices, + faces, # Optional parameters - simplify=0.95, # Ratio of triangles to remove in the simplification process - texture_size=1024, # Size of the texture used for the GLB + simplify_ratio=simplify_ratio, # Ratio of triangles to remove in the simplification process ) - - vertices, faces, uvs, texture = torch.from_numpy(vertices).to(DEVICE), torch.from_numpy(faces).to(torch.int64).to(DEVICE), torch.from_numpy(uvs).to(DEVICE), torch.from_numpy(texture).to(DEVICE) - mesh = Mesh(v=vertices, f=faces, vt=uvs, ft=faces, albedo=texture, device=DEVICE) + + vertices, faces, uvs = postprocessing_utils.parametrize_mesh(vertices, faces) + + observations, extrinsics, intrinsics = postprocessing_utils.render_multiview( + gs, + resolution=render_resolution, + nviews=100 + ) + masks = [np.any(observation > 0, axis=-1) for observation in observations] + extrinsics = [extrinsics[i].cpu().numpy() for i in range(len(extrinsics))] + intrinsics = [intrinsics[i].cpu().numpy() for i in range(len(intrinsics))] + texture = postprocessing_utils.bake_texture( + vertices, faces, uvs, + observations, masks, extrinsics, intrinsics, + texture_size=texture_size, mode='opt', + lambda_tv=0.01, + verbose=False + ) + + #texture = np.transpose(texture / 255.0, (2,0,1)) + #texture_raw = texture + + vertices = vertices @ np.array([[1, 0, 0], [0, 0, -1], [0, 1, 0]]) + + # glb = postprocessing_utils.to_glb( + # outputs['gaussian'][0], + # outputs['mesh'][0], + # simplify = 0.95, + # texture_size = 2048, + # ) + texture_0 = torch.flip(torch.tensor(torch.from_numpy(texture / 255.0).unsqueeze(0)), (1,)) + + vertices, faces, uvs = torch.from_numpy(vertices).to(DEVICE), torch.from_numpy(faces).to(torch.int64).to(DEVICE), torch.from_numpy(uvs).to(DEVICE) + mesh = Mesh(v=vertices, f=faces, vt=uvs, ft=faces, albedo=texture_0[0], device=DEVICE) mesh.auto_normal() - return (mesh,) + return (mesh,texture_0, ) From e0c2591d1487cdda17806005d90ba1f143560ca6 Mon Sep 17 00:00:00 2001 From: iiiCpu <40638625+iiiCpu@users.noreply.github.com> Date: Sun, 12 Jan 2025 15:58:55 +0700 Subject: [PATCH 2/6] Changed Save_3D_Mesh node to follow Save_Image node behavior: now node creates new file every time instead of rewriting it. New output format option to select between [glb, ply, obj] instead of reading the file name extension. New "create_folder" option to create new directory for every output and put all files into the same place. --- nodes.py | 66 +++++++++++++++++++++++++++++++++++++++++++++++++------- 1 file changed, 58 insertions(+), 8 deletions(-) diff --git a/nodes.py b/nodes.py index 2b353b25..a229f7ec 100644 --- a/nodes.py +++ b/nodes.py @@ -7,6 +7,7 @@ import folder_paths as comfy_paths from omegaconf import OmegaConf import json +from pathlib import Path import torch from torch.utils.data import DataLoader @@ -238,6 +239,39 @@ def preview_mesh(self, mesh_file_path): ] return {"ui": {"previews": previews}, "result": ()} +class Preview_3DMesh2: + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "mesh": ("MESH", ), + }, + } + + OUTPUT_NODE = True + RETURN_TYPES = () + FUNCTION = "preview_mesh2" + CATEGORY = "Comfy3D/Visualize" + + def preview_mesh2(self, mesh): + + mesh_folder_path, filename = os.path.split(mesh_file_path) + + if not os.path.isabs(mesh_file_path): + mesh_file_path = os.path.join(comfy_paths.output_directory, mesh_folder_path) + + if not filename.lower().endswith(SUPPORTED_3D_EXTENSIONS): + cstr(f"[{self.__class__.__name__}] File name {filename} does not end with supported 3D file extensions: {SUPPORTED_3D_EXTENSIONS}").error.print() + mesh_file_path = "" + + previews = [ + { + "filepath": mesh_file_path, + } + ] + return {"ui": {"previews": previews}, "result": ()} + class Load_3D_Mesh: @classmethod @@ -322,27 +356,43 @@ def INPUT_TYPES(cls): return { "required": { "mesh": ("MESH",), - "save_path": ("STRING", {"default": 'Mesh_%Y-%m-%d-%M-%S-%f.glb', "multiline": False}), + "format": (["glb", "obj", "ply"], ), + "create_subfolders": ("BOOLEAN", {"default": False},), + "save_path": ("STRING", {"default": 'Mesh', "multiline": False}), }, } OUTPUT_NODE = True RETURN_TYPES = ( "STRING", + "STRING", ) RETURN_NAMES = ( "save_path", + "folder_path" ) FUNCTION = "save_mesh" CATEGORY = "Comfy3D/Import|Export" - def save_mesh(self, mesh, save_path): - save_path = parse_save_filename(save_path, comfy_paths.output_directory, SUPPORTED_3D_EXTENSIONS, self.__class__.__name__) - - if save_path is not None: - mesh.write(save_path) - - return (save_path, ) + def save_mesh(self, mesh, format, create_subfolders, save_path): + full_output_folder, filename, counter, subfolder, filename_prefix = comfy_paths.get_save_image_path(save_path, comfy_paths.output_directory, 0, 0) + if create_subfolders: + if not full_output_folder.endswith('/'): + full_output_folder += '/' + full_output_folder = f"{full_output_folder}{filename}_{counter:05}_/" + Path(full_output_folder).mkdir(parents=True, exist_ok=True) + filename_prefix = f"{filename_prefix}.{format}" + else: + filename_prefix = f"{filename_prefix}_{counter:05}_.{format}" + save_path = os.path.join(full_output_folder, filename_prefix) + match format: + case "glb": + mesh.write_glb(save_path) + case "obj": + mesh.write_obj(save_path) + case "ply": + mesh.write_ply(save_path) + return (save_path, full_output_folder, ) class Save_3DGS: From 81f498a17fa4538a3ee781521a67c0e3b429d487 Mon Sep 17 00:00:00 2001 From: iiiCpu <40638625+iiiCpu@users.noreply.github.com> Date: Sun, 12 Jan 2025 16:07:48 +0700 Subject: [PATCH 3/6] Changed Save_3DGS node also --- nodes.py | 37 ++++++++++++++++++++++++------------- 1 file changed, 24 insertions(+), 13 deletions(-) diff --git a/nodes.py b/nodes.py index a229f7ec..757ee6dc 100644 --- a/nodes.py +++ b/nodes.py @@ -366,10 +366,12 @@ def INPUT_TYPES(cls): RETURN_TYPES = ( "STRING", "STRING", + "STRING", ) RETURN_NAMES = ( "save_path", - "folder_path" + "folder_full_path", + "subfolder", ) FUNCTION = "save_mesh" CATEGORY = "Comfy3D/Import|Export" @@ -380,6 +382,7 @@ def save_mesh(self, mesh, format, create_subfolders, save_path): if not full_output_folder.endswith('/'): full_output_folder += '/' full_output_folder = f"{full_output_folder}{filename}_{counter:05}_/" + subfolder = f"{subfolder}{filename}_{counter:05}_/" Path(full_output_folder).mkdir(parents=True, exist_ok=True) filename_prefix = f"{filename_prefix}.{format}" else: @@ -392,7 +395,7 @@ def save_mesh(self, mesh, format, create_subfolders, save_path): mesh.write_obj(save_path) case "ply": mesh.write_ply(save_path) - return (save_path, full_output_folder, ) + return (save_path, full_output_folder, subfolder, ) class Save_3DGS: @@ -401,28 +404,42 @@ def INPUT_TYPES(cls): return { "required": { "gs_ply": ("GS_PLY",), - "save_path": ("STRING", {"default": '3DGS_%Y-%m-%d-%M-%S-%f.ply', "multiline": False}), + "create_subfolders": ("BOOLEAN", {"default": False},), + "save_path": ("STRING", {"default": '3DGS', "multiline": False}), }, } OUTPUT_NODE = True RETURN_TYPES = ( "STRING", + "STRING", + "STRING", ) RETURN_NAMES = ( "save_path", + "folder_full_path", + "subfolder", ) FUNCTION = "save_gs" CATEGORY = "Comfy3D/Import|Export" - def save_gs(self, gs_ply, save_path): - - save_path = parse_save_filename(save_path, comfy_paths.output_directory, SUPPORTED_3DGS_EXTENSIONS, self.__class__.__name__) + def save_gs(self, gs_ply, create_subfolders, save_path): + full_output_folder, filename, counter, subfolder, filename_prefix = comfy_paths.get_save_image_path(save_path, comfy_paths.output_directory, 0, 0) + if create_subfolders: + if not full_output_folder.endswith('/'): + full_output_folder += '/' + full_output_folder = f"{full_output_folder}{filename}_{counter:05}_/" + subfolder = f"{subfolder}{filename}_{counter:05}_/" + Path(full_output_folder).mkdir(parents=True, exist_ok=True) + filename_prefix = f"{filename_prefix}.ply" + else: + filename_prefix = f"{filename_prefix}_{counter:05}_.ply" + save_path = os.path.join(full_output_folder, filename_prefix) if save_path is not None: gs_ply.write(save_path) - return (save_path, ) + return (save_path, full_output_folder, subfolder, ) class Image_Add_Pure_Color_Background: @classmethod @@ -4076,12 +4093,6 @@ def run_model( vertices = vertices @ np.array([[1, 0, 0], [0, 0, -1], [0, 1, 0]]) - # glb = postprocessing_utils.to_glb( - # outputs['gaussian'][0], - # outputs['mesh'][0], - # simplify = 0.95, - # texture_size = 2048, - # ) texture_0 = torch.flip(torch.tensor(torch.from_numpy(texture / 255.0).unsqueeze(0)), (1,)) vertices, faces, uvs = torch.from_numpy(vertices).to(DEVICE), torch.from_numpy(faces).to(torch.int64).to(DEVICE), torch.from_numpy(uvs).to(DEVICE) From 92f8109fd9ae2ff73fee881bb059135e8a936aa5 Mon Sep 17 00:00:00 2001 From: iiiCpu <40638625+iiiCpu@users.noreply.github.com> Date: Sun, 12 Jan 2025 19:54:15 +0700 Subject: [PATCH 4/6] Fix: create path on save --- nodes.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/nodes.py b/nodes.py index 757ee6dc..c21700f1 100644 --- a/nodes.py +++ b/nodes.py @@ -383,10 +383,10 @@ def save_mesh(self, mesh, format, create_subfolders, save_path): full_output_folder += '/' full_output_folder = f"{full_output_folder}{filename}_{counter:05}_/" subfolder = f"{subfolder}{filename}_{counter:05}_/" - Path(full_output_folder).mkdir(parents=True, exist_ok=True) filename_prefix = f"{filename_prefix}.{format}" else: filename_prefix = f"{filename_prefix}_{counter:05}_.{format}" + Path(full_output_folder).mkdir(parents=True, exist_ok=True) save_path = os.path.join(full_output_folder, filename_prefix) match format: case "glb": @@ -431,10 +431,10 @@ def save_gs(self, gs_ply, create_subfolders, save_path): full_output_folder += '/' full_output_folder = f"{full_output_folder}{filename}_{counter:05}_/" subfolder = f"{subfolder}{filename}_{counter:05}_/" - Path(full_output_folder).mkdir(parents=True, exist_ok=True) filename_prefix = f"{filename_prefix}.ply" else: filename_prefix = f"{filename_prefix}_{counter:05}_.ply" + Path(full_output_folder).mkdir(parents=True, exist_ok=True) save_path = os.path.join(full_output_folder, filename_prefix) if save_path is not None: gs_ply.write(save_path) From e6cf71d7499476c83d7f7638488c2cadac094777 Mon Sep 17 00:00:00 2001 From: iiiCpu <40638625+iiiCpu@users.noreply.github.com> Date: Sun, 12 Jan 2025 20:06:52 +0700 Subject: [PATCH 5/6] Fix invalid output path --- nodes.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/nodes.py b/nodes.py index c21700f1..d99f7119 100644 --- a/nodes.py +++ b/nodes.py @@ -379,15 +379,15 @@ def INPUT_TYPES(cls): def save_mesh(self, mesh, format, create_subfolders, save_path): full_output_folder, filename, counter, subfolder, filename_prefix = comfy_paths.get_save_image_path(save_path, comfy_paths.output_directory, 0, 0) if create_subfolders: - if not full_output_folder.endswith('/'): + if not full_output_folder.endswith('/') and not full_output_folder.endswith('\\'): full_output_folder += '/' full_output_folder = f"{full_output_folder}{filename}_{counter:05}_/" subfolder = f"{subfolder}{filename}_{counter:05}_/" - filename_prefix = f"{filename_prefix}.{format}" + filename = f"{filename}.{format}" else: - filename_prefix = f"{filename_prefix}_{counter:05}_.{format}" + filename = f"{filename}_{counter:05}_.{format}" Path(full_output_folder).mkdir(parents=True, exist_ok=True) - save_path = os.path.join(full_output_folder, filename_prefix) + save_path = os.path.join(full_output_folder, filename) match format: case "glb": mesh.write_glb(save_path) @@ -427,15 +427,15 @@ def save_gs(self, gs_ply, create_subfolders, save_path): full_output_folder, filename, counter, subfolder, filename_prefix = comfy_paths.get_save_image_path(save_path, comfy_paths.output_directory, 0, 0) if create_subfolders: - if not full_output_folder.endswith('/'): + if not full_output_folder.endswith('/') and not full_output_folder.endswith('\\'): full_output_folder += '/' full_output_folder = f"{full_output_folder}{filename}_{counter:05}_/" subfolder = f"{subfolder}{filename}_{counter:05}_/" - filename_prefix = f"{filename_prefix}.ply" + filename = f"{filename}.ply" else: - filename_prefix = f"{filename_prefix}_{counter:05}_.ply" + filename = f"{filename}_{counter:05}_.ply" Path(full_output_folder).mkdir(parents=True, exist_ok=True) - save_path = os.path.join(full_output_folder, filename_prefix) + save_path = os.path.join(full_output_folder, filename) if save_path is not None: gs_ply.write(save_path) From 3613bcfc1e1ebc38e6cd81b9beacb6ca7b8467c2 Mon Sep 17 00:00:00 2001 From: iiiCpu <40638625+iiiCpu@users.noreply.github.com> Date: Tue, 21 Jan 2025 08:07:58 +0700 Subject: [PATCH 6/6] Removed `Gen_3D_Modules/TRELLIS/trellis_` directory. Updated `Trellis_Structured_3D_Latents_Models` so, if provided with batch of images, it will process in `run_multi_image` pipe. Cleanup in `nodes.py`. --- Gen_3D_Modules/TRELLIS/trellis_/__init__.py | 6 - .../TRELLIS/trellis_/models/__init__.py | 70 -- .../trellis_/models/sparse_structure_flow.py | 200 ----- .../trellis_/models/sparse_structure_vae.py | 306 ------- .../trellis_/models/structured_latent_flow.py | 262 ------ .../models/structured_latent_vae/__init__.py | 4 - .../models/structured_latent_vae/base.py | 117 --- .../structured_latent_vae/decoder_gs.py | 122 --- .../structured_latent_vae/decoder_mesh.py | 167 ---- .../structured_latent_vae/decoder_rf.py | 104 --- .../models/structured_latent_vae/encoder.py | 72 -- .../trellis_/modules/attention/__init__.py | 36 - .../trellis_/modules/attention/full_attn.py | 140 ---- .../trellis_/modules/attention/modules.py | 146 ---- .../TRELLIS/trellis_/modules/norm.py | 25 - .../trellis_/modules/sparse/__init__.py | 102 --- .../modules/sparse/attention/__init__.py | 4 - .../modules/sparse/attention/full_attn.py | 215 ----- .../modules/sparse/attention/modules.py | 139 --- .../sparse/attention/serialized_attn.py | 193 ----- .../modules/sparse/attention/windowed_attn.py | 135 --- .../TRELLIS/trellis_/modules/sparse/basic.py | 459 ---------- .../trellis_/modules/sparse/conv/__init__.py | 21 - .../modules/sparse/conv/conv_spconv.py | 80 -- .../modules/sparse/conv/conv_torchsparse.py | 38 - .../TRELLIS/trellis_/modules/sparse/linear.py | 15 - .../trellis_/modules/sparse/nonlinearity.py | 35 - .../TRELLIS/trellis_/modules/sparse/norm.py | 58 -- .../trellis_/modules/sparse/spatial.py | 110 --- .../modules/sparse/transformer/__init__.py | 2 - .../modules/sparse/transformer/blocks.py | 151 ---- .../modules/sparse/transformer/modulated.py | 166 ---- .../TRELLIS/trellis_/modules/spatial.py | 48 -- .../trellis_/modules/transformer/__init__.py | 2 - .../trellis_/modules/transformer/blocks.py | 182 ---- .../trellis_/modules/transformer/modulated.py | 157 ---- .../TRELLIS/trellis_/modules/utils.py | 54 -- .../TRELLIS/trellis_/pipelines/__init__.py | 24 - .../TRELLIS/trellis_/pipelines/base.py | 66 -- .../trellis_/pipelines/samplers/__init__.py | 2 - .../trellis_/pipelines/samplers/base.py | 20 - .../classifier_free_guidance_mixin.py | 12 - .../trellis_/pipelines/samplers/flow_euler.py | 202 ----- .../samplers/guidance_interval_mixin.py | 15 - .../trellis_/pipelines/trellis_image_to_3d.py | 283 ------- .../TRELLIS/trellis_/renderers/__init__.py | 31 - .../trellis_/renderers/gaussian_render.py | 235 ------ .../trellis_/renderers/mesh_renderer.py | 133 --- .../trellis_/renderers/octree_renderer.py | 300 ------- .../TRELLIS/trellis_/renderers/sh_utils.py | 118 --- .../trellis_/representations/__init__.py | 4 - .../representations/gaussian/__init__.py | 1 - .../gaussian/gaussian_model.py | 194 ----- .../representations/gaussian/general_utils.py | 133 --- .../trellis_/representations/mesh/__init__.py | 1 - .../representations/mesh/cube2mesh.py | 146 ---- .../mesh/flexicubes/flexicubes.py | 417 --------- .../representations/mesh/flexicubes/tables.py | 791 ------------------ .../representations/mesh/utils_cube.py | 61 -- .../representations/octree/__init__.py | 1 - .../representations/octree/octree_dfs.py | 362 -------- .../radiance_field/__init__.py | 1 - .../representations/radiance_field/strivec.py | 28 - .../TRELLIS/trellis_/utils/__init__.py | 0 .../TRELLIS/trellis_/utils/general_utils.py | 187 ----- .../trellis_/utils/postprocessing_utils.py | 467 ----------- .../TRELLIS/trellis_/utils/random_utils.py | 30 - .../TRELLIS/trellis_/utils/render_utils.py | 116 --- nodes.py | 92 +- 69 files changed, 37 insertions(+), 8579 deletions(-) delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/__init__.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/models/__init__.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/models/sparse_structure_flow.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/models/sparse_structure_vae.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_flow.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/__init__.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/base.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/decoder_gs.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/decoder_mesh.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/decoder_rf.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/encoder.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/attention/__init__.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/attention/full_attn.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/attention/modules.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/norm.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/__init__.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/__init__.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/full_attn.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/modules.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/serialized_attn.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/windowed_attn.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/basic.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/conv/__init__.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/conv/conv_spconv.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/conv/conv_torchsparse.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/linear.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/nonlinearity.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/norm.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/spatial.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/transformer/__init__.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/transformer/blocks.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/transformer/modulated.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/spatial.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/transformer/__init__.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/transformer/blocks.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/transformer/modulated.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/modules/utils.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/pipelines/__init__.py delete mode 100644 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Gen_3D_Modules/TRELLIS/trellis_/representations/radiance_field/__init__.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/representations/radiance_field/strivec.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/utils/__init__.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/utils/general_utils.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/utils/postprocessing_utils.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/utils/random_utils.py delete mode 100644 Gen_3D_Modules/TRELLIS/trellis_/utils/render_utils.py diff --git a/Gen_3D_Modules/TRELLIS/trellis_/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/__init__.py deleted file mode 100644 index 20d240af..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -from . import models -from . import modules -from . import pipelines -from . import renderers -from . import representations -from . import utils diff --git a/Gen_3D_Modules/TRELLIS/trellis_/models/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/models/__init__.py deleted file mode 100644 index d90e9f9a..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/models/__init__.py +++ /dev/null @@ -1,70 +0,0 @@ -import importlib - -__attributes = { - 'SparseStructureEncoder': 'sparse_structure_vae', - 'SparseStructureDecoder': 'sparse_structure_vae', - 'SparseStructureFlowModel': 'sparse_structure_flow', - 'SLatEncoder': 'structured_latent_vae', - 'SLatGaussianDecoder': 'structured_latent_vae', - 'SLatRadianceFieldDecoder': 'structured_latent_vae', - 'SLatMeshDecoder': 'structured_latent_vae', - 'SLatFlowModel': 'structured_latent_flow', -} - -__submodules = [] - -__all__ = list(__attributes.keys()) + __submodules - -def __getattr__(name): - if name not in globals(): - if name in __attributes: - module_name = __attributes[name] - module = importlib.import_module(f".{module_name}", __name__) - globals()[name] = getattr(module, name) - elif name in __submodules: - module = importlib.import_module(f".{name}", __name__) - globals()[name] = module - else: - raise AttributeError(f"module {__name__} has no attribute {name}") - return globals()[name] - - -def from_pretrained(path: str, **kwargs): - """ - Load a model from a pretrained checkpoint. - - Args: - path: The path to the checkpoint. Can be either local path or a Hugging Face model name. - NOTE: config file and model file should take the name f'{path}.json' and f'{path}.safetensors' respectively. - **kwargs: Additional arguments for the model constructor. - """ - import os - import json - from safetensors.torch import load_file - is_local = os.path.exists(f"{path}.json") and os.path.exists(f"{path}.safetensors") - - if is_local: - config_file = f"{path}.json" - model_file = f"{path}.safetensors" - else: - from huggingface_hub import hf_hub_download - path_parts = path.split('/') - repo_id = f'{path_parts[0]}/{path_parts[1]}' - model_name = '/'.join(path_parts[2:]) - config_file = hf_hub_download(repo_id, f"{model_name}.json") - model_file = hf_hub_download(repo_id, f"{model_name}.safetensors") - - with open(config_file, 'r') as f: - config = json.load(f) - model = __getattr__(config['name'])(**config['args'], **kwargs) - model.load_state_dict(load_file(model_file)) - - return model - - -# For Pylance -if __name__ == '__main__': - from .sparse_structure_vae import SparseStructureEncoder, SparseStructureDecoder - from .sparse_structure_flow import SparseStructureFlowModel - from .structured_latent_vae import SLatEncoder, SLatGaussianDecoder, SLatRadianceFieldDecoder, SLatMeshDecoder - from .structured_latent_flow import SLatFlowModel diff --git a/Gen_3D_Modules/TRELLIS/trellis_/models/sparse_structure_flow.py b/Gen_3D_Modules/TRELLIS/trellis_/models/sparse_structure_flow.py deleted file mode 100644 index aee71a96..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/models/sparse_structure_flow.py +++ /dev/null @@ -1,200 +0,0 @@ -from typing import * -import torch -import torch.nn as nn -import torch.nn.functional as F -import numpy as np -from ..modules.utils import convert_module_to_f16, convert_module_to_f32 -from ..modules.transformer import AbsolutePositionEmbedder, ModulatedTransformerCrossBlock -from ..modules.spatial import patchify, unpatchify - - -class TimestepEmbedder(nn.Module): - """ - Embeds scalar timesteps into vector representations. - """ - def __init__(self, hidden_size, frequency_embedding_size=256): - super().__init__() - self.mlp = nn.Sequential( - nn.Linear(frequency_embedding_size, hidden_size, bias=True), - nn.SiLU(), - nn.Linear(hidden_size, hidden_size, bias=True), - ) - self.frequency_embedding_size = frequency_embedding_size - - @staticmethod - def timestep_embedding(t, dim, max_period=10000): - """ - Create sinusoidal timestep embeddings. - - Args: - t: a 1-D Tensor of N indices, one per batch element. - These may be fractional. - dim: the dimension of the output. - max_period: controls the minimum frequency of the embeddings. - - Returns: - an (N, D) Tensor of positional embeddings. - """ - # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py - half = dim // 2 - freqs = torch.exp( - -np.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half - ).to(device=t.device) - args = t[:, None].float() * freqs[None] - embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) - if dim % 2: - embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) - return embedding - - def forward(self, t): - t_freq = self.timestep_embedding(t, self.frequency_embedding_size) - t_emb = self.mlp(t_freq) - return t_emb - - -class SparseStructureFlowModel(nn.Module): - def __init__( - self, - resolution: int, - in_channels: int, - model_channels: int, - cond_channels: int, - out_channels: int, - num_blocks: int, - num_heads: Optional[int] = None, - num_head_channels: Optional[int] = 64, - mlp_ratio: float = 4, - patch_size: int = 2, - pe_mode: Literal["ape", "rope"] = "ape", - use_fp16: bool = False, - use_checkpoint: bool = False, - share_mod: bool = False, - qk_rms_norm: bool = False, - qk_rms_norm_cross: bool = False, - ): - super().__init__() - self.resolution = resolution - self.in_channels = in_channels - self.model_channels = model_channels - self.cond_channels = cond_channels - self.out_channels = out_channels - self.num_blocks = num_blocks - self.num_heads = num_heads or model_channels // num_head_channels - self.mlp_ratio = mlp_ratio - self.patch_size = patch_size - self.pe_mode = pe_mode - self.use_fp16 = use_fp16 - self.use_checkpoint = use_checkpoint - self.share_mod = share_mod - self.qk_rms_norm = qk_rms_norm - self.qk_rms_norm_cross = qk_rms_norm_cross - self.dtype = torch.float16 if use_fp16 else torch.float32 - - self.t_embedder = TimestepEmbedder(model_channels) - if share_mod: - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), - nn.Linear(model_channels, 6 * model_channels, bias=True) - ) - - if pe_mode == "ape": - pos_embedder = AbsolutePositionEmbedder(model_channels, 3) - coords = torch.meshgrid(*[torch.arange(res, device=self.device) for res in [resolution // patch_size] * 3], indexing='ij') - coords = torch.stack(coords, dim=-1).reshape(-1, 3) - pos_emb = pos_embedder(coords) - self.register_buffer("pos_emb", pos_emb) - - self.input_layer = nn.Linear(in_channels * patch_size**3, model_channels) - - self.blocks = nn.ModuleList([ - ModulatedTransformerCrossBlock( - model_channels, - cond_channels, - num_heads=self.num_heads, - mlp_ratio=self.mlp_ratio, - attn_mode='full', - use_checkpoint=self.use_checkpoint, - use_rope=(pe_mode == "rope"), - share_mod=share_mod, - qk_rms_norm=self.qk_rms_norm, - qk_rms_norm_cross=self.qk_rms_norm_cross, - ) - for _ in range(num_blocks) - ]) - - self.out_layer = nn.Linear(model_channels, out_channels * patch_size**3) - - self.initialize_weights() - if use_fp16: - self.convert_to_fp16() - - @property - def device(self) -> torch.device: - """ - Return the device of the model. - """ - return next(self.parameters()).device - - def convert_to_fp16(self) -> None: - """ - Convert the torso of the model to float16. - """ - self.blocks.apply(convert_module_to_f16) - - def convert_to_fp32(self) -> None: - """ - Convert the torso of the model to float32. - """ - self.blocks.apply(convert_module_to_f32) - - def initialize_weights(self) -> None: - # Initialize transformer layers: - def _basic_init(module): - if isinstance(module, nn.Linear): - torch.nn.init.xavier_uniform_(module.weight) - if module.bias is not None: - nn.init.constant_(module.bias, 0) - self.apply(_basic_init) - - # Initialize timestep embedding MLP: - nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02) - nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02) - - # Zero-out adaLN modulation layers in DiT blocks: - if self.share_mod: - nn.init.constant_(self.adaLN_modulation[-1].weight, 0) - nn.init.constant_(self.adaLN_modulation[-1].bias, 0) - else: - for block in self.blocks: - nn.init.constant_(block.adaLN_modulation[-1].weight, 0) - nn.init.constant_(block.adaLN_modulation[-1].bias, 0) - - # Zero-out output layers: - nn.init.constant_(self.out_layer.weight, 0) - nn.init.constant_(self.out_layer.bias, 0) - - def forward(self, x: torch.Tensor, t: torch.Tensor, cond: torch.Tensor) -> torch.Tensor: - assert [*x.shape] == [x.shape[0], self.in_channels, *[self.resolution] * 3], \ - f"Input shape mismatch, got {x.shape}, expected {[x.shape[0], self.in_channels, *[self.resolution] * 3]}" - - h = patchify(x, self.patch_size) - h = h.view(*h.shape[:2], -1).permute(0, 2, 1).contiguous() - - h = self.input_layer(h) - h = h + self.pos_emb[None] - t_emb = self.t_embedder(t) - if self.share_mod: - t_emb = self.adaLN_modulation(t_emb) - t_emb = t_emb.type(self.dtype) - h = h.type(self.dtype) - cond = cond.type(self.dtype) - for block in self.blocks: - h = block(h, t_emb, cond) - h = h.type(x.dtype) - h = F.layer_norm(h, h.shape[-1:]) - h = self.out_layer(h) - - h = h.permute(0, 2, 1).view(h.shape[0], h.shape[2], *[self.resolution // self.patch_size] * 3) - h = unpatchify(h, self.patch_size).contiguous() - - return h diff --git a/Gen_3D_Modules/TRELLIS/trellis_/models/sparse_structure_vae.py b/Gen_3D_Modules/TRELLIS/trellis_/models/sparse_structure_vae.py deleted file mode 100644 index c3e09136..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/models/sparse_structure_vae.py +++ /dev/null @@ -1,306 +0,0 @@ -from typing import * -import torch -import torch.nn as nn -import torch.nn.functional as F -from ..modules.norm import GroupNorm32, ChannelLayerNorm32 -from ..modules.spatial import pixel_shuffle_3d -from ..modules.utils import zero_module, convert_module_to_f16, convert_module_to_f32 - - -def norm_layer(norm_type: str, *args, **kwargs) -> nn.Module: - """ - Return a normalization layer. - """ - if norm_type == "group": - return GroupNorm32(32, *args, **kwargs) - elif norm_type == "layer": - return ChannelLayerNorm32(*args, **kwargs) - else: - raise ValueError(f"Invalid norm type {norm_type}") - - -class ResBlock3d(nn.Module): - def __init__( - self, - channels: int, - out_channels: Optional[int] = None, - norm_type: Literal["group", "layer"] = "layer", - ): - super().__init__() - self.channels = channels - self.out_channels = out_channels or channels - - self.norm1 = norm_layer(norm_type, channels) - self.norm2 = norm_layer(norm_type, self.out_channels) - self.conv1 = nn.Conv3d(channels, self.out_channels, 3, padding=1) - self.conv2 = zero_module(nn.Conv3d(self.out_channels, self.out_channels, 3, padding=1)) - self.skip_connection = nn.Conv3d(channels, self.out_channels, 1) if channels != self.out_channels else nn.Identity() - - def forward(self, x: torch.Tensor) -> torch.Tensor: - h = self.norm1(x) - h = F.silu(h) - h = self.conv1(h) - h = self.norm2(h) - h = F.silu(h) - h = self.conv2(h) - h = h + self.skip_connection(x) - return h - - -class DownsampleBlock3d(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - mode: Literal["conv", "avgpool"] = "conv", - ): - assert mode in ["conv", "avgpool"], f"Invalid mode {mode}" - - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - - if mode == "conv": - self.conv = nn.Conv3d(in_channels, out_channels, 2, stride=2) - elif mode == "avgpool": - assert in_channels == out_channels, "Pooling mode requires in_channels to be equal to out_channels" - - def forward(self, x: torch.Tensor) -> torch.Tensor: - if hasattr(self, "conv"): - return self.conv(x) - else: - return F.avg_pool3d(x, 2) - - -class UpsampleBlock3d(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - mode: Literal["conv", "nearest"] = "conv", - ): - assert mode in ["conv", "nearest"], f"Invalid mode {mode}" - - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - - if mode == "conv": - self.conv = nn.Conv3d(in_channels, out_channels*8, 3, padding=1) - elif mode == "nearest": - assert in_channels == out_channels, "Nearest mode requires in_channels to be equal to out_channels" - - def forward(self, x: torch.Tensor) -> torch.Tensor: - if hasattr(self, "conv"): - x = self.conv(x) - return pixel_shuffle_3d(x, 2) - else: - return F.interpolate(x, scale_factor=2, mode="nearest") - - -class SparseStructureEncoder(nn.Module): - """ - Encoder for Sparse Structure (\mathcal{E}_S in the paper Sec. 3.3). - - Args: - in_channels (int): Channels of the input. - latent_channels (int): Channels of the latent representation. - num_res_blocks (int): Number of residual blocks at each resolution. - channels (List[int]): Channels of the encoder blocks. - num_res_blocks_middle (int): Number of residual blocks in the middle. - norm_type (Literal["group", "layer"]): Type of normalization layer. - use_fp16 (bool): Whether to use FP16. - """ - def __init__( - self, - in_channels: int, - latent_channels: int, - num_res_blocks: int, - channels: List[int], - num_res_blocks_middle: int = 2, - norm_type: Literal["group", "layer"] = "layer", - use_fp16: bool = False, - ): - super().__init__() - self.in_channels = in_channels - self.latent_channels = latent_channels - self.num_res_blocks = num_res_blocks - self.channels = channels - self.num_res_blocks_middle = num_res_blocks_middle - self.norm_type = norm_type - self.use_fp16 = use_fp16 - self.dtype = torch.float16 if use_fp16 else torch.float32 - - self.input_layer = nn.Conv3d(in_channels, channels[0], 3, padding=1) - - self.blocks = nn.ModuleList([]) - for i, ch in enumerate(channels): - self.blocks.extend([ - ResBlock3d(ch, ch) - for _ in range(num_res_blocks) - ]) - if i < len(channels) - 1: - self.blocks.append( - DownsampleBlock3d(ch, channels[i+1]) - ) - - self.middle_block = nn.Sequential(*[ - ResBlock3d(channels[-1], channels[-1]) - for _ in range(num_res_blocks_middle) - ]) - - self.out_layer = nn.Sequential( - norm_layer(norm_type, channels[-1]), - nn.SiLU(), - nn.Conv3d(channels[-1], latent_channels*2, 3, padding=1) - ) - - if use_fp16: - self.convert_to_fp16() - - @property - def device(self) -> torch.device: - """ - Return the device of the model. - """ - return next(self.parameters()).device - - def convert_to_fp16(self) -> None: - """ - Convert the torso of the model to float16. - """ - self.use_fp16 = True - self.dtype = torch.float16 - self.blocks.apply(convert_module_to_f16) - self.middle_block.apply(convert_module_to_f16) - - def convert_to_fp32(self) -> None: - """ - Convert the torso of the model to float32. - """ - self.use_fp16 = False - self.dtype = torch.float32 - self.blocks.apply(convert_module_to_f32) - self.middle_block.apply(convert_module_to_f32) - - def forward(self, x: torch.Tensor, sample_posterior: bool = False, return_raw: bool = False) -> torch.Tensor: - h = self.input_layer(x) - h = h.type(self.dtype) - - for block in self.blocks: - h = block(h) - h = self.middle_block(h) - - h = h.type(x.dtype) - h = self.out_layer(h) - - mean, logvar = h.chunk(2, dim=1) - - if sample_posterior: - std = torch.exp(0.5 * logvar) - z = mean + std * torch.randn_like(std) - else: - z = mean - - if return_raw: - return z, mean, logvar - return z - - -class SparseStructureDecoder(nn.Module): - """ - Decoder for Sparse Structure (\mathcal{D}_S in the paper Sec. 3.3). - - Args: - out_channels (int): Channels of the output. - latent_channels (int): Channels of the latent representation. - num_res_blocks (int): Number of residual blocks at each resolution. - channels (List[int]): Channels of the decoder blocks. - num_res_blocks_middle (int): Number of residual blocks in the middle. - norm_type (Literal["group", "layer"]): Type of normalization layer. - use_fp16 (bool): Whether to use FP16. - """ - def __init__( - self, - out_channels: int, - latent_channels: int, - num_res_blocks: int, - channels: List[int], - num_res_blocks_middle: int = 2, - norm_type: Literal["group", "layer"] = "layer", - use_fp16: bool = False, - ): - super().__init__() - self.out_channels = out_channels - self.latent_channels = latent_channels - self.num_res_blocks = num_res_blocks - self.channels = channels - self.num_res_blocks_middle = num_res_blocks_middle - self.norm_type = norm_type - self.use_fp16 = use_fp16 - self.dtype = torch.float16 if use_fp16 else torch.float32 - - self.input_layer = nn.Conv3d(latent_channels, channels[0], 3, padding=1) - - self.middle_block = nn.Sequential(*[ - ResBlock3d(channels[0], channels[0]) - for _ in range(num_res_blocks_middle) - ]) - - self.blocks = nn.ModuleList([]) - for i, ch in enumerate(channels): - self.blocks.extend([ - ResBlock3d(ch, ch) - for _ in range(num_res_blocks) - ]) - if i < len(channels) - 1: - self.blocks.append( - UpsampleBlock3d(ch, channels[i+1]) - ) - - self.out_layer = nn.Sequential( - norm_layer(norm_type, channels[-1]), - nn.SiLU(), - nn.Conv3d(channels[-1], out_channels, 3, padding=1) - ) - - if use_fp16: - self.convert_to_fp16() - - @property - def device(self) -> torch.device: - """ - Return the device of the model. - """ - return next(self.parameters()).device - - def convert_to_fp16(self) -> None: - """ - Convert the torso of the model to float16. - """ - self.use_fp16 = True - self.dtype = torch.float16 - self.blocks.apply(convert_module_to_f16) - self.middle_block.apply(convert_module_to_f16) - - def convert_to_fp32(self) -> None: - """ - Convert the torso of the model to float32. - """ - self.use_fp16 = False - self.dtype = torch.float32 - self.blocks.apply(convert_module_to_f32) - self.middle_block.apply(convert_module_to_f32) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - h = self.input_layer(x) - - h = h.type(self.dtype) - - h = self.middle_block(h) - for block in self.blocks: - h = block(h) - - h = h.type(x.dtype) - h = self.out_layer(h) - return h diff --git a/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_flow.py b/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_flow.py deleted file mode 100644 index f1463d79..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_flow.py +++ /dev/null @@ -1,262 +0,0 @@ -from typing import * -import torch -import torch.nn as nn -import torch.nn.functional as F -import numpy as np -from ..modules.utils import zero_module, convert_module_to_f16, convert_module_to_f32 -from ..modules.transformer import AbsolutePositionEmbedder -from ..modules.norm import LayerNorm32 -from ..modules import sparse as sp -from ..modules.sparse.transformer import ModulatedSparseTransformerCrossBlock -from .sparse_structure_flow import TimestepEmbedder - - -class SparseResBlock3d(nn.Module): - def __init__( - self, - channels: int, - emb_channels: int, - out_channels: Optional[int] = None, - downsample: bool = False, - upsample: bool = False, - ): - super().__init__() - self.channels = channels - self.emb_channels = emb_channels - self.out_channels = out_channels or channels - self.downsample = downsample - self.upsample = upsample - - assert not (downsample and upsample), "Cannot downsample and upsample at the same time" - - self.norm1 = LayerNorm32(channels, elementwise_affine=True, eps=1e-6) - self.norm2 = LayerNorm32(self.out_channels, elementwise_affine=False, eps=1e-6) - self.conv1 = sp.SparseConv3d(channels, self.out_channels, 3) - self.conv2 = zero_module(sp.SparseConv3d(self.out_channels, self.out_channels, 3)) - self.emb_layers = nn.Sequential( - nn.SiLU(), - nn.Linear(emb_channels, 2 * self.out_channels, bias=True), - ) - self.skip_connection = sp.SparseLinear(channels, self.out_channels) if channels != self.out_channels else nn.Identity() - self.updown = None - if self.downsample: - self.updown = sp.SparseDownsample(2) - elif self.upsample: - self.updown = sp.SparseUpsample(2) - - def _updown(self, x: sp.SparseTensor) -> sp.SparseTensor: - if self.updown is not None: - x = self.updown(x) - return x - - def forward(self, x: sp.SparseTensor, emb: torch.Tensor) -> sp.SparseTensor: - emb_out = self.emb_layers(emb).type(x.dtype) - scale, shift = torch.chunk(emb_out, 2, dim=1) - - x = self._updown(x) - h = x.replace(self.norm1(x.feats)) - h = h.replace(F.silu(h.feats)) - h = self.conv1(h) - h = h.replace(self.norm2(h.feats)) * (1 + scale) + shift - h = h.replace(F.silu(h.feats)) - h = self.conv2(h) - h = h + self.skip_connection(x) - - return h - - -class SLatFlowModel(nn.Module): - def __init__( - self, - resolution: int, - in_channels: int, - model_channels: int, - cond_channels: int, - out_channels: int, - num_blocks: int, - num_heads: Optional[int] = None, - num_head_channels: Optional[int] = 64, - mlp_ratio: float = 4, - patch_size: int = 2, - num_io_res_blocks: int = 2, - io_block_channels: List[int] = None, - pe_mode: Literal["ape", "rope"] = "ape", - use_fp16: bool = False, - use_checkpoint: bool = False, - use_skip_connection: bool = True, - share_mod: bool = False, - qk_rms_norm: bool = False, - qk_rms_norm_cross: bool = False, - ): - super().__init__() - self.resolution = resolution - self.in_channels = in_channels - self.model_channels = model_channels - self.cond_channels = cond_channels - self.out_channels = out_channels - self.num_blocks = num_blocks - self.num_heads = num_heads or model_channels // num_head_channels - self.mlp_ratio = mlp_ratio - self.patch_size = patch_size - self.num_io_res_blocks = num_io_res_blocks - self.io_block_channels = io_block_channels - self.pe_mode = pe_mode - self.use_fp16 = use_fp16 - self.use_checkpoint = use_checkpoint - self.use_skip_connection = use_skip_connection - self.share_mod = share_mod - self.qk_rms_norm = qk_rms_norm - self.qk_rms_norm_cross = qk_rms_norm_cross - self.dtype = torch.float16 if use_fp16 else torch.float32 - - assert int(np.log2(patch_size)) == np.log2(patch_size), "Patch size must be a power of 2" - assert np.log2(patch_size) == len(io_block_channels), "Number of IO ResBlocks must match the number of stages" - - self.t_embedder = TimestepEmbedder(model_channels) - if share_mod: - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), - nn.Linear(model_channels, 6 * model_channels, bias=True) - ) - - if pe_mode == "ape": - self.pos_embedder = AbsolutePositionEmbedder(model_channels) - - self.input_layer = sp.SparseLinear(in_channels, io_block_channels[0]) - self.input_blocks = nn.ModuleList([]) - for chs, next_chs in zip(io_block_channels, io_block_channels[1:] + [model_channels]): - self.input_blocks.extend([ - SparseResBlock3d( - chs, - model_channels, - out_channels=chs, - ) - for _ in range(num_io_res_blocks-1) - ]) - self.input_blocks.append( - SparseResBlock3d( - chs, - model_channels, - out_channels=next_chs, - downsample=True, - ) - ) - - self.blocks = nn.ModuleList([ - ModulatedSparseTransformerCrossBlock( - model_channels, - cond_channels, - num_heads=self.num_heads, - mlp_ratio=self.mlp_ratio, - attn_mode='full', - use_checkpoint=self.use_checkpoint, - use_rope=(pe_mode == "rope"), - share_mod=self.share_mod, - qk_rms_norm=self.qk_rms_norm, - qk_rms_norm_cross=self.qk_rms_norm_cross, - ) - for _ in range(num_blocks) - ]) - - self.out_blocks = nn.ModuleList([]) - for chs, prev_chs in zip(reversed(io_block_channels), [model_channels] + list(reversed(io_block_channels[1:]))): - self.out_blocks.append( - SparseResBlock3d( - prev_chs * 2 if self.use_skip_connection else prev_chs, - model_channels, - out_channels=chs, - upsample=True, - ) - ) - self.out_blocks.extend([ - SparseResBlock3d( - chs * 2 if self.use_skip_connection else chs, - model_channels, - out_channels=chs, - ) - for _ in range(num_io_res_blocks-1) - ]) - self.out_layer = sp.SparseLinear(io_block_channels[0], out_channels) - - self.initialize_weights() - if use_fp16: - self.convert_to_fp16() - - @property - def device(self) -> torch.device: - """ - Return the device of the model. - """ - return next(self.parameters()).device - - def convert_to_fp16(self) -> None: - """ - Convert the torso of the model to float16. - """ - self.input_blocks.apply(convert_module_to_f16) - self.blocks.apply(convert_module_to_f16) - self.out_blocks.apply(convert_module_to_f16) - - def convert_to_fp32(self) -> None: - """ - Convert the torso of the model to float32. - """ - self.input_blocks.apply(convert_module_to_f32) - self.blocks.apply(convert_module_to_f32) - self.out_blocks.apply(convert_module_to_f32) - - def initialize_weights(self) -> None: - # Initialize transformer layers: - def _basic_init(module): - if isinstance(module, nn.Linear): - torch.nn.init.xavier_uniform_(module.weight) - if module.bias is not None: - nn.init.constant_(module.bias, 0) - self.apply(_basic_init) - - # Initialize timestep embedding MLP: - nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02) - nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02) - - # Zero-out adaLN modulation layers in DiT blocks: - if self.share_mod: - nn.init.constant_(self.adaLN_modulation[-1].weight, 0) - nn.init.constant_(self.adaLN_modulation[-1].bias, 0) - else: - for block in self.blocks: - nn.init.constant_(block.adaLN_modulation[-1].weight, 0) - nn.init.constant_(block.adaLN_modulation[-1].bias, 0) - - # Zero-out output layers: - nn.init.constant_(self.out_layer.weight, 0) - nn.init.constant_(self.out_layer.bias, 0) - - def forward(self, x: sp.SparseTensor, t: torch.Tensor, cond: torch.Tensor) -> sp.SparseTensor: - h = self.input_layer(x).type(self.dtype) - t_emb = self.t_embedder(t) - if self.share_mod: - t_emb = self.adaLN_modulation(t_emb) - t_emb = t_emb.type(self.dtype) - cond = cond.type(self.dtype) - - skips = [] - # pack with input blocks - for block in self.input_blocks: - h = block(h, t_emb) - skips.append(h.feats) - - if self.pe_mode == "ape": - h = h + self.pos_embedder(h.coords[:, 1:]).type(self.dtype) - for block in self.blocks: - h = block(h, t_emb, cond) - - # unpack with output blocks - for block, skip in zip(self.out_blocks, reversed(skips)): - if self.use_skip_connection: - h = block(h.replace(torch.cat([h.feats, skip], dim=1)), t_emb) - else: - h = block(h, t_emb) - - h = h.replace(F.layer_norm(h.feats, h.feats.shape[-1:])) - h = self.out_layer(h.type(x.dtype)) - return h diff --git a/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/__init__.py deleted file mode 100644 index 75603bc1..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .encoder import SLatEncoder -from .decoder_gs import SLatGaussianDecoder -from .decoder_rf import SLatRadianceFieldDecoder -from .decoder_mesh import SLatMeshDecoder diff --git a/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/base.py b/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/base.py deleted file mode 100644 index ab0bf6a8..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/base.py +++ /dev/null @@ -1,117 +0,0 @@ -from typing import * -import torch -import torch.nn as nn -from ...modules.utils import convert_module_to_f16, convert_module_to_f32 -from ...modules import sparse as sp -from ...modules.transformer import AbsolutePositionEmbedder -from ...modules.sparse.transformer import SparseTransformerBlock - - -def block_attn_config(self): - """ - Return the attention configuration of the model. - """ - for i in range(self.num_blocks): - if self.attn_mode == "shift_window": - yield "serialized", self.window_size, 0, (16 * (i % 2),) * 3, sp.SerializeMode.Z_ORDER - elif self.attn_mode == "shift_sequence": - yield "serialized", self.window_size, self.window_size // 2 * (i % 2), (0, 0, 0), sp.SerializeMode.Z_ORDER - elif self.attn_mode == "shift_order": - yield "serialized", self.window_size, 0, (0, 0, 0), sp.SerializeModes[i % 4] - elif self.attn_mode == "full": - yield "full", None, None, None, None - elif self.attn_mode == "swin": - yield "windowed", self.window_size, None, self.window_size // 2 * (i % 2), None - - -class SparseTransformerBase(nn.Module): - """ - Sparse Transformer without output layers. - Serve as the base class for encoder and decoder. - """ - def __init__( - self, - in_channels: int, - model_channels: int, - num_blocks: int, - num_heads: Optional[int] = None, - num_head_channels: Optional[int] = 64, - mlp_ratio: float = 4.0, - attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "full", - window_size: Optional[int] = None, - pe_mode: Literal["ape", "rope"] = "ape", - use_fp16: bool = False, - use_checkpoint: bool = False, - qk_rms_norm: bool = False, - ): - super().__init__() - self.in_channels = in_channels - self.model_channels = model_channels - self.num_blocks = num_blocks - self.window_size = window_size - self.num_heads = num_heads or model_channels // num_head_channels - self.mlp_ratio = mlp_ratio - self.attn_mode = attn_mode - self.pe_mode = pe_mode - self.use_fp16 = use_fp16 - self.use_checkpoint = use_checkpoint - self.qk_rms_norm = qk_rms_norm - self.dtype = torch.float16 if use_fp16 else torch.float32 - - if pe_mode == "ape": - self.pos_embedder = AbsolutePositionEmbedder(model_channels) - - self.input_layer = sp.SparseLinear(in_channels, model_channels) - self.blocks = nn.ModuleList([ - SparseTransformerBlock( - model_channels, - num_heads=self.num_heads, - mlp_ratio=self.mlp_ratio, - attn_mode=attn_mode, - window_size=window_size, - shift_sequence=shift_sequence, - shift_window=shift_window, - serialize_mode=serialize_mode, - use_checkpoint=self.use_checkpoint, - use_rope=(pe_mode == "rope"), - qk_rms_norm=self.qk_rms_norm, - ) - for attn_mode, window_size, shift_sequence, shift_window, serialize_mode in block_attn_config(self) - ]) - - @property - def device(self) -> torch.device: - """ - Return the device of the model. - """ - return next(self.parameters()).device - - def convert_to_fp16(self) -> None: - """ - Convert the torso of the model to float16. - """ - self.blocks.apply(convert_module_to_f16) - - def convert_to_fp32(self) -> None: - """ - Convert the torso of the model to float32. - """ - self.blocks.apply(convert_module_to_f32) - - def initialize_weights(self) -> None: - # Initialize transformer layers: - def _basic_init(module): - if isinstance(module, nn.Linear): - torch.nn.init.xavier_uniform_(module.weight) - if module.bias is not None: - nn.init.constant_(module.bias, 0) - self.apply(_basic_init) - - def forward(self, x: sp.SparseTensor) -> sp.SparseTensor: - h = self.input_layer(x) - if self.pe_mode == "ape": - h = h + self.pos_embedder(x.coords[:, 1:]) - h = h.type(self.dtype) - for block in self.blocks: - h = block(h) - return h diff --git a/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/decoder_gs.py b/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/decoder_gs.py deleted file mode 100644 index b893cfcf..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/decoder_gs.py +++ /dev/null @@ -1,122 +0,0 @@ -from typing import * -import torch -import torch.nn as nn -import torch.nn.functional as F -from ...modules import sparse as sp -from ...utils.random_utils import hammersley_sequence -from .base import SparseTransformerBase -from ...representations import Gaussian - - -class SLatGaussianDecoder(SparseTransformerBase): - def __init__( - self, - resolution: int, - model_channels: int, - latent_channels: int, - num_blocks: int, - num_heads: Optional[int] = None, - num_head_channels: Optional[int] = 64, - mlp_ratio: float = 4, - attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "swin", - window_size: int = 8, - pe_mode: Literal["ape", "rope"] = "ape", - use_fp16: bool = False, - use_checkpoint: bool = False, - qk_rms_norm: bool = False, - representation_config: dict = None, - ): - super().__init__( - in_channels=latent_channels, - model_channels=model_channels, - num_blocks=num_blocks, - num_heads=num_heads, - num_head_channels=num_head_channels, - mlp_ratio=mlp_ratio, - attn_mode=attn_mode, - window_size=window_size, - pe_mode=pe_mode, - use_fp16=use_fp16, - use_checkpoint=use_checkpoint, - qk_rms_norm=qk_rms_norm, - ) - self.resolution = resolution - self.rep_config = representation_config - self._calc_layout() - self.out_layer = sp.SparseLinear(model_channels, self.out_channels) - self._build_perturbation() - - self.initialize_weights() - if use_fp16: - self.convert_to_fp16() - - def initialize_weights(self) -> None: - super().initialize_weights() - # Zero-out output layers: - nn.init.constant_(self.out_layer.weight, 0) - nn.init.constant_(self.out_layer.bias, 0) - - def _build_perturbation(self) -> None: - perturbation = [hammersley_sequence(3, i, self.rep_config['num_gaussians']) for i in range(self.rep_config['num_gaussians'])] - perturbation = torch.tensor(perturbation).float() * 2 - 1 - perturbation = perturbation / self.rep_config['voxel_size'] - perturbation = torch.atanh(perturbation).to(self.device) - self.register_buffer('offset_perturbation', perturbation) - - def _calc_layout(self) -> None: - self.layout = { - '_xyz' : {'shape': (self.rep_config['num_gaussians'], 3), 'size': self.rep_config['num_gaussians'] * 3}, - '_features_dc' : {'shape': (self.rep_config['num_gaussians'], 1, 3), 'size': self.rep_config['num_gaussians'] * 3}, - '_scaling' : {'shape': (self.rep_config['num_gaussians'], 3), 'size': self.rep_config['num_gaussians'] * 3}, - '_rotation' : {'shape': (self.rep_config['num_gaussians'], 4), 'size': self.rep_config['num_gaussians'] * 4}, - '_opacity' : {'shape': (self.rep_config['num_gaussians'], 1), 'size': self.rep_config['num_gaussians']}, - } - start = 0 - for k, v in self.layout.items(): - v['range'] = (start, start + v['size']) - start += v['size'] - self.out_channels = start - - def to_representation(self, x: sp.SparseTensor) -> List[Gaussian]: - """ - Convert a batch of network outputs to 3D representations. - - Args: - x: The [N x * x C] sparse tensor output by the network. - - Returns: - list of representations - """ - ret = [] - for i in range(x.shape[0]): - representation = Gaussian( - sh_degree=0, - aabb=[-0.5, -0.5, -0.5, 1.0, 1.0, 1.0], - mininum_kernel_size = self.rep_config['3d_filter_kernel_size'], - scaling_bias = self.rep_config['scaling_bias'], - opacity_bias = self.rep_config['opacity_bias'], - scaling_activation = self.rep_config['scaling_activation'] - ) - xyz = (x.coords[x.layout[i]][:, 1:].float() + 0.5) / self.resolution - for k, v in self.layout.items(): - if k == '_xyz': - offset = x.feats[x.layout[i]][:, v['range'][0]:v['range'][1]].reshape(-1, *v['shape']) - offset = offset * self.rep_config['lr'][k] - if self.rep_config['perturb_offset']: - offset = offset + self.offset_perturbation - offset = torch.tanh(offset) / self.resolution * 0.5 * self.rep_config['voxel_size'] - _xyz = xyz.unsqueeze(1) + offset - setattr(representation, k, _xyz.flatten(0, 1)) - else: - feats = x.feats[x.layout[i]][:, v['range'][0]:v['range'][1]].reshape(-1, *v['shape']).flatten(0, 1) - feats = feats * self.rep_config['lr'][k] - setattr(representation, k, feats) - ret.append(representation) - return ret - - def forward(self, x: sp.SparseTensor) -> List[Gaussian]: - h = super().forward(x) - h = h.type(x.dtype) - h = h.replace(F.layer_norm(h.feats, h.feats.shape[-1:])) - h = self.out_layer(h) - return self.to_representation(h) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/decoder_mesh.py b/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/decoder_mesh.py deleted file mode 100644 index 75c1b1ec..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/decoder_mesh.py +++ /dev/null @@ -1,167 +0,0 @@ -from typing import * -import torch -import torch.nn as nn -import torch.nn.functional as F -import numpy as np -from ...modules.utils import zero_module, convert_module_to_f16, convert_module_to_f32 -from ...modules import sparse as sp -from .base import SparseTransformerBase -from ...representations import MeshExtractResult -from ...representations.mesh import SparseFeatures2Mesh - - -class SparseSubdivideBlock3d(nn.Module): - """ - A 3D subdivide block that can subdivide the sparse tensor. - - Args: - channels: channels in the inputs and outputs. - out_channels: if specified, the number of output channels. - num_groups: the number of groups for the group norm. - """ - def __init__( - self, - channels: int, - resolution: int, - out_channels: Optional[int] = None, - num_groups: int = 32 - ): - super().__init__() - self.channels = channels - self.resolution = resolution - self.out_resolution = resolution * 2 - self.out_channels = out_channels or channels - - self.act_layers = nn.Sequential( - sp.SparseGroupNorm32(num_groups, channels), - sp.SparseSiLU() - ) - - self.sub = sp.SparseSubdivide() - - self.out_layers = nn.Sequential( - sp.SparseConv3d(channels, self.out_channels, 3, indice_key=f"res_{self.out_resolution}"), - sp.SparseGroupNorm32(num_groups, self.out_channels), - sp.SparseSiLU(), - zero_module(sp.SparseConv3d(self.out_channels, self.out_channels, 3, indice_key=f"res_{self.out_resolution}")), - ) - - if self.out_channels == channels: - self.skip_connection = nn.Identity() - else: - self.skip_connection = sp.SparseConv3d(channels, self.out_channels, 1, indice_key=f"res_{self.out_resolution}") - - def forward(self, x: sp.SparseTensor) -> sp.SparseTensor: - """ - Apply the block to a Tensor, conditioned on a timestep embedding. - - Args: - x: an [N x C x ...] Tensor of features. - Returns: - an [N x C x ...] Tensor of outputs. - """ - h = self.act_layers(x) - h = self.sub(h) - x = self.sub(x) - h = self.out_layers(h) - h = h + self.skip_connection(x) - return h - - -class SLatMeshDecoder(SparseTransformerBase): - def __init__( - self, - resolution: int, - model_channels: int, - latent_channels: int, - num_blocks: int, - num_heads: Optional[int] = None, - num_head_channels: Optional[int] = 64, - mlp_ratio: float = 4, - attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "swin", - window_size: int = 8, - pe_mode: Literal["ape", "rope"] = "ape", - use_fp16: bool = False, - use_checkpoint: bool = False, - qk_rms_norm: bool = False, - representation_config: dict = None, - ): - super().__init__( - in_channels=latent_channels, - model_channels=model_channels, - num_blocks=num_blocks, - num_heads=num_heads, - num_head_channels=num_head_channels, - mlp_ratio=mlp_ratio, - attn_mode=attn_mode, - window_size=window_size, - pe_mode=pe_mode, - use_fp16=use_fp16, - use_checkpoint=use_checkpoint, - qk_rms_norm=qk_rms_norm, - ) - self.resolution = resolution - self.rep_config = representation_config - self.mesh_extractor = SparseFeatures2Mesh(res=self.resolution*4, use_color=self.rep_config.get('use_color', False)) - self.out_channels = self.mesh_extractor.feats_channels - self.upsample = nn.ModuleList([ - SparseSubdivideBlock3d( - channels=model_channels, - resolution=resolution, - out_channels=model_channels // 4 - ), - SparseSubdivideBlock3d( - channels=model_channels // 4, - resolution=resolution * 2, - out_channels=model_channels // 8 - ) - ]) - self.out_layer = sp.SparseLinear(model_channels // 8, self.out_channels) - - self.initialize_weights() - if use_fp16: - self.convert_to_fp16() - - def initialize_weights(self) -> None: - super().initialize_weights() - # Zero-out output layers: - nn.init.constant_(self.out_layer.weight, 0) - nn.init.constant_(self.out_layer.bias, 0) - - def convert_to_fp16(self) -> None: - """ - Convert the torso of the model to float16. - """ - super().convert_to_fp16() - self.upsample.apply(convert_module_to_f16) - - def convert_to_fp32(self) -> None: - """ - Convert the torso of the model to float32. - """ - super().convert_to_fp32() - self.upsample.apply(convert_module_to_f32) - - def to_representation(self, x: sp.SparseTensor) -> List[MeshExtractResult]: - """ - Convert a batch of network outputs to 3D representations. - - Args: - x: The [N x * x C] sparse tensor output by the network. - - Returns: - list of representations - """ - ret = [] - for i in range(x.shape[0]): - mesh = self.mesh_extractor(x[i], training=self.training) - ret.append(mesh) - return ret - - def forward(self, x: sp.SparseTensor) -> List[MeshExtractResult]: - h = super().forward(x) - for block in self.upsample: - h = block(h) - h = h.type(x.dtype) - h = self.out_layer(h) - return self.to_representation(h) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/decoder_rf.py b/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/decoder_rf.py deleted file mode 100644 index 968bb305..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/decoder_rf.py +++ /dev/null @@ -1,104 +0,0 @@ -from typing import * -import torch -import torch.nn as nn -import torch.nn.functional as F -import numpy as np -from ...modules import sparse as sp -from .base import SparseTransformerBase -from ...representations import Strivec - - -class SLatRadianceFieldDecoder(SparseTransformerBase): - def __init__( - self, - resolution: int, - model_channels: int, - latent_channels: int, - num_blocks: int, - num_heads: Optional[int] = None, - num_head_channels: Optional[int] = 64, - mlp_ratio: float = 4, - attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "swin", - window_size: int = 8, - pe_mode: Literal["ape", "rope"] = "ape", - use_fp16: bool = False, - use_checkpoint: bool = False, - qk_rms_norm: bool = False, - representation_config: dict = None, - ): - super().__init__( - in_channels=latent_channels, - model_channels=model_channels, - num_blocks=num_blocks, - num_heads=num_heads, - num_head_channels=num_head_channels, - mlp_ratio=mlp_ratio, - attn_mode=attn_mode, - window_size=window_size, - pe_mode=pe_mode, - use_fp16=use_fp16, - use_checkpoint=use_checkpoint, - qk_rms_norm=qk_rms_norm, - ) - self.resolution = resolution - self.rep_config = representation_config - self._calc_layout() - self.out_layer = sp.SparseLinear(model_channels, self.out_channels) - - self.initialize_weights() - if use_fp16: - self.convert_to_fp16() - - def initialize_weights(self) -> None: - super().initialize_weights() - # Zero-out output layers: - nn.init.constant_(self.out_layer.weight, 0) - nn.init.constant_(self.out_layer.bias, 0) - - def _calc_layout(self) -> None: - self.layout = { - 'trivec': {'shape': (self.rep_config['rank'], 3, self.rep_config['dim']), 'size': self.rep_config['rank'] * 3 * self.rep_config['dim']}, - 'density': {'shape': (self.rep_config['rank'],), 'size': self.rep_config['rank']}, - 'features_dc': {'shape': (self.rep_config['rank'], 1, 3), 'size': self.rep_config['rank'] * 3}, - } - start = 0 - for k, v in self.layout.items(): - v['range'] = (start, start + v['size']) - start += v['size'] - self.out_channels = start - - def to_representation(self, x: sp.SparseTensor) -> List[Strivec]: - """ - Convert a batch of network outputs to 3D representations. - - Args: - x: The [N x * x C] sparse tensor output by the network. - - Returns: - list of representations - """ - ret = [] - for i in range(x.shape[0]): - representation = Strivec( - sh_degree=0, - resolution=self.resolution, - aabb=[-0.5, -0.5, -0.5, 1, 1, 1], - rank=self.rep_config['rank'], - dim=self.rep_config['dim'], - device='cuda', - ) - representation.density_shift = 0.0 - representation.position = (x.coords[x.layout[i]][:, 1:].float() + 0.5) / self.resolution - representation.depth = torch.full((representation.position.shape[0], 1), int(np.log2(self.resolution)), dtype=torch.uint8, device='cuda') - for k, v in self.layout.items(): - setattr(representation, k, x.feats[x.layout[i]][:, v['range'][0]:v['range'][1]].reshape(-1, *v['shape'])) - representation.trivec = representation.trivec + 1 - ret.append(representation) - return ret - - def forward(self, x: sp.SparseTensor) -> List[Strivec]: - h = super().forward(x) - h = h.type(x.dtype) - h = h.replace(F.layer_norm(h.feats, h.feats.shape[-1:])) - h = self.out_layer(h) - return self.to_representation(h) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/encoder.py b/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/encoder.py deleted file mode 100644 index 8370921d..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/models/structured_latent_vae/encoder.py +++ /dev/null @@ -1,72 +0,0 @@ -from typing import * -import torch -import torch.nn as nn -import torch.nn.functional as F -from ...modules import sparse as sp -from .base import SparseTransformerBase - - -class SLatEncoder(SparseTransformerBase): - def __init__( - self, - resolution: int, - in_channels: int, - model_channels: int, - latent_channels: int, - num_blocks: int, - num_heads: Optional[int] = None, - num_head_channels: Optional[int] = 64, - mlp_ratio: float = 4, - attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "swin", - window_size: int = 8, - pe_mode: Literal["ape", "rope"] = "ape", - use_fp16: bool = False, - use_checkpoint: bool = False, - qk_rms_norm: bool = False, - ): - super().__init__( - in_channels=in_channels, - model_channels=model_channels, - num_blocks=num_blocks, - num_heads=num_heads, - num_head_channels=num_head_channels, - mlp_ratio=mlp_ratio, - attn_mode=attn_mode, - window_size=window_size, - pe_mode=pe_mode, - use_fp16=use_fp16, - use_checkpoint=use_checkpoint, - qk_rms_norm=qk_rms_norm, - ) - self.resolution = resolution - self.out_layer = sp.SparseLinear(model_channels, 2 * latent_channels) - - self.initialize_weights() - if use_fp16: - self.convert_to_fp16() - - def initialize_weights(self) -> None: - super().initialize_weights() - # Zero-out output layers: - nn.init.constant_(self.out_layer.weight, 0) - nn.init.constant_(self.out_layer.bias, 0) - - def forward(self, x: sp.SparseTensor, sample_posterior=True, return_raw=False): - h = super().forward(x) - h = h.type(x.dtype) - h = h.replace(F.layer_norm(h.feats, h.feats.shape[-1:])) - h = self.out_layer(h) - - # Sample from the posterior distribution - mean, logvar = h.feats.chunk(2, dim=-1) - if sample_posterior: - std = torch.exp(0.5 * logvar) - z = mean + std * torch.randn_like(std) - else: - z = mean - z = h.replace(z) - - if return_raw: - return z, mean, logvar - else: - return z diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/attention/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/attention/__init__.py deleted file mode 100644 index b77197d6..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/modules/attention/__init__.py +++ /dev/null @@ -1,36 +0,0 @@ -from typing import * - -BACKEND = 'xformers' -DEBUG = False - -def __from_env(): - import os - - global BACKEND - global DEBUG - - env_attn_backend = os.environ.get('ATTN_BACKEND') - env_sttn_debug = os.environ.get('ATTN_DEBUG') - - if env_attn_backend is not None and env_attn_backend in ['xformers', 'flash_attn', 'sdpa', 'naive']: - BACKEND = env_attn_backend - if env_sttn_debug is not None: - DEBUG = env_sttn_debug == '1' - - print(f"[ATTENTION] Using backend: {BACKEND}") - - -__from_env() - - -def set_backend(backend: Literal['xformers', 'flash_attn']): - global BACKEND - BACKEND = backend - -def set_debug(debug: bool): - global DEBUG - DEBUG = debug - - -from .full_attn import * -from .modules import * diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/attention/full_attn.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/attention/full_attn.py deleted file mode 100644 index d9ebf638..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/modules/attention/full_attn.py +++ /dev/null @@ -1,140 +0,0 @@ -from typing import * -import torch -import math -from . import DEBUG, BACKEND - -if BACKEND == 'xformers': - import xformers.ops as xops -elif BACKEND == 'flash_attn': - import flash_attn -elif BACKEND == 'sdpa': - from torch.nn.functional import scaled_dot_product_attention as sdpa -elif BACKEND == 'naive': - pass -else: - raise ValueError(f"Unknown attention backend: {BACKEND}") - - -__all__ = [ - 'scaled_dot_product_attention', -] - - -def _naive_sdpa(q, k, v): - """ - Naive implementation of scaled dot product attention. - """ - q = q.permute(0, 2, 1, 3) # [N, H, L, C] - k = k.permute(0, 2, 1, 3) # [N, H, L, C] - v = v.permute(0, 2, 1, 3) # [N, H, L, C] - scale_factor = 1 / math.sqrt(q.size(-1)) - attn_weight = q @ k.transpose(-2, -1) * scale_factor - attn_weight = torch.softmax(attn_weight, dim=-1) - out = attn_weight @ v - out = out.permute(0, 2, 1, 3) # [N, L, H, C] - return out - - -@overload -def scaled_dot_product_attention(qkv: torch.Tensor) -> torch.Tensor: - """ - Apply scaled dot product attention. - - Args: - qkv (torch.Tensor): A [N, L, 3, H, C] tensor containing Qs, Ks, and Vs. - """ - ... - -@overload -def scaled_dot_product_attention(q: torch.Tensor, kv: torch.Tensor) -> torch.Tensor: - """ - Apply scaled dot product attention. - - Args: - q (torch.Tensor): A [N, L, H, C] tensor containing Qs. - kv (torch.Tensor): A [N, L, 2, H, C] tensor containing Ks and Vs. - """ - ... - -@overload -def scaled_dot_product_attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> torch.Tensor: - """ - Apply scaled dot product attention. - - Args: - q (torch.Tensor): A [N, L, H, Ci] tensor containing Qs. - k (torch.Tensor): A [N, L, H, Ci] tensor containing Ks. - v (torch.Tensor): A [N, L, H, Co] tensor containing Vs. - - Note: - k and v are assumed to have the same coordinate map. - """ - ... - -def scaled_dot_product_attention(*args, **kwargs): - arg_names_dict = { - 1: ['qkv'], - 2: ['q', 'kv'], - 3: ['q', 'k', 'v'] - } - num_all_args = len(args) + len(kwargs) - assert num_all_args in arg_names_dict, f"Invalid number of arguments, got {num_all_args}, expected 1, 2, or 3" - for key in arg_names_dict[num_all_args][len(args):]: - assert key in kwargs, f"Missing argument {key}" - - if num_all_args == 1: - qkv = args[0] if len(args) > 0 else kwargs['qkv'] - assert len(qkv.shape) == 5 and qkv.shape[2] == 3, f"Invalid shape for qkv, got {qkv.shape}, expected [N, L, 3, H, C]" - device = qkv.device - - elif num_all_args == 2: - q = args[0] if len(args) > 0 else kwargs['q'] - kv = args[1] if len(args) > 1 else kwargs['kv'] - assert q.shape[0] == kv.shape[0], f"Batch size mismatch, got {q.shape[0]} and {kv.shape[0]}" - assert len(q.shape) == 4, f"Invalid shape for q, got {q.shape}, expected [N, L, H, C]" - assert len(kv.shape) == 5, f"Invalid shape for kv, got {kv.shape}, expected [N, L, 2, H, C]" - device = q.device - - elif num_all_args == 3: - q = args[0] if len(args) > 0 else kwargs['q'] - k = args[1] if len(args) > 1 else kwargs['k'] - v = args[2] if len(args) > 2 else kwargs['v'] - assert q.shape[0] == k.shape[0] == v.shape[0], f"Batch size mismatch, got {q.shape[0]}, {k.shape[0]}, and {v.shape[0]}" - assert len(q.shape) == 4, f"Invalid shape for q, got {q.shape}, expected [N, L, H, Ci]" - assert len(k.shape) == 4, f"Invalid shape for k, got {k.shape}, expected [N, L, H, Ci]" - assert len(v.shape) == 4, f"Invalid shape for v, got {v.shape}, expected [N, L, H, Co]" - device = q.device - - if BACKEND == 'xformers': - if num_all_args == 1: - q, k, v = qkv.unbind(dim=2) - elif num_all_args == 2: - k, v = kv.unbind(dim=2) - out = xops.memory_efficient_attention(q, k, v) - elif BACKEND == 'flash_attn': - if num_all_args == 1: - out = flash_attn.flash_attn_qkvpacked_func(qkv) - elif num_all_args == 2: - out = flash_attn.flash_attn_kvpacked_func(q, kv) - elif num_all_args == 3: - out = flash_attn.flash_attn_func(q, k, v) - elif BACKEND == 'sdpa': - if num_all_args == 1: - q, k, v = qkv.unbind(dim=2) - elif num_all_args == 2: - k, v = kv.unbind(dim=2) - q = q.permute(0, 2, 1, 3) # [N, H, L, C] - k = k.permute(0, 2, 1, 3) # [N, H, L, C] - v = v.permute(0, 2, 1, 3) # [N, H, L, C] - out = sdpa(q, k, v) # [N, H, L, C] - out = out.permute(0, 2, 1, 3) # [N, L, H, C] - elif BACKEND == 'naive': - if num_all_args == 1: - q, k, v = qkv.unbind(dim=2) - elif num_all_args == 2: - k, v = kv.unbind(dim=2) - out = _naive_sdpa(q, k, v) - else: - raise ValueError(f"Unknown attention module: {BACKEND}") - - return out diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/attention/modules.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/attention/modules.py deleted file mode 100644 index dbe6235c..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/modules/attention/modules.py +++ /dev/null @@ -1,146 +0,0 @@ -from typing import * -import torch -import torch.nn as nn -import torch.nn.functional as F -from .full_attn import scaled_dot_product_attention - - -class MultiHeadRMSNorm(nn.Module): - def __init__(self, dim: int, heads: int): - super().__init__() - self.scale = dim ** 0.5 - self.gamma = nn.Parameter(torch.ones(heads, dim)) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - return (F.normalize(x.float(), dim = -1) * self.gamma * self.scale).to(x.dtype) - - -class RotaryPositionEmbedder(nn.Module): - def __init__(self, hidden_size: int, in_channels: int = 3): - super().__init__() - assert hidden_size % 2 == 0, "Hidden size must be divisible by 2" - self.hidden_size = hidden_size - self.in_channels = in_channels - self.freq_dim = hidden_size // in_channels // 2 - self.freqs = torch.arange(self.freq_dim, dtype=torch.float32) / self.freq_dim - self.freqs = 1.0 / (10000 ** self.freqs) - - def _get_phases(self, indices: torch.Tensor) -> torch.Tensor: - self.freqs = self.freqs.to(indices.device) - phases = torch.outer(indices, self.freqs) - phases = torch.polar(torch.ones_like(phases), phases) - return phases - - def _rotary_embedding(self, x: torch.Tensor, phases: torch.Tensor) -> torch.Tensor: - x_complex = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2)) - x_rotated = x_complex * phases - x_embed = torch.view_as_real(x_rotated).reshape(*x_rotated.shape[:-1], -1).to(x.dtype) - return x_embed - - def forward(self, q: torch.Tensor, k: torch.Tensor, indices: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, torch.Tensor]: - """ - Args: - q (sp.SparseTensor): [..., N, D] tensor of queries - k (sp.SparseTensor): [..., N, D] tensor of keys - indices (torch.Tensor): [..., N, C] tensor of spatial positions - """ - if indices is None: - indices = torch.arange(q.shape[-2], device=q.device) - if len(q.shape) > 2: - indices = indices.unsqueeze(0).expand(q.shape[:-2] + (-1,)) - - phases = self._get_phases(indices.reshape(-1)).reshape(*indices.shape[:-1], -1) - if phases.shape[1] < self.hidden_size // 2: - phases = torch.cat([phases, torch.polar( - torch.ones(*phases.shape[:-1], self.hidden_size // 2 - phases.shape[1], device=phases.device), - torch.zeros(*phases.shape[:-1], self.hidden_size // 2 - phases.shape[1], device=phases.device) - )], dim=-1) - q_embed = self._rotary_embedding(q, phases) - k_embed = self._rotary_embedding(k, phases) - return q_embed, k_embed - - -class MultiHeadAttention(nn.Module): - def __init__( - self, - channels: int, - num_heads: int, - ctx_channels: Optional[int]=None, - type: Literal["self", "cross"] = "self", - attn_mode: Literal["full", "windowed"] = "full", - window_size: Optional[int] = None, - shift_window: Optional[Tuple[int, int, int]] = None, - qkv_bias: bool = True, - use_rope: bool = False, - qk_rms_norm: bool = False, - ): - super().__init__() - assert channels % num_heads == 0 - assert type in ["self", "cross"], f"Invalid attention type: {type}" - assert attn_mode in ["full", "windowed"], f"Invalid attention mode: {attn_mode}" - assert type == "self" or attn_mode == "full", "Cross-attention only supports full attention" - - if attn_mode == "windowed": - raise NotImplementedError("Windowed attention is not yet implemented") - - self.channels = channels - self.head_dim = channels // num_heads - self.ctx_channels = ctx_channels if ctx_channels is not None else channels - self.num_heads = num_heads - self._type = type - self.attn_mode = attn_mode - self.window_size = window_size - self.shift_window = shift_window - self.use_rope = use_rope - self.qk_rms_norm = qk_rms_norm - - if self._type == "self": - self.to_qkv = nn.Linear(channels, channels * 3, bias=qkv_bias) - else: - self.to_q = nn.Linear(channels, channels, bias=qkv_bias) - self.to_kv = nn.Linear(self.ctx_channels, channels * 2, bias=qkv_bias) - - if self.qk_rms_norm: - self.q_rms_norm = MultiHeadRMSNorm(self.head_dim, num_heads) - self.k_rms_norm = MultiHeadRMSNorm(self.head_dim, num_heads) - - self.to_out = nn.Linear(channels, channels) - - if use_rope: - self.rope = RotaryPositionEmbedder(channels) - - def forward(self, x: torch.Tensor, context: Optional[torch.Tensor] = None, indices: Optional[torch.Tensor] = None) -> torch.Tensor: - B, L, C = x.shape - if self._type == "self": - qkv = self.to_qkv(x) - qkv = qkv.reshape(B, L, 3, self.num_heads, -1) - if self.use_rope: - q, k, v = qkv.unbind(dim=2) - q, k = self.rope(q, k, indices) - qkv = torch.stack([q, k, v], dim=2) - if self.attn_mode == "full": - if self.qk_rms_norm: - q, k, v = qkv.unbind(dim=2) - q = self.q_rms_norm(q) - k = self.k_rms_norm(k) - h = scaled_dot_product_attention(q, k, v) - else: - h = scaled_dot_product_attention(qkv) - elif self.attn_mode == "windowed": - raise NotImplementedError("Windowed attention is not yet implemented") - else: - Lkv = context.shape[1] - q = self.to_q(x) - kv = self.to_kv(context) - q = q.reshape(B, L, self.num_heads, -1) - kv = kv.reshape(B, Lkv, 2, self.num_heads, -1) - if self.qk_rms_norm: - q = self.q_rms_norm(q) - k, v = kv.unbind(dim=2) - k = self.k_rms_norm(k) - h = scaled_dot_product_attention(q, k, v) - else: - h = scaled_dot_product_attention(q, kv) - h = h.reshape(B, L, -1) - h = self.to_out(h) - return h diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/norm.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/norm.py deleted file mode 100644 index 09035726..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/modules/norm.py +++ /dev/null @@ -1,25 +0,0 @@ -import torch -import torch.nn as nn - - -class LayerNorm32(nn.LayerNorm): - def forward(self, x: torch.Tensor) -> torch.Tensor: - return super().forward(x.float()).type(x.dtype) - - -class GroupNorm32(nn.GroupNorm): - """ - A GroupNorm layer that converts to float32 before the forward pass. - """ - def forward(self, x: torch.Tensor) -> torch.Tensor: - return super().forward(x.float()).type(x.dtype) - - -class ChannelLayerNorm32(LayerNorm32): - def forward(self, x: torch.Tensor) -> torch.Tensor: - DIM = x.dim() - x = x.permute(0, *range(2, DIM), 1).contiguous() - x = super().forward(x) - x = x.permute(0, DIM-1, *range(1, DIM-1)).contiguous() - return x - \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/__init__.py deleted file mode 100644 index 77108a63..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/__init__.py +++ /dev/null @@ -1,102 +0,0 @@ -from typing import * - -BACKEND = 'spconv' -DEBUG = False -ATTN = 'xformers' - -def __from_env(): - import os - - global BACKEND - global DEBUG - global ATTN - - env_sparse_backend = os.environ.get('SPARSE_BACKEND') - env_sparse_debug = os.environ.get('SPARSE_DEBUG') - env_sparse_attn = os.environ.get('SPARSE_ATTN_BACKEND') - if env_sparse_attn is None: - env_sparse_attn = os.environ.get('ATTN_BACKEND') - - if env_sparse_backend is not None and env_sparse_backend in ['spconv', 'torchsparse']: - BACKEND = env_sparse_backend - if env_sparse_debug is not None: - DEBUG = env_sparse_debug == '1' - if env_sparse_attn is not None and env_sparse_attn in ['xformers', 'flash_attn']: - ATTN = env_sparse_attn - - print(f"[SPARSE] Backend: {BACKEND}, Attention: {ATTN}") - - -__from_env() - - -def set_backend(backend: Literal['spconv', 'torchsparse']): - global BACKEND - BACKEND = backend - -def set_debug(debug: bool): - global DEBUG - DEBUG = debug - -def set_attn(attn: Literal['xformers', 'flash_attn']): - global ATTN - ATTN = attn - - -import importlib - -__attributes = { - 'SparseTensor': 'basic', - 'sparse_batch_broadcast': 'basic', - 'sparse_batch_op': 'basic', - 'sparse_cat': 'basic', - 'sparse_unbind': 'basic', - 'SparseGroupNorm': 'norm', - 'SparseLayerNorm': 'norm', - 'SparseGroupNorm32': 'norm', - 'SparseLayerNorm32': 'norm', - 'SparseReLU': 'nonlinearity', - 'SparseSiLU': 'nonlinearity', - 'SparseGELU': 'nonlinearity', - 'SparseActivation': 'nonlinearity', - 'SparseLinear': 'linear', - 'sparse_scaled_dot_product_attention': 'attention', - 'SerializeMode': 'attention', - 'sparse_serialized_scaled_dot_product_self_attention': 'attention', - 'sparse_windowed_scaled_dot_product_self_attention': 'attention', - 'SparseMultiHeadAttention': 'attention', - 'SparseConv3d': 'conv', - 'SparseInverseConv3d': 'conv', - 'SparseDownsample': 'spatial', - 'SparseUpsample': 'spatial', - 'SparseSubdivide' : 'spatial' -} - -__submodules = ['transformer'] - -__all__ = list(__attributes.keys()) + __submodules - -def __getattr__(name): - if name not in globals(): - if name in __attributes: - module_name = __attributes[name] - module = importlib.import_module(f".{module_name}", __name__) - globals()[name] = getattr(module, name) - elif name in __submodules: - module = importlib.import_module(f".{name}", __name__) - globals()[name] = module - else: - raise AttributeError(f"module {__name__} has no attribute {name}") - return globals()[name] - - -# For Pylance -if __name__ == '__main__': - from .basic import * - from .norm import * - from .nonlinearity import * - from .linear import * - from .attention import * - from .conv import * - from .spatial import * - import transformer diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/__init__.py deleted file mode 100644 index 32b3c2c8..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .full_attn import * -from .serialized_attn import * -from .windowed_attn import * -from .modules import * diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/full_attn.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/full_attn.py deleted file mode 100644 index e9e27aeb..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/full_attn.py +++ /dev/null @@ -1,215 +0,0 @@ -from typing import * -import torch -from .. import SparseTensor -from .. import DEBUG, ATTN - -if ATTN == 'xformers': - import xformers.ops as xops -elif ATTN == 'flash_attn': - import flash_attn -else: - raise ValueError(f"Unknown attention module: {ATTN}") - - -__all__ = [ - 'sparse_scaled_dot_product_attention', -] - - -@overload -def sparse_scaled_dot_product_attention(qkv: SparseTensor) -> SparseTensor: - """ - Apply scaled dot product attention to a sparse tensor. - - Args: - qkv (SparseTensor): A [N, *, 3, H, C] sparse tensor containing Qs, Ks, and Vs. - """ - ... - -@overload -def sparse_scaled_dot_product_attention(q: SparseTensor, kv: Union[SparseTensor, torch.Tensor]) -> SparseTensor: - """ - Apply scaled dot product attention to a sparse tensor. - - Args: - q (SparseTensor): A [N, *, H, C] sparse tensor containing Qs. - kv (SparseTensor or torch.Tensor): A [N, *, 2, H, C] sparse tensor or a [N, L, 2, H, C] dense tensor containing Ks and Vs. - """ - ... - -@overload -def sparse_scaled_dot_product_attention(q: torch.Tensor, kv: SparseTensor) -> torch.Tensor: - """ - Apply scaled dot product attention to a sparse tensor. - - Args: - q (SparseTensor): A [N, L, H, C] dense tensor containing Qs. - kv (SparseTensor or torch.Tensor): A [N, *, 2, H, C] sparse tensor containing Ks and Vs. - """ - ... - -@overload -def sparse_scaled_dot_product_attention(q: SparseTensor, k: SparseTensor, v: SparseTensor) -> SparseTensor: - """ - Apply scaled dot product attention to a sparse tensor. - - Args: - q (SparseTensor): A [N, *, H, Ci] sparse tensor containing Qs. - k (SparseTensor): A [N, *, H, Ci] sparse tensor containing Ks. - v (SparseTensor): A [N, *, H, Co] sparse tensor containing Vs. - - Note: - k and v are assumed to have the same coordinate map. - """ - ... - -@overload -def sparse_scaled_dot_product_attention(q: SparseTensor, k: torch.Tensor, v: torch.Tensor) -> SparseTensor: - """ - Apply scaled dot product attention to a sparse tensor. - - Args: - q (SparseTensor): A [N, *, H, Ci] sparse tensor containing Qs. - k (torch.Tensor): A [N, L, H, Ci] dense tensor containing Ks. - v (torch.Tensor): A [N, L, H, Co] dense tensor containing Vs. - """ - ... - -@overload -def sparse_scaled_dot_product_attention(q: torch.Tensor, k: SparseTensor, v: SparseTensor) -> torch.Tensor: - """ - Apply scaled dot product attention to a sparse tensor. - - Args: - q (torch.Tensor): A [N, L, H, Ci] dense tensor containing Qs. - k (SparseTensor): A [N, *, H, Ci] sparse tensor containing Ks. - v (SparseTensor): A [N, *, H, Co] sparse tensor containing Vs. - """ - ... - -def sparse_scaled_dot_product_attention(*args, **kwargs): - arg_names_dict = { - 1: ['qkv'], - 2: ['q', 'kv'], - 3: ['q', 'k', 'v'] - } - num_all_args = len(args) + len(kwargs) - assert num_all_args in arg_names_dict, f"Invalid number of arguments, got {num_all_args}, expected 1, 2, or 3" - for key in arg_names_dict[num_all_args][len(args):]: - assert key in kwargs, f"Missing argument {key}" - - if num_all_args == 1: - qkv = args[0] if len(args) > 0 else kwargs['qkv'] - assert isinstance(qkv, SparseTensor), f"qkv must be a SparseTensor, got {type(qkv)}" - assert len(qkv.shape) == 4 and qkv.shape[1] == 3, f"Invalid shape for qkv, got {qkv.shape}, expected [N, *, 3, H, C]" - device = qkv.device - - s = qkv - q_seqlen = [qkv.layout[i].stop - qkv.layout[i].start for i in range(qkv.shape[0])] - kv_seqlen = q_seqlen - qkv = qkv.feats # [T, 3, H, C] - - elif num_all_args == 2: - q = args[0] if len(args) > 0 else kwargs['q'] - kv = args[1] if len(args) > 1 else kwargs['kv'] - assert isinstance(q, SparseTensor) and isinstance(kv, (SparseTensor, torch.Tensor)) or \ - isinstance(q, torch.Tensor) and isinstance(kv, SparseTensor), \ - f"Invalid types, got {type(q)} and {type(kv)}" - assert q.shape[0] == kv.shape[0], f"Batch size mismatch, got {q.shape[0]} and {kv.shape[0]}" - device = q.device - - if isinstance(q, SparseTensor): - assert len(q.shape) == 3, f"Invalid shape for q, got {q.shape}, expected [N, *, H, C]" - s = q - q_seqlen = [q.layout[i].stop - q.layout[i].start for i in range(q.shape[0])] - q = q.feats # [T_Q, H, C] - else: - assert len(q.shape) == 4, f"Invalid shape for q, got {q.shape}, expected [N, L, H, C]" - s = None - N, L, H, C = q.shape - q_seqlen = [L] * N - q = q.reshape(N * L, H, C) # [T_Q, H, C] - - if isinstance(kv, SparseTensor): - assert len(kv.shape) == 4 and kv.shape[1] == 2, f"Invalid shape for kv, got {kv.shape}, expected [N, *, 2, H, C]" - kv_seqlen = [kv.layout[i].stop - kv.layout[i].start for i in range(kv.shape[0])] - kv = kv.feats # [T_KV, 2, H, C] - else: - assert len(kv.shape) == 5, f"Invalid shape for kv, got {kv.shape}, expected [N, L, 2, H, C]" - N, L, _, H, C = kv.shape - kv_seqlen = [L] * N - kv = kv.reshape(N * L, 2, H, C) # [T_KV, 2, H, C] - - elif num_all_args == 3: - q = args[0] if len(args) > 0 else kwargs['q'] - k = args[1] if len(args) > 1 else kwargs['k'] - v = args[2] if len(args) > 2 else kwargs['v'] - assert isinstance(q, SparseTensor) and isinstance(k, (SparseTensor, torch.Tensor)) and type(k) == type(v) or \ - isinstance(q, torch.Tensor) and isinstance(k, SparseTensor) and isinstance(v, SparseTensor), \ - f"Invalid types, got {type(q)}, {type(k)}, and {type(v)}" - assert q.shape[0] == k.shape[0] == v.shape[0], f"Batch size mismatch, got {q.shape[0]}, {k.shape[0]}, and {v.shape[0]}" - device = q.device - - if isinstance(q, SparseTensor): - assert len(q.shape) == 3, f"Invalid shape for q, got {q.shape}, expected [N, *, H, Ci]" - s = q - q_seqlen = [q.layout[i].stop - q.layout[i].start for i in range(q.shape[0])] - q = q.feats # [T_Q, H, Ci] - else: - assert len(q.shape) == 4, f"Invalid shape for q, got {q.shape}, expected [N, L, H, Ci]" - s = None - N, L, H, CI = q.shape - q_seqlen = [L] * N - q = q.reshape(N * L, H, CI) # [T_Q, H, Ci] - - if isinstance(k, SparseTensor): - assert len(k.shape) == 3, f"Invalid shape for k, got {k.shape}, expected [N, *, H, Ci]" - assert len(v.shape) == 3, f"Invalid shape for v, got {v.shape}, expected [N, *, H, Co]" - kv_seqlen = [k.layout[i].stop - k.layout[i].start for i in range(k.shape[0])] - k = k.feats # [T_KV, H, Ci] - v = v.feats # [T_KV, H, Co] - else: - assert len(k.shape) == 4, f"Invalid shape for k, got {k.shape}, expected [N, L, H, Ci]" - assert len(v.shape) == 4, f"Invalid shape for v, got {v.shape}, expected [N, L, H, Co]" - N, L, H, CI, CO = *k.shape, v.shape[-1] - kv_seqlen = [L] * N - k = k.reshape(N * L, H, CI) # [T_KV, H, Ci] - v = v.reshape(N * L, H, CO) # [T_KV, H, Co] - - if DEBUG: - if s is not None: - for i in range(s.shape[0]): - assert (s.coords[s.layout[i]] == i).all(), f"SparseScaledDotProductSelfAttention: batch index mismatch" - if num_all_args in [2, 3]: - assert q.shape[:2] == [1, sum(q_seqlen)], f"SparseScaledDotProductSelfAttention: q shape mismatch" - if num_all_args == 3: - assert k.shape[:2] == [1, sum(kv_seqlen)], f"SparseScaledDotProductSelfAttention: k shape mismatch" - assert v.shape[:2] == [1, sum(kv_seqlen)], f"SparseScaledDotProductSelfAttention: v shape mismatch" - - if ATTN == 'xformers': - if num_all_args == 1: - q, k, v = qkv.unbind(dim=1) - elif num_all_args == 2: - k, v = kv.unbind(dim=1) - q = q.unsqueeze(0) - k = k.unsqueeze(0) - v = v.unsqueeze(0) - mask = xops.fmha.BlockDiagonalMask.from_seqlens(q_seqlen, kv_seqlen) - out = xops.memory_efficient_attention(q, k, v, mask)[0] - elif ATTN == 'flash_attn': - cu_seqlens_q = torch.cat([torch.tensor([0]), torch.cumsum(torch.tensor(q_seqlen), dim=0)]).int().to(device) - if num_all_args in [2, 3]: - cu_seqlens_kv = torch.cat([torch.tensor([0]), torch.cumsum(torch.tensor(kv_seqlen), dim=0)]).int().to(device) - if num_all_args == 1: - out = flash_attn.flash_attn_varlen_qkvpacked_func(qkv, cu_seqlens_q, max(q_seqlen)) - elif num_all_args == 2: - out = flash_attn.flash_attn_varlen_kvpacked_func(q, kv, cu_seqlens_q, cu_seqlens_kv, max(q_seqlen), max(kv_seqlen)) - elif num_all_args == 3: - out = flash_attn.flash_attn_varlen_func(q, k, v, cu_seqlens_q, cu_seqlens_kv, max(q_seqlen), max(kv_seqlen)) - else: - raise ValueError(f"Unknown attention module: {ATTN}") - - if s is not None: - return s.replace(out) - else: - return out.reshape(N, L, H, -1) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/modules.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/modules.py deleted file mode 100644 index 5d2fe782..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/modules.py +++ /dev/null @@ -1,139 +0,0 @@ -from typing import * -import torch -import torch.nn as nn -import torch.nn.functional as F -from .. import SparseTensor -from .full_attn import sparse_scaled_dot_product_attention -from .serialized_attn import SerializeMode, sparse_serialized_scaled_dot_product_self_attention -from .windowed_attn import sparse_windowed_scaled_dot_product_self_attention -from ...attention import RotaryPositionEmbedder - - -class SparseMultiHeadRMSNorm(nn.Module): - def __init__(self, dim: int, heads: int): - super().__init__() - self.scale = dim ** 0.5 - self.gamma = nn.Parameter(torch.ones(heads, dim)) - - def forward(self, x: Union[SparseTensor, torch.Tensor]) -> Union[SparseTensor, torch.Tensor]: - x_type = x.dtype - x = x.float() - if isinstance(x, SparseTensor): - x = x.replace(F.normalize(x.feats, dim=-1)) - else: - x = F.normalize(x, dim=-1) - return (x * self.gamma * self.scale).to(x_type) - - -class SparseMultiHeadAttention(nn.Module): - def __init__( - self, - channels: int, - num_heads: int, - ctx_channels: Optional[int] = None, - type: Literal["self", "cross"] = "self", - attn_mode: Literal["full", "serialized", "windowed"] = "full", - window_size: Optional[int] = None, - shift_sequence: Optional[int] = None, - shift_window: Optional[Tuple[int, int, int]] = None, - serialize_mode: Optional[SerializeMode] = None, - qkv_bias: bool = True, - use_rope: bool = False, - qk_rms_norm: bool = False, - ): - super().__init__() - assert channels % num_heads == 0 - assert type in ["self", "cross"], f"Invalid attention type: {type}" - assert attn_mode in ["full", "serialized", "windowed"], f"Invalid attention mode: {attn_mode}" - assert type == "self" or attn_mode == "full", "Cross-attention only supports full attention" - assert type == "self" or use_rope is False, "Rotary position embeddings only supported for self-attention" - self.channels = channels - self.ctx_channels = ctx_channels if ctx_channels is not None else channels - self.num_heads = num_heads - self._type = type - self.attn_mode = attn_mode - self.window_size = window_size - self.shift_sequence = shift_sequence - self.shift_window = shift_window - self.serialize_mode = serialize_mode - self.use_rope = use_rope - self.qk_rms_norm = qk_rms_norm - - if self._type == "self": - self.to_qkv = nn.Linear(channels, channels * 3, bias=qkv_bias) - else: - self.to_q = nn.Linear(channels, channels, bias=qkv_bias) - self.to_kv = nn.Linear(self.ctx_channels, channels * 2, bias=qkv_bias) - - if self.qk_rms_norm: - self.q_rms_norm = SparseMultiHeadRMSNorm(channels // num_heads, num_heads) - self.k_rms_norm = SparseMultiHeadRMSNorm(channels // num_heads, num_heads) - - self.to_out = nn.Linear(channels, channels) - - if use_rope: - self.rope = RotaryPositionEmbedder(channels) - - @staticmethod - def _linear(module: nn.Linear, x: Union[SparseTensor, torch.Tensor]) -> Union[SparseTensor, torch.Tensor]: - if isinstance(x, SparseTensor): - return x.replace(module(x.feats)) - else: - return module(x) - - @staticmethod - def _reshape_chs(x: Union[SparseTensor, torch.Tensor], shape: Tuple[int, ...]) -> Union[SparseTensor, torch.Tensor]: - if isinstance(x, SparseTensor): - return x.reshape(*shape) - else: - return x.reshape(*x.shape[:2], *shape) - - def _fused_pre(self, x: Union[SparseTensor, torch.Tensor], num_fused: int) -> Union[SparseTensor, torch.Tensor]: - if isinstance(x, SparseTensor): - x_feats = x.feats.unsqueeze(0) - else: - x_feats = x - x_feats = x_feats.reshape(*x_feats.shape[:2], num_fused, self.num_heads, -1) - return x.replace(x_feats.squeeze(0)) if isinstance(x, SparseTensor) else x_feats - - def _rope(self, qkv: SparseTensor) -> SparseTensor: - q, k, v = qkv.feats.unbind(dim=1) # [T, H, C] - q, k = self.rope(q, k, qkv.coords[:, 1:]) - qkv = qkv.replace(torch.stack([q, k, v], dim=1)) - return qkv - - def forward(self, x: Union[SparseTensor, torch.Tensor], context: Optional[Union[SparseTensor, torch.Tensor]] = None) -> Union[SparseTensor, torch.Tensor]: - if self._type == "self": - qkv = self._linear(self.to_qkv, x) - qkv = self._fused_pre(qkv, num_fused=3) - if self.use_rope: - qkv = self._rope(qkv) - if self.qk_rms_norm: - q, k, v = qkv.unbind(dim=1) - q = self.q_rms_norm(q) - k = self.k_rms_norm(k) - qkv = qkv.replace(torch.stack([q.feats, k.feats, v.feats], dim=1)) - if self.attn_mode == "full": - h = sparse_scaled_dot_product_attention(qkv) - elif self.attn_mode == "serialized": - h = sparse_serialized_scaled_dot_product_self_attention( - qkv, self.window_size, serialize_mode=self.serialize_mode, shift_sequence=self.shift_sequence, shift_window=self.shift_window - ) - elif self.attn_mode == "windowed": - h = sparse_windowed_scaled_dot_product_self_attention( - qkv, self.window_size, shift_window=self.shift_window - ) - else: - q = self._linear(self.to_q, x) - q = self._reshape_chs(q, (self.num_heads, -1)) - kv = self._linear(self.to_kv, context) - kv = self._fused_pre(kv, num_fused=2) - if self.qk_rms_norm: - q = self.q_rms_norm(q) - k, v = kv.unbind(dim=1) - k = self.k_rms_norm(k) - kv = kv.replace(torch.stack([k.feats, v.feats], dim=1)) - h = sparse_scaled_dot_product_attention(q, kv) - h = self._reshape_chs(h, (-1,)) - h = self._linear(self.to_out, h) - return h diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/serialized_attn.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/serialized_attn.py deleted file mode 100644 index 5950b75b..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/serialized_attn.py +++ /dev/null @@ -1,193 +0,0 @@ -from typing import * -from enum import Enum -import torch -import math -from .. import SparseTensor -from .. import DEBUG, ATTN - -if ATTN == 'xformers': - import xformers.ops as xops -elif ATTN == 'flash_attn': - import flash_attn -else: - raise ValueError(f"Unknown attention module: {ATTN}") - - -__all__ = [ - 'sparse_serialized_scaled_dot_product_self_attention', -] - - -class SerializeMode(Enum): - Z_ORDER = 0 - Z_ORDER_TRANSPOSED = 1 - HILBERT = 2 - HILBERT_TRANSPOSED = 3 - - -SerializeModes = [ - SerializeMode.Z_ORDER, - SerializeMode.Z_ORDER_TRANSPOSED, - SerializeMode.HILBERT, - SerializeMode.HILBERT_TRANSPOSED -] - - -def calc_serialization( - tensor: SparseTensor, - window_size: int, - serialize_mode: SerializeMode = SerializeMode.Z_ORDER, - shift_sequence: int = 0, - shift_window: Tuple[int, int, int] = (0, 0, 0) -) -> Tuple[torch.Tensor, torch.Tensor, List[int]]: - """ - Calculate serialization and partitioning for a set of coordinates. - - Args: - tensor (SparseTensor): The input tensor. - window_size (int): The window size to use. - serialize_mode (SerializeMode): The serialization mode to use. - shift_sequence (int): The shift of serialized sequence. - shift_window (Tuple[int, int, int]): The shift of serialized coordinates. - - Returns: - (torch.Tensor, torch.Tensor): Forwards and backwards indices. - """ - fwd_indices = [] - bwd_indices = [] - seq_lens = [] - seq_batch_indices = [] - offsets = [0] - - if 'vox2seq' not in globals(): - import vox2seq - - # Serialize the input - serialize_coords = tensor.coords[:, 1:].clone() - serialize_coords += torch.tensor(shift_window, dtype=torch.int32, device=tensor.device).reshape(1, 3) - if serialize_mode == SerializeMode.Z_ORDER: - code = vox2seq.encode(serialize_coords, mode='z_order', permute=[0, 1, 2]) - elif serialize_mode == SerializeMode.Z_ORDER_TRANSPOSED: - code = vox2seq.encode(serialize_coords, mode='z_order', permute=[1, 0, 2]) - elif serialize_mode == SerializeMode.HILBERT: - code = vox2seq.encode(serialize_coords, mode='hilbert', permute=[0, 1, 2]) - elif serialize_mode == SerializeMode.HILBERT_TRANSPOSED: - code = vox2seq.encode(serialize_coords, mode='hilbert', permute=[1, 0, 2]) - else: - raise ValueError(f"Unknown serialize mode: {serialize_mode}") - - for bi, s in enumerate(tensor.layout): - num_points = s.stop - s.start - num_windows = (num_points + window_size - 1) // window_size - valid_window_size = num_points / num_windows - to_ordered = torch.argsort(code[s.start:s.stop]) - if num_windows == 1: - fwd_indices.append(to_ordered) - bwd_indices.append(torch.zeros_like(to_ordered).scatter_(0, to_ordered, torch.arange(num_points, device=tensor.device))) - fwd_indices[-1] += s.start - bwd_indices[-1] += offsets[-1] - seq_lens.append(num_points) - seq_batch_indices.append(bi) - offsets.append(offsets[-1] + seq_lens[-1]) - else: - # Partition the input - offset = 0 - mids = [(i + 0.5) * valid_window_size + shift_sequence for i in range(num_windows)] - split = [math.floor(i * valid_window_size + shift_sequence) for i in range(num_windows + 1)] - bwd_index = torch.zeros((num_points,), dtype=torch.int64, device=tensor.device) - for i in range(num_windows): - mid = mids[i] - valid_start = split[i] - valid_end = split[i + 1] - padded_start = math.floor(mid - 0.5 * window_size) - padded_end = padded_start + window_size - fwd_indices.append(to_ordered[torch.arange(padded_start, padded_end, device=tensor.device) % num_points]) - offset += valid_start - padded_start - bwd_index.scatter_(0, fwd_indices[-1][valid_start-padded_start:valid_end-padded_start], torch.arange(offset, offset + valid_end - valid_start, device=tensor.device)) - offset += padded_end - valid_start - fwd_indices[-1] += s.start - seq_lens.extend([window_size] * num_windows) - seq_batch_indices.extend([bi] * num_windows) - bwd_indices.append(bwd_index + offsets[-1]) - offsets.append(offsets[-1] + num_windows * window_size) - - fwd_indices = torch.cat(fwd_indices) - bwd_indices = torch.cat(bwd_indices) - - return fwd_indices, bwd_indices, seq_lens, seq_batch_indices - - -def sparse_serialized_scaled_dot_product_self_attention( - qkv: SparseTensor, - window_size: int, - serialize_mode: SerializeMode = SerializeMode.Z_ORDER, - shift_sequence: int = 0, - shift_window: Tuple[int, int, int] = (0, 0, 0) -) -> SparseTensor: - """ - Apply serialized scaled dot product self attention to a sparse tensor. - - Args: - qkv (SparseTensor): [N, *, 3, H, C] sparse tensor containing Qs, Ks, and Vs. - window_size (int): The window size to use. - serialize_mode (SerializeMode): The serialization mode to use. - shift_sequence (int): The shift of serialized sequence. - shift_window (Tuple[int, int, int]): The shift of serialized coordinates. - shift (int): The shift to use. - """ - assert len(qkv.shape) == 4 and qkv.shape[1] == 3, f"Invalid shape for qkv, got {qkv.shape}, expected [N, *, 3, H, C]" - - serialization_spatial_cache_name = f'serialization_{serialize_mode}_{window_size}_{shift_sequence}_{shift_window}' - serialization_spatial_cache = qkv.get_spatial_cache(serialization_spatial_cache_name) - if serialization_spatial_cache is None: - fwd_indices, bwd_indices, seq_lens, seq_batch_indices = calc_serialization(qkv, window_size, serialize_mode, shift_sequence, shift_window) - qkv.register_spatial_cache(serialization_spatial_cache_name, (fwd_indices, bwd_indices, seq_lens, seq_batch_indices)) - else: - fwd_indices, bwd_indices, seq_lens, seq_batch_indices = serialization_spatial_cache - - M = fwd_indices.shape[0] - T = qkv.feats.shape[0] - H = qkv.feats.shape[2] - C = qkv.feats.shape[3] - - qkv_feats = qkv.feats[fwd_indices] # [M, 3, H, C] - - if DEBUG: - start = 0 - qkv_coords = qkv.coords[fwd_indices] - for i in range(len(seq_lens)): - assert (qkv_coords[start:start+seq_lens[i], 0] == seq_batch_indices[i]).all(), f"SparseWindowedScaledDotProductSelfAttention: batch index mismatch" - start += seq_lens[i] - - if all([seq_len == window_size for seq_len in seq_lens]): - B = len(seq_lens) - N = window_size - qkv_feats = qkv_feats.reshape(B, N, 3, H, C) - if ATTN == 'xformers': - q, k, v = qkv_feats.unbind(dim=2) # [B, N, H, C] - out = xops.memory_efficient_attention(q, k, v) # [B, N, H, C] - elif ATTN == 'flash_attn': - out = flash_attn.flash_attn_qkvpacked_func(qkv_feats) # [B, N, H, C] - else: - raise ValueError(f"Unknown attention module: {ATTN}") - out = out.reshape(B * N, H, C) # [M, H, C] - else: - if ATTN == 'xformers': - q, k, v = qkv_feats.unbind(dim=1) # [M, H, C] - q = q.unsqueeze(0) # [1, M, H, C] - k = k.unsqueeze(0) # [1, M, H, C] - v = v.unsqueeze(0) # [1, M, H, C] - mask = xops.fmha.BlockDiagonalMask.from_seqlens(seq_lens) - out = xops.memory_efficient_attention(q, k, v, mask)[0] # [M, H, C] - elif ATTN == 'flash_attn': - cu_seqlens = torch.cat([torch.tensor([0]), torch.cumsum(torch.tensor(seq_lens), dim=0)], dim=0) \ - .to(qkv.device).int() - out = flash_attn.flash_attn_varlen_qkvpacked_func(qkv_feats, cu_seqlens, max(seq_lens)) # [M, H, C] - - out = out[bwd_indices] # [T, H, C] - - if DEBUG: - qkv_coords = qkv_coords[bwd_indices] - assert torch.equal(qkv_coords, qkv.coords), "SparseWindowedScaledDotProductSelfAttention: coordinate mismatch" - - return qkv.replace(out) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/windowed_attn.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/windowed_attn.py deleted file mode 100644 index cd642c52..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/attention/windowed_attn.py +++ /dev/null @@ -1,135 +0,0 @@ -from typing import * -import torch -import math -from .. import SparseTensor -from .. import DEBUG, ATTN - -if ATTN == 'xformers': - import xformers.ops as xops -elif ATTN == 'flash_attn': - import flash_attn -else: - raise ValueError(f"Unknown attention module: {ATTN}") - - -__all__ = [ - 'sparse_windowed_scaled_dot_product_self_attention', -] - - -def calc_window_partition( - tensor: SparseTensor, - window_size: Union[int, Tuple[int, ...]], - shift_window: Union[int, Tuple[int, ...]] = 0 -) -> Tuple[torch.Tensor, torch.Tensor, List[int], List[int]]: - """ - Calculate serialization and partitioning for a set of coordinates. - - Args: - tensor (SparseTensor): The input tensor. - window_size (int): The window size to use. - shift_window (Tuple[int, ...]): The shift of serialized coordinates. - - Returns: - (torch.Tensor): Forwards indices. - (torch.Tensor): Backwards indices. - (List[int]): Sequence lengths. - (List[int]): Sequence batch indices. - """ - DIM = tensor.coords.shape[1] - 1 - shift_window = (shift_window,) * DIM if isinstance(shift_window, int) else shift_window - window_size = (window_size,) * DIM if isinstance(window_size, int) else window_size - shifted_coords = tensor.coords.clone().detach() - shifted_coords[:, 1:] += torch.tensor(shift_window, device=tensor.device, dtype=torch.int32).unsqueeze(0) - - MAX_COORDS = shifted_coords[:, 1:].max(dim=0).values.tolist() - NUM_WINDOWS = [math.ceil((mc + 1) / ws) for mc, ws in zip(MAX_COORDS, window_size)] - OFFSET = torch.cumprod(torch.tensor([1] + NUM_WINDOWS[::-1]), dim=0).tolist()[::-1] - - shifted_coords[:, 1:] //= torch.tensor(window_size, device=tensor.device, dtype=torch.int32).unsqueeze(0) - shifted_indices = (shifted_coords * torch.tensor(OFFSET, device=tensor.device, dtype=torch.int32).unsqueeze(0)).sum(dim=1) - fwd_indices = torch.argsort(shifted_indices) - bwd_indices = torch.empty_like(fwd_indices) - bwd_indices[fwd_indices] = torch.arange(fwd_indices.shape[0], device=tensor.device) - seq_lens = torch.bincount(shifted_indices) - seq_batch_indices = torch.arange(seq_lens.shape[0], device=tensor.device, dtype=torch.int32) // OFFSET[0] - mask = seq_lens != 0 - seq_lens = seq_lens[mask].tolist() - seq_batch_indices = seq_batch_indices[mask].tolist() - - return fwd_indices, bwd_indices, seq_lens, seq_batch_indices - - -def sparse_windowed_scaled_dot_product_self_attention( - qkv: SparseTensor, - window_size: int, - shift_window: Tuple[int, int, int] = (0, 0, 0) -) -> SparseTensor: - """ - Apply windowed scaled dot product self attention to a sparse tensor. - - Args: - qkv (SparseTensor): [N, *, 3, H, C] sparse tensor containing Qs, Ks, and Vs. - window_size (int): The window size to use. - shift_window (Tuple[int, int, int]): The shift of serialized coordinates. - shift (int): The shift to use. - """ - assert len(qkv.shape) == 4 and qkv.shape[1] == 3, f"Invalid shape for qkv, got {qkv.shape}, expected [N, *, 3, H, C]" - - serialization_spatial_cache_name = f'window_partition_{window_size}_{shift_window}' - serialization_spatial_cache = qkv.get_spatial_cache(serialization_spatial_cache_name) - if serialization_spatial_cache is None: - fwd_indices, bwd_indices, seq_lens, seq_batch_indices = calc_window_partition(qkv, window_size, shift_window) - qkv.register_spatial_cache(serialization_spatial_cache_name, (fwd_indices, bwd_indices, seq_lens, seq_batch_indices)) - else: - fwd_indices, bwd_indices, seq_lens, seq_batch_indices = serialization_spatial_cache - - M = fwd_indices.shape[0] - T = qkv.feats.shape[0] - H = qkv.feats.shape[2] - C = qkv.feats.shape[3] - - qkv_feats = qkv.feats[fwd_indices] # [M, 3, H, C] - - if DEBUG: - start = 0 - qkv_coords = qkv.coords[fwd_indices] - for i in range(len(seq_lens)): - seq_coords = qkv_coords[start:start+seq_lens[i]] - assert (seq_coords[:, 0] == seq_batch_indices[i]).all(), f"SparseWindowedScaledDotProductSelfAttention: batch index mismatch" - assert (seq_coords[:, 1:].max(dim=0).values - seq_coords[:, 1:].min(dim=0).values < window_size).all(), \ - f"SparseWindowedScaledDotProductSelfAttention: window size exceeded" - start += seq_lens[i] - - if all([seq_len == window_size for seq_len in seq_lens]): - B = len(seq_lens) - N = window_size - qkv_feats = qkv_feats.reshape(B, N, 3, H, C) - if ATTN == 'xformers': - q, k, v = qkv_feats.unbind(dim=2) # [B, N, H, C] - out = xops.memory_efficient_attention(q, k, v) # [B, N, H, C] - elif ATTN == 'flash_attn': - out = flash_attn.flash_attn_qkvpacked_func(qkv_feats) # [B, N, H, C] - else: - raise ValueError(f"Unknown attention module: {ATTN}") - out = out.reshape(B * N, H, C) # [M, H, C] - else: - if ATTN == 'xformers': - q, k, v = qkv_feats.unbind(dim=1) # [M, H, C] - q = q.unsqueeze(0) # [1, M, H, C] - k = k.unsqueeze(0) # [1, M, H, C] - v = v.unsqueeze(0) # [1, M, H, C] - mask = xops.fmha.BlockDiagonalMask.from_seqlens(seq_lens) - out = xops.memory_efficient_attention(q, k, v, mask)[0] # [M, H, C] - elif ATTN == 'flash_attn': - cu_seqlens = torch.cat([torch.tensor([0]), torch.cumsum(torch.tensor(seq_lens), dim=0)], dim=0) \ - .to(qkv.device).int() - out = flash_attn.flash_attn_varlen_qkvpacked_func(qkv_feats, cu_seqlens, max(seq_lens)) # [M, H, C] - - out = out[bwd_indices] # [T, H, C] - - if DEBUG: - qkv_coords = qkv_coords[bwd_indices] - assert torch.equal(qkv_coords, qkv.coords), "SparseWindowedScaledDotProductSelfAttention: coordinate mismatch" - - return qkv.replace(out) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/basic.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/basic.py deleted file mode 100644 index 8837f440..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/basic.py +++ /dev/null @@ -1,459 +0,0 @@ -from typing import * -import torch -import torch.nn as nn -from . import BACKEND, DEBUG -SparseTensorData = None # Lazy import - - -__all__ = [ - 'SparseTensor', - 'sparse_batch_broadcast', - 'sparse_batch_op', - 'sparse_cat', - 'sparse_unbind', -] - - -class SparseTensor: - """ - Sparse tensor with support for both torchsparse and spconv backends. - - Parameters: - - feats (torch.Tensor): Features of the sparse tensor. - - coords (torch.Tensor): Coordinates of the sparse tensor. - - shape (torch.Size): Shape of the sparse tensor. - - layout (List[slice]): Layout of the sparse tensor for each batch - - data (SparseTensorData): Sparse tensor data used for convolusion - - NOTE: - - Data corresponding to a same batch should be contiguous. - - Coords should be in [0, 1023] - """ - @overload - def __init__(self, feats: torch.Tensor, coords: torch.Tensor, shape: Optional[torch.Size] = None, layout: Optional[List[slice]] = None, **kwargs): ... - - @overload - def __init__(self, data, shape: Optional[torch.Size] = None, layout: Optional[List[slice]] = None, **kwargs): ... - - def __init__(self, *args, **kwargs): - # Lazy import of sparse tensor backend - global SparseTensorData - if SparseTensorData is None: - import importlib - if BACKEND == 'torchsparse': - SparseTensorData = importlib.import_module('torchsparse').SparseTensor - elif BACKEND == 'spconv': - SparseTensorData = importlib.import_module('spconv.pytorch').SparseConvTensor - - method_id = 0 - if len(args) != 0: - method_id = 0 if isinstance(args[0], torch.Tensor) else 1 - else: - method_id = 1 if 'data' in kwargs else 0 - - if method_id == 0: - feats, coords, shape, layout = args + (None,) * (4 - len(args)) - if 'feats' in kwargs: - feats = kwargs['feats'] - del kwargs['feats'] - if 'coords' in kwargs: - coords = kwargs['coords'] - del kwargs['coords'] - if 'shape' in kwargs: - shape = kwargs['shape'] - del kwargs['shape'] - if 'layout' in kwargs: - layout = kwargs['layout'] - del kwargs['layout'] - - if shape is None: - shape = self.__cal_shape(feats, coords) - if layout is None: - layout = self.__cal_layout(coords, shape[0]) - if BACKEND == 'torchsparse': - self.data = SparseTensorData(feats, coords, **kwargs) - elif BACKEND == 'spconv': - spatial_shape = list(coords.max(0)[0] + 1)[1:] - self.data = SparseTensorData(feats.reshape(feats.shape[0], -1), coords, spatial_shape, shape[0], **kwargs) - self.data._features = feats - elif method_id == 1: - data, shape, layout = args + (None,) * (3 - len(args)) - if 'data' in kwargs: - data = kwargs['data'] - del kwargs['data'] - if 'shape' in kwargs: - shape = kwargs['shape'] - del kwargs['shape'] - if 'layout' in kwargs: - layout = kwargs['layout'] - del kwargs['layout'] - - self.data = data - if shape is None: - shape = self.__cal_shape(self.feats, self.coords) - if layout is None: - layout = self.__cal_layout(self.coords, shape[0]) - - self._shape = shape - self._layout = layout - self._scale = kwargs.get('scale', (1, 1, 1)) - self._spatial_cache = kwargs.get('spatial_cache', {}) - - if DEBUG: - try: - assert self.feats.shape[0] == self.coords.shape[0], f"Invalid feats shape: {self.feats.shape}, coords shape: {self.coords.shape}" - assert self.shape == self.__cal_shape(self.feats, self.coords), f"Invalid shape: {self.shape}" - assert self.layout == self.__cal_layout(self.coords, self.shape[0]), f"Invalid layout: {self.layout}" - for i in range(self.shape[0]): - assert torch.all(self.coords[self.layout[i], 0] == i), f"The data of batch {i} is not contiguous" - except Exception as e: - print('Debugging information:') - print(f"- Shape: {self.shape}") - print(f"- Layout: {self.layout}") - print(f"- Scale: {self._scale}") - print(f"- Coords: {self.coords}") - raise e - - def __cal_shape(self, feats, coords): - shape = [] - shape.append(coords[:, 0].max().item() + 1) - shape.extend([*feats.shape[1:]]) - return torch.Size(shape) - - def __cal_layout(self, coords, batch_size): - seq_len = torch.bincount(coords[:, 0], minlength=batch_size) - offset = torch.cumsum(seq_len, dim=0) - layout = [slice((offset[i] - seq_len[i]).item(), offset[i].item()) for i in range(batch_size)] - return layout - - @property - def shape(self) -> torch.Size: - return self._shape - - def dim(self) -> int: - return len(self.shape) - - @property - def layout(self) -> List[slice]: - return self._layout - - @property - def feats(self) -> torch.Tensor: - if BACKEND == 'torchsparse': - return self.data.F - elif BACKEND == 'spconv': - return self.data.features - - @feats.setter - def feats(self, value: torch.Tensor): - if BACKEND == 'torchsparse': - self.data.F = value - elif BACKEND == 'spconv': - self.data.features = value - - @property - def coords(self) -> torch.Tensor: - if BACKEND == 'torchsparse': - return self.data.C - elif BACKEND == 'spconv': - return self.data.indices - - @coords.setter - def coords(self, value: torch.Tensor): - if BACKEND == 'torchsparse': - self.data.C = value - elif BACKEND == 'spconv': - self.data.indices = value - - @property - def dtype(self): - return self.feats.dtype - - @property - def device(self): - return self.feats.device - - @overload - def to(self, dtype: torch.dtype) -> 'SparseTensor': ... - - @overload - def to(self, device: Optional[Union[str, torch.device]] = None, dtype: Optional[torch.dtype] = None) -> 'SparseTensor': ... - - def to(self, *args, **kwargs) -> 'SparseTensor': - device = None - dtype = None - if len(args) == 2: - device, dtype = args - elif len(args) == 1: - if isinstance(args[0], torch.dtype): - dtype = args[0] - else: - device = args[0] - if 'dtype' in kwargs: - assert dtype is None, "to() received multiple values for argument 'dtype'" - dtype = kwargs['dtype'] - if 'device' in kwargs: - assert device is None, "to() received multiple values for argument 'device'" - device = kwargs['device'] - - new_feats = self.feats.to(device=device, dtype=dtype) - new_coords = self.coords.to(device=device) - return self.replace(new_feats, new_coords) - - def type(self, dtype): - new_feats = self.feats.type(dtype) - return self.replace(new_feats) - - def cpu(self) -> 'SparseTensor': - new_feats = self.feats.cpu() - new_coords = self.coords.cpu() - return self.replace(new_feats, new_coords) - - def cuda(self) -> 'SparseTensor': - new_feats = self.feats.cuda() - new_coords = self.coords.cuda() - return self.replace(new_feats, new_coords) - - def half(self) -> 'SparseTensor': - new_feats = self.feats.half() - return self.replace(new_feats) - - def float(self) -> 'SparseTensor': - new_feats = self.feats.float() - return self.replace(new_feats) - - def detach(self) -> 'SparseTensor': - new_coords = self.coords.detach() - new_feats = self.feats.detach() - return self.replace(new_feats, new_coords) - - def dense(self) -> torch.Tensor: - if BACKEND == 'torchsparse': - return self.data.dense() - elif BACKEND == 'spconv': - return self.data.dense() - - def reshape(self, *shape) -> 'SparseTensor': - new_feats = self.feats.reshape(self.feats.shape[0], *shape) - return self.replace(new_feats) - - def unbind(self, dim: int) -> List['SparseTensor']: - return sparse_unbind(self, dim) - - def replace(self, feats: torch.Tensor, coords: Optional[torch.Tensor] = None) -> 'SparseTensor': - new_shape = [self.shape[0]] - new_shape.extend(feats.shape[1:]) - if BACKEND == 'torchsparse': - new_data = SparseTensorData( - feats=feats, - coords=self.data.coords if coords is None else coords, - stride=self.data.stride, - spatial_range=self.data.spatial_range, - ) - new_data._caches = self.data._caches - elif BACKEND == 'spconv': - new_data = SparseTensorData( - self.data.features.reshape(self.data.features.shape[0], -1), - self.data.indices, - self.data.spatial_shape, - self.data.batch_size, - self.data.grid, - self.data.voxel_num, - self.data.indice_dict - ) - new_data._features = feats - new_data.benchmark = self.data.benchmark - new_data.benchmark_record = self.data.benchmark_record - new_data.thrust_allocator = self.data.thrust_allocator - new_data._timer = self.data._timer - new_data.force_algo = self.data.force_algo - new_data.int8_scale = self.data.int8_scale - if coords is not None: - new_data.indices = coords - new_tensor = SparseTensor(new_data, shape=torch.Size(new_shape), layout=self.layout, scale=self._scale, spatial_cache=self._spatial_cache) - return new_tensor - - @staticmethod - def full(aabb, dim, value, dtype=torch.float32, device=None) -> 'SparseTensor': - N, C = dim - x = torch.arange(aabb[0], aabb[3] + 1) - y = torch.arange(aabb[1], aabb[4] + 1) - z = torch.arange(aabb[2], aabb[5] + 1) - coords = torch.stack(torch.meshgrid(x, y, z, indexing='ij'), dim=-1).reshape(-1, 3) - coords = torch.cat([ - torch.arange(N).view(-1, 1).repeat(1, coords.shape[0]).view(-1, 1), - coords.repeat(N, 1), - ], dim=1).to(dtype=torch.int32, device=device) - feats = torch.full((coords.shape[0], C), value, dtype=dtype, device=device) - return SparseTensor(feats=feats, coords=coords) - - def __merge_sparse_cache(self, other: 'SparseTensor') -> dict: - new_cache = {} - for k in set(list(self._spatial_cache.keys()) + list(other._spatial_cache.keys())): - if k in self._spatial_cache: - new_cache[k] = self._spatial_cache[k] - if k in other._spatial_cache: - if k not in new_cache: - new_cache[k] = other._spatial_cache[k] - else: - new_cache[k].update(other._spatial_cache[k]) - return new_cache - - def __neg__(self) -> 'SparseTensor': - return self.replace(-self.feats) - - def __elemwise__(self, other: Union[torch.Tensor, 'SparseTensor'], op: callable) -> 'SparseTensor': - if isinstance(other, torch.Tensor): - try: - other = torch.broadcast_to(other, self.shape) - other = sparse_batch_broadcast(self, other) - except: - pass - if isinstance(other, SparseTensor): - other = other.feats - new_feats = op(self.feats, other) - new_tensor = self.replace(new_feats) - if isinstance(other, SparseTensor): - new_tensor._spatial_cache = self.__merge_sparse_cache(other) - return new_tensor - - def __add__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor': - return self.__elemwise__(other, torch.add) - - def __radd__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor': - return self.__elemwise__(other, torch.add) - - def __sub__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor': - return self.__elemwise__(other, torch.sub) - - def __rsub__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor': - return self.__elemwise__(other, lambda x, y: torch.sub(y, x)) - - def __mul__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor': - return self.__elemwise__(other, torch.mul) - - def __rmul__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor': - return self.__elemwise__(other, torch.mul) - - def __truediv__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor': - return self.__elemwise__(other, torch.div) - - def __rtruediv__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor': - return self.__elemwise__(other, lambda x, y: torch.div(y, x)) - - def __getitem__(self, idx): - if isinstance(idx, int): - idx = [idx] - elif isinstance(idx, slice): - idx = range(*idx.indices(self.shape[0])) - elif isinstance(idx, torch.Tensor): - if idx.dtype == torch.bool: - assert idx.shape == (self.shape[0],), f"Invalid index shape: {idx.shape}" - idx = idx.nonzero().squeeze(1) - elif idx.dtype in [torch.int32, torch.int64]: - assert len(idx.shape) == 1, f"Invalid index shape: {idx.shape}" - else: - raise ValueError(f"Unknown index type: {idx.dtype}") - else: - raise ValueError(f"Unknown index type: {type(idx)}") - - coords = [] - feats = [] - for new_idx, old_idx in enumerate(idx): - coords.append(self.coords[self.layout[old_idx]].clone()) - coords[-1][:, 0] = new_idx - feats.append(self.feats[self.layout[old_idx]]) - coords = torch.cat(coords, dim=0).contiguous() - feats = torch.cat(feats, dim=0).contiguous() - return SparseTensor(feats=feats, coords=coords) - - def register_spatial_cache(self, key, value) -> None: - """ - Register a spatial cache. - The spatial cache can be any thing you want to cache. - The registery and retrieval of the cache is based on current scale. - """ - scale_key = str(self._scale) - if scale_key not in self._spatial_cache: - self._spatial_cache[scale_key] = {} - self._spatial_cache[scale_key][key] = value - - def get_spatial_cache(self, key=None): - """ - Get a spatial cache. - """ - scale_key = str(self._scale) - cur_scale_cache = self._spatial_cache.get(scale_key, {}) - if key is None: - return cur_scale_cache - return cur_scale_cache.get(key, None) - - -def sparse_batch_broadcast(input: SparseTensor, other: torch.Tensor) -> torch.Tensor: - """ - Broadcast a 1D tensor to a sparse tensor along the batch dimension then perform an operation. - - Args: - input (torch.Tensor): 1D tensor to broadcast. - target (SparseTensor): Sparse tensor to broadcast to. - op (callable): Operation to perform after broadcasting. Defaults to torch.add. - """ - coords, feats = input.coords, input.feats - broadcasted = torch.zeros_like(feats) - for k in range(input.shape[0]): - broadcasted[input.layout[k]] = other[k] - return broadcasted - - -def sparse_batch_op(input: SparseTensor, other: torch.Tensor, op: callable = torch.add) -> SparseTensor: - """ - Broadcast a 1D tensor to a sparse tensor along the batch dimension then perform an operation. - - Args: - input (torch.Tensor): 1D tensor to broadcast. - target (SparseTensor): Sparse tensor to broadcast to. - op (callable): Operation to perform after broadcasting. Defaults to torch.add. - """ - return input.replace(op(input.feats, sparse_batch_broadcast(input, other))) - - -def sparse_cat(inputs: List[SparseTensor], dim: int = 0) -> SparseTensor: - """ - Concatenate a list of sparse tensors. - - Args: - inputs (List[SparseTensor]): List of sparse tensors to concatenate. - """ - if dim == 0: - start = 0 - coords = [] - for input in inputs: - coords.append(input.coords.clone()) - coords[-1][:, 0] += start - start += input.shape[0] - coords = torch.cat(coords, dim=0) - feats = torch.cat([input.feats for input in inputs], dim=0) - output = SparseTensor( - coords=coords, - feats=feats, - ) - else: - feats = torch.cat([input.feats for input in inputs], dim=dim) - output = inputs[0].replace(feats) - - return output - - -def sparse_unbind(input: SparseTensor, dim: int) -> List[SparseTensor]: - """ - Unbind a sparse tensor along a dimension. - - Args: - input (SparseTensor): Sparse tensor to unbind. - dim (int): Dimension to unbind. - """ - if dim == 0: - return [input[i] for i in range(input.shape[0])] - else: - feats = input.feats.unbind(dim) - return [input.replace(f) for f in feats] diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/conv/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/conv/__init__.py deleted file mode 100644 index 340a8712..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/conv/__init__.py +++ /dev/null @@ -1,21 +0,0 @@ -from .. import BACKEND - - -SPCONV_ALGO = 'auto' # 'auto', 'implicit_gemm', 'native' - -def __from_env(): - import os - - global SPCONV_ALGO - env_spconv_algo = os.environ.get('SPCONV_ALGO') - if env_spconv_algo is not None and env_spconv_algo in ['auto', 'implicit_gemm', 'native']: - SPCONV_ALGO = env_spconv_algo - print(f"[SPARSE][CONV] spconv algo: {SPCONV_ALGO}") - - -__from_env() - -if BACKEND == 'torchsparse': - from .conv_torchsparse import * -elif BACKEND == 'spconv': - from .conv_spconv import * diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/conv/conv_spconv.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/conv/conv_spconv.py deleted file mode 100644 index 524bcd4a..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/conv/conv_spconv.py +++ /dev/null @@ -1,80 +0,0 @@ -import torch -import torch.nn as nn -from .. import SparseTensor -from .. import DEBUG -from . import SPCONV_ALGO - -class SparseConv3d(nn.Module): - def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, padding=None, bias=True, indice_key=None): - super(SparseConv3d, self).__init__() - if 'spconv' not in globals(): - import spconv.pytorch as spconv - algo = None - if SPCONV_ALGO == 'native': - algo = spconv.ConvAlgo.Native - elif SPCONV_ALGO == 'implicit_gemm': - algo = spconv.ConvAlgo.MaskImplicitGemm - if stride == 1 and (padding is None): - self.conv = spconv.SubMConv3d(in_channels, out_channels, kernel_size, dilation=dilation, bias=bias, indice_key=indice_key, algo=algo) - else: - self.conv = spconv.SparseConv3d(in_channels, out_channels, kernel_size, stride=stride, dilation=dilation, padding=padding, bias=bias, indice_key=indice_key, algo=algo) - self.stride = tuple(stride) if isinstance(stride, (list, tuple)) else (stride, stride, stride) - self.padding = padding - - def forward(self, x: SparseTensor) -> SparseTensor: - spatial_changed = any(s != 1 for s in self.stride) or (self.padding is not None) - new_data = self.conv(x.data) - new_shape = [x.shape[0], self.conv.out_channels] - new_layout = None if spatial_changed else x.layout - - if spatial_changed and (x.shape[0] != 1): - # spconv was non-1 stride will break the contiguous of the output tensor, sort by the coords - fwd = new_data.indices[:, 0].argsort() - bwd = torch.zeros_like(fwd).scatter_(0, fwd, torch.arange(fwd.shape[0], device=fwd.device)) - sorted_feats = new_data.features[fwd] - sorted_coords = new_data.indices[fwd] - unsorted_data = new_data - new_data = spconv.SparseConvTensor(sorted_feats, sorted_coords, unsorted_data.spatial_shape, unsorted_data.batch_size) # type: ignore - - out = SparseTensor( - new_data, shape=torch.Size(new_shape), layout=new_layout, - scale=tuple([s * stride for s, stride in zip(x._scale, self.stride)]), - spatial_cache=x._spatial_cache, - ) - - if spatial_changed and (x.shape[0] != 1): - out.register_spatial_cache(f'conv_{self.stride}_unsorted_data', unsorted_data) - out.register_spatial_cache(f'conv_{self.stride}_sort_bwd', bwd) - - return out - - -class SparseInverseConv3d(nn.Module): - def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, bias=True, indice_key=None): - super(SparseInverseConv3d, self).__init__() - if 'spconv' not in globals(): - import spconv.pytorch as spconv - self.conv = spconv.SparseInverseConv3d(in_channels, out_channels, kernel_size, bias=bias, indice_key=indice_key) - self.stride = tuple(stride) if isinstance(stride, (list, tuple)) else (stride, stride, stride) - - def forward(self, x: SparseTensor) -> SparseTensor: - spatial_changed = any(s != 1 for s in self.stride) - if spatial_changed: - # recover the original spconv order - data = x.get_spatial_cache(f'conv_{self.stride}_unsorted_data') - bwd = x.get_spatial_cache(f'conv_{self.stride}_sort_bwd') - data = data.replace_feature(x.feats[bwd]) - if DEBUG: - assert torch.equal(data.indices, x.coords[bwd]), 'Recover the original order failed' - else: - data = x.data - - new_data = self.conv(data) - new_shape = [x.shape[0], self.conv.out_channels] - new_layout = None if spatial_changed else x.layout - out = SparseTensor( - new_data, shape=torch.Size(new_shape), layout=new_layout, - scale=tuple([s // stride for s, stride in zip(x._scale, self.stride)]), - spatial_cache=x._spatial_cache, - ) - return out diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/conv/conv_torchsparse.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/conv/conv_torchsparse.py deleted file mode 100644 index 1d612582..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/conv/conv_torchsparse.py +++ /dev/null @@ -1,38 +0,0 @@ -import torch -import torch.nn as nn -from .. import SparseTensor - - -class SparseConv3d(nn.Module): - def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, bias=True, indice_key=None): - super(SparseConv3d, self).__init__() - if 'torchsparse' not in globals(): - import torchsparse - self.conv = torchsparse.nn.Conv3d(in_channels, out_channels, kernel_size, stride, 0, dilation, bias) - - def forward(self, x: SparseTensor) -> SparseTensor: - out = self.conv(x.data) - new_shape = [x.shape[0], self.conv.out_channels] - out = SparseTensor(out, shape=torch.Size(new_shape), layout=x.layout if all(s == 1 for s in self.conv.stride) else None) - out._spatial_cache = x._spatial_cache - out._scale = tuple([s * stride for s, stride in zip(x._scale, self.conv.stride)]) - return out - - -class SparseInverseConv3d(nn.Module): - def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, bias=True, indice_key=None): - super(SparseInverseConv3d, self).__init__() - if 'torchsparse' not in globals(): - import torchsparse - self.conv = torchsparse.nn.Conv3d(in_channels, out_channels, kernel_size, stride, 0, dilation, bias, transposed=True) - - def forward(self, x: SparseTensor) -> SparseTensor: - out = self.conv(x.data) - new_shape = [x.shape[0], self.conv.out_channels] - out = SparseTensor(out, shape=torch.Size(new_shape), layout=x.layout if all(s == 1 for s in self.conv.stride) else None) - out._spatial_cache = x._spatial_cache - out._scale = tuple([s // stride for s, stride in zip(x._scale, self.conv.stride)]) - return out - - - diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/linear.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/linear.py deleted file mode 100644 index a854e77c..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/linear.py +++ /dev/null @@ -1,15 +0,0 @@ -import torch -import torch.nn as nn -from . import SparseTensor - -__all__ = [ - 'SparseLinear' -] - - -class SparseLinear(nn.Linear): - def __init__(self, in_features, out_features, bias=True): - super(SparseLinear, self).__init__(in_features, out_features, bias) - - def forward(self, input: SparseTensor) -> SparseTensor: - return input.replace(super().forward(input.feats)) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/nonlinearity.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/nonlinearity.py deleted file mode 100644 index f200098d..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/nonlinearity.py +++ /dev/null @@ -1,35 +0,0 @@ -import torch -import torch.nn as nn -from . import SparseTensor - -__all__ = [ - 'SparseReLU', - 'SparseSiLU', - 'SparseGELU', - 'SparseActivation' -] - - -class SparseReLU(nn.ReLU): - def forward(self, input: SparseTensor) -> SparseTensor: - return input.replace(super().forward(input.feats)) - - -class SparseSiLU(nn.SiLU): - def forward(self, input: SparseTensor) -> SparseTensor: - return input.replace(super().forward(input.feats)) - - -class SparseGELU(nn.GELU): - def forward(self, input: SparseTensor) -> SparseTensor: - return input.replace(super().forward(input.feats)) - - -class SparseActivation(nn.Module): - def __init__(self, activation: nn.Module): - super().__init__() - self.activation = activation - - def forward(self, input: SparseTensor) -> SparseTensor: - return input.replace(self.activation(input.feats)) - diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/norm.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/norm.py deleted file mode 100644 index 6b38a366..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/norm.py +++ /dev/null @@ -1,58 +0,0 @@ -import torch -import torch.nn as nn -from . import SparseTensor -from . import DEBUG - -__all__ = [ - 'SparseGroupNorm', - 'SparseLayerNorm', - 'SparseGroupNorm32', - 'SparseLayerNorm32', -] - - -class SparseGroupNorm(nn.GroupNorm): - def __init__(self, num_groups, num_channels, eps=1e-5, affine=True): - super(SparseGroupNorm, self).__init__(num_groups, num_channels, eps, affine) - - def forward(self, input: SparseTensor) -> SparseTensor: - nfeats = torch.zeros_like(input.feats) - for k in range(input.shape[0]): - if DEBUG: - assert (input.coords[input.layout[k], 0] == k).all(), f"SparseGroupNorm: batch index mismatch" - bfeats = input.feats[input.layout[k]] - bfeats = bfeats.permute(1, 0).reshape(1, input.shape[1], -1) - bfeats = super().forward(bfeats) - bfeats = bfeats.reshape(input.shape[1], -1).permute(1, 0) - nfeats[input.layout[k]] = bfeats - return input.replace(nfeats) - - -class SparseLayerNorm(nn.LayerNorm): - def __init__(self, normalized_shape, eps=1e-5, elementwise_affine=True): - super(SparseLayerNorm, self).__init__(normalized_shape, eps, elementwise_affine) - - def forward(self, input: SparseTensor) -> SparseTensor: - nfeats = torch.zeros_like(input.feats) - for k in range(input.shape[0]): - bfeats = input.feats[input.layout[k]] - bfeats = bfeats.permute(1, 0).reshape(1, input.shape[1], -1) - bfeats = super().forward(bfeats) - bfeats = bfeats.reshape(input.shape[1], -1).permute(1, 0) - nfeats[input.layout[k]] = bfeats - return input.replace(nfeats) - - -class SparseGroupNorm32(SparseGroupNorm): - """ - A GroupNorm layer that converts to float32 before the forward pass. - """ - def forward(self, x: SparseTensor) -> SparseTensor: - return super().forward(x.float()).type(x.dtype) - -class SparseLayerNorm32(SparseLayerNorm): - """ - A LayerNorm layer that converts to float32 before the forward pass. - """ - def forward(self, x: SparseTensor) -> SparseTensor: - return super().forward(x.float()).type(x.dtype) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/spatial.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/spatial.py deleted file mode 100644 index ad712147..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/spatial.py +++ /dev/null @@ -1,110 +0,0 @@ -from typing import * -import torch -import torch.nn as nn -from . import SparseTensor - -__all__ = [ - 'SparseDownsample', - 'SparseUpsample', - 'SparseSubdivide' -] - - -class SparseDownsample(nn.Module): - """ - Downsample a sparse tensor by a factor of `factor`. - Implemented as average pooling. - """ - def __init__(self, factor: Union[int, Tuple[int, ...], List[int]]): - super(SparseDownsample, self).__init__() - self.factor = tuple(factor) if isinstance(factor, (list, tuple)) else factor - - def forward(self, input: SparseTensor) -> SparseTensor: - DIM = input.coords.shape[-1] - 1 - factor = self.factor if isinstance(self.factor, tuple) else (self.factor,) * DIM - assert DIM == len(factor), 'Input coordinates must have the same dimension as the downsample factor.' - - coord = list(input.coords.unbind(dim=-1)) - for i, f in enumerate(factor): - coord[i+1] = coord[i+1] // f - - MAX = [coord[i+1].max().item() + 1 for i in range(DIM)] - OFFSET = torch.cumprod(torch.tensor(MAX[::-1]), 0).tolist()[::-1] + [1] - code = sum([c * o for c, o in zip(coord, OFFSET)]) - code, idx = code.unique(return_inverse=True) - - new_feats = torch.scatter_reduce( - torch.zeros(code.shape[0], input.feats.shape[1], device=input.feats.device, dtype=input.feats.dtype), - dim=0, - index=idx.unsqueeze(1).expand(-1, input.feats.shape[1]), - src=input.feats, - reduce='mean' - ) - new_coords = torch.stack( - [code // OFFSET[0]] + - [(code // OFFSET[i+1]) % MAX[i] for i in range(DIM)], - dim=-1 - ) - out = SparseTensor(new_feats, new_coords, input.shape,) - out._scale = tuple([s // f for s, f in zip(input._scale, factor)]) - out._spatial_cache = input._spatial_cache - - out.register_spatial_cache(f'upsample_{factor}_coords', input.coords) - out.register_spatial_cache(f'upsample_{factor}_layout', input.layout) - out.register_spatial_cache(f'upsample_{factor}_idx', idx) - - return out - - -class SparseUpsample(nn.Module): - """ - Upsample a sparse tensor by a factor of `factor`. - Implemented as nearest neighbor interpolation. - """ - def __init__(self, factor: Union[int, Tuple[int, int, int], List[int]]): - super(SparseUpsample, self).__init__() - self.factor = tuple(factor) if isinstance(factor, (list, tuple)) else factor - - def forward(self, input: SparseTensor) -> SparseTensor: - DIM = input.coords.shape[-1] - 1 - factor = self.factor if isinstance(self.factor, tuple) else (self.factor,) * DIM - assert DIM == len(factor), 'Input coordinates must have the same dimension as the upsample factor.' - - new_coords = input.get_spatial_cache(f'upsample_{factor}_coords') - new_layout = input.get_spatial_cache(f'upsample_{factor}_layout') - idx = input.get_spatial_cache(f'upsample_{factor}_idx') - if any([x is None for x in [new_coords, new_layout, idx]]): - raise ValueError('Upsample cache not found. SparseUpsample must be paired with SparseDownsample.') - new_feats = input.feats[idx] - out = SparseTensor(new_feats, new_coords, input.shape, new_layout) - out._scale = tuple([s * f for s, f in zip(input._scale, factor)]) - out._spatial_cache = input._spatial_cache - return out - -class SparseSubdivide(nn.Module): - """ - Upsample a sparse tensor by a factor of `factor`. - Implemented as nearest neighbor interpolation. - """ - def __init__(self): - super(SparseSubdivide, self).__init__() - - def forward(self, input: SparseTensor) -> SparseTensor: - DIM = input.coords.shape[-1] - 1 - # upsample scale=2^DIM - n_cube = torch.ones([2] * DIM, device=input.device, dtype=torch.int) - n_coords = torch.nonzero(n_cube) - n_coords = torch.cat([torch.zeros_like(n_coords[:, :1]), n_coords], dim=-1) - factor = n_coords.shape[0] - assert factor == 2 ** DIM - # print(n_coords.shape) - new_coords = input.coords.clone() - new_coords[:, 1:] *= 2 - new_coords = new_coords.unsqueeze(1) + n_coords.unsqueeze(0).to(new_coords.dtype) - - new_feats = input.feats.unsqueeze(1).expand(input.feats.shape[0], factor, *input.feats.shape[1:]) - out = SparseTensor(new_feats.flatten(0, 1), new_coords.flatten(0, 1), input.shape) - out._scale = input._scale * 2 - out._spatial_cache = input._spatial_cache - return out - diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/transformer/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/transformer/__init__.py deleted file mode 100644 index b08b0d4e..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/transformer/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .blocks import * -from .modulated import * \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/transformer/blocks.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/transformer/blocks.py deleted file mode 100644 index 9d037a49..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/transformer/blocks.py +++ /dev/null @@ -1,151 +0,0 @@ -from typing import * -import torch -import torch.nn as nn -from ..basic import SparseTensor -from ..linear import SparseLinear -from ..nonlinearity import SparseGELU -from ..attention import SparseMultiHeadAttention, SerializeMode -from ...norm import LayerNorm32 - - -class SparseFeedForwardNet(nn.Module): - def __init__(self, channels: int, mlp_ratio: float = 4.0): - super().__init__() - self.mlp = nn.Sequential( - SparseLinear(channels, int(channels * mlp_ratio)), - SparseGELU(approximate="tanh"), - SparseLinear(int(channels * mlp_ratio), channels), - ) - - def forward(self, x: SparseTensor) -> SparseTensor: - return self.mlp(x) - - -class SparseTransformerBlock(nn.Module): - """ - Sparse Transformer block (MSA + FFN). - """ - def __init__( - self, - channels: int, - num_heads: int, - mlp_ratio: float = 4.0, - attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "full", - window_size: Optional[int] = None, - shift_sequence: Optional[int] = None, - shift_window: Optional[Tuple[int, int, int]] = None, - serialize_mode: Optional[SerializeMode] = None, - use_checkpoint: bool = False, - use_rope: bool = False, - qk_rms_norm: bool = False, - qkv_bias: bool = True, - ln_affine: bool = False, - ): - super().__init__() - self.use_checkpoint = use_checkpoint - self.norm1 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6) - self.norm2 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6) - self.attn = SparseMultiHeadAttention( - channels, - num_heads=num_heads, - attn_mode=attn_mode, - window_size=window_size, - shift_sequence=shift_sequence, - shift_window=shift_window, - serialize_mode=serialize_mode, - qkv_bias=qkv_bias, - use_rope=use_rope, - qk_rms_norm=qk_rms_norm, - ) - self.mlp = SparseFeedForwardNet( - channels, - mlp_ratio=mlp_ratio, - ) - - def _forward(self, x: SparseTensor) -> SparseTensor: - h = x.replace(self.norm1(x.feats)) - h = self.attn(h) - x = x + h - h = x.replace(self.norm2(x.feats)) - h = self.mlp(h) - x = x + h - return x - - def forward(self, x: SparseTensor) -> SparseTensor: - if self.use_checkpoint: - return torch.utils.checkpoint.checkpoint(self._forward, x, use_reentrant=False) - else: - return self._forward(x) - - -class SparseTransformerCrossBlock(nn.Module): - """ - Sparse Transformer cross-attention block (MSA + MCA + FFN). - """ - def __init__( - self, - channels: int, - ctx_channels: int, - num_heads: int, - mlp_ratio: float = 4.0, - attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "full", - window_size: Optional[int] = None, - shift_sequence: Optional[int] = None, - shift_window: Optional[Tuple[int, int, int]] = None, - serialize_mode: Optional[SerializeMode] = None, - use_checkpoint: bool = False, - use_rope: bool = False, - qk_rms_norm: bool = False, - qk_rms_norm_cross: bool = False, - qkv_bias: bool = True, - ln_affine: bool = False, - ): - super().__init__() - self.use_checkpoint = use_checkpoint - self.norm1 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6) - self.norm2 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6) - self.norm3 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6) - self.self_attn = SparseMultiHeadAttention( - channels, - num_heads=num_heads, - type="self", - attn_mode=attn_mode, - window_size=window_size, - shift_sequence=shift_sequence, - shift_window=shift_window, - serialize_mode=serialize_mode, - qkv_bias=qkv_bias, - use_rope=use_rope, - qk_rms_norm=qk_rms_norm, - ) - self.cross_attn = SparseMultiHeadAttention( - channels, - ctx_channels=ctx_channels, - num_heads=num_heads, - type="cross", - attn_mode="full", - qkv_bias=qkv_bias, - qk_rms_norm=qk_rms_norm_cross, - ) - self.mlp = SparseFeedForwardNet( - channels, - mlp_ratio=mlp_ratio, - ) - - def _forward(self, x: SparseTensor, mod: torch.Tensor, context: torch.Tensor): - h = x.replace(self.norm1(x.feats)) - h = self.self_attn(h) - x = x + h - h = x.replace(self.norm2(x.feats)) - h = self.cross_attn(h, context) - x = x + h - h = x.replace(self.norm3(x.feats)) - h = self.mlp(h) - x = x + h - return x - - def forward(self, x: SparseTensor, context: torch.Tensor): - if self.use_checkpoint: - return torch.utils.checkpoint.checkpoint(self._forward, x, context, use_reentrant=False) - else: - return self._forward(x, context) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/transformer/modulated.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/transformer/modulated.py deleted file mode 100644 index 4a841655..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/modules/sparse/transformer/modulated.py +++ /dev/null @@ -1,166 +0,0 @@ -from typing import * -import torch -import torch.nn as nn -from ..basic import SparseTensor -from ..attention import SparseMultiHeadAttention, SerializeMode -from ...norm import LayerNorm32 -from .blocks import SparseFeedForwardNet - - -class ModulatedSparseTransformerBlock(nn.Module): - """ - Sparse Transformer block (MSA + FFN) with adaptive layer norm conditioning. - """ - def __init__( - self, - channels: int, - num_heads: int, - mlp_ratio: float = 4.0, - attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "full", - window_size: Optional[int] = None, - shift_sequence: Optional[int] = None, - shift_window: Optional[Tuple[int, int, int]] = None, - serialize_mode: Optional[SerializeMode] = None, - use_checkpoint: bool = False, - use_rope: bool = False, - qk_rms_norm: bool = False, - qkv_bias: bool = True, - share_mod: bool = False, - ): - super().__init__() - self.use_checkpoint = use_checkpoint - self.share_mod = share_mod - self.norm1 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6) - self.norm2 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6) - self.attn = SparseMultiHeadAttention( - channels, - num_heads=num_heads, - attn_mode=attn_mode, - window_size=window_size, - shift_sequence=shift_sequence, - shift_window=shift_window, - serialize_mode=serialize_mode, - qkv_bias=qkv_bias, - use_rope=use_rope, - qk_rms_norm=qk_rms_norm, - ) - self.mlp = SparseFeedForwardNet( - channels, - mlp_ratio=mlp_ratio, - ) - if not share_mod: - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), - nn.Linear(channels, 6 * channels, bias=True) - ) - - def _forward(self, x: SparseTensor, mod: torch.Tensor) -> SparseTensor: - if self.share_mod: - shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = mod.chunk(6, dim=1) - else: - shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(mod).chunk(6, dim=1) - h = x.replace(self.norm1(x.feats)) - h = h * (1 + scale_msa) + shift_msa - h = self.attn(h) - h = h * gate_msa - x = x + h - h = x.replace(self.norm2(x.feats)) - h = h * (1 + scale_mlp) + shift_mlp - h = self.mlp(h) - h = h * gate_mlp - x = x + h - return x - - def forward(self, x: SparseTensor, mod: torch.Tensor) -> SparseTensor: - if self.use_checkpoint: - return torch.utils.checkpoint.checkpoint(self._forward, x, mod, use_reentrant=False) - else: - return self._forward(x, mod) - - -class ModulatedSparseTransformerCrossBlock(nn.Module): - """ - Sparse Transformer cross-attention block (MSA + MCA + FFN) with adaptive layer norm conditioning. - """ - def __init__( - self, - channels: int, - ctx_channels: int, - num_heads: int, - mlp_ratio: float = 4.0, - attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "full", - window_size: Optional[int] = None, - shift_sequence: Optional[int] = None, - shift_window: Optional[Tuple[int, int, int]] = None, - serialize_mode: Optional[SerializeMode] = None, - use_checkpoint: bool = False, - use_rope: bool = False, - qk_rms_norm: bool = False, - qk_rms_norm_cross: bool = False, - qkv_bias: bool = True, - share_mod: bool = False, - - ): - super().__init__() - self.use_checkpoint = use_checkpoint - self.share_mod = share_mod - self.norm1 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6) - self.norm2 = LayerNorm32(channels, elementwise_affine=True, eps=1e-6) - self.norm3 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6) - self.self_attn = SparseMultiHeadAttention( - channels, - num_heads=num_heads, - type="self", - attn_mode=attn_mode, - window_size=window_size, - shift_sequence=shift_sequence, - shift_window=shift_window, - serialize_mode=serialize_mode, - qkv_bias=qkv_bias, - use_rope=use_rope, - qk_rms_norm=qk_rms_norm, - ) - self.cross_attn = SparseMultiHeadAttention( - channels, - ctx_channels=ctx_channels, - num_heads=num_heads, - type="cross", - attn_mode="full", - qkv_bias=qkv_bias, - qk_rms_norm=qk_rms_norm_cross, - ) - self.mlp = SparseFeedForwardNet( - channels, - mlp_ratio=mlp_ratio, - ) - if not share_mod: - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), - nn.Linear(channels, 6 * channels, bias=True) - ) - - def _forward(self, x: SparseTensor, mod: torch.Tensor, context: torch.Tensor) -> SparseTensor: - if self.share_mod: - shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = mod.chunk(6, dim=1) - else: - shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(mod).chunk(6, dim=1) - h = x.replace(self.norm1(x.feats)) - h = h * (1 + scale_msa) + shift_msa - h = self.self_attn(h) - h = h * gate_msa - x = x + h - h = x.replace(self.norm2(x.feats)) - h = self.cross_attn(h, context) - x = x + h - h = x.replace(self.norm3(x.feats)) - h = h * (1 + scale_mlp) + shift_mlp - h = self.mlp(h) - h = h * gate_mlp - x = x + h - return x - - def forward(self, x: SparseTensor, mod: torch.Tensor, context: torch.Tensor) -> SparseTensor: - if self.use_checkpoint: - return torch.utils.checkpoint.checkpoint(self._forward, x, mod, context, use_reentrant=False) - else: - return self._forward(x, mod, context) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/spatial.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/spatial.py deleted file mode 100644 index 79e268d3..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/modules/spatial.py +++ /dev/null @@ -1,48 +0,0 @@ -import torch - - -def pixel_shuffle_3d(x: torch.Tensor, scale_factor: int) -> torch.Tensor: - """ - 3D pixel shuffle. - """ - B, C, H, W, D = x.shape - C_ = C // scale_factor**3 - x = x.reshape(B, C_, scale_factor, scale_factor, scale_factor, H, W, D) - x = x.permute(0, 1, 5, 2, 6, 3, 7, 4) - x = x.reshape(B, C_, H*scale_factor, W*scale_factor, D*scale_factor) - return x - - -def patchify(x: torch.Tensor, patch_size: int): - """ - Patchify a tensor. - - Args: - x (torch.Tensor): (N, C, *spatial) tensor - patch_size (int): Patch size - """ - DIM = x.dim() - 2 - for d in range(2, DIM + 2): - assert x.shape[d] % patch_size == 0, f"Dimension {d} of input tensor must be divisible by patch size, got {x.shape[d]} and {patch_size}" - - x = x.reshape(*x.shape[:2], *sum([[x.shape[d] // patch_size, patch_size] for d in range(2, DIM + 2)], [])) - x = x.permute(0, 1, *([2 * i + 3 for i in range(DIM)] + [2 * i + 2 for i in range(DIM)])) - x = x.reshape(x.shape[0], x.shape[1] * (patch_size ** DIM), *(x.shape[-DIM:])) - return x - - -def unpatchify(x: torch.Tensor, patch_size: int): - """ - Unpatchify a tensor. - - Args: - x (torch.Tensor): (N, C, *spatial) tensor - patch_size (int): Patch size - """ - DIM = x.dim() - 2 - assert x.shape[1] % (patch_size ** DIM) == 0, f"Second dimension of input tensor must be divisible by patch size to unpatchify, got {x.shape[1]} and {patch_size ** DIM}" - - x = x.reshape(x.shape[0], x.shape[1] // (patch_size ** DIM), *([patch_size] * DIM), *(x.shape[-DIM:])) - x = x.permute(0, 1, *(sum([[2 + DIM + i, 2 + i] for i in range(DIM)], []))) - x = x.reshape(x.shape[0], x.shape[1], *[x.shape[2 + 2 * i] * patch_size for i in range(DIM)]) - return x diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/transformer/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/transformer/__init__.py deleted file mode 100644 index b08b0d4e..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/modules/transformer/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .blocks import * -from .modulated import * \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/transformer/blocks.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/transformer/blocks.py deleted file mode 100644 index c37eb7ed..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/modules/transformer/blocks.py +++ /dev/null @@ -1,182 +0,0 @@ -from typing import * -import torch -import torch.nn as nn -from ..attention import MultiHeadAttention -from ..norm import LayerNorm32 - - -class AbsolutePositionEmbedder(nn.Module): - """ - Embeds spatial positions into vector representations. - """ - def __init__(self, channels: int, in_channels: int = 3): - super().__init__() - self.channels = channels - self.in_channels = in_channels - self.freq_dim = channels // in_channels // 2 - self.freqs = torch.arange(self.freq_dim, dtype=torch.float32) / self.freq_dim - self.freqs = 1.0 / (10000 ** self.freqs) - - def _sin_cos_embedding(self, x: torch.Tensor) -> torch.Tensor: - """ - Create sinusoidal position embeddings. - - Args: - x: a 1-D Tensor of N indices - - Returns: - an (N, D) Tensor of positional embeddings. - """ - self.freqs = self.freqs.to(x.device) - out = torch.outer(x, self.freqs) - out = torch.cat([torch.sin(out), torch.cos(out)], dim=-1) - return out - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """ - Args: - x (torch.Tensor): (N, D) tensor of spatial positions - """ - N, D = x.shape - assert D == self.in_channels, "Input dimension must match number of input channels" - embed = self._sin_cos_embedding(x.reshape(-1)) - embed = embed.reshape(N, -1) - if embed.shape[1] < self.channels: - embed = torch.cat([embed, torch.zeros(N, self.channels - embed.shape[1], device=embed.device)], dim=-1) - return embed - - -class FeedForwardNet(nn.Module): - def __init__(self, channels: int, mlp_ratio: float = 4.0): - super().__init__() - self.mlp = nn.Sequential( - nn.Linear(channels, int(channels * mlp_ratio)), - nn.GELU(approximate="tanh"), - nn.Linear(int(channels * mlp_ratio), channels), - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - return self.mlp(x) - - -class TransformerBlock(nn.Module): - """ - Transformer block (MSA + FFN). - """ - def __init__( - self, - channels: int, - num_heads: int, - mlp_ratio: float = 4.0, - attn_mode: Literal["full", "windowed"] = "full", - window_size: Optional[int] = None, - shift_window: Optional[int] = None, - use_checkpoint: bool = False, - use_rope: bool = False, - qk_rms_norm: bool = False, - qkv_bias: bool = True, - ln_affine: bool = False, - ): - super().__init__() - self.use_checkpoint = use_checkpoint - self.norm1 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6) - self.norm2 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6) - self.attn = MultiHeadAttention( - channels, - num_heads=num_heads, - attn_mode=attn_mode, - window_size=window_size, - shift_window=shift_window, - qkv_bias=qkv_bias, - use_rope=use_rope, - qk_rms_norm=qk_rms_norm, - ) - self.mlp = FeedForwardNet( - channels, - mlp_ratio=mlp_ratio, - ) - - def _forward(self, x: torch.Tensor) -> torch.Tensor: - h = self.norm1(x) - h = self.attn(h) - x = x + h - h = self.norm2(x) - h = self.mlp(h) - x = x + h - return x - - def forward(self, x: torch.Tensor) -> torch.Tensor: - if self.use_checkpoint: - return torch.utils.checkpoint.checkpoint(self._forward, x, use_reentrant=False) - else: - return self._forward(x) - - -class TransformerCrossBlock(nn.Module): - """ - Transformer cross-attention block (MSA + MCA + FFN). - """ - def __init__( - self, - channels: int, - ctx_channels: int, - num_heads: int, - mlp_ratio: float = 4.0, - attn_mode: Literal["full", "windowed"] = "full", - window_size: Optional[int] = None, - shift_window: Optional[Tuple[int, int, int]] = None, - use_checkpoint: bool = False, - use_rope: bool = False, - qk_rms_norm: bool = False, - qk_rms_norm_cross: bool = False, - qkv_bias: bool = True, - ln_affine: bool = False, - ): - super().__init__() - self.use_checkpoint = use_checkpoint - self.norm1 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6) - self.norm2 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6) - self.norm3 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6) - self.self_attn = MultiHeadAttention( - channels, - num_heads=num_heads, - type="self", - attn_mode=attn_mode, - window_size=window_size, - shift_window=shift_window, - qkv_bias=qkv_bias, - use_rope=use_rope, - qk_rms_norm=qk_rms_norm, - ) - self.cross_attn = MultiHeadAttention( - channels, - ctx_channels=ctx_channels, - num_heads=num_heads, - type="cross", - attn_mode="full", - qkv_bias=qkv_bias, - qk_rms_norm=qk_rms_norm_cross, - ) - self.mlp = FeedForwardNet( - channels, - mlp_ratio=mlp_ratio, - ) - - def _forward(self, x: torch.Tensor, context: torch.Tensor): - h = self.norm1(x) - h = self.self_attn(h) - x = x + h - h = self.norm2(x) - h = self.cross_attn(h, context) - x = x + h - h = self.norm3(x) - h = self.mlp(h) - x = x + h - return x - - def forward(self, x: torch.Tensor, context: torch.Tensor): - if self.use_checkpoint: - return torch.utils.checkpoint.checkpoint(self._forward, x, context, use_reentrant=False) - else: - return self._forward(x, context) - \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/transformer/modulated.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/transformer/modulated.py deleted file mode 100644 index d4aeca06..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/modules/transformer/modulated.py +++ /dev/null @@ -1,157 +0,0 @@ -from typing import * -import torch -import torch.nn as nn -from ..attention import MultiHeadAttention -from ..norm import LayerNorm32 -from .blocks import FeedForwardNet - - -class ModulatedTransformerBlock(nn.Module): - """ - Transformer block (MSA + FFN) with adaptive layer norm conditioning. - """ - def __init__( - self, - channels: int, - num_heads: int, - mlp_ratio: float = 4.0, - attn_mode: Literal["full", "windowed"] = "full", - window_size: Optional[int] = None, - shift_window: Optional[Tuple[int, int, int]] = None, - use_checkpoint: bool = False, - use_rope: bool = False, - qk_rms_norm: bool = False, - qkv_bias: bool = True, - share_mod: bool = False, - ): - super().__init__() - self.use_checkpoint = use_checkpoint - self.share_mod = share_mod - self.norm1 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6) - self.norm2 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6) - self.attn = MultiHeadAttention( - channels, - num_heads=num_heads, - attn_mode=attn_mode, - window_size=window_size, - shift_window=shift_window, - qkv_bias=qkv_bias, - use_rope=use_rope, - qk_rms_norm=qk_rms_norm, - ) - self.mlp = FeedForwardNet( - channels, - mlp_ratio=mlp_ratio, - ) - if not share_mod: - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), - nn.Linear(channels, 6 * channels, bias=True) - ) - - def _forward(self, x: torch.Tensor, mod: torch.Tensor) -> torch.Tensor: - if self.share_mod: - shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = mod.chunk(6, dim=1) - else: - shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(mod).chunk(6, dim=1) - h = self.norm1(x) - h = h * (1 + scale_msa.unsqueeze(1)) + shift_msa.unsqueeze(1) - h = self.attn(h) - h = h * gate_msa.unsqueeze(1) - x = x + h - h = self.norm2(x) - h = h * (1 + scale_mlp.unsqueeze(1)) + shift_mlp.unsqueeze(1) - h = self.mlp(h) - h = h * gate_mlp.unsqueeze(1) - x = x + h - return x - - def forward(self, x: torch.Tensor, mod: torch.Tensor) -> torch.Tensor: - if self.use_checkpoint: - return torch.utils.checkpoint.checkpoint(self._forward, x, mod, use_reentrant=False) - else: - return self._forward(x, mod) - - -class ModulatedTransformerCrossBlock(nn.Module): - """ - Transformer cross-attention block (MSA + MCA + FFN) with adaptive layer norm conditioning. - """ - def __init__( - self, - channels: int, - ctx_channels: int, - num_heads: int, - mlp_ratio: float = 4.0, - attn_mode: Literal["full", "windowed"] = "full", - window_size: Optional[int] = None, - shift_window: Optional[Tuple[int, int, int]] = None, - use_checkpoint: bool = False, - use_rope: bool = False, - qk_rms_norm: bool = False, - qk_rms_norm_cross: bool = False, - qkv_bias: bool = True, - share_mod: bool = False, - ): - super().__init__() - self.use_checkpoint = use_checkpoint - self.share_mod = share_mod - self.norm1 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6) - self.norm2 = LayerNorm32(channels, elementwise_affine=True, eps=1e-6) - self.norm3 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6) - self.self_attn = MultiHeadAttention( - channels, - num_heads=num_heads, - type="self", - attn_mode=attn_mode, - window_size=window_size, - shift_window=shift_window, - qkv_bias=qkv_bias, - use_rope=use_rope, - qk_rms_norm=qk_rms_norm, - ) - self.cross_attn = MultiHeadAttention( - channels, - ctx_channels=ctx_channels, - num_heads=num_heads, - type="cross", - attn_mode="full", - qkv_bias=qkv_bias, - qk_rms_norm=qk_rms_norm_cross, - ) - self.mlp = FeedForwardNet( - channels, - mlp_ratio=mlp_ratio, - ) - if not share_mod: - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), - nn.Linear(channels, 6 * channels, bias=True) - ) - - def _forward(self, x: torch.Tensor, mod: torch.Tensor, context: torch.Tensor): - if self.share_mod: - shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = mod.chunk(6, dim=1) - else: - shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(mod).chunk(6, dim=1) - h = self.norm1(x) - h = h * (1 + scale_msa.unsqueeze(1)) + shift_msa.unsqueeze(1) - h = self.self_attn(h) - h = h * gate_msa.unsqueeze(1) - x = x + h - h = self.norm2(x) - h = self.cross_attn(h, context) - x = x + h - h = self.norm3(x) - h = h * (1 + scale_mlp.unsqueeze(1)) + shift_mlp.unsqueeze(1) - h = self.mlp(h) - h = h * gate_mlp.unsqueeze(1) - x = x + h - return x - - def forward(self, x: torch.Tensor, mod: torch.Tensor, context: torch.Tensor): - if self.use_checkpoint: - return torch.utils.checkpoint.checkpoint(self._forward, x, mod, context, use_reentrant=False) - else: - return self._forward(x, mod, context) - \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/modules/utils.py b/Gen_3D_Modules/TRELLIS/trellis_/modules/utils.py deleted file mode 100644 index f0afb1b6..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/modules/utils.py +++ /dev/null @@ -1,54 +0,0 @@ -import torch.nn as nn -from ..modules import sparse as sp - -FP16_MODULES = ( - nn.Conv1d, - nn.Conv2d, - nn.Conv3d, - nn.ConvTranspose1d, - nn.ConvTranspose2d, - nn.ConvTranspose3d, - nn.Linear, - sp.SparseConv3d, - sp.SparseInverseConv3d, - sp.SparseLinear, -) - -def convert_module_to_f16(l): - """ - Convert primitive modules to float16. - """ - if isinstance(l, FP16_MODULES): - for p in l.parameters(): - p.data = p.data.half() - - -def convert_module_to_f32(l): - """ - Convert primitive modules to float32, undoing convert_module_to_f16(). - """ - if isinstance(l, FP16_MODULES): - for p in l.parameters(): - p.data = p.data.float() - - -def zero_module(module): - """ - Zero out the parameters of a module and return it. - """ - for p in module.parameters(): - p.detach().zero_() - return module - - -def scale_module(module, scale): - """ - Scale the parameters of a module and return it. - """ - for p in module.parameters(): - p.detach().mul_(scale) - return module - - -def modulate(x, shift, scale): - return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/pipelines/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/pipelines/__init__.py deleted file mode 100644 index f9e8548b..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/pipelines/__init__.py +++ /dev/null @@ -1,24 +0,0 @@ -from . import samplers -from .trellis_image_to_3d import TrellisImageTo3DPipeline - - -def from_pretrained(path: str): - """ - Load a pipeline from a model folder or a Hugging Face model hub. - - Args: - path: The path to the model. Can be either local path or a Hugging Face model name. - """ - import os - import json - is_local = os.path.exists(f"{path}/pipeline.json") - - if is_local: - config_file = f"{path}/pipeline.json" - else: - from huggingface_hub import hf_hub_download - config_file = hf_hub_download(path, "pipeline.json") - - with open(config_file, 'r') as f: - config = json.load(f) - return globals()[config['name']].from_pretrained(path) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/pipelines/base.py b/Gen_3D_Modules/TRELLIS/trellis_/pipelines/base.py deleted file mode 100644 index 3a9e0df4..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/pipelines/base.py +++ /dev/null @@ -1,66 +0,0 @@ -from typing import * -import torch -import torch.nn as nn -from .. import models - - -class Pipeline: - """ - A base class for pipelines. - """ - def __init__( - self, - models: dict[str, nn.Module] = None, - ): - if models is None: - return - self.models = models - for model in self.models.values(): - model.eval() - - @staticmethod - def from_pretrained(path: str) -> "Pipeline": - """ - Load a pretrained model. - """ - import os - import json - is_local = os.path.exists(f"{path}/pipeline.json") - - if is_local: - config_file = f"{path}/pipeline.json" - else: - from huggingface_hub import hf_hub_download - config_file = hf_hub_download(path, "pipeline.json") - - with open(config_file, 'r') as f: - args = json.load(f)['args'] - - _models = { - k: models.from_pretrained(f"{path}/{v}") - for k, v in args['models'].items() - } - - new_pipeline = Pipeline(_models) - new_pipeline._pretrained_args = args - return new_pipeline - - @property - def device(self) -> torch.device: - for model in self.models.values(): - if hasattr(model, 'device'): - return model.device - for model in self.models.values(): - if hasattr(model, 'parameters'): - return next(model.parameters()).device - raise RuntimeError("No device found.") - - def to(self, device: torch.device) -> None: - for model in self.models.values(): - model.to(device) - - def cuda(self) -> None: - self.to(torch.device("cuda")) - - def cpu(self) -> None: - self.to(torch.device("cpu")) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/__init__.py deleted file mode 100644 index 54d412fc..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .base import Sampler -from .flow_euler import FlowEulerSampler, FlowEulerCfgSampler, FlowEulerGuidanceIntervalSampler \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/base.py b/Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/base.py deleted file mode 100644 index 1966ce78..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/base.py +++ /dev/null @@ -1,20 +0,0 @@ -from typing import * -from abc import ABC, abstractmethod - - -class Sampler(ABC): - """ - A base class for samplers. - """ - - @abstractmethod - def sample( - self, - model, - **kwargs - ): - """ - Sample from a model. - """ - pass - \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/classifier_free_guidance_mixin.py b/Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/classifier_free_guidance_mixin.py deleted file mode 100644 index 5701b25f..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/classifier_free_guidance_mixin.py +++ /dev/null @@ -1,12 +0,0 @@ -from typing import * - - -class ClassifierFreeGuidanceSamplerMixin: - """ - A mixin class for samplers that apply classifier-free guidance. - """ - - def _inference_model(self, model, x_t, t, cond, neg_cond, cfg_strength, **kwargs): - pred = super()._inference_model(model, x_t, t, cond, **kwargs) - neg_pred = super()._inference_model(model, x_t, t, neg_cond, **kwargs) - return (1 + cfg_strength) * pred - cfg_strength * neg_pred diff --git a/Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/flow_euler.py b/Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/flow_euler.py deleted file mode 100644 index b2d48607..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/flow_euler.py +++ /dev/null @@ -1,202 +0,0 @@ -from typing import * -import torch -import numpy as np -from tqdm import tqdm -import comfy.utils -from easydict import EasyDict as edict -from .base import Sampler -from .classifier_free_guidance_mixin import ClassifierFreeGuidanceSamplerMixin -from .guidance_interval_mixin import GuidanceIntervalSamplerMixin - - -class FlowEulerSampler(Sampler): - """ - Generate samples from a flow-matching model using Euler sampling. - - Args: - sigma_min: The minimum scale of noise in flow. - """ - def __init__( - self, - sigma_min: float, - ): - self.sigma_min = sigma_min - - def _eps_to_xstart(self, x_t, t, eps): - assert x_t.shape == eps.shape - return (x_t - (self.sigma_min + (1 - self.sigma_min) * t) * eps) / (1 - t) - - def _xstart_to_eps(self, x_t, t, x_0): - assert x_t.shape == x_0.shape - return (x_t - (1 - t) * x_0) / (self.sigma_min + (1 - self.sigma_min) * t) - - def _v_to_xstart_eps(self, x_t, t, v): - assert x_t.shape == v.shape - eps = (1 - t) * v + x_t - x_0 = (1 - self.sigma_min) * x_t - (self.sigma_min + (1 - self.sigma_min) * t) * v - return x_0, eps - - def _inference_model(self, model, x_t, t, cond=None, **kwargs): - t = torch.tensor([1000 * t] * x_t.shape[0], device=x_t.device, dtype=torch.float32) - return model(x_t, t, cond, **kwargs) - - def _get_model_prediction(self, model, x_t, t, cond=None, **kwargs): - pred_v = self._inference_model(model, x_t, t, cond, **kwargs) - pred_x_0, pred_eps = self._v_to_xstart_eps(x_t=x_t, t=t, v=pred_v) - return pred_x_0, pred_eps, pred_v - - @torch.no_grad() - def sample_once( - self, - model, - x_t, - t: float, - t_prev: float, - cond: Optional[Any] = None, - **kwargs - ): - """ - Sample x_{t-1} from the model using Euler method. - - Args: - model: The model to sample from. - x_t: The [N x C x ...] tensor of noisy inputs at time t. - t: The current timestep. - t_prev: The previous timestep. - cond: conditional information. - **kwargs: Additional arguments for model inference. - - Returns: - a dict containing the following - - 'pred_x_prev': x_{t-1}. - - 'pred_x_0': a prediction of x_0. - """ - pred_x_0, pred_eps, pred_v = self._get_model_prediction(model, x_t, t, cond, **kwargs) - pred_x_prev = x_t - (t - t_prev) * pred_v - return edict({"pred_x_prev": pred_x_prev, "pred_x_0": pred_x_0}) - - @torch.no_grad() - def sample( - self, - model, - noise, - cond: Optional[Any] = None, - steps: int = 50, - rescale_t: float = 1.0, - verbose: bool = True, - **kwargs - ): - """ - Generate samples from the model using Euler method. - - Args: - model: The model to sample from. - noise: The initial noise tensor. - cond: conditional information. - steps: The number of steps to sample. - rescale_t: The rescale factor for t. - verbose: If True, show a progress bar. - **kwargs: Additional arguments for model_inference. - - Returns: - a dict containing the following - - 'samples': the model samples. - - 'pred_x_t': a list of prediction of x_t. - - 'pred_x_0': a list of prediction of x_0. - """ - sample = noise - t_seq = np.linspace(1, 0, steps + 1) - t_seq = rescale_t * t_seq / (1 + (rescale_t - 1) * t_seq) - t_pairs = list((t_seq[i], t_seq[i + 1]) for i in range(steps)) - ret = edict({"samples": None, "pred_x_t": [], "pred_x_0": []}) - comfy_pbar = comfy.utils.ProgressBar(steps) - for i, (t, t_prev) in enumerate(tqdm(t_pairs, desc="Sampling", disable=not verbose)): - out = self.sample_once(model, sample, t, t_prev, cond, **kwargs) - sample = out.pred_x_prev - ret.pred_x_t.append(out.pred_x_prev) - ret.pred_x_0.append(out.pred_x_0) - comfy_pbar.update_absolute(i + 1) - ret.samples = sample - return ret - - -class FlowEulerCfgSampler(ClassifierFreeGuidanceSamplerMixin, FlowEulerSampler): - """ - Generate samples from a flow-matching model using Euler sampling with classifier-free guidance. - """ - @torch.no_grad() - def sample( - self, - model, - noise, - cond, - neg_cond, - steps: int = 50, - rescale_t: float = 1.0, - cfg_strength: float = 3.0, - verbose: bool = True, - **kwargs - ): - """ - Generate samples from the model using Euler method. - - Args: - model: The model to sample from. - noise: The initial noise tensor. - cond: conditional information. - neg_cond: negative conditional information. - steps: The number of steps to sample. - rescale_t: The rescale factor for t. - cfg_strength: The strength of classifier-free guidance. - verbose: If True, show a progress bar. - **kwargs: Additional arguments for model_inference. - - Returns: - a dict containing the following - - 'samples': the model samples. - - 'pred_x_t': a list of prediction of x_t. - - 'pred_x_0': a list of prediction of x_0. - """ - return super().sample(model, noise, cond, steps, rescale_t, verbose, neg_cond=neg_cond, cfg_strength=cfg_strength, **kwargs) - - -class FlowEulerGuidanceIntervalSampler(GuidanceIntervalSamplerMixin, FlowEulerSampler): - """ - Generate samples from a flow-matching model using Euler sampling with classifier-free guidance and interval. - """ - @torch.no_grad() - def sample( - self, - model, - noise, - cond, - neg_cond, - steps: int = 50, - rescale_t: float = 1.0, - cfg_strength: float = 3.0, - cfg_interval: Tuple[float, float] = (0.0, 1.0), - verbose: bool = True, - **kwargs - ): - """ - Generate samples from the model using Euler method. - - Args: - model: The model to sample from. - noise: The initial noise tensor. - cond: conditional information. - neg_cond: negative conditional information. - steps: The number of steps to sample. - rescale_t: The rescale factor for t. - cfg_strength: The strength of classifier-free guidance. - cfg_interval: The interval for classifier-free guidance. - verbose: If True, show a progress bar. - **kwargs: Additional arguments for model_inference. - - Returns: - a dict containing the following - - 'samples': the model samples. - - 'pred_x_t': a list of prediction of x_t. - - 'pred_x_0': a list of prediction of x_0. - """ - return super().sample(model, noise, cond, steps, rescale_t, verbose, neg_cond=neg_cond, cfg_strength=cfg_strength, cfg_interval=cfg_interval, **kwargs) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/guidance_interval_mixin.py b/Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/guidance_interval_mixin.py deleted file mode 100644 index 7074a4d5..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/pipelines/samplers/guidance_interval_mixin.py +++ /dev/null @@ -1,15 +0,0 @@ -from typing import * - - -class GuidanceIntervalSamplerMixin: - """ - A mixin class for samplers that apply classifier-free guidance with interval. - """ - - def _inference_model(self, model, x_t, t, cond, neg_cond, cfg_strength, cfg_interval, **kwargs): - if cfg_interval[0] <= t <= cfg_interval[1]: - pred = super()._inference_model(model, x_t, t, cond, **kwargs) - neg_pred = super()._inference_model(model, x_t, t, neg_cond, **kwargs) - return (1 + cfg_strength) * pred - cfg_strength * neg_pred - else: - return super()._inference_model(model, x_t, t, cond, **kwargs) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/pipelines/trellis_image_to_3d.py b/Gen_3D_Modules/TRELLIS/trellis_/pipelines/trellis_image_to_3d.py deleted file mode 100644 index 033083e0..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/pipelines/trellis_image_to_3d.py +++ /dev/null @@ -1,283 +0,0 @@ -from typing import * -import torch -import torch.nn as nn -import torch.nn.functional as F -import numpy as np -from tqdm import tqdm -from easydict import EasyDict as edict -from torchvision import transforms -from PIL import Image -from .base import Pipeline -from . import samplers -from ..modules import sparse as sp -from ..representations import Gaussian, Strivec, MeshExtractResult - - -class TrellisImageTo3DPipeline(Pipeline): - """ - Pipeline for inferring Trellis image-to-3D models. - - Args: - models (dict[str, nn.Module]): The models to use in the pipeline. - sparse_structure_sampler (samplers.Sampler): The sampler for the sparse structure. - slat_sampler (samplers.Sampler): The sampler for the structured latent. - slat_normalization (dict): The normalization parameters for the structured latent. - image_cond_model (str): The name of the image conditioning model. - """ - def __init__( - self, - models: dict[str, nn.Module] = None, - sparse_structure_sampler: samplers.Sampler = None, - slat_sampler: samplers.Sampler = None, - slat_normalization: dict = None, - image_cond_model: str = None, - ): - if models is None: - return - super().__init__(models) - self.sparse_structure_sampler = sparse_structure_sampler - self.slat_sampler = slat_sampler - self.sparse_structure_sampler_params = {} - self.slat_sampler_params = {} - self.slat_normalization = slat_normalization - self.rembg_session = None - self._init_image_cond_model(image_cond_model) - - @staticmethod - def from_pretrained(path: str) -> "TrellisImageTo3DPipeline": - """ - Load a pretrained model. - - Args: - path (str): The path to the model. Can be either local path or a Hugging Face repository. - """ - pipeline = super(TrellisImageTo3DPipeline, TrellisImageTo3DPipeline).from_pretrained(path) - new_pipeline = TrellisImageTo3DPipeline() - new_pipeline.__dict__ = pipeline.__dict__ - args = pipeline._pretrained_args - - new_pipeline.sparse_structure_sampler = getattr(samplers, args['sparse_structure_sampler']['name'])(**args['sparse_structure_sampler']['args']) - new_pipeline.sparse_structure_sampler_params = args['sparse_structure_sampler']['params'] - - new_pipeline.slat_sampler = getattr(samplers, args['slat_sampler']['name'])(**args['slat_sampler']['args']) - new_pipeline.slat_sampler_params = args['slat_sampler']['params'] - - new_pipeline.slat_normalization = args['slat_normalization'] - - new_pipeline._init_image_cond_model(args['image_cond_model']) - - return new_pipeline - - def _init_image_cond_model(self, name: str): - """ - Initialize the image conditioning model. - """ - dinov2_model = torch.hub.load('facebookresearch/dinov2', name, pretrained=True) - dinov2_model.eval() - self.models['image_cond_model'] = dinov2_model - transform = transforms.Compose([ - transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), - ]) - self.image_cond_model_transform = transform - - def preprocess_image(self, input: Image.Image) -> Image.Image: - """ - Preprocess the input image. - """ - # if has alpha channel, use it directly; otherwise, remove background - has_alpha = False - if input.mode == 'RGBA': - alpha = np.array(input)[:, :, 3] - if not np.all(alpha == 255): - has_alpha = True - if has_alpha: - output = input - else: - import rembg - input = input.convert('RGB') - max_size = max(input.size) - scale = min(1, 1024 / max_size) - if scale < 1: - input = input.resize((int(input.width * scale), int(input.height * scale)), Image.Resampling.LANCZOS) - if getattr(self, 'rembg_session', None) is None: - self.rembg_session = rembg.new_session('u2net') - output = rembg.remove(input, session=self.rembg_session) - output_np = np.array(output) - alpha = output_np[:, :, 3] - bbox = np.argwhere(alpha > 0.8 * 255) - bbox = np.min(bbox[:, 1]), np.min(bbox[:, 0]), np.max(bbox[:, 1]), np.max(bbox[:, 0]) - center = (bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2 - size = max(bbox[2] - bbox[0], bbox[3] - bbox[1]) - size = int(size * 1.2) - bbox = center[0] - size // 2, center[1] - size // 2, center[0] + size // 2, center[1] + size // 2 - output = output.crop(bbox) # type: ignore - output = output.resize((518, 518), Image.Resampling.LANCZOS) - output = np.array(output).astype(np.float32) / 255 - output = output[:, :, :3] * output[:, :, 3:4] - output = Image.fromarray((output * 255).astype(np.uint8)) - return output - - @torch.no_grad() - def encode_image(self, image: Union[torch.Tensor, list[Image.Image]]) -> torch.Tensor: - """ - Encode the image. - - Args: - image (Union[torch.Tensor, list[Image.Image]]): The image to encode - - Returns: - torch.Tensor: The encoded features. - """ - if isinstance(image, torch.Tensor): - assert image.ndim == 4, "Image tensor should be batched (B, C, H, W)" - elif isinstance(image, list): - assert all(isinstance(i, Image.Image) for i in image), "Image list should be list of PIL images" - image = [i.resize((518, 518), Image.LANCZOS) for i in image] - image = [np.array(i.convert('RGB')).astype(np.float32) / 255 for i in image] - image = [torch.from_numpy(i).permute(2, 0, 1).float() for i in image] - image = torch.stack(image).to(self.device) - else: - raise ValueError(f"Unsupported type of image: {type(image)}") - - image = self.image_cond_model_transform(image).to(self.device) - features = self.models['image_cond_model'](image, is_training=True)['x_prenorm'] - patchtokens = F.layer_norm(features, features.shape[-1:]) - return patchtokens - - def get_cond(self, image: Union[torch.Tensor, list[Image.Image]]) -> dict: - """ - Get the conditioning information for the model. - - Args: - image (Union[torch.Tensor, list[Image.Image]]): The image prompts. - - Returns: - dict: The conditioning information - """ - cond = self.encode_image(image) - neg_cond = torch.zeros_like(cond) - return { - 'cond': cond, - 'neg_cond': neg_cond, - } - - def sample_sparse_structure( - self, - cond: dict, - num_samples: int = 1, - sampler_params: dict = {}, - ) -> torch.Tensor: - """ - Sample sparse structures with the given conditioning. - - Args: - cond (dict): The conditioning information. - num_samples (int): The number of samples to generate. - sampler_params (dict): Additional parameters for the sampler. - """ - # Sample occupancy latent - flow_model = self.models['sparse_structure_flow_model'] - reso = flow_model.resolution - noise = torch.randn(num_samples, flow_model.in_channels, reso, reso, reso).to(self.device) - sampler_params = {**self.sparse_structure_sampler_params, **sampler_params} - z_s = self.sparse_structure_sampler.sample( - flow_model, - noise, - **cond, - **sampler_params, - verbose=True - ).samples - - # Decode occupancy latent - decoder = self.models['sparse_structure_decoder'] - coords = torch.argwhere(decoder(z_s)>0)[:, [0, 2, 3, 4]].int() - - return coords - - def decode_slat( - self, - slat: sp.SparseTensor, - formats: List[str] = ['mesh', 'gaussian', 'radiance_field'], - ) -> dict: - """ - Decode the structured latent. - - Args: - slat (sp.SparseTensor): The structured latent. - formats (List[str]): The formats to decode the structured latent to. - - Returns: - dict: The decoded structured latent. - """ - ret = {} - if 'mesh' in formats: - ret['mesh'] = self.models['slat_decoder_mesh'](slat) - if 'gaussian' in formats: - ret['gaussian'] = self.models['slat_decoder_gs'](slat) - if 'radiance_field' in formats: - ret['radiance_field'] = self.models['slat_decoder_rf'](slat) - return ret - - def sample_slat( - self, - cond: dict, - coords: torch.Tensor, - sampler_params: dict = {}, - ) -> sp.SparseTensor: - """ - Sample structured latent with the given conditioning. - - Args: - cond (dict): The conditioning information. - coords (torch.Tensor): The coordinates of the sparse structure. - sampler_params (dict): Additional parameters for the sampler. - """ - # Sample structured latent - flow_model = self.models['slat_flow_model'] - noise = sp.SparseTensor( - feats=torch.randn(coords.shape[0], flow_model.in_channels).to(self.device), - coords=coords, - ) - sampler_params = {**self.slat_sampler_params, **sampler_params} - slat = self.slat_sampler.sample( - flow_model, - noise, - **cond, - **sampler_params, - verbose=True - ).samples - - std = torch.tensor(self.slat_normalization['std'])[None].to(slat.device) - mean = torch.tensor(self.slat_normalization['mean'])[None].to(slat.device) - slat = slat * std + mean - - return slat - - @torch.no_grad() - def run( - self, - image: Image.Image, - num_samples: int = 1, - seed: int = 42, - sparse_structure_sampler_params: dict = {}, - slat_sampler_params: dict = {}, - formats: List[str] = ['mesh', 'gaussian', 'radiance_field'], - preprocess_image: bool = True, - ) -> dict: - """ - Run the pipeline. - - Args: - image (Image.Image): The image prompt. - num_samples (int): The number of samples to generate. - sparse_structure_sampler_params (dict): Additional parameters for the sparse structure sampler. - slat_sampler_params (dict): Additional parameters for the structured latent sampler. - preprocess_image (bool): Whether to preprocess the image. - """ - if preprocess_image: - image = self.preprocess_image(image) - cond = self.get_cond([image]) - torch.manual_seed(seed) - coords = self.sample_sparse_structure(cond, num_samples, sparse_structure_sampler_params) - slat = self.sample_slat(cond, coords, slat_sampler_params) - return self.decode_slat(slat, formats) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/renderers/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/renderers/__init__.py deleted file mode 100644 index 0339355c..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/renderers/__init__.py +++ /dev/null @@ -1,31 +0,0 @@ -import importlib - -__attributes = { - 'OctreeRenderer': 'octree_renderer', - 'GaussianRenderer': 'gaussian_render', - 'MeshRenderer': 'mesh_renderer', -} - -__submodules = [] - -__all__ = list(__attributes.keys()) + __submodules - -def __getattr__(name): - if name not in globals(): - if name in __attributes: - module_name = __attributes[name] - module = importlib.import_module(f".{module_name}", __name__) - globals()[name] = getattr(module, name) - elif name in __submodules: - module = importlib.import_module(f".{name}", __name__) - globals()[name] = module - else: - raise AttributeError(f"module {__name__} has no attribute {name}") - return globals()[name] - - -# For Pylance -if __name__ == '__main__': - from .octree_renderer import OctreeRenderer - from .gaussian_render import GaussianRenderer - from .mesh_renderer import MeshRenderer \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/renderers/gaussian_render.py b/Gen_3D_Modules/TRELLIS/trellis_/renderers/gaussian_render.py deleted file mode 100644 index ef3ef8c4..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/renderers/gaussian_render.py +++ /dev/null @@ -1,235 +0,0 @@ -# -# Copyright (C) 2023, Inria -# GRAPHDECO research group, https://team.inria.fr/graphdeco -# All rights reserved. -# -# This software is free for non-commercial, research and evaluation use -# under the terms of the LICENSE.md file. -# -# For inquiries contact george.drettakis@inria.fr -# - -import torch -import math -from easydict import EasyDict as edict -import numpy as np -from ..representations.gaussian import Gaussian -from .sh_utils import eval_sh -import torch.nn.functional as F -from easydict import EasyDict as edict - - -def intrinsics_to_projection( - intrinsics: torch.Tensor, - near: float, - far: float, - ) -> torch.Tensor: - """ - OpenCV intrinsics to OpenGL perspective matrix - - Args: - intrinsics (torch.Tensor): [3, 3] OpenCV intrinsics matrix - near (float): near plane to clip - far (float): far plane to clip - Returns: - (torch.Tensor): [4, 4] OpenGL perspective matrix - """ - fx, fy = intrinsics[0, 0], intrinsics[1, 1] - cx, cy = intrinsics[0, 2], intrinsics[1, 2] - ret = torch.zeros((4, 4), dtype=intrinsics.dtype, device=intrinsics.device) - ret[0, 0] = 2 * fx - ret[1, 1] = 2 * fy - ret[0, 2] = 2 * cx - 1 - ret[1, 2] = - 2 * cy + 1 - ret[2, 2] = far / (far - near) - ret[2, 3] = near * far / (near - far) - ret[3, 2] = 1. - return ret - - -def render(viewpoint_camera, pc : Gaussian, pipe, bg_color : torch.Tensor, scaling_modifier = 1.0, override_color = None): - """ - Render the scene. - - Background tensor (bg_color) must be on GPU! - - Original code use the Differential Gaussian Rasterization from https://github.com/autonomousvision/mip-splatting/tree/main/submodules/diff-gaussian-rasterization - Modified to use the GaussianRasterizer from https://github.com/ashawkey/diff-gaussian-rasterization - Only changes are the inputs to GaussianRasterizationSettings: kernel_size and subpixel_offset are commented out. - """ - # lazy import - if 'GaussianRasterizer' not in globals(): - from diff_gaussian_rasterization import GaussianRasterizer, GaussianRasterizationSettings - - # Create zero tensor. We will use it to make pytorch return gradients of the 2D (screen-space) means - screenspace_points = torch.zeros_like(pc.get_xyz, dtype=pc.get_xyz.dtype, requires_grad=True, device="cuda") + 0 - try: - screenspace_points.retain_grad() - except: - pass - # Set up rasterization configuration - tanfovx = math.tan(viewpoint_camera.FoVx * 0.5) - tanfovy = math.tan(viewpoint_camera.FoVy * 0.5) - - #kernel_size = pipe.kernel_size - #subpixel_offset = torch.zeros((int(viewpoint_camera.image_height), int(viewpoint_camera.image_width), 2), dtype=torch.float32, device="cuda") - - raster_settings = GaussianRasterizationSettings( - image_height=int(viewpoint_camera.image_height), - image_width=int(viewpoint_camera.image_width), - tanfovx=tanfovx, - tanfovy=tanfovy, - #kernel_size=kernel_size, - #subpixel_offset=subpixel_offset, - bg=bg_color, - scale_modifier=scaling_modifier, - viewmatrix=viewpoint_camera.world_view_transform, - projmatrix=viewpoint_camera.full_proj_transform, - sh_degree=pc.active_sh_degree, - campos=viewpoint_camera.camera_center, - prefiltered=False, - debug=pipe.debug - ) - - rasterizer = GaussianRasterizer(raster_settings=raster_settings) - - means3D = pc.get_xyz - means2D = screenspace_points - opacity = pc.get_opacity - - # If precomputed 3d covariance is provided, use it. If not, then it will be computed from - # scaling / rotation by the rasterizer. - scales = None - rotations = None - cov3D_precomp = None - if pipe.compute_cov3D_python: - cov3D_precomp = pc.get_covariance(scaling_modifier) - else: - scales = pc.get_scaling - rotations = pc.get_rotation - - # If precomputed colors are provided, use them. Otherwise, if it is desired to precompute colors - # from SHs in Python, do it. If not, then SH -> RGB conversion will be done by rasterizer. - shs = None - colors_precomp = None - if override_color is None: - if pipe.convert_SHs_python: - shs_view = pc.get_features.transpose(1, 2).view(-1, 3, (pc.max_sh_degree+1)**2) - dir_pp = (pc.get_xyz - viewpoint_camera.camera_center.repeat(pc.get_features.shape[0], 1)) - dir_pp_normalized = dir_pp/dir_pp.norm(dim=1, keepdim=True) - sh2rgb = eval_sh(pc.active_sh_degree, shs_view, dir_pp_normalized) - colors_precomp = torch.clamp_min(sh2rgb + 0.5, 0.0) - else: - shs = pc.get_features - else: - colors_precomp = override_color - - # Rasterize visible Gaussians to image, obtain their radii (on screen). - rendered_image, radii, rendered_depth, rendered_alpha = rasterizer( - means3D = means3D, - means2D = means2D, - shs = shs, - colors_precomp = colors_precomp, - opacities = opacity, - scales = scales, - rotations = rotations, - cov3D_precomp = cov3D_precomp - ) - - # Those Gaussians that were frustum culled or had a radius of 0 were not visible. - # They will be excluded from value updates used in the splitting criteria. - return edict({"render": rendered_image, - "viewspace_points": screenspace_points, - "visibility_filter" : radii > 0, - "radii": radii}) - - -class GaussianRenderer: - """ - Renderer for the Voxel representation. - - Args: - rendering_options (dict): Rendering options. - """ - - def __init__(self, rendering_options={}) -> None: - self.pipe = edict({ - "kernel_size": 0.1, - "convert_SHs_python": False, - "compute_cov3D_python": False, - "scale_modifier": 1.0, - "debug": False - }) - self.rendering_options = edict({ - "resolution": None, - "near": None, - "far": None, - "ssaa": 1, - "bg_color": 'random', - }) - self.rendering_options.update(rendering_options) - self.bg_color = None - - def render( - self, - gausssian: Gaussian, - extrinsics: torch.Tensor, - intrinsics: torch.Tensor, - colors_overwrite: torch.Tensor = None - ) -> edict: - """ - Render the gausssian. - - Args: - gaussian : gaussianmodule - extrinsics (torch.Tensor): (4, 4) camera extrinsics - intrinsics (torch.Tensor): (3, 3) camera intrinsics - colors_overwrite (torch.Tensor): (N, 3) override color - - Returns: - edict containing: - color (torch.Tensor): (3, H, W) rendered color image - """ - resolution = self.rendering_options["resolution"] - near = self.rendering_options["near"] - far = self.rendering_options["far"] - ssaa = self.rendering_options["ssaa"] - - if self.rendering_options["bg_color"] == 'random': - self.bg_color = torch.zeros(3, dtype=torch.float32, device="cuda") - if np.random.rand() < 0.5: - self.bg_color += 1 - else: - self.bg_color = torch.tensor(self.rendering_options["bg_color"], dtype=torch.float32, device="cuda") - - view = extrinsics - perspective = intrinsics_to_projection(intrinsics, near, far) - camera = torch.inverse(view)[:3, 3] - focalx = intrinsics[0, 0] - focaly = intrinsics[1, 1] - fovx = 2 * torch.atan(0.5 / focalx) - fovy = 2 * torch.atan(0.5 / focaly) - - camera_dict = edict({ - "image_height": resolution * ssaa, - "image_width": resolution * ssaa, - "FoVx": fovx, - "FoVy": fovy, - "znear": near, - "zfar": far, - "world_view_transform": view.T.contiguous(), - "projection_matrix": perspective.T.contiguous(), - "full_proj_transform": (perspective @ view).T.contiguous(), - "camera_center": camera - }) - - # Render - render_ret = render(camera_dict, gausssian, self.pipe, self.bg_color, override_color=colors_overwrite, scaling_modifier=self.pipe.scale_modifier) - - if ssaa > 1: - render_ret.render = F.interpolate(render_ret.render[None], size=(resolution, resolution), mode='bilinear', align_corners=False, antialias=True).squeeze() - - ret = edict({ - 'color': render_ret['render'] - }) - return ret diff --git a/Gen_3D_Modules/TRELLIS/trellis_/renderers/mesh_renderer.py b/Gen_3D_Modules/TRELLIS/trellis_/renderers/mesh_renderer.py deleted file mode 100644 index b504fa4d..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/renderers/mesh_renderer.py +++ /dev/null @@ -1,133 +0,0 @@ -import torch -import nvdiffrast.torch as dr -from easydict import EasyDict as edict -from ..representations.mesh import MeshExtractResult -import torch.nn.functional as F - - -def intrinsics_to_projection( - intrinsics: torch.Tensor, - near: float, - far: float, - ) -> torch.Tensor: - """ - OpenCV intrinsics to OpenGL perspective matrix - - Args: - intrinsics (torch.Tensor): [3, 3] OpenCV intrinsics matrix - near (float): near plane to clip - far (float): far plane to clip - Returns: - (torch.Tensor): [4, 4] OpenGL perspective matrix - """ - fx, fy = intrinsics[0, 0], intrinsics[1, 1] - cx, cy = intrinsics[0, 2], intrinsics[1, 2] - ret = torch.zeros((4, 4), dtype=intrinsics.dtype, device=intrinsics.device) - ret[0, 0] = 2 * fx - ret[1, 1] = 2 * fy - ret[0, 2] = 2 * cx - 1 - ret[1, 2] = - 2 * cy + 1 - ret[2, 2] = far / (far - near) - ret[2, 3] = near * far / (near - far) - ret[3, 2] = 1. - return ret - - -class MeshRenderer: - """ - Renderer for the Mesh representation. - - Args: - rendering_options (dict): Rendering options. - glctx (nvdiffrast.torch.RasterizeGLContext): RasterizeGLContext object for CUDA/OpenGL interop. - """ - def __init__(self, rendering_options={}, device='cuda'): - self.rendering_options = edict({ - "resolution": None, - "near": None, - "far": None, - "ssaa": 1 - }) - self.rendering_options.update(rendering_options) - self.glctx = dr.RasterizeCudaContext(device=device) - self.device=device - - def render( - self, - mesh : MeshExtractResult, - extrinsics: torch.Tensor, - intrinsics: torch.Tensor, - return_types = ["mask", "normal", "depth"] - ) -> edict: - """ - Render the mesh. - - Args: - mesh : meshmodel - extrinsics (torch.Tensor): (4, 4) camera extrinsics - intrinsics (torch.Tensor): (3, 3) camera intrinsics - return_types (list): list of return types, can be "mask", "depth", "normal_map", "normal", "color" - - Returns: - edict based on return_types containing: - color (torch.Tensor): [3, H, W] rendered color image - depth (torch.Tensor): [H, W] rendered depth image - normal (torch.Tensor): [3, H, W] rendered normal image - normal_map (torch.Tensor): [3, H, W] rendered normal map image - mask (torch.Tensor): [H, W] rendered mask image - """ - resolution = self.rendering_options["resolution"] - near = self.rendering_options["near"] - far = self.rendering_options["far"] - ssaa = self.rendering_options["ssaa"] - - if mesh.vertices.shape[0] == 0 or mesh.faces.shape[0] == 0: - default_img = torch.zeros((1, resolution, resolution, 3), dtype=torch.float32, device=self.device) - ret_dict = {k : default_img if k in ['normal', 'normal_map', 'color'] else default_img[..., :1] for k in return_types} - return ret_dict - - perspective = intrinsics_to_projection(intrinsics, near, far) - - RT = extrinsics.unsqueeze(0) - full_proj = (perspective @ extrinsics).unsqueeze(0) - - vertices = mesh.vertices.unsqueeze(0) - - vertices_homo = torch.cat([vertices, torch.ones_like(vertices[..., :1])], dim=-1) - vertices_camera = torch.bmm(vertices_homo, RT.transpose(-1, -2)) - vertices_clip = torch.bmm(vertices_homo, full_proj.transpose(-1, -2)) - faces_int = mesh.faces.int() - rast, _ = dr.rasterize( - self.glctx, vertices_clip, faces_int, (resolution * ssaa, resolution * ssaa)) - - out_dict = edict() - for type in return_types: - img = None - if type == "mask" : - img = dr.antialias((rast[..., -1:] > 0).float(), rast, vertices_clip, faces_int) - elif type == "depth": - img = dr.interpolate(vertices_camera[..., 2:3].contiguous(), rast, faces_int)[0] - img = dr.antialias(img, rast, vertices_clip, faces_int) - elif type == "normal" : - img = dr.interpolate( - mesh.face_normal.reshape(1, -1, 3), rast, - torch.arange(mesh.faces.shape[0] * 3, device=self.device, dtype=torch.int).reshape(-1, 3) - )[0] - img = dr.antialias(img, rast, vertices_clip, faces_int) - # normalize norm pictures - img = (img + 1) / 2 - elif type == "normal_map" : - img = dr.interpolate(mesh.vertex_attrs[:, 3:].contiguous(), rast, faces_int)[0] - img = dr.antialias(img, rast, vertices_clip, faces_int) - elif type == "color" : - img = dr.interpolate(mesh.vertex_attrs[:, :3].contiguous(), rast, faces_int)[0] - img = dr.antialias(img, rast, vertices_clip, faces_int) - - if ssaa > 1: - img = F.interpolate(img.permute(0, 3, 1, 2), (resolution, resolution), mode='bilinear', align_corners=False, antialias=True) - img = img.squeeze() - else: - img = img.permute(0, 3, 1, 2).squeeze() - out_dict[type] = img - - return out_dict diff --git a/Gen_3D_Modules/TRELLIS/trellis_/renderers/octree_renderer.py b/Gen_3D_Modules/TRELLIS/trellis_/renderers/octree_renderer.py deleted file mode 100644 index 136069cd..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/renderers/octree_renderer.py +++ /dev/null @@ -1,300 +0,0 @@ -import numpy as np -import torch -import torch.nn.functional as F -import math -import cv2 -from scipy.stats import qmc -from easydict import EasyDict as edict -from ..representations.octree import DfsOctree - - -def intrinsics_to_projection( - intrinsics: torch.Tensor, - near: float, - far: float, - ) -> torch.Tensor: - """ - OpenCV intrinsics to OpenGL perspective matrix - - Args: - intrinsics (torch.Tensor): [3, 3] OpenCV intrinsics matrix - near (float): near plane to clip - far (float): far plane to clip - Returns: - (torch.Tensor): [4, 4] OpenGL perspective matrix - """ - fx, fy = intrinsics[0, 0], intrinsics[1, 1] - cx, cy = intrinsics[0, 2], intrinsics[1, 2] - ret = torch.zeros((4, 4), dtype=intrinsics.dtype, device=intrinsics.device) - ret[0, 0] = 2 * fx - ret[1, 1] = 2 * fy - ret[0, 2] = 2 * cx - 1 - ret[1, 2] = - 2 * cy + 1 - ret[2, 2] = far / (far - near) - ret[2, 3] = near * far / (near - far) - ret[3, 2] = 1. - return ret - - -def render(viewpoint_camera, octree : DfsOctree, pipe, bg_color : torch.Tensor, scaling_modifier = 1.0, used_rank = None, colors_overwrite = None, aux=None, halton_sampler=None): - """ - Render the scene. - - Background tensor (bg_color) must be on GPU! - """ - # lazy import - if 'OctreeTrivecRasterizer' not in globals(): - from diffoctreerast import OctreeVoxelRasterizer, OctreeGaussianRasterizer, OctreeTrivecRasterizer, OctreeDecoupolyRasterizer - - # Set up rasterization configuration - tanfovx = math.tan(viewpoint_camera.FoVx * 0.5) - tanfovy = math.tan(viewpoint_camera.FoVy * 0.5) - - raster_settings = edict( - image_height=int(viewpoint_camera.image_height), - image_width=int(viewpoint_camera.image_width), - tanfovx=tanfovx, - tanfovy=tanfovy, - bg=bg_color, - scale_modifier=scaling_modifier, - viewmatrix=viewpoint_camera.world_view_transform, - projmatrix=viewpoint_camera.full_proj_transform, - sh_degree=octree.active_sh_degree, - campos=viewpoint_camera.camera_center, - with_distloss=pipe.with_distloss, - jitter=pipe.jitter, - debug=pipe.debug, - ) - - positions = octree.get_xyz - if octree.primitive == "voxel": - densities = octree.get_density - elif octree.primitive == "gaussian": - opacities = octree.get_opacity - elif octree.primitive == "trivec": - trivecs = octree.get_trivec - densities = octree.get_density - raster_settings.density_shift = octree.density_shift - elif octree.primitive == "decoupoly": - decoupolys_V, decoupolys_g = octree.get_decoupoly - densities = octree.get_density - raster_settings.density_shift = octree.density_shift - else: - raise ValueError(f"Unknown primitive {octree.primitive}") - depths = octree.get_depth - - # If precomputed colors are provided, use them. Otherwise, if it is desired to precompute colors - # from SHs in Python, do it. If not, then SH -> RGB conversion will be done by rasterizer. - colors_precomp = None - shs = octree.get_features - if octree.primitive in ["voxel", "gaussian"] and colors_overwrite is not None: - colors_precomp = colors_overwrite - shs = None - - ret = edict() - - if octree.primitive == "voxel": - renderer = OctreeVoxelRasterizer(raster_settings=raster_settings) - rgb, depth, alpha, distloss = renderer( - positions = positions, - densities = densities, - shs = shs, - colors_precomp = colors_precomp, - depths = depths, - aabb = octree.aabb, - aux = aux, - ) - ret['rgb'] = rgb - ret['depth'] = depth - ret['alpha'] = alpha - ret['distloss'] = distloss - elif octree.primitive == "gaussian": - renderer = OctreeGaussianRasterizer(raster_settings=raster_settings) - rgb, depth, alpha = renderer( - positions = positions, - opacities = opacities, - shs = shs, - colors_precomp = colors_precomp, - depths = depths, - aabb = octree.aabb, - aux = aux, - ) - ret['rgb'] = rgb - ret['depth'] = depth - ret['alpha'] = alpha - elif octree.primitive == "trivec": - raster_settings.used_rank = used_rank if used_rank is not None else trivecs.shape[1] - renderer = OctreeTrivecRasterizer(raster_settings=raster_settings) - rgb, depth, alpha, percent_depth = renderer( - positions = positions, - trivecs = trivecs, - densities = densities, - shs = shs, - colors_precomp = colors_precomp, - colors_overwrite = colors_overwrite, - depths = depths, - aabb = octree.aabb, - aux = aux, - halton_sampler = halton_sampler, - ) - ret['percent_depth'] = percent_depth - ret['rgb'] = rgb - ret['depth'] = depth - ret['alpha'] = alpha - elif octree.primitive == "decoupoly": - raster_settings.used_rank = used_rank if used_rank is not None else decoupolys_V.shape[1] - renderer = OctreeDecoupolyRasterizer(raster_settings=raster_settings) - rgb, depth, alpha = renderer( - positions = positions, - decoupolys_V = decoupolys_V, - decoupolys_g = decoupolys_g, - densities = densities, - shs = shs, - colors_precomp = colors_precomp, - depths = depths, - aabb = octree.aabb, - aux = aux, - ) - ret['rgb'] = rgb - ret['depth'] = depth - ret['alpha'] = alpha - - return ret - - -class OctreeRenderer: - """ - Renderer for the Voxel representation. - - Args: - rendering_options (dict): Rendering options. - """ - - def __init__(self, rendering_options={}) -> None: - try: - import diffoctreerast - except ImportError: - print("\033[93m[WARNING] diffoctreerast is not installed. The renderer will be disabled.\033[0m") - self.unsupported = True - else: - self.unsupported = False - - self.pipe = edict({ - "with_distloss": False, - "with_aux": False, - "scale_modifier": 1.0, - "used_rank": None, - "jitter": False, - "debug": False, - }) - self.rendering_options = edict({ - "resolution": None, - "near": None, - "far": None, - "ssaa": 1, - "bg_color": 'random', - }) - self.halton_sampler = qmc.Halton(2, scramble=False) - self.rendering_options.update(rendering_options) - self.bg_color = None - - def render( - self, - octree: DfsOctree, - extrinsics: torch.Tensor, - intrinsics: torch.Tensor, - colors_overwrite: torch.Tensor = None, - ) -> edict: - """ - Render the octree. - - Args: - octree (Octree): octree - extrinsics (torch.Tensor): (4, 4) camera extrinsics - intrinsics (torch.Tensor): (3, 3) camera intrinsics - colors_overwrite (torch.Tensor): (N, 3) override color - - Returns: - edict containing: - color (torch.Tensor): (3, H, W) rendered color - depth (torch.Tensor): (H, W) rendered depth - alpha (torch.Tensor): (H, W) rendered alpha - distloss (Optional[torch.Tensor]): (H, W) rendered distance loss - percent_depth (Optional[torch.Tensor]): (H, W) rendered percent depth - aux (Optional[edict]): auxiliary tensors - """ - resolution = self.rendering_options["resolution"] - near = self.rendering_options["near"] - far = self.rendering_options["far"] - ssaa = self.rendering_options["ssaa"] - - if self.unsupported: - image = np.zeros((512, 512, 3), dtype=np.uint8) - text_bbox = cv2.getTextSize("Unsupported", cv2.FONT_HERSHEY_SIMPLEX, 2, 3)[0] - origin = (512 - text_bbox[0]) // 2, (512 - text_bbox[1]) // 2 - image = cv2.putText(image, "Unsupported", origin, cv2.FONT_HERSHEY_SIMPLEX, 2, (255, 255, 255), 3, cv2.LINE_AA) - return { - 'color': torch.tensor(image, dtype=torch.float32).permute(2, 0, 1) / 255, - } - - if self.rendering_options["bg_color"] == 'random': - self.bg_color = torch.zeros(3, dtype=torch.float32, device="cuda") - if np.random.rand() < 0.5: - self.bg_color += 1 - else: - self.bg_color = torch.tensor(self.rendering_options["bg_color"], dtype=torch.float32, device="cuda") - - if self.pipe["with_aux"]: - aux = { - 'grad_color2': torch.zeros((octree.num_leaf_nodes, 3), dtype=torch.float32, requires_grad=True, device="cuda") + 0, - 'contributions': torch.zeros((octree.num_leaf_nodes, 1), dtype=torch.float32, requires_grad=True, device="cuda") + 0, - } - for k in aux.keys(): - aux[k].requires_grad_() - aux[k].retain_grad() - else: - aux = None - - view = extrinsics - perspective = intrinsics_to_projection(intrinsics, near, far) - camera = torch.inverse(view)[:3, 3] - focalx = intrinsics[0, 0] - focaly = intrinsics[1, 1] - fovx = 2 * torch.atan(0.5 / focalx) - fovy = 2 * torch.atan(0.5 / focaly) - - camera_dict = edict({ - "image_height": resolution * ssaa, - "image_width": resolution * ssaa, - "FoVx": fovx, - "FoVy": fovy, - "znear": near, - "zfar": far, - "world_view_transform": view.T.contiguous(), - "projection_matrix": perspective.T.contiguous(), - "full_proj_transform": (perspective @ view).T.contiguous(), - "camera_center": camera - }) - - # Render - render_ret = render(camera_dict, octree, self.pipe, self.bg_color, aux=aux, colors_overwrite=colors_overwrite, scaling_modifier=self.pipe.scale_modifier, used_rank=self.pipe.used_rank, halton_sampler=self.halton_sampler) - - if ssaa > 1: - render_ret.rgb = F.interpolate(render_ret.rgb[None], size=(resolution, resolution), mode='bilinear', align_corners=False, antialias=True).squeeze() - render_ret.depth = F.interpolate(render_ret.depth[None, None], size=(resolution, resolution), mode='bilinear', align_corners=False, antialias=True).squeeze() - render_ret.alpha = F.interpolate(render_ret.alpha[None, None], size=(resolution, resolution), mode='bilinear', align_corners=False, antialias=True).squeeze() - if hasattr(render_ret, 'percent_depth'): - render_ret.percent_depth = F.interpolate(render_ret.percent_depth[None, None], size=(resolution, resolution), mode='bilinear', align_corners=False, antialias=True).squeeze() - - ret = edict({ - 'color': render_ret.rgb, - 'depth': render_ret.depth, - 'alpha': render_ret.alpha, - }) - if self.pipe["with_distloss"] and 'distloss' in render_ret: - ret['distloss'] = render_ret.distloss - if self.pipe["with_aux"]: - ret['aux'] = aux - if hasattr(render_ret, 'percent_depth'): - ret['percent_depth'] = render_ret.percent_depth - return ret diff --git a/Gen_3D_Modules/TRELLIS/trellis_/renderers/sh_utils.py b/Gen_3D_Modules/TRELLIS/trellis_/renderers/sh_utils.py deleted file mode 100644 index bbca7d19..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/renderers/sh_utils.py +++ /dev/null @@ -1,118 +0,0 @@ -# Copyright 2021 The PlenOctree Authors. -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: -# -# 1. Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE -# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -# POSSIBILITY OF SUCH DAMAGE. - -import torch - -C0 = 0.28209479177387814 -C1 = 0.4886025119029199 -C2 = [ - 1.0925484305920792, - -1.0925484305920792, - 0.31539156525252005, - -1.0925484305920792, - 0.5462742152960396 -] -C3 = [ - -0.5900435899266435, - 2.890611442640554, - -0.4570457994644658, - 0.3731763325901154, - -0.4570457994644658, - 1.445305721320277, - -0.5900435899266435 -] -C4 = [ - 2.5033429417967046, - -1.7701307697799304, - 0.9461746957575601, - -0.6690465435572892, - 0.10578554691520431, - -0.6690465435572892, - 0.47308734787878004, - -1.7701307697799304, - 0.6258357354491761, -] - - -def eval_sh(deg, sh, dirs): - """ - Evaluate spherical harmonics at unit directions - using hardcoded SH polynomials. - Works with torch/np/jnp. - ... Can be 0 or more batch dimensions. - Args: - deg: int SH deg. Currently, 0-3 supported - sh: jnp.ndarray SH coeffs [..., C, (deg + 1) ** 2] - dirs: jnp.ndarray unit directions [..., 3] - Returns: - [..., C] - """ - assert deg <= 4 and deg >= 0 - coeff = (deg + 1) ** 2 - assert sh.shape[-1] >= coeff - - result = C0 * sh[..., 0] - if deg > 0: - x, y, z = dirs[..., 0:1], dirs[..., 1:2], dirs[..., 2:3] - result = (result - - C1 * y * sh[..., 1] + - C1 * z * sh[..., 2] - - C1 * x * sh[..., 3]) - - if deg > 1: - xx, yy, zz = x * x, y * y, z * z - xy, yz, xz = x * y, y * z, x * z - result = (result + - C2[0] * xy * sh[..., 4] + - C2[1] * yz * sh[..., 5] + - C2[2] * (2.0 * zz - xx - yy) * sh[..., 6] + - C2[3] * xz * sh[..., 7] + - C2[4] * (xx - yy) * sh[..., 8]) - - if deg > 2: - result = (result + - C3[0] * y * (3 * xx - yy) * sh[..., 9] + - C3[1] * xy * z * sh[..., 10] + - C3[2] * y * (4 * zz - xx - yy)* sh[..., 11] + - C3[3] * z * (2 * zz - 3 * xx - 3 * yy) * sh[..., 12] + - C3[4] * x * (4 * zz - xx - yy) * sh[..., 13] + - C3[5] * z * (xx - yy) * sh[..., 14] + - C3[6] * x * (xx - 3 * yy) * sh[..., 15]) - - if deg > 3: - result = (result + C4[0] * xy * (xx - yy) * sh[..., 16] + - C4[1] * yz * (3 * xx - yy) * sh[..., 17] + - C4[2] * xy * (7 * zz - 1) * sh[..., 18] + - C4[3] * yz * (7 * zz - 3) * sh[..., 19] + - C4[4] * (zz * (35 * zz - 30) + 3) * sh[..., 20] + - C4[5] * xz * (7 * zz - 3) * sh[..., 21] + - C4[6] * (xx - yy) * (7 * zz - 1) * sh[..., 22] + - C4[7] * xz * (xx - 3 * yy) * sh[..., 23] + - C4[8] * (xx * (xx - 3 * yy) - yy * (3 * xx - yy)) * sh[..., 24]) - return result - -def RGB2SH(rgb): - return (rgb - 0.5) / C0 - -def SH2RGB(sh): - return sh * C0 + 0.5 \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/representations/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/representations/__init__.py deleted file mode 100644 index 549ffdb9..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/representations/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .radiance_field import Strivec -from .octree import DfsOctree as Octree -from .gaussian import Gaussian -from .mesh import MeshExtractResult diff --git a/Gen_3D_Modules/TRELLIS/trellis_/representations/gaussian/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/representations/gaussian/__init__.py deleted file mode 100644 index e3de6e18..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/representations/gaussian/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .gaussian_model import Gaussian \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/representations/gaussian/gaussian_model.py b/Gen_3D_Modules/TRELLIS/trellis_/representations/gaussian/gaussian_model.py deleted file mode 100644 index 2dc70552..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/representations/gaussian/gaussian_model.py +++ /dev/null @@ -1,194 +0,0 @@ -import torch -import numpy as np -from plyfile import PlyData, PlyElement -from .general_utils import inverse_sigmoid, strip_symmetric, build_scaling_rotation - - -class Gaussian: - def __init__( - self, - aabb : list, - sh_degree : int = 0, - mininum_kernel_size : float = 0.0, - scaling_bias : float = 0.01, - opacity_bias : float = 0.1, - scaling_activation : str = "exp", - device='cuda' - ): - self.init_params = { - 'aabb': aabb, - 'sh_degree': sh_degree, - 'mininum_kernel_size': mininum_kernel_size, - 'scaling_bias': scaling_bias, - 'opacity_bias': opacity_bias, - 'scaling_activation': scaling_activation, - } - - self.sh_degree = sh_degree - self.active_sh_degree = sh_degree - self.mininum_kernel_size = mininum_kernel_size - self.scaling_bias = scaling_bias - self.opacity_bias = opacity_bias - self.scaling_activation_type = scaling_activation - self.device = device - self.aabb = torch.tensor(aabb, dtype=torch.float32, device=device) - self.setup_functions() - - self._xyz = None - self._features_dc = None - self._features_rest = None - self._scaling = None - self._rotation = None - self._opacity = None - - def setup_functions(self): - def build_covariance_from_scaling_rotation(scaling, scaling_modifier, rotation): - L = build_scaling_rotation(scaling_modifier * scaling, rotation) - actual_covariance = L @ L.transpose(1, 2) - symm = strip_symmetric(actual_covariance) - return symm - - if self.scaling_activation_type == "exp": - self.scaling_activation = torch.exp - self.inverse_scaling_activation = torch.log - elif self.scaling_activation_type == "softplus": - self.scaling_activation = torch.nn.functional.softplus - self.inverse_scaling_activation = lambda x: x + torch.log(-torch.expm1(-x)) - - self.covariance_activation = build_covariance_from_scaling_rotation - - self.opacity_activation = torch.sigmoid - self.inverse_opacity_activation = inverse_sigmoid - - self.rotation_activation = torch.nn.functional.normalize - - self.scale_bias = self.inverse_scaling_activation(torch.tensor(self.scaling_bias)).cuda() - self.rots_bias = torch.zeros((4)).cuda() - self.rots_bias[0] = 1 - self.opacity_bias = self.inverse_opacity_activation(torch.tensor(self.opacity_bias)).cuda() - - @property - def get_scaling(self): - scales = self.scaling_activation(self._scaling + self.scale_bias) - scales = torch.square(scales) + self.mininum_kernel_size ** 2 - scales = torch.sqrt(scales) - return scales - - @property - def get_rotation(self): - return self.rotation_activation(self._rotation + self.rots_bias[None, :]) - - @property - def get_xyz(self): - return self._xyz * self.aabb[None, 3:] + self.aabb[None, :3] - - @property - def get_features(self): - return torch.cat((self._features_dc, self._features_rest), dim=2) if self._features_rest is not None else self._features_dc - - @property - def get_opacity(self): - return self.opacity_activation(self._opacity + self.opacity_bias) - - def get_covariance(self, scaling_modifier = 1): - return self.covariance_activation(self.get_scaling, scaling_modifier, self._rotation + self.rots_bias[None, :]) - - def from_scaling(self, scales): - scales = torch.sqrt(torch.square(scales) - self.mininum_kernel_size ** 2) - self._scaling = self.inverse_scaling_activation(scales) - self.scale_bias - - def from_rotation(self, rots): - self._rotation = rots - self.rots_bias[None, :] - - def from_xyz(self, xyz): - self._xyz = (xyz - self.aabb[None, :3]) / self.aabb[None, 3:] - - def from_features(self, features): - self._features_dc = features - - def from_opacity(self, opacities): - self._opacity = self.inverse_opacity_activation(opacities) - self.opacity_bias - - def construct_list_of_attributes(self): - l = ['x', 'y', 'z', 'nx', 'ny', 'nz'] - # All channels except the 3 DC - for i in range(self._features_dc.shape[1]*self._features_dc.shape[2]): - l.append('f_dc_{}'.format(i)) - l.append('opacity') - for i in range(self._scaling.shape[1]): - l.append('scale_{}'.format(i)) - for i in range(self._rotation.shape[1]): - l.append('rot_{}'.format(i)) - return l - - def save_ply(self, path): - xyz = self.get_xyz.detach().cpu().numpy() - normals = np.zeros_like(xyz) - f_dc = self._features_dc.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy() - opacities = inverse_sigmoid(self.get_opacity).detach().cpu().numpy() - scale = torch.log(self.get_scaling).detach().cpu().numpy() - rotation = (self._rotation + self.rots_bias[None, :]).detach().cpu().numpy() - - dtype_full = [(attribute, 'f4') for attribute in self.construct_list_of_attributes()] - - elements = np.empty(xyz.shape[0], dtype=dtype_full) - attributes = np.concatenate((xyz, normals, f_dc, opacities, scale, rotation), axis=1) - elements[:] = list(map(tuple, attributes)) - el = PlyElement.describe(elements, 'vertex') - PlyData([el]).write(path) - - def load_ply(self, path): - plydata = PlyData.read(path) - - xyz = np.stack((np.asarray(plydata.elements[0]["x"]), - np.asarray(plydata.elements[0]["y"]), - np.asarray(plydata.elements[0]["z"])), axis=1) - opacities = np.asarray(plydata.elements[0]["opacity"])[..., np.newaxis] - - features_dc = np.zeros((xyz.shape[0], 3, 1)) - features_dc[:, 0, 0] = np.asarray(plydata.elements[0]["f_dc_0"]) - features_dc[:, 1, 0] = np.asarray(plydata.elements[0]["f_dc_1"]) - features_dc[:, 2, 0] = np.asarray(plydata.elements[0]["f_dc_2"]) - - if self.sh_degree > 0: - extra_f_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("f_rest_")] - extra_f_names = sorted(extra_f_names, key = lambda x: int(x.split('_')[-1])) - assert len(extra_f_names)==3*(self.sh_degree + 1) ** 2 - 3 - features_extra = np.zeros((xyz.shape[0], len(extra_f_names))) - for idx, attr_name in enumerate(extra_f_names): - features_extra[:, idx] = np.asarray(plydata.elements[0][attr_name]) - # Reshape (P,F*SH_coeffs) to (P, F, SH_coeffs except DC) - features_extra = features_extra.reshape((features_extra.shape[0], 3, (self.max_sh_degree + 1) ** 2 - 1)) - - scale_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("scale_")] - scale_names = sorted(scale_names, key = lambda x: int(x.split('_')[-1])) - scales = np.zeros((xyz.shape[0], len(scale_names))) - for idx, attr_name in enumerate(scale_names): - scales[:, idx] = np.asarray(plydata.elements[0][attr_name]) - - rot_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("rot")] - rot_names = sorted(rot_names, key = lambda x: int(x.split('_')[-1])) - rots = np.zeros((xyz.shape[0], len(rot_names))) - for idx, attr_name in enumerate(rot_names): - rots[:, idx] = np.asarray(plydata.elements[0][attr_name]) - - # convert to actual gaussian attributes - xyz = torch.tensor(xyz, dtype=torch.float, device=self.device) - features_dc = torch.tensor(features_dc, dtype=torch.float, device=self.device).transpose(1, 2).contiguous() - if self.sh_degree > 0: - features_extra = torch.tensor(features_extra, dtype=torch.float, device=self.device).transpose(1, 2).contiguous() - opacities = torch.sigmoid(torch.tensor(opacities, dtype=torch.float, device=self.device)) - scales = torch.exp(torch.tensor(scales, dtype=torch.float, device=self.device)) - rots = torch.tensor(rots, dtype=torch.float, device=self.device) - - # convert to _hidden attributes - self._xyz = (xyz - self.aabb[None, :3]) / self.aabb[None, 3:] - self._features_dc = features_dc - if self.sh_degree > 0: - self._features_rest = features_extra - else: - self._features_rest = None - self._opacity = self.inverse_opacity_activation(opacities) - self.opacity_bias - self._scaling = self.inverse_scaling_activation(torch.sqrt(torch.square(scales) - self.mininum_kernel_size ** 2)) - self.scale_bias - self._rotation = rots - self.rots_bias[None, :] - \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/representations/gaussian/general_utils.py b/Gen_3D_Modules/TRELLIS/trellis_/representations/gaussian/general_utils.py deleted file mode 100644 index 541c0825..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/representations/gaussian/general_utils.py +++ /dev/null @@ -1,133 +0,0 @@ -# -# Copyright (C) 2023, Inria -# GRAPHDECO research group, https://team.inria.fr/graphdeco -# All rights reserved. -# -# This software is free for non-commercial, research and evaluation use -# under the terms of the LICENSE.md file. -# -# For inquiries contact george.drettakis@inria.fr -# - -import torch -import sys -from datetime import datetime -import numpy as np -import random - -def inverse_sigmoid(x): - return torch.log(x/(1-x)) - -def PILtoTorch(pil_image, resolution): - resized_image_PIL = pil_image.resize(resolution) - resized_image = torch.from_numpy(np.array(resized_image_PIL)) / 255.0 - if len(resized_image.shape) == 3: - return resized_image.permute(2, 0, 1) - else: - return resized_image.unsqueeze(dim=-1).permute(2, 0, 1) - -def get_expon_lr_func( - lr_init, lr_final, lr_delay_steps=0, lr_delay_mult=1.0, max_steps=1000000 -): - """ - Copied from Plenoxels - - Continuous learning rate decay function. Adapted from JaxNeRF - The returned rate is lr_init when step=0 and lr_final when step=max_steps, and - is log-linearly interpolated elsewhere (equivalent to exponential decay). - If lr_delay_steps>0 then the learning rate will be scaled by some smooth - function of lr_delay_mult, such that the initial learning rate is - lr_init*lr_delay_mult at the beginning of optimization but will be eased back - to the normal learning rate when steps>lr_delay_steps. - :param conf: config subtree 'lr' or similar - :param max_steps: int, the number of steps during optimization. - :return HoF which takes step as input - """ - - def helper(step): - if step < 0 or (lr_init == 0.0 and lr_final == 0.0): - # Disable this parameter - return 0.0 - if lr_delay_steps > 0: - # A kind of reverse cosine decay. - delay_rate = lr_delay_mult + (1 - lr_delay_mult) * np.sin( - 0.5 * np.pi * np.clip(step / lr_delay_steps, 0, 1) - ) - else: - delay_rate = 1.0 - t = np.clip(step / max_steps, 0, 1) - log_lerp = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t) - return delay_rate * log_lerp - - return helper - -def strip_lowerdiag(L): - uncertainty = torch.zeros((L.shape[0], 6), dtype=torch.float, device="cuda") - - uncertainty[:, 0] = L[:, 0, 0] - uncertainty[:, 1] = L[:, 0, 1] - uncertainty[:, 2] = L[:, 0, 2] - uncertainty[:, 3] = L[:, 1, 1] - uncertainty[:, 4] = L[:, 1, 2] - uncertainty[:, 5] = L[:, 2, 2] - return uncertainty - -def strip_symmetric(sym): - return strip_lowerdiag(sym) - -def build_rotation(r): - norm = torch.sqrt(r[:,0]*r[:,0] + r[:,1]*r[:,1] + r[:,2]*r[:,2] + r[:,3]*r[:,3]) - - q = r / norm[:, None] - - R = torch.zeros((q.size(0), 3, 3), device='cuda') - - r = q[:, 0] - x = q[:, 1] - y = q[:, 2] - z = q[:, 3] - - R[:, 0, 0] = 1 - 2 * (y*y + z*z) - R[:, 0, 1] = 2 * (x*y - r*z) - R[:, 0, 2] = 2 * (x*z + r*y) - R[:, 1, 0] = 2 * (x*y + r*z) - R[:, 1, 1] = 1 - 2 * (x*x + z*z) - R[:, 1, 2] = 2 * (y*z - r*x) - R[:, 2, 0] = 2 * (x*z - r*y) - R[:, 2, 1] = 2 * (y*z + r*x) - R[:, 2, 2] = 1 - 2 * (x*x + y*y) - return R - -def build_scaling_rotation(s, r): - L = torch.zeros((s.shape[0], 3, 3), dtype=torch.float, device="cuda") - R = build_rotation(r) - - L[:,0,0] = s[:,0] - L[:,1,1] = s[:,1] - L[:,2,2] = s[:,2] - - L = R @ L - return L - -def safe_state(silent): - old_f = sys.stdout - class F: - def __init__(self, silent): - self.silent = silent - - def write(self, x): - if not self.silent: - if x.endswith("\n"): - old_f.write(x.replace("\n", " [{}]\n".format(str(datetime.now().strftime("%d/%m %H:%M:%S"))))) - else: - old_f.write(x) - - def flush(self): - old_f.flush() - - sys.stdout = F(silent) - - random.seed(0) - np.random.seed(0) - torch.manual_seed(0) - torch.cuda.set_device(torch.device("cuda:0")) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/__init__.py deleted file mode 100644 index 38cf35c0..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .cube2mesh import SparseFeatures2Mesh, MeshExtractResult diff --git a/Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/cube2mesh.py b/Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/cube2mesh.py deleted file mode 100644 index fe2cca76..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/cube2mesh.py +++ /dev/null @@ -1,146 +0,0 @@ -import torch -from ...modules.sparse import SparseTensor -from easydict import EasyDict as edict -from .utils_cube import * -try: - from .flexicubes.flexicubes import FlexiCubes -except: - print("Please install kaolin and diso to use the mesh extractor.") - - -class MeshExtractResult: - def __init__(self, - vertices, - faces, - vertex_attrs=None, - res=64 - ): - self.vertices = vertices - self.faces = faces.long() - self.vertex_attrs = vertex_attrs - self.face_normal = self.comput_face_normals(vertices, faces) - self.res = res - self.success = (vertices.shape[0] != 0 and faces.shape[0] != 0) - - # training only - self.tsdf_v = None - self.tsdf_s = None - self.reg_loss = None - - def comput_face_normals(self, verts, faces): - i0 = faces[..., 0].long() - i1 = faces[..., 1].long() - i2 = faces[..., 2].long() - - v0 = verts[i0, :] - v1 = verts[i1, :] - v2 = verts[i2, :] - face_normals = torch.cross(v1 - v0, v2 - v0, dim=-1) - face_normals = torch.nn.functional.normalize(face_normals, dim=1) - # print(face_normals.min(), face_normals.max(), face_normals.shape) - return face_normals[:, None, :].repeat(1, 3, 1) - - def comput_v_normals(self, verts, faces): - i0 = faces[..., 0].long() - i1 = faces[..., 1].long() - i2 = faces[..., 2].long() - - v0 = verts[i0, :] - v1 = verts[i1, :] - v2 = verts[i2, :] - face_normals = torch.cross(v1 - v0, v2 - v0, dim=-1) - v_normals = torch.zeros_like(verts) - v_normals.scatter_add_(0, i0[..., None].repeat(1, 3), face_normals) - v_normals.scatter_add_(0, i1[..., None].repeat(1, 3), face_normals) - v_normals.scatter_add_(0, i2[..., None].repeat(1, 3), face_normals) - - v_normals = torch.nn.functional.normalize(v_normals, dim=1) - return v_normals - - -class SparseFeatures2Mesh: - def __init__(self, device="cuda", res=64, use_color=True): - ''' - a model to generate a mesh from sparse features structures using flexicube - ''' - super().__init__() - self.device=device - self.res = res - self.mesh_extractor = FlexiCubes(device=device) - self.sdf_bias = -1.0 / res - verts, cube = construct_dense_grid(self.res, self.device) - self.reg_c = cube.to(self.device) - self.reg_v = verts.to(self.device) - self.use_color = use_color - self._calc_layout() - - def _calc_layout(self): - LAYOUTS = { - 'sdf': {'shape': (8, 1), 'size': 8}, - 'deform': {'shape': (8, 3), 'size': 8 * 3}, - 'weights': {'shape': (21,), 'size': 21} - } - if self.use_color: - ''' - 6 channel color including normal map - ''' - LAYOUTS['color'] = {'shape': (8, 6,), 'size': 8 * 6} - self.layouts = edict(LAYOUTS) - start = 0 - for k, v in self.layouts.items(): - v['range'] = (start, start + v['size']) - start += v['size'] - self.feats_channels = start - - def get_layout(self, feats : torch.Tensor, name : str): - if name not in self.layouts: - return None - return feats[:, self.layouts[name]['range'][0]:self.layouts[name]['range'][1]].reshape(-1, *self.layouts[name]['shape']) - - def __call__(self, cubefeats : SparseTensor, training=False): - """ - Generates a mesh based on the specified sparse voxel structures. - Args: - cube_attrs [Nx21] : Sparse Tensor attrs about cube weights - verts_attrs [Nx10] : [0:1] SDF [1:4] deform [4:7] color [7:10] normal - Returns: - return the success tag and ni you loss, - """ - # add sdf bias to verts_attrs - coords = cubefeats.coords[:, 1:] - feats = cubefeats.feats - - sdf, deform, color, weights = [self.get_layout(feats, name) for name in ['sdf', 'deform', 'color', 'weights']] - sdf += self.sdf_bias - v_attrs = [sdf, deform, color] if self.use_color else [sdf, deform] - v_pos, v_attrs, reg_loss = sparse_cube2verts(coords, torch.cat(v_attrs, dim=-1), training=training) - v_attrs_d = get_dense_attrs(v_pos, v_attrs, res=self.res+1, sdf_init=True) - weights_d = get_dense_attrs(coords, weights, res=self.res, sdf_init=False) - if self.use_color: - sdf_d, deform_d, colors_d = v_attrs_d[..., 0], v_attrs_d[..., 1:4], v_attrs_d[..., 4:] - else: - sdf_d, deform_d = v_attrs_d[..., 0], v_attrs_d[..., 1:4] - colors_d = None - - x_nx3 = get_defomed_verts(self.reg_v, deform_d, self.res) - - vertices, faces, L_dev, colors = self.mesh_extractor( - voxelgrid_vertices=x_nx3, - scalar_field=sdf_d, - cube_idx=self.reg_c, - resolution=self.res, - beta=weights_d[:, :12], - alpha=weights_d[:, 12:20], - gamma_f=weights_d[:, 20], - voxelgrid_colors=colors_d, - training=training) - - mesh = MeshExtractResult(vertices=vertices, faces=faces, vertex_attrs=colors, res=self.res) - if training: - if mesh.success: - reg_loss += L_dev.mean() * 0.5 - reg_loss += (weights[:,:20]).abs().mean() * 0.2 - mesh.reg_loss = reg_loss - mesh.tsdf_v = get_defomed_verts(v_pos, v_attrs[:, 1:4], self.res) - mesh.tsdf_s = v_attrs[:, 0] - return mesh diff --git a/Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/flexicubes/flexicubes.py b/Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/flexicubes/flexicubes.py deleted file mode 100644 index 297c5137..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/flexicubes/flexicubes.py +++ /dev/null @@ -1,417 +0,0 @@ -# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited. - -import torch -from .tables import * - -__all__ = [ - 'FlexiCubes' -] - -def check_tensor(tensor, shape=None, dtype=None, device=None, throw=True): - """Check if :class:`torch.Tensor` is valid given set of criteria. - - Args: - tensor (torch.Tensor): the tensor to be tested. - shape (list or tuple of int, optional): the expected shape, - if a dimension is set at ``None`` then it's not verified. - dtype (torch.dtype, optional): the expected dtype. - device (torch.device, optional): the expected device. - throw (bool): if true (default), will throw if checks fail - - Return: - (bool) True if checks pass - """ - if shape is not None: - if len(shape) != tensor.ndim: - if throw: - raise ValueError(f"tensor have {tensor.ndim} ndim, should have {len(shape)}") - return False - for i, dim in enumerate(shape): - if dim is not None and tensor.shape[i] != dim: - if throw: - raise ValueError(f"tensor shape is {tensor.shape}, should be {shape}") - return False - if dtype is not None and dtype != tensor.dtype: - if throw: - raise TypeError(f"tensor dtype is {tensor.dtype}, should be {dtype}") - return False - if device is not None and device != tensor.device.type: - if throw: - raise TypeError(f"tensor device is {tensor.device.type}, should be {device}") - return False - return True - - -class FlexiCubes: - def __init__(self, device="cuda"): - - self.device = device - self.dmc_table = torch.tensor(dmc_table, dtype=torch.long, device=device, requires_grad=False) - self.num_vd_table = torch.tensor(num_vd_table, - dtype=torch.long, device=device, requires_grad=False) - self.check_table = torch.tensor( - check_table, - dtype=torch.long, device=device, requires_grad=False) - - self.tet_table = torch.tensor(tet_table, dtype=torch.long, device=device, requires_grad=False) - self.quad_split_1 = torch.tensor([0, 1, 2, 0, 2, 3], dtype=torch.long, device=device, requires_grad=False) - self.quad_split_2 = torch.tensor([0, 1, 3, 3, 1, 2], dtype=torch.long, device=device, requires_grad=False) - self.quad_split_train = torch.tensor( - [0, 1, 1, 2, 2, 3, 3, 0], dtype=torch.long, device=device, requires_grad=False) - - self.cube_corners = torch.tensor([[0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0], [0, 0, 1], [ - 1, 0, 1], [0, 1, 1], [1, 1, 1]], dtype=torch.float, device=device) - self.cube_corners_idx = torch.pow(2, torch.arange(8, requires_grad=False)) - self.cube_edges = torch.tensor([0, 1, 1, 5, 4, 5, 0, 4, 2, 3, 3, 7, 6, 7, 2, 6, - 2, 0, 3, 1, 7, 5, 6, 4], dtype=torch.long, device=device, requires_grad=False) - - self.edge_dir_table = torch.tensor([0, 2, 0, 2, 0, 2, 0, 2, 1, 1, 1, 1], - dtype=torch.long, device=device) - self.dir_faces_table = torch.tensor([ - [[5, 4], [3, 2], [4, 5], [2, 3]], - [[5, 4], [1, 0], [4, 5], [0, 1]], - [[3, 2], [1, 0], [2, 3], [0, 1]] - ], dtype=torch.long, device=device) - self.adj_pairs = torch.tensor([0, 1, 1, 3, 3, 2, 2, 0], dtype=torch.long, device=device) - - def __call__(self, voxelgrid_vertices, scalar_field, cube_idx, resolution, qef_reg_scale=1e-3, - weight_scale=0.99, beta=None, alpha=None, gamma_f=None, voxelgrid_colors=None, training=False): - assert torch.is_tensor(voxelgrid_vertices) and \ - check_tensor(voxelgrid_vertices, (None, 3), throw=False), \ - "'voxelgrid_vertices' should be a tensor of shape (num_vertices, 3)" - num_vertices = voxelgrid_vertices.shape[0] - assert torch.is_tensor(scalar_field) and \ - check_tensor(scalar_field, (num_vertices,), throw=False), \ - "'scalar_field' should be a tensor of shape (num_vertices,)" - assert torch.is_tensor(cube_idx) and \ - check_tensor(cube_idx, (None, 8), throw=False), \ - "'cube_idx' should be a tensor of shape (num_cubes, 8)" - num_cubes = cube_idx.shape[0] - assert beta is None or ( - torch.is_tensor(beta) and - check_tensor(beta, (num_cubes, 12), throw=False) - ), "'beta' should be a tensor of shape (num_cubes, 12)" - assert alpha is None or ( - torch.is_tensor(alpha) and - check_tensor(alpha, (num_cubes, 8), throw=False) - ), "'alpha' should be a tensor of shape (num_cubes, 8)" - assert gamma_f is None or ( - torch.is_tensor(gamma_f) and - check_tensor(gamma_f, (num_cubes,), throw=False) - ), "'gamma_f' should be a tensor of shape (num_cubes,)" - - surf_cubes, occ_fx8 = self._identify_surf_cubes(scalar_field, cube_idx) - if surf_cubes.sum() == 0: - return ( - torch.zeros((0, 3), device=self.device), - torch.zeros((0, 3), dtype=torch.long, device=self.device), - torch.zeros((0), device=self.device), - torch.zeros((0, voxelgrid_colors.shape[-1]), device=self.device) if voxelgrid_colors is not None else None - ) - beta, alpha, gamma_f = self._normalize_weights( - beta, alpha, gamma_f, surf_cubes, weight_scale) - - if voxelgrid_colors is not None: - voxelgrid_colors = torch.sigmoid(voxelgrid_colors) - - case_ids = self._get_case_id(occ_fx8, surf_cubes, resolution) - - surf_edges, idx_map, edge_counts, surf_edges_mask = self._identify_surf_edges( - scalar_field, cube_idx, surf_cubes - ) - - vd, L_dev, vd_gamma, vd_idx_map, vd_color = self._compute_vd( - voxelgrid_vertices, cube_idx[surf_cubes], surf_edges, scalar_field, - case_ids, beta, alpha, gamma_f, idx_map, qef_reg_scale, voxelgrid_colors) - vertices, faces, s_edges, edge_indices, vertices_color = self._triangulate( - scalar_field, surf_edges, vd, vd_gamma, edge_counts, idx_map, - vd_idx_map, surf_edges_mask, training, vd_color) - return vertices, faces, L_dev, vertices_color - - def _compute_reg_loss(self, vd, ue, edge_group_to_vd, vd_num_edges): - """ - Regularizer L_dev as in Equation 8 - """ - dist = torch.norm(ue - torch.index_select(input=vd, index=edge_group_to_vd, dim=0), dim=-1) - mean_l2 = torch.zeros_like(vd[:, 0]) - mean_l2 = (mean_l2).index_add_(0, edge_group_to_vd, dist) / vd_num_edges.squeeze(1).float() - mad = (dist - torch.index_select(input=mean_l2, index=edge_group_to_vd, dim=0)).abs() - return mad - - def _normalize_weights(self, beta, alpha, gamma_f, surf_cubes, weight_scale): - """ - Normalizes the given weights to be non-negative. If input weights are None, it creates and returns a set of weights of ones. - """ - n_cubes = surf_cubes.shape[0] - - if beta is not None: - beta = (torch.tanh(beta) * weight_scale + 1) - else: - beta = torch.ones((n_cubes, 12), dtype=torch.float, device=self.device) - - if alpha is not None: - alpha = (torch.tanh(alpha) * weight_scale + 1) - else: - alpha = torch.ones((n_cubes, 8), dtype=torch.float, device=self.device) - - if gamma_f is not None: - gamma_f = torch.sigmoid(gamma_f) * weight_scale + (1 - weight_scale) / 2 - else: - gamma_f = torch.ones((n_cubes), dtype=torch.float, device=self.device) - - return beta[surf_cubes], alpha[surf_cubes], gamma_f[surf_cubes] - - @torch.no_grad() - def _get_case_id(self, occ_fx8, surf_cubes, res): - """ - Obtains the ID of topology cases based on cell corner occupancy. This function resolves the - ambiguity in the Dual Marching Cubes (DMC) configurations as described in Section 1.3 of the - supplementary material. It should be noted that this function assumes a regular grid. - """ - case_ids = (occ_fx8[surf_cubes] * self.cube_corners_idx.to(self.device).unsqueeze(0)).sum(-1) - - problem_config = self.check_table.to(self.device)[case_ids] - to_check = problem_config[..., 0] == 1 - problem_config = problem_config[to_check] - if not isinstance(res, (list, tuple)): - res = [res, res, res] - - # The 'problematic_configs' only contain configurations for surface cubes. Next, we construct a 3D array, - # 'problem_config_full', to store configurations for all cubes (with default config for non-surface cubes). - # This allows efficient checking on adjacent cubes. - problem_config_full = torch.zeros(list(res) + [5], device=self.device, dtype=torch.long) - vol_idx = torch.nonzero(problem_config_full[..., 0] == 0) # N, 3 - vol_idx_problem = vol_idx[surf_cubes][to_check] - problem_config_full[vol_idx_problem[..., 0], vol_idx_problem[..., 1], vol_idx_problem[..., 2]] = problem_config - vol_idx_problem_adj = vol_idx_problem + problem_config[..., 1:4] - - within_range = ( - vol_idx_problem_adj[..., 0] >= 0) & ( - vol_idx_problem_adj[..., 0] < res[0]) & ( - vol_idx_problem_adj[..., 1] >= 0) & ( - vol_idx_problem_adj[..., 1] < res[1]) & ( - vol_idx_problem_adj[..., 2] >= 0) & ( - vol_idx_problem_adj[..., 2] < res[2]) - - vol_idx_problem = vol_idx_problem[within_range] - vol_idx_problem_adj = vol_idx_problem_adj[within_range] - problem_config = problem_config[within_range] - problem_config_adj = problem_config_full[vol_idx_problem_adj[..., 0], - vol_idx_problem_adj[..., 1], vol_idx_problem_adj[..., 2]] - # If two cubes with cases C16 and C19 share an ambiguous face, both cases are inverted. - to_invert = (problem_config_adj[..., 0] == 1) - idx = torch.arange(case_ids.shape[0], device=self.device)[to_check][within_range][to_invert] - case_ids.index_put_((idx,), problem_config[to_invert][..., -1]) - return case_ids - - @torch.no_grad() - def _identify_surf_edges(self, scalar_field, cube_idx, surf_cubes): - """ - Identifies grid edges that intersect with the underlying surface by checking for opposite signs. As each edge - can be shared by multiple cubes, this function also assigns a unique index to each surface-intersecting edge - and marks the cube edges with this index. - """ - occ_n = scalar_field < 0 - all_edges = cube_idx[surf_cubes][:, self.cube_edges].reshape(-1, 2) - unique_edges, _idx_map, counts = torch.unique(all_edges, dim=0, return_inverse=True, return_counts=True) - - unique_edges = unique_edges.long() - mask_edges = occ_n[unique_edges.reshape(-1)].reshape(-1, 2).sum(-1) == 1 - - surf_edges_mask = mask_edges[_idx_map] - counts = counts[_idx_map] - - mapping = torch.ones((unique_edges.shape[0]), dtype=torch.long, device=cube_idx.device) * -1 - mapping[mask_edges] = torch.arange(mask_edges.sum(), device=cube_idx.device) - # Shaped as [number of cubes x 12 edges per cube]. This is later used to map a cube edge to the unique index - # for a surface-intersecting edge. Non-surface-intersecting edges are marked with -1. - idx_map = mapping[_idx_map] - surf_edges = unique_edges[mask_edges] - return surf_edges, idx_map, counts, surf_edges_mask - - @torch.no_grad() - def _identify_surf_cubes(self, scalar_field, cube_idx): - """ - Identifies grid cubes that intersect with the underlying surface by checking if the signs at - all corners are not identical. - """ - occ_n = scalar_field < 0 - occ_fx8 = occ_n[cube_idx.reshape(-1)].reshape(-1, 8) - _occ_sum = torch.sum(occ_fx8, -1) - surf_cubes = (_occ_sum > 0) & (_occ_sum < 8) - return surf_cubes, occ_fx8 - - def _linear_interp(self, edges_weight, edges_x): - """ - Computes the location of zero-crossings on 'edges_x' using linear interpolation with 'edges_weight'. - """ - edge_dim = edges_weight.dim() - 2 - assert edges_weight.shape[edge_dim] == 2 - edges_weight = torch.cat([torch.index_select(input=edges_weight, index=torch.tensor(1, device=self.device), dim=edge_dim), - - torch.index_select(input=edges_weight, index=torch.tensor(0, device=self.device), dim=edge_dim)] - , edge_dim) - denominator = edges_weight.sum(edge_dim) - ue = (edges_x * edges_weight).sum(edge_dim) / denominator - return ue - - def _solve_vd_QEF(self, p_bxnx3, norm_bxnx3, c_bx3, qef_reg_scale): - p_bxnx3 = p_bxnx3.reshape(-1, 7, 3) - norm_bxnx3 = norm_bxnx3.reshape(-1, 7, 3) - c_bx3 = c_bx3.reshape(-1, 3) - A = norm_bxnx3 - B = ((p_bxnx3) * norm_bxnx3).sum(-1, keepdims=True) - - A_reg = (torch.eye(3, device=p_bxnx3.device) * qef_reg_scale).unsqueeze(0).repeat(p_bxnx3.shape[0], 1, 1) - B_reg = (qef_reg_scale * c_bx3).unsqueeze(-1) - A = torch.cat([A, A_reg], 1) - B = torch.cat([B, B_reg], 1) - dual_verts = torch.linalg.lstsq(A, B).solution.squeeze(-1) - return dual_verts - - def _compute_vd(self, voxelgrid_vertices, surf_cubes_fx8, surf_edges, scalar_field, - case_ids, beta, alpha, gamma_f, idx_map, qef_reg_scale, voxelgrid_colors): - """ - Computes the location of dual vertices as described in Section 4.2 - """ - alpha_nx12x2 = torch.index_select(input=alpha, index=self.cube_edges, dim=1).reshape(-1, 12, 2) - surf_edges_x = torch.index_select(input=voxelgrid_vertices, index=surf_edges.reshape(-1), dim=0).reshape(-1, 2, 3) - surf_edges_s = torch.index_select(input=scalar_field, index=surf_edges.reshape(-1), dim=0).reshape(-1, 2, 1) - zero_crossing = self._linear_interp(surf_edges_s, surf_edges_x) - - if voxelgrid_colors is not None: - C = voxelgrid_colors.shape[-1] - surf_edges_c = torch.index_select(input=voxelgrid_colors, index=surf_edges.reshape(-1), dim=0).reshape(-1, 2, C) - - idx_map = idx_map.reshape(-1, 12) - num_vd = torch.index_select(input=self.num_vd_table, index=case_ids, dim=0) - edge_group, edge_group_to_vd, edge_group_to_cube, vd_num_edges, vd_gamma = [], [], [], [], [] - - # if color is not None: - # vd_color = [] - - total_num_vd = 0 - vd_idx_map = torch.zeros((case_ids.shape[0], 12), dtype=torch.long, device=self.device, requires_grad=False) - - for num in torch.unique(num_vd): - cur_cubes = (num_vd == num) # consider cubes with the same numbers of vd emitted (for batching) - curr_num_vd = cur_cubes.sum() * num - curr_edge_group = self.dmc_table[case_ids[cur_cubes], :num].reshape(-1, num * 7) - curr_edge_group_to_vd = torch.arange( - curr_num_vd, device=self.device).unsqueeze(-1).repeat(1, 7) + total_num_vd - total_num_vd += curr_num_vd - curr_edge_group_to_cube = torch.arange(idx_map.shape[0], device=self.device)[ - cur_cubes].unsqueeze(-1).repeat(1, num * 7).reshape_as(curr_edge_group) - - curr_mask = (curr_edge_group != -1) - edge_group.append(torch.masked_select(curr_edge_group, curr_mask)) - edge_group_to_vd.append(torch.masked_select(curr_edge_group_to_vd.reshape_as(curr_edge_group), curr_mask)) - edge_group_to_cube.append(torch.masked_select(curr_edge_group_to_cube, curr_mask)) - vd_num_edges.append(curr_mask.reshape(-1, 7).sum(-1, keepdims=True)) - vd_gamma.append(torch.masked_select(gamma_f, cur_cubes).unsqueeze(-1).repeat(1, num).reshape(-1)) - # if color is not None: - # vd_color.append(color[cur_cubes].unsqueeze(1).repeat(1, num, 1).reshape(-1, 3)) - - edge_group = torch.cat(edge_group) - edge_group_to_vd = torch.cat(edge_group_to_vd) - edge_group_to_cube = torch.cat(edge_group_to_cube) - vd_num_edges = torch.cat(vd_num_edges) - vd_gamma = torch.cat(vd_gamma) - # if color is not None: - # vd_color = torch.cat(vd_color) - # else: - # vd_color = None - - vd = torch.zeros((total_num_vd, 3), device=self.device) - beta_sum = torch.zeros((total_num_vd, 1), device=self.device) - - idx_group = torch.gather(input=idx_map.reshape(-1), dim=0, index=edge_group_to_cube * 12 + edge_group) - - x_group = torch.index_select(input=surf_edges_x, index=idx_group.reshape(-1), dim=0).reshape(-1, 2, 3) - s_group = torch.index_select(input=surf_edges_s, index=idx_group.reshape(-1), dim=0).reshape(-1, 2, 1) - - - zero_crossing_group = torch.index_select( - input=zero_crossing, index=idx_group.reshape(-1), dim=0).reshape(-1, 3) - - alpha_group = torch.index_select(input=alpha_nx12x2.reshape(-1, 2), dim=0, - index=edge_group_to_cube * 12 + edge_group).reshape(-1, 2, 1) - ue_group = self._linear_interp(s_group * alpha_group, x_group) - - beta_group = torch.gather(input=beta.reshape(-1), dim=0, - index=edge_group_to_cube * 12 + edge_group).reshape(-1, 1) - beta_sum = beta_sum.index_add_(0, index=edge_group_to_vd, source=beta_group) - vd = vd.index_add_(0, index=edge_group_to_vd, source=ue_group * beta_group) / beta_sum - - ''' - interpolate colors use the same method as dual vertices - ''' - if voxelgrid_colors is not None: - vd_color = torch.zeros((total_num_vd, C), device=self.device) - c_group = torch.index_select(input=surf_edges_c, index=idx_group.reshape(-1), dim=0).reshape(-1, 2, C) - uc_group = self._linear_interp(s_group * alpha_group, c_group) - vd_color = vd_color.index_add_(0, index=edge_group_to_vd, source=uc_group * beta_group) / beta_sum - else: - vd_color = None - - L_dev = self._compute_reg_loss(vd, zero_crossing_group, edge_group_to_vd, vd_num_edges) - - v_idx = torch.arange(vd.shape[0], device=self.device) # + total_num_vd - - vd_idx_map = (vd_idx_map.reshape(-1)).scatter(dim=0, index=edge_group_to_cube * - 12 + edge_group, src=v_idx[edge_group_to_vd]) - - return vd, L_dev, vd_gamma, vd_idx_map, vd_color - - def _triangulate(self, scalar_field, surf_edges, vd, vd_gamma, edge_counts, idx_map, vd_idx_map, surf_edges_mask, training, vd_color): - """ - Connects four neighboring dual vertices to form a quadrilateral. The quadrilaterals are then split into - triangles based on the gamma parameter, as described in Section 4.3. - """ - with torch.no_grad(): - group_mask = (edge_counts == 4) & surf_edges_mask # surface edges shared by 4 cubes. - group = idx_map.reshape(-1)[group_mask] - vd_idx = vd_idx_map[group_mask] - edge_indices, indices = torch.sort(group, stable=True) - quad_vd_idx = vd_idx[indices].reshape(-1, 4) - - # Ensure all face directions point towards the positive SDF to maintain consistent winding. - s_edges = scalar_field[surf_edges[edge_indices.reshape(-1, 4)[:, 0]].reshape(-1)].reshape(-1, 2) - flip_mask = s_edges[:, 0] > 0 - quad_vd_idx = torch.cat((quad_vd_idx[flip_mask][:, [0, 1, 3, 2]], - quad_vd_idx[~flip_mask][:, [2, 3, 1, 0]])) - - quad_gamma = torch.index_select(input=vd_gamma, index=quad_vd_idx.reshape(-1), dim=0).reshape(-1, 4) - gamma_02 = quad_gamma[:, 0] * quad_gamma[:, 2] - gamma_13 = quad_gamma[:, 1] * quad_gamma[:, 3] - if not training: - mask = (gamma_02 > gamma_13) - faces = torch.zeros((quad_gamma.shape[0], 6), dtype=torch.long, device=quad_vd_idx.device) - faces[mask] = quad_vd_idx[mask][:, self.quad_split_1] - faces[~mask] = quad_vd_idx[~mask][:, self.quad_split_2] - faces = faces.reshape(-1, 3) - else: - vd_quad = torch.index_select(input=vd, index=quad_vd_idx.reshape(-1), dim=0).reshape(-1, 4, 3) - vd_02 = (vd_quad[:, 0] + vd_quad[:, 2]) / 2 - vd_13 = (vd_quad[:, 1] + vd_quad[:, 3]) / 2 - weight_sum = (gamma_02 + gamma_13) + 1e-8 - vd_center = (vd_02 * gamma_02.unsqueeze(-1) + vd_13 * gamma_13.unsqueeze(-1)) / weight_sum.unsqueeze(-1) - - if vd_color is not None: - color_quad = torch.index_select(input=vd_color, index=quad_vd_idx.reshape(-1), dim=0).reshape(-1, 4, vd_color.shape[-1]) - color_02 = (color_quad[:, 0] + color_quad[:, 2]) / 2 - color_13 = (color_quad[:, 1] + color_quad[:, 3]) / 2 - color_center = (color_02 * gamma_02.unsqueeze(-1) + color_13 * gamma_13.unsqueeze(-1)) / weight_sum.unsqueeze(-1) - vd_color = torch.cat([vd_color, color_center]) - - - vd_center_idx = torch.arange(vd_center.shape[0], device=self.device) + vd.shape[0] - vd = torch.cat([vd, vd_center]) - faces = quad_vd_idx[:, self.quad_split_train].reshape(-1, 4, 2) - faces = torch.cat([faces, vd_center_idx.reshape(-1, 1, 1).repeat(1, 4, 1)], -1).reshape(-1, 3) - return vd, faces, s_edges, edge_indices, vd_color \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/flexicubes/tables.py b/Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/flexicubes/tables.py deleted file mode 100644 index 5873e772..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/flexicubes/tables.py +++ /dev/null @@ -1,791 +0,0 @@ -# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited. -dmc_table = [ -[[-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], -[[0, 3, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], -[[0, 1, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], -[[1, 3, 8, 9, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], -[[4, 7, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], -[[0, 3, 4, 7, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], -[[0, 1, 9, -1, -1, -1, -1], [4, 7, 8, -1, -1, -1, -1], [-1, -1, 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-1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], -[[0, 1, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], -[[0, 3, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], -[[-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]] -] -num_vd_table = [0, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 3, 1, 2, 2, -2, 1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 2, 2, 1, 2, 3, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, -1, 2, 1, 2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 1, 2, 3, 2, 2, 1, 1, 1, 1, -1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 2, 1, 1, 1, 1, 1, 2, 3, 2, 2, 2, 2, 2, 1, 3, 4, 2, -2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 2, 2, 2, 2, -3, 2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 3, 2, 3, 2, 4, 2, 2, 2, 2, 1, 2, 1, 2, 1, 1, -2, 1, 1, 2, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, -1, 2, 1, 1, 1, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, -1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0] -check_table = [ -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[1, 1, 0, 0, 194], -[1, -1, 0, 0, 193], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[1, 0, 1, 0, 164], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[1, 0, -1, 0, 161], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[1, 0, 0, 1, 152], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[1, 0, 0, 1, 145], -[1, 0, 0, 1, 144], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[1, 0, 0, -1, 137], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[1, 0, 1, 0, 133], -[1, 0, 1, 0, 132], -[1, 1, 0, 0, 131], -[1, 1, 0, 0, 130], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[1, 0, 0, 1, 100], -[0, 0, 0, 0, 0], -[1, 0, 0, 1, 98], -[0, 0, 0, 0, 0], -[1, 0, 0, 1, 96], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[1, 0, 1, 0, 88], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[1, 0, -1, 0, 82], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[1, 0, 1, 0, 74], -[0, 0, 0, 0, 0], -[1, 0, 1, 0, 72], -[0, 0, 0, 0, 0], -[1, 0, 0, -1, 70], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[1, -1, 0, 0, 67], -[0, 0, 0, 0, 0], -[1, -1, 0, 0, 65], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[1, 1, 0, 0, 56], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[1, -1, 0, 0, 52], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[1, 1, 0, 0, 44], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[1, 1, 0, 0, 40], -[0, 0, 0, 0, 0], -[1, 0, 0, -1, 38], -[1, 0, -1, 0, 37], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[1, 0, -1, 0, 33], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[1, -1, 0, 0, 28], -[0, 0, 0, 0, 0], -[1, 0, -1, 0, 26], -[1, 0, 0, -1, 25], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[1, -1, 0, 0, 20], -[0, 0, 0, 0, 0], -[1, 0, -1, 0, 18], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[1, 0, 0, -1, 9], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[1, 0, 0, -1, 6], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0], -[0, 0, 0, 0, 0] -] -tet_table = [ -[-1, -1, -1, -1, -1, -1], -[0, 0, 0, 0, 0, 0], -[0, 0, 0, 0, 0, 0], -[1, 1, 1, 1, 1, 1], -[4, 4, 4, 4, 4, 4], -[0, 0, 0, 0, 0, 0], -[4, 0, 0, 4, 4, -1], -[1, 1, 1, 1, 1, 1], -[4, 4, 4, 4, 4, 4], -[0, 4, 0, 4, 4, -1], -[0, 0, 0, 0, 0, 0], -[1, 1, 1, 1, 1, 1], -[5, 5, 5, 5, 5, 5], -[0, 0, 0, 0, 0, 0], -[0, 0, 0, 0, 0, 0], -[1, 1, 1, 1, 1, 1], -[2, 2, 2, 2, 2, 2], -[0, 0, 0, 0, 0, 0], -[2, 0, 2, -1, 0, 2], -[1, 1, 1, 1, 1, 1], -[2, -1, 2, 4, 4, 2], -[0, 0, 0, 0, 0, 0], -[2, 0, 2, 4, 4, 2], -[1, 1, 1, 1, 1, 1], -[2, 4, 2, 4, 4, 2], -[0, 4, 0, 4, 4, 0], -[2, 0, 2, 0, 0, 2], -[1, 1, 1, 1, 1, 1], -[2, 5, 2, 5, 5, 2], -[0, 0, 0, 0, 0, 0], -[2, 0, 2, 0, 0, 2], -[1, 1, 1, 1, 1, 1], -[1, 1, 1, 1, 1, 1], -[0, 1, 1, -1, 0, 1], -[0, 0, 0, 0, 0, 0], -[2, 2, 2, 2, 2, 2], -[4, 1, 1, 4, 4, 1], -[0, 1, 1, 0, 0, 1], -[4, 0, 0, 4, 4, 0], -[2, 2, 2, 2, 2, 2], -[-1, 1, 1, 4, 4, 1], -[0, 1, 1, 4, 4, 1], -[0, 0, 0, 0, 0, 0], -[2, 2, 2, 2, 2, 2], -[5, 1, 1, 5, 5, 1], -[0, 1, 1, 0, 0, 1], -[0, 0, 0, 0, 0, 0], -[2, 2, 2, 2, 2, 2], -[1, 1, 1, 1, 1, 1], -[0, 0, 0, 0, 0, 0], -[0, 0, 0, 0, 0, 0], -[8, 8, 8, 8, 8, 8], -[1, 1, 1, 4, 4, 1], -[0, 0, 0, 0, 0, 0], -[4, 0, 0, 4, 4, 0], -[4, 4, 4, 4, 4, 4], -[1, 1, 1, 4, 4, 1], -[0, 4, 0, 4, 4, 0], -[0, 0, 0, 0, 0, 0], -[4, 4, 4, 4, 4, 4], -[1, 1, 1, 5, 5, 1], -[0, 0, 0, 0, 0, 0], -[0, 0, 0, 0, 0, 0], -[5, 5, 5, 5, 5, 5], -[6, 6, 6, 6, 6, 6], -[6, -1, 0, 6, 0, 6], -[6, 0, 0, 6, 0, 6], -[6, 1, 1, 6, 1, 6], -[4, 4, 4, 4, 4, 4], -[0, 0, 0, 0, 0, 0], -[4, 0, 0, 4, 4, 4], -[1, 1, 1, 1, 1, 1], -[6, 4, -1, 6, 4, 6], -[6, 4, 0, 6, 4, 6], -[6, 0, 0, 6, 0, 6], -[6, 1, 1, 6, 1, 6], -[5, 5, 5, 5, 5, 5], -[0, 0, 0, 0, 0, 0], -[0, 0, 0, 0, 0, 0], -[1, 1, 1, 1, 1, 1], -[2, 2, 2, 2, 2, 2], -[0, 0, 0, 0, 0, 0], -[2, 0, 2, 2, 0, 2], -[1, 1, 1, 1, 1, 1], -[2, 2, 2, 2, 2, 2], -[0, 0, 0, 0, 0, 0], -[2, 0, 2, 2, 2, 2], -[1, 1, 1, 1, 1, 1], -[2, 4, 2, 2, 4, 2], -[0, 4, 0, 4, 4, 0], -[2, 0, 2, 2, 0, 2], -[1, 1, 1, 1, 1, 1], -[2, 2, 2, 2, 2, 2], -[0, 0, 0, 0, 0, 0], -[0, 0, 0, 0, 0, 0], -[1, 1, 1, 1, 1, 1], -[6, 1, 1, 6, -1, 6], -[6, 1, 1, 6, 0, 6], -[6, 0, 0, 6, 0, 6], -[6, 2, 2, 6, 2, 6], -[4, 1, 1, 4, 4, 1], -[0, 1, 1, 0, 0, 1], -[4, 0, 0, 4, 4, 4], -[2, 2, 2, 2, 2, 2], -[6, 1, 1, 6, 4, 6], -[6, 1, 1, 6, 4, 6], -[6, 0, 0, 6, 0, 6], -[6, 2, 2, 6, 2, 6], -[5, 1, 1, 5, 5, 1], -[0, 1, 1, 0, 0, 1], -[0, 0, 0, 0, 0, 0], -[2, 2, 2, 2, 2, 2], -[1, 1, 1, 1, 1, 1], -[0, 0, 0, 0, 0, 0], -[0, 0, 0, 0, 0, 0], -[6, 6, 6, 6, 6, 6], -[1, 1, 1, 1, 1, 1], -[0, 0, 0, 0, 0, 0], -[0, 0, 0, 0, 0, 0], -[4, 4, 4, 4, 4, 4], -[1, 1, 1, 1, 4, 1], -[0, 4, 0, 4, 4, 0], -[0, 0, 0, 0, 0, 0], -[4, 4, 4, 4, 4, 4], -[1, 1, 1, 1, 1, 1], -[0, 0, 0, 0, 0, 0], -[0, 5, 0, 5, 0, 5], -[5, 5, 5, 5, 5, 5], -[5, 5, 5, 5, 5, 5], -[0, 5, 0, 5, 0, 5], -[-1, 5, 0, 5, 0, 5], -[1, 5, 1, 5, 1, 5], -[4, 5, -1, 5, 4, 5], -[0, 5, 0, 5, 0, 5], -[4, 5, 0, 5, 4, 5], -[1, 5, 1, 5, 1, 5], -[4, 4, 4, 4, 4, 4], -[0, 4, 0, 4, 4, 4], -[0, 0, 0, 0, 0, 0], -[1, 1, 1, 1, 1, 1], -[6, 6, 6, 6, 6, 6], -[0, 0, 0, 0, 0, 0], -[0, 0, 0, 0, 0, 0], -[1, 1, 1, 1, 1, 1], -[2, 5, 2, 5, -1, 5], -[0, 5, 0, 5, 0, 5], -[2, 5, 2, 5, 0, 5], -[1, 5, 1, 5, 1, 5], -[2, 5, 2, 5, 4, 5], -[0, 5, 0, 5, 0, 5], -[2, 5, 2, 5, 4, 5], -[1, 5, 1, 5, 1, 5], -[2, 4, 2, 4, 4, 2], -[0, 4, 0, 4, 4, 4], -[2, 0, 2, 0, 0, 2], -[1, 1, 1, 1, 1, 1], -[2, 6, 2, 6, 6, 2], -[0, 0, 0, 0, 0, 0], -[2, 0, 2, 0, 0, 2], -[1, 1, 1, 1, 1, 1], -[1, 1, 1, 1, 1, 1], -[0, 1, 1, 1, 0, 1], -[0, 0, 0, 0, 0, 0], -[2, 2, 2, 2, 2, 2], -[4, 1, 1, 1, 4, 1], -[0, 1, 1, 1, 0, 1], -[4, 0, 0, 4, 4, 0], -[2, 2, 2, 2, 2, 2], -[1, 1, 1, 1, 1, 1], -[0, 1, 1, 1, 1, 1], -[0, 0, 0, 0, 0, 0], -[2, 2, 2, 2, 2, 2], -[1, 1, 1, 1, 1, 1], -[0, 0, 0, 0, 0, 0], -[0, 0, 0, 0, 0, 0], -[2, 2, 2, 2, 2, 2], -[1, 1, 1, 1, 1, 1], -[0, 0, 0, 0, 0, 0], -[0, 0, 0, 0, 0, 0], -[5, 5, 5, 5, 5, 5], -[1, 1, 1, 1, 4, 1], -[0, 0, 0, 0, 0, 0], -[4, 0, 0, 4, 4, 0], -[4, 4, 4, 4, 4, 4], -[1, 1, 1, 1, 1, 1], -[0, 0, 0, 0, 0, 0], -[0, 0, 0, 0, 0, 0], -[4, 4, 4, 4, 4, 4], -[1, 1, 1, 1, 1, 1], -[6, 0, 0, 6, 0, 6], -[0, 0, 0, 0, 0, 0], -[6, 6, 6, 6, 6, 6], -[5, 5, 5, 5, 5, 5], -[5, 5, 0, 5, 0, 5], -[5, 5, 0, 5, 0, 5], -[5, 5, 1, 5, 1, 5], -[4, 4, 4, 4, 4, 4], -[0, 0, 0, 0, 0, 0], -[4, 4, 0, 4, 4, 4], -[1, 1, 1, 1, 1, 1], -[4, 4, 4, 4, 4, 4], -[4, 4, 0, 4, 4, 4], -[0, 0, 0, 0, 0, 0], -[1, 1, 1, 1, 1, 1], -[8, 8, 8, 8, 8, 8], -[0, 0, 0, 0, 0, 0], -[0, 0, 0, 0, 0, 0], -[1, 1, 1, 1, 1, 1], -[2, 2, 2, 2, 2, 2], -[0, 0, 0, 0, 0, 0], -[2, 2, 2, 2, 0, 2], -[1, 1, 1, 1, 1, 1], -[2, 2, 2, 2, 2, 2], -[0, 0, 0, 0, 0, 0], -[2, 2, 2, 2, 2, 2], -[1, 1, 1, 1, 1, 1], -[2, 2, 2, 2, 2, 2], -[0, 0, 0, 0, 0, 0], -[0, 0, 0, 0, 0, 0], -[4, 1, 1, 4, 4, 1], -[2, 2, 2, 2, 2, 2], -[0, 0, 0, 0, 0, 0], -[0, 0, 0, 0, 0, 0], -[1, 1, 1, 1, 1, 1], -[1, 1, 1, 1, 1, 1], -[1, 1, 1, 1, 0, 1], -[0, 0, 0, 0, 0, 0], -[2, 2, 2, 2, 2, 2], -[1, 1, 1, 1, 1, 1], -[0, 0, 0, 0, 0, 0], -[0, 0, 0, 0, 0, 0], -[2, 4, 2, 4, 4, 2], -[1, 1, 1, 1, 1, 1], -[1, 1, 1, 1, 1, 1], -[0, 0, 0, 0, 0, 0], -[2, 2, 2, 2, 2, 2], -[1, 1, 1, 1, 1, 1], -[0, 0, 0, 0, 0, 0], -[0, 0, 0, 0, 0, 0], -[2, 2, 2, 2, 2, 2], -[1, 1, 1, 1, 1, 1], -[0, 0, 0, 0, 0, 0], -[0, 0, 0, 0, 0, 0], -[5, 5, 5, 5, 5, 5], -[1, 1, 1, 1, 1, 1], -[0, 0, 0, 0, 0, 0], -[0, 0, 0, 0, 0, 0], -[4, 4, 4, 4, 4, 4], -[1, 1, 1, 1, 1, 1], -[0, 0, 0, 0, 0, 0], -[0, 0, 0, 0, 0, 0], -[4, 4, 4, 4, 4, 4], -[1, 1, 1, 1, 1, 1], -[0, 0, 0, 0, 0, 0], -[0, 0, 0, 0, 0, 0], -[12, 12, 12, 12, 12, 12] -] diff --git a/Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/utils_cube.py b/Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/utils_cube.py deleted file mode 100644 index 23913c97..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/representations/mesh/utils_cube.py +++ /dev/null @@ -1,61 +0,0 @@ -import torch -cube_corners = torch.tensor([[0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0], [0, 0, 1], [ - 1, 0, 1], [0, 1, 1], [1, 1, 1]], dtype=torch.int) -cube_neighbor = torch.tensor([[1, 0, 0], [-1, 0, 0], [0, 1, 0], [0, -1, 0], [0, 0, 1], [0, 0, -1]]) -cube_edges = torch.tensor([0, 1, 1, 5, 4, 5, 0, 4, 2, 3, 3, 7, 6, 7, 2, 6, - 2, 0, 3, 1, 7, 5, 6, 4], dtype=torch.long, requires_grad=False) - -def construct_dense_grid(res, device='cuda'): - '''construct a dense grid based on resolution''' - res_v = res + 1 - vertsid = torch.arange(res_v ** 3, device=device) - coordsid = vertsid.reshape(res_v, res_v, res_v)[:res, :res, :res].flatten() - cube_corners_bias = (cube_corners[:, 0] * res_v + cube_corners[:, 1]) * res_v + cube_corners[:, 2] - cube_fx8 = (coordsid.unsqueeze(1) + cube_corners_bias.unsqueeze(0).to(device)) - verts = torch.stack([vertsid // (res_v ** 2), (vertsid // res_v) % res_v, vertsid % res_v], dim=1) - return verts, cube_fx8 - - -def construct_voxel_grid(coords): - verts = (cube_corners.unsqueeze(0).to(coords) + coords.unsqueeze(1)).reshape(-1, 3) - verts_unique, inverse_indices = torch.unique(verts, dim=0, return_inverse=True) - cubes = inverse_indices.reshape(-1, 8) - return verts_unique, cubes - - -def cubes_to_verts(num_verts, cubes, value, reduce='mean'): - """ - Args: - cubes [Vx8] verts index for each cube - value [Vx8xM] value to be scattered - Operation: - reduced[cubes[i][j]][k] += value[i][k] - """ - M = value.shape[2] # number of channels - reduced = torch.zeros(num_verts, M, device=cubes.device) - return torch.scatter_reduce(reduced, 0, - cubes.unsqueeze(-1).expand(-1, -1, M).flatten(0, 1), - value.flatten(0, 1), reduce=reduce, include_self=False) - -def sparse_cube2verts(coords, feats, training=True): - new_coords, cubes = construct_voxel_grid(coords) - new_feats = cubes_to_verts(new_coords.shape[0], cubes, feats) - if training: - con_loss = torch.mean((feats - new_feats[cubes]) ** 2) - else: - con_loss = 0.0 - return new_coords, new_feats, con_loss - - -def get_dense_attrs(coords : torch.Tensor, feats : torch.Tensor, res : int, sdf_init=True): - F = feats.shape[-1] - dense_attrs = torch.zeros([res] * 3 + [F], device=feats.device) - if sdf_init: - dense_attrs[..., 0] = 1 # initial outside sdf value - dense_attrs[coords[:, 0], coords[:, 1], coords[:, 2], :] = feats - return dense_attrs.reshape(-1, F) - - -def get_defomed_verts(v_pos : torch.Tensor, deform : torch.Tensor, res): - return v_pos / res - 0.5 + (1 - 1e-8) / (res * 2) * torch.tanh(deform) - \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/representations/octree/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/representations/octree/__init__.py deleted file mode 100644 index f66a39a5..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/representations/octree/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .octree_dfs import DfsOctree \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/representations/octree/octree_dfs.py b/Gen_3D_Modules/TRELLIS/trellis_/representations/octree/octree_dfs.py deleted file mode 100644 index 9d1f7898..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/representations/octree/octree_dfs.py +++ /dev/null @@ -1,362 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - - -DEFAULT_TRIVEC_CONFIG = { - 'dim': 8, - 'rank': 8, -} - -DEFAULT_VOXEL_CONFIG = { - 'solid': False, -} - -DEFAULT_DECOPOLY_CONFIG = { - 'degree': 8, - 'rank': 16, -} - - -class DfsOctree: - """ - Sparse Voxel Octree (SVO) implementation for PyTorch. - Using Depth-First Search (DFS) order to store the octree. - DFS order suits rendering and ray tracing. - - The structure and data are separatedly stored. - Structure is stored as a continuous array, each element is a 3*32 bits descriptor. - |-----------------------------------------| - | 0:3 bits | 4:31 bits | - | leaf num | unused | - |-----------------------------------------| - | 0:31 bits | - | child ptr | - |-----------------------------------------| - | 0:31 bits | - | data ptr | - |-----------------------------------------| - Each element represents a non-leaf node in the octree. - The valid mask is used to indicate whether the children are valid. - The leaf mask is used to indicate whether the children are leaf nodes. - The child ptr is used to point to the first non-leaf child. Non-leaf children descriptors are stored continuously from the child ptr. - The data ptr is used to point to the data of leaf children. Leaf children data are stored continuously from the data ptr. - - There are also auxiliary arrays to store the additional structural information to facilitate parallel processing. - - Position: the position of the octree nodes. - - Depth: the depth of the octree nodes. - - Args: - depth (int): the depth of the octree. - """ - - def __init__( - self, - depth, - aabb=[0,0,0,1,1,1], - sh_degree=2, - primitive='voxel', - primitive_config={}, - device='cuda', - ): - self.max_depth = depth - self.aabb = torch.tensor(aabb, dtype=torch.float32, device=device) - self.device = device - self.sh_degree = sh_degree - self.active_sh_degree = sh_degree - self.primitive = primitive - self.primitive_config = primitive_config - - self.structure = torch.tensor([[8, 1, 0]], dtype=torch.int32, device=self.device) - self.position = torch.zeros((8, 3), dtype=torch.float32, device=self.device) - self.depth = torch.zeros((8, 1), dtype=torch.uint8, device=self.device) - self.position[:, 0] = torch.tensor([0.25, 0.75, 0.25, 0.75, 0.25, 0.75, 0.25, 0.75], device=self.device) - self.position[:, 1] = torch.tensor([0.25, 0.25, 0.75, 0.75, 0.25, 0.25, 0.75, 0.75], device=self.device) - self.position[:, 2] = torch.tensor([0.25, 0.25, 0.25, 0.25, 0.75, 0.75, 0.75, 0.75], device=self.device) - self.depth[:, 0] = 1 - - self.data = ['position', 'depth'] - self.param_names = [] - - if primitive == 'voxel': - self.features_dc = torch.zeros((8, 1, 3), dtype=torch.float32, device=self.device) - self.features_ac = torch.zeros((8, (sh_degree+1)**2-1, 3), dtype=torch.float32, device=self.device) - self.data += ['features_dc', 'features_ac'] - self.param_names += ['features_dc', 'features_ac'] - if not primitive_config.get('solid', False): - self.density = torch.zeros((8, 1), dtype=torch.float32, device=self.device) - self.data.append('density') - self.param_names.append('density') - elif primitive == 'gaussian': - self.features_dc = torch.zeros((8, 1, 3), dtype=torch.float32, device=self.device) - self.features_ac = torch.zeros((8, (sh_degree+1)**2-1, 3), dtype=torch.float32, device=self.device) - self.opacity = torch.zeros((8, 1), dtype=torch.float32, device=self.device) - self.data += ['features_dc', 'features_ac', 'opacity'] - self.param_names += ['features_dc', 'features_ac', 'opacity'] - elif primitive == 'trivec': - self.trivec = torch.zeros((8, primitive_config['rank'], 3, primitive_config['dim']), dtype=torch.float32, device=self.device) - self.density = torch.zeros((8, primitive_config['rank']), dtype=torch.float32, device=self.device) - self.features_dc = torch.zeros((8, primitive_config['rank'], 1, 3), dtype=torch.float32, device=self.device) - self.features_ac = torch.zeros((8, primitive_config['rank'], (sh_degree+1)**2-1, 3), dtype=torch.float32, device=self.device) - self.density_shift = 0 - self.data += ['trivec', 'density', 'features_dc', 'features_ac'] - self.param_names += ['trivec', 'density', 'features_dc', 'features_ac'] - elif primitive == 'decoupoly': - self.decoupoly_V = torch.zeros((8, primitive_config['rank'], 3), dtype=torch.float32, device=self.device) - self.decoupoly_g = torch.zeros((8, primitive_config['rank'], primitive_config['degree']), dtype=torch.float32, device=self.device) - self.density = torch.zeros((8, primitive_config['rank']), dtype=torch.float32, device=self.device) - self.features_dc = torch.zeros((8, primitive_config['rank'], 1, 3), dtype=torch.float32, device=self.device) - self.features_ac = torch.zeros((8, primitive_config['rank'], (sh_degree+1)**2-1, 3), dtype=torch.float32, device=self.device) - self.density_shift = 0 - self.data += ['decoupoly_V', 'decoupoly_g', 'density', 'features_dc', 'features_ac'] - self.param_names += ['decoupoly_V', 'decoupoly_g', 'density', 'features_dc', 'features_ac'] - - self.setup_functions() - - def setup_functions(self): - self.density_activation = (lambda x: torch.exp(x - 2)) if self.primitive != 'trivec' else (lambda x: x) - self.opacity_activation = lambda x: torch.sigmoid(x - 6) - self.inverse_opacity_activation = lambda x: torch.log(x / (1 - x)) + 6 - self.color_activation = lambda x: torch.sigmoid(x) - - @property - def num_non_leaf_nodes(self): - return self.structure.shape[0] - - @property - def num_leaf_nodes(self): - return self.depth.shape[0] - - @property - def cur_depth(self): - return self.depth.max().item() - - @property - def occupancy(self): - return self.num_leaf_nodes / 8 ** self.cur_depth - - @property - def get_xyz(self): - return self.position - - @property - def get_depth(self): - return self.depth - - @property - def get_density(self): - if self.primitive == 'voxel' and self.voxel_config['solid']: - return torch.full((self.position.shape[0], 1), 1000, dtype=torch.float32, device=self.device) - return self.density_activation(self.density) - - @property - def get_opacity(self): - return self.opacity_activation(self.density) - - @property - def get_trivec(self): - return self.trivec - - @property - def get_decoupoly(self): - return F.normalize(self.decoupoly_V, dim=-1), self.decoupoly_g - - @property - def get_color(self): - return self.color_activation(self.colors) - - @property - def get_features(self): - if self.sh_degree == 0: - return self.features_dc - return torch.cat([self.features_dc, self.features_ac], dim=-2) - - def state_dict(self): - ret = {'structure': self.structure, 'position': self.position, 'depth': self.depth, 'sh_degree': self.sh_degree, 'active_sh_degree': self.active_sh_degree, 'trivec_config': self.trivec_config, 'voxel_config': self.voxel_config, 'primitive': self.primitive} - if hasattr(self, 'density_shift'): - ret['density_shift'] = self.density_shift - for data in set(self.data + self.param_names): - if not isinstance(getattr(self, data), nn.Module): - ret[data] = getattr(self, data) - else: - ret[data] = getattr(self, data).state_dict() - return ret - - def load_state_dict(self, state_dict): - keys = list(set(self.data + self.param_names + list(state_dict.keys()) + ['structure', 'position', 'depth'])) - for key in keys: - if key not in state_dict: - print(f"Warning: key {key} not found in the state_dict.") - continue - try: - if not isinstance(getattr(self, key), nn.Module): - setattr(self, key, state_dict[key]) - else: - getattr(self, key).load_state_dict(state_dict[key]) - except Exception as e: - print(e) - raise ValueError(f"Error loading key {key}.") - - def gather_from_leaf_children(self, data): - """ - Gather the data from the leaf children. - - Args: - data (torch.Tensor): the data to gather. The first dimension should be the number of leaf nodes. - """ - leaf_cnt = self.structure[:, 0] - leaf_cnt_masks = [leaf_cnt == i for i in range(1, 9)] - ret = torch.zeros((self.num_non_leaf_nodes,), dtype=data.dtype, device=self.device) - for i in range(8): - if leaf_cnt_masks[i].sum() == 0: - continue - start = self.structure[leaf_cnt_masks[i], 2] - for j in range(i+1): - ret[leaf_cnt_masks[i]] += data[start + j] - return ret - - def gather_from_non_leaf_children(self, data): - """ - Gather the data from the non-leaf children. - - Args: - data (torch.Tensor): the data to gather. The first dimension should be the number of leaf nodes. - """ - non_leaf_cnt = 8 - self.structure[:, 0] - non_leaf_cnt_masks = [non_leaf_cnt == i for i in range(1, 9)] - ret = torch.zeros_like(data, device=self.device) - for i in range(8): - if non_leaf_cnt_masks[i].sum() == 0: - continue - start = self.structure[non_leaf_cnt_masks[i], 1] - for j in range(i+1): - ret[non_leaf_cnt_masks[i]] += data[start + j] - return ret - - def structure_control(self, mask): - """ - Control the structure of the octree. - - Args: - mask (torch.Tensor): the mask to control the structure. 1 for subdivide, -1 for merge, 0 for keep. - """ - # Dont subdivide when the depth is the maximum. - mask[self.depth.squeeze() == self.max_depth] = torch.clamp_max(mask[self.depth.squeeze() == self.max_depth], 0) - # Dont merge when the depth is the minimum. - mask[self.depth.squeeze() == 1] = torch.clamp_min(mask[self.depth.squeeze() == 1], 0) - - # Gather control mask - structre_ctrl = self.gather_from_leaf_children(mask) - structre_ctrl[structre_ctrl==-8] = -1 - - new_leaf_num = self.structure[:, 0].clone() - # Modify the leaf num. - structre_valid = structre_ctrl >= 0 - new_leaf_num[structre_valid] -= structre_ctrl[structre_valid] # Add the new nodes. - structre_delete = structre_ctrl < 0 - merged_nodes = self.gather_from_non_leaf_children(structre_delete.int()) - new_leaf_num += merged_nodes # Delete the merged nodes. - - # Update the structure array to allocate new nodes. - mem_offset = torch.zeros((self.num_non_leaf_nodes + 1,), dtype=torch.int32, device=self.device) - mem_offset.index_add_(0, self.structure[structre_valid, 1], structre_ctrl[structre_valid]) # Add the new nodes. - mem_offset[:-1] -= structre_delete.int() # Delete the merged nodes. - new_structre_idx = torch.arange(0, self.num_non_leaf_nodes + 1, dtype=torch.int32, device=self.device) + mem_offset.cumsum(0) - new_structure_length = new_structre_idx[-1].item() - new_structre_idx = new_structre_idx[:-1] - new_structure = torch.empty((new_structure_length, 3), dtype=torch.int32, device=self.device) - new_structure[new_structre_idx[structre_valid], 0] = new_leaf_num[structre_valid] - - # Initialize the new nodes. - new_node_mask = torch.ones((new_structure_length,), dtype=torch.bool, device=self.device) - new_node_mask[new_structre_idx[structre_valid]] = False - new_structure[new_node_mask, 0] = 8 # Initialize to all leaf nodes. - new_node_num = new_node_mask.sum().item() - - # Rebuild child ptr. - non_leaf_cnt = 8 - new_structure[:, 0] - new_child_ptr = torch.cat([torch.zeros((1,), dtype=torch.int32, device=self.device), non_leaf_cnt.cumsum(0)[:-1]]) - new_structure[:, 1] = new_child_ptr + 1 - - # Rebuild data ptr with old data. - leaf_cnt = torch.zeros((new_structure_length,), dtype=torch.int32, device=self.device) - leaf_cnt.index_add_(0, new_structre_idx, self.structure[:, 0]) - old_data_ptr = torch.cat([torch.zeros((1,), dtype=torch.int32, device=self.device), leaf_cnt.cumsum(0)[:-1]]) - - # Update the data array - subdivide_mask = mask == 1 - merge_mask = mask == -1 - data_valid = ~(subdivide_mask | merge_mask) - mem_offset = torch.zeros((self.num_leaf_nodes + 1,), dtype=torch.int32, device=self.device) - mem_offset.index_add_(0, old_data_ptr[new_node_mask], torch.full((new_node_num,), 8, dtype=torch.int32, device=self.device)) # Add data array for new nodes - mem_offset[:-1] -= subdivide_mask.int() # Delete data elements for subdivide nodes - mem_offset[:-1] -= merge_mask.int() # Delete data elements for merge nodes - mem_offset.index_add_(0, self.structure[structre_valid, 2], merged_nodes[structre_valid]) # Add data elements for merge nodes - new_data_idx = torch.arange(0, self.num_leaf_nodes + 1, dtype=torch.int32, device=self.device) + mem_offset.cumsum(0) - new_data_length = new_data_idx[-1].item() - new_data_idx = new_data_idx[:-1] - new_data = {data: torch.empty((new_data_length,) + getattr(self, data).shape[1:], dtype=getattr(self, data).dtype, device=self.device) for data in self.data} - for data in self.data: - new_data[data][new_data_idx[data_valid]] = getattr(self, data)[data_valid] - - # Rebuild data ptr - leaf_cnt = new_structure[:, 0] - new_data_ptr = torch.cat([torch.zeros((1,), dtype=torch.int32, device=self.device), leaf_cnt.cumsum(0)[:-1]]) - new_structure[:, 2] = new_data_ptr - - # Initialize the new data array - ## For subdivide nodes - if subdivide_mask.sum() > 0: - subdivide_data_ptr = new_structure[new_node_mask, 2] - for data in self.data: - for i in range(8): - if data == 'position': - offset = torch.tensor([i // 4, (i // 2) % 2, i % 2], dtype=torch.float32, device=self.device) - 0.5 - scale = 2 ** (-1.0 - self.depth[subdivide_mask]) - new_data['position'][subdivide_data_ptr + i] = self.position[subdivide_mask] + offset * scale - elif data == 'depth': - new_data['depth'][subdivide_data_ptr + i] = self.depth[subdivide_mask] + 1 - elif data == 'opacity': - new_data['opacity'][subdivide_data_ptr + i] = self.inverse_opacity_activation(torch.sqrt(self.opacity_activation(self.opacity[subdivide_mask]))) - elif data == 'trivec': - offset = torch.tensor([i // 4, (i // 2) % 2, i % 2], dtype=torch.float32, device=self.device) * 0.5 - coord = (torch.linspace(0, 0.5, self.trivec.shape[-1], dtype=torch.float32, device=self.device)[None] + offset[:, None]).reshape(1, 3, self.trivec.shape[-1], 1) - axis = torch.linspace(0, 1, 3, dtype=torch.float32, device=self.device).reshape(1, 3, 1, 1).repeat(1, 1, self.trivec.shape[-1], 1) - coord = torch.stack([coord, axis], dim=3).reshape(1, 3, self.trivec.shape[-1], 2).expand(self.trivec[subdivide_mask].shape[0], -1, -1, -1) * 2 - 1 - new_data['trivec'][subdivide_data_ptr + i] = F.grid_sample(self.trivec[subdivide_mask], coord, align_corners=True) - else: - new_data[data][subdivide_data_ptr + i] = getattr(self, data)[subdivide_mask] - ## For merge nodes - if merge_mask.sum() > 0: - merge_data_ptr = torch.empty((merged_nodes.sum().item(),), dtype=torch.int32, device=self.device) - merge_nodes_cumsum = torch.cat([torch.zeros((1,), dtype=torch.int32, device=self.device), merged_nodes.cumsum(0)[:-1]]) - for i in range(8): - merge_data_ptr[merge_nodes_cumsum[merged_nodes > i] + i] = new_structure[new_structre_idx[merged_nodes > i], 2] + i - old_merge_data_ptr = self.structure[structre_delete, 2] - for data in self.data: - if data == 'position': - scale = 2 ** (1.0 - self.depth[old_merge_data_ptr]) - new_data['position'][merge_data_ptr] = ((self.position[old_merge_data_ptr] + 0.5) / scale).floor() * scale + 0.5 * scale - 0.5 - elif data == 'depth': - new_data['depth'][merge_data_ptr] = self.depth[old_merge_data_ptr] - 1 - elif data == 'opacity': - new_data['opacity'][subdivide_data_ptr + i] = self.inverse_opacity_activation(self.opacity_activation(self.opacity[subdivide_mask])**2) - elif data == 'trivec': - new_data['trivec'][merge_data_ptr] = self.trivec[old_merge_data_ptr] - else: - new_data[data][merge_data_ptr] = getattr(self, data)[old_merge_data_ptr] - - # Update the structure and data array - self.structure = new_structure - for data in self.data: - setattr(self, data, new_data[data]) - - # Save data array control temp variables - self.data_rearrange_buffer = { - 'subdivide_mask': subdivide_mask, - 'merge_mask': merge_mask, - 'data_valid': data_valid, - 'new_data_idx': new_data_idx, - 'new_data_length': new_data_length, - 'new_data': new_data - } diff --git a/Gen_3D_Modules/TRELLIS/trellis_/representations/radiance_field/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/representations/radiance_field/__init__.py deleted file mode 100644 index b72a1b7e..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/representations/radiance_field/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .strivec import Strivec \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/representations/radiance_field/strivec.py b/Gen_3D_Modules/TRELLIS/trellis_/representations/radiance_field/strivec.py deleted file mode 100644 index 8fc4b749..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/representations/radiance_field/strivec.py +++ /dev/null @@ -1,28 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -import numpy as np -from ..octree import DfsOctree as Octree - - -class Strivec(Octree): - def __init__( - self, - resolution: int, - aabb: list, - sh_degree: int = 0, - rank: int = 8, - dim: int = 8, - device: str = "cuda", - ): - assert np.log2(resolution) % 1 == 0, "Resolution must be a power of 2" - self.resolution = resolution - depth = int(np.round(np.log2(resolution))) - super().__init__( - depth=depth, - aabb=aabb, - sh_degree=sh_degree, - primitive="trivec", - primitive_config={"rank": rank, "dim": dim}, - device=device, - ) diff --git a/Gen_3D_Modules/TRELLIS/trellis_/utils/__init__.py b/Gen_3D_Modules/TRELLIS/trellis_/utils/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/Gen_3D_Modules/TRELLIS/trellis_/utils/general_utils.py b/Gen_3D_Modules/TRELLIS/trellis_/utils/general_utils.py deleted file mode 100644 index 3b454d9c..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/utils/general_utils.py +++ /dev/null @@ -1,187 +0,0 @@ -import numpy as np -import cv2 -import torch - - -# Dictionary utils -def _dict_merge(dicta, dictb, prefix=''): - """ - Merge two dictionaries. - """ - assert isinstance(dicta, dict), 'input must be a dictionary' - assert isinstance(dictb, dict), 'input must be a dictionary' - dict_ = {} - all_keys = set(dicta.keys()).union(set(dictb.keys())) - for key in all_keys: - if key in dicta.keys() and key in dictb.keys(): - if isinstance(dicta[key], dict) and isinstance(dictb[key], dict): - dict_[key] = _dict_merge(dicta[key], dictb[key], prefix=f'{prefix}.{key}') - else: - raise ValueError(f'Duplicate key {prefix}.{key} found in both dictionaries. Types: {type(dicta[key])}, {type(dictb[key])}') - elif key in dicta.keys(): - dict_[key] = dicta[key] - else: - dict_[key] = dictb[key] - return dict_ - - -def dict_merge(dicta, dictb): - """ - Merge two dictionaries. - """ - return _dict_merge(dicta, dictb, prefix='') - - -def dict_foreach(dic, func, special_func={}): - """ - Recursively apply a function to all non-dictionary leaf values in a dictionary. - """ - assert isinstance(dic, dict), 'input must be a dictionary' - for key in dic.keys(): - if isinstance(dic[key], dict): - dic[key] = dict_foreach(dic[key], func) - else: - if key in special_func.keys(): - dic[key] = special_func[key](dic[key]) - else: - dic[key] = func(dic[key]) - return dic - - -def dict_reduce(dicts, func, special_func={}): - """ - Reduce a list of dictionaries. Leaf values must be scalars. - """ - assert isinstance(dicts, list), 'input must be a list of dictionaries' - assert all([isinstance(d, dict) for d in dicts]), 'input must be a list of dictionaries' - assert len(dicts) > 0, 'input must be a non-empty list of dictionaries' - all_keys = set([key for dict_ in dicts for key in dict_.keys()]) - reduced_dict = {} - for key in all_keys: - vlist = [dict_[key] for dict_ in dicts if key in dict_.keys()] - if isinstance(vlist[0], dict): - reduced_dict[key] = dict_reduce(vlist, func, special_func) - else: - if key in special_func.keys(): - reduced_dict[key] = special_func[key](vlist) - else: - reduced_dict[key] = func(vlist) - return reduced_dict - - -def dict_any(dic, func): - """ - Recursively apply a function to all non-dictionary leaf values in a dictionary. - """ - assert isinstance(dic, dict), 'input must be a dictionary' - for key in dic.keys(): - if isinstance(dic[key], dict): - if dict_any(dic[key], func): - return True - else: - if func(dic[key]): - return True - return False - - -def dict_all(dic, func): - """ - Recursively apply a function to all non-dictionary leaf values in a dictionary. - """ - assert isinstance(dic, dict), 'input must be a dictionary' - for key in dic.keys(): - if isinstance(dic[key], dict): - if not dict_all(dic[key], func): - return False - else: - if not func(dic[key]): - return False - return True - - -def dict_flatten(dic, sep='.'): - """ - Flatten a nested dictionary into a dictionary with no nested dictionaries. - """ - assert isinstance(dic, dict), 'input must be a dictionary' - flat_dict = {} - for key in dic.keys(): - if isinstance(dic[key], dict): - sub_dict = dict_flatten(dic[key], sep=sep) - for sub_key in sub_dict.keys(): - flat_dict[str(key) + sep + str(sub_key)] = sub_dict[sub_key] - else: - flat_dict[key] = dic[key] - return flat_dict - - -def make_grid(images, nrow=None, ncol=None, aspect_ratio=None): - num_images = len(images) - if nrow is None and ncol is None: - if aspect_ratio is not None: - nrow = int(np.round(np.sqrt(num_images / aspect_ratio))) - else: - nrow = int(np.sqrt(num_images)) - ncol = (num_images + nrow - 1) // nrow - elif nrow is None and ncol is not None: - nrow = (num_images + ncol - 1) // ncol - elif nrow is not None and ncol is None: - ncol = (num_images + nrow - 1) // nrow - else: - assert nrow * ncol >= num_images, 'nrow * ncol must be greater than or equal to the number of images' - - grid = np.zeros((nrow * images[0].shape[0], ncol * images[0].shape[1], images[0].shape[2]), dtype=images[0].dtype) - for i, img in enumerate(images): - row = i // ncol - col = i % ncol - grid[row * img.shape[0]:(row + 1) * img.shape[0], col * img.shape[1]:(col + 1) * img.shape[1]] = img - return grid - - -def notes_on_image(img, notes=None): - img = np.pad(img, ((0, 32), (0, 0), (0, 0)), 'constant', constant_values=0) - img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) - if notes is not None: - img = cv2.putText(img, notes, (0, img.shape[0] - 4), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1) - img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) - return img - - -def save_image_with_notes(img, path, notes=None): - """ - Save an image with notes. - """ - if isinstance(img, torch.Tensor): - img = img.cpu().numpy().transpose(1, 2, 0) - if img.dtype == np.float32 or img.dtype == np.float64: - img = np.clip(img * 255, 0, 255).astype(np.uint8) - img = notes_on_image(img, notes) - cv2.imwrite(path, cv2.cvtColor(img, cv2.COLOR_RGB2BGR)) - - -# debug utils - -def atol(x, y): - """ - Absolute tolerance. - """ - return torch.abs(x - y) - - -def rtol(x, y): - """ - Relative tolerance. - """ - return torch.abs(x - y) / torch.clamp_min(torch.maximum(torch.abs(x), torch.abs(y)), 1e-12) - - -# print utils -def indent(s, n=4): - """ - Indent a string. - """ - lines = s.split('\n') - for i in range(1, len(lines)): - lines[i] = ' ' * n + lines[i] - return '\n'.join(lines) - diff --git a/Gen_3D_Modules/TRELLIS/trellis_/utils/postprocessing_utils.py b/Gen_3D_Modules/TRELLIS/trellis_/utils/postprocessing_utils.py deleted file mode 100644 index 8f5c42b4..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/utils/postprocessing_utils.py +++ /dev/null @@ -1,467 +0,0 @@ -from typing import * -import numpy as np -import torch -import utils3d -import nvdiffrast.torch as dr -from tqdm import tqdm -import comfy.utils -import xatlas -import pyvista as pv -from pymeshfix import _meshfix -import igraph -import cv2 -from .random_utils import sphere_hammersley_sequence -from .render_utils import render_multiview -from ..representations import Strivec, Gaussian, MeshExtractResult - - -@torch.no_grad() -def _fill_holes( - verts, - faces, - max_hole_size=0.04, - max_hole_nbe=32, - resolution=128, - num_views=500, - debug=False, - verbose=False -): - """ - Rasterize a mesh from multiple views and remove invisible faces. - Also includes postprocessing to: - 1. Remove connected components that are have low visibility. - 2. Mincut to remove faces at the inner side of the mesh connected to the outer side with a small hole. - - Args: - verts (torch.Tensor): Vertices of the mesh. Shape (V, 3). - faces (torch.Tensor): Faces of the mesh. Shape (F, 3). - max_hole_size (float): Maximum area of a hole to fill. - resolution (int): Resolution of the rasterization. - num_views (int): Number of views to rasterize the mesh. - verbose (bool): Whether to print progress. - """ - # Construct cameras - yaws = [] - pitchs = [] - for i in range(num_views): - y, p = sphere_hammersley_sequence(i, num_views) - yaws.append(y) - pitchs.append(p) - yaws = torch.tensor(yaws).cuda() - pitchs = torch.tensor(pitchs).cuda() - radius = 2.0 - fov = torch.deg2rad(torch.tensor(40)).cuda() - projection = utils3d.torch.perspective_from_fov_xy(fov, fov, 1, 3) - views = [] - for (yaw, pitch) in zip(yaws, pitchs): - orig = torch.tensor([ - torch.sin(yaw) * torch.cos(pitch), - torch.cos(yaw) * torch.cos(pitch), - torch.sin(pitch), - ]).cuda().float() * radius - view = utils3d.torch.view_look_at(orig, torch.tensor([0, 0, 0]).float().cuda(), torch.tensor([0, 0, 1]).float().cuda()) - views.append(view) - views = torch.stack(views, dim=0) - - # Rasterize - visblity = torch.zeros(faces.shape[0], dtype=torch.int32, device=verts.device) - rastctx = utils3d.torch.RastContext(backend='cuda') - for i in tqdm(range(views.shape[0]), total=views.shape[0], disable=not verbose, desc='Rasterizing'): - view = views[i] - buffers = utils3d.torch.rasterize_triangle_faces( - rastctx, verts[None], faces, resolution, resolution, view=view, projection=projection - ) - face_id = buffers['face_id'][0][buffers['mask'][0] > 0.95] - 1 - face_id = torch.unique(face_id).long() - visblity[face_id] += 1 - visblity = visblity.float() / num_views - - # Mincut - ## construct outer faces - edges, face2edge, edge_degrees = utils3d.torch.compute_edges(faces) - boundary_edge_indices = torch.nonzero(edge_degrees == 1).reshape(-1) - connected_components = utils3d.torch.compute_connected_components(faces, edges, face2edge) - outer_face_indices = torch.zeros(faces.shape[0], dtype=torch.bool, device=faces.device) - for i in range(len(connected_components)): - outer_face_indices[connected_components[i]] = visblity[connected_components[i]] > min(max(visblity[connected_components[i]].quantile(0.75).item(), 0.25), 0.5) - outer_face_indices = outer_face_indices.nonzero().reshape(-1) - - ## construct inner faces - inner_face_indices = torch.nonzero(visblity == 0).reshape(-1) - if verbose: - tqdm.write(f'Found {inner_face_indices.shape[0]} invisible faces') - if inner_face_indices.shape[0] == 0: - return verts, faces - - ## Construct dual graph (faces as nodes, edges as edges) - dual_edges, dual_edge2edge = utils3d.torch.compute_dual_graph(face2edge) - dual_edge2edge = edges[dual_edge2edge] - dual_edges_weights = torch.norm(verts[dual_edge2edge[:, 0]] - verts[dual_edge2edge[:, 1]], dim=1) - if verbose: - tqdm.write(f'Dual graph: {dual_edges.shape[0]} edges') - - ## solve mincut problem - ### construct main graph - g = igraph.Graph() - g.add_vertices(faces.shape[0]) - g.add_edges(dual_edges.cpu().numpy()) - g.es['weight'] = dual_edges_weights.cpu().numpy() - - ### source and target - g.add_vertex('s') - g.add_vertex('t') - - ### connect invisible faces to source - g.add_edges([(f, 's') for f in inner_face_indices], attributes={'weight': torch.ones(inner_face_indices.shape[0], dtype=torch.float32).cpu().numpy()}) - - ### connect outer faces to target - g.add_edges([(f, 't') for f in outer_face_indices], attributes={'weight': torch.ones(outer_face_indices.shape[0], dtype=torch.float32).cpu().numpy()}) - - ### solve mincut - cut = g.mincut('s', 't', (np.array(g.es['weight']) * 1000).tolist()) - remove_face_indices = torch.tensor([v for v in cut.partition[0] if v < faces.shape[0]], dtype=torch.long, device=faces.device) - if verbose: - tqdm.write(f'Mincut solved, start checking the cut') - - ### check if the cut is valid with each connected component - to_remove_cc = utils3d.torch.compute_connected_components(faces[remove_face_indices]) - if debug: - tqdm.write(f'Number of connected components of the cut: {len(to_remove_cc)}') - valid_remove_cc = [] - cutting_edges = [] - for cc in to_remove_cc: - #### check if the connected component has low visibility - visblity_median = visblity[remove_face_indices[cc]].median() - if debug: - tqdm.write(f'visblity_median: {visblity_median}') - if visblity_median > 0.25: - continue - - #### check if the cuting loop is small enough - cc_edge_indices, cc_edges_degree = torch.unique(face2edge[remove_face_indices[cc]], return_counts=True) - cc_boundary_edge_indices = cc_edge_indices[cc_edges_degree == 1] - cc_new_boundary_edge_indices = cc_boundary_edge_indices[~torch.isin(cc_boundary_edge_indices, boundary_edge_indices)] - if len(cc_new_boundary_edge_indices) > 0: - cc_new_boundary_edge_cc = utils3d.torch.compute_edge_connected_components(edges[cc_new_boundary_edge_indices]) - cc_new_boundary_edges_cc_center = [verts[edges[cc_new_boundary_edge_indices[edge_cc]]].mean(dim=1).mean(dim=0) for edge_cc in cc_new_boundary_edge_cc] - cc_new_boundary_edges_cc_area = [] - for i, edge_cc in enumerate(cc_new_boundary_edge_cc): - _e1 = verts[edges[cc_new_boundary_edge_indices[edge_cc]][:, 0]] - cc_new_boundary_edges_cc_center[i] - _e2 = verts[edges[cc_new_boundary_edge_indices[edge_cc]][:, 1]] - cc_new_boundary_edges_cc_center[i] - cc_new_boundary_edges_cc_area.append(torch.norm(torch.cross(_e1, _e2, dim=-1), dim=1).sum() * 0.5) - if debug: - cutting_edges.append(cc_new_boundary_edge_indices) - tqdm.write(f'Area of the cutting loop: {cc_new_boundary_edges_cc_area}') - if any([l > max_hole_size for l in cc_new_boundary_edges_cc_area]): - continue - - valid_remove_cc.append(cc) - - if debug: - face_v = verts[faces].mean(dim=1).cpu().numpy() - vis_dual_edges = dual_edges.cpu().numpy() - vis_colors = np.zeros((faces.shape[0], 3), dtype=np.uint8) - vis_colors[inner_face_indices.cpu().numpy()] = [0, 0, 255] - vis_colors[outer_face_indices.cpu().numpy()] = [0, 255, 0] - vis_colors[remove_face_indices.cpu().numpy()] = [255, 0, 255] - if len(valid_remove_cc) > 0: - vis_colors[remove_face_indices[torch.cat(valid_remove_cc)].cpu().numpy()] = [255, 0, 0] - utils3d.io.write_ply('dbg_dual.ply', face_v, edges=vis_dual_edges, vertex_colors=vis_colors) - - vis_verts = verts.cpu().numpy() - vis_edges = edges[torch.cat(cutting_edges)].cpu().numpy() - utils3d.io.write_ply('dbg_cut.ply', vis_verts, edges=vis_edges) - - - if len(valid_remove_cc) > 0: - remove_face_indices = remove_face_indices[torch.cat(valid_remove_cc)] - mask = torch.ones(faces.shape[0], dtype=torch.bool, device=faces.device) - mask[remove_face_indices] = 0 - faces = faces[mask] - faces, verts = utils3d.torch.remove_unreferenced_vertices(faces, verts) - if verbose: - tqdm.write(f'Removed {(~mask).sum()} faces by mincut') - else: - if verbose: - tqdm.write(f'Removed 0 faces by mincut') - - mesh = _meshfix.PyTMesh() - mesh.load_array(verts.cpu().numpy(), faces.cpu().numpy()) - mesh.fill_small_boundaries(nbe=max_hole_nbe, refine=True) - verts, faces = mesh.return_arrays() - verts, faces = torch.tensor(verts, device='cuda', dtype=torch.float32), torch.tensor(faces, device='cuda', dtype=torch.int32) - - return verts, faces - - -def postprocess_mesh( - vertices: np.array, - faces: np.array, - simplify: bool = True, - simplify_ratio: float = 0.9, - fill_holes: bool = True, - fill_holes_max_hole_size: float = 0.04, - fill_holes_max_hole_nbe: int = 32, - fill_holes_resolution: int = 1024, - fill_holes_num_views: int = 1000, - debug: bool = False, - verbose: bool = False, -): - """ - Postprocess a mesh by simplifying, removing invisible faces, and removing isolated pieces. - - Args: - vertices (np.array): Vertices of the mesh. Shape (V, 3). - faces (np.array): Faces of the mesh. Shape (F, 3). - simplify (bool): Whether to simplify the mesh, using quadric edge collapse. - simplify_ratio (float): Ratio of faces to keep after simplification. - fill_holes (bool): Whether to fill holes in the mesh. - fill_holes_max_hole_size (float): Maximum area of a hole to fill. - fill_holes_max_hole_nbe (int): Maximum number of boundary edges of a hole to fill. - fill_holes_resolution (int): Resolution of the rasterization. - fill_holes_num_views (int): Number of views to rasterize the mesh. - verbose (bool): Whether to print progress. - """ - - if verbose: - tqdm.write(f'Before postprocess: {vertices.shape[0]} vertices, {faces.shape[0]} faces') - - # Simplify - if simplify and simplify_ratio > 0: - mesh = pv.PolyData(vertices, np.concatenate([np.full((faces.shape[0], 1), 3), faces], axis=1)) - mesh = mesh.decimate(simplify_ratio, progress_bar=verbose) - vertices, faces = mesh.points, mesh.faces.reshape(-1, 4)[:, 1:] - if verbose: - tqdm.write(f'After decimate: {vertices.shape[0]} vertices, {faces.shape[0]} faces') - - # Remove invisible faces - if fill_holes: - vertices, faces = torch.tensor(vertices).cuda(), torch.tensor(faces.astype(np.int32)).cuda() - vertices, faces = _fill_holes( - vertices, faces, - max_hole_size=fill_holes_max_hole_size, - max_hole_nbe=fill_holes_max_hole_nbe, - resolution=fill_holes_resolution, - num_views=fill_holes_num_views, - debug=debug, - verbose=verbose, - ) - vertices, faces = vertices.cpu().numpy(), faces.cpu().numpy() - if verbose: - tqdm.write(f'After remove invisible faces: {vertices.shape[0]} vertices, {faces.shape[0]} faces') - - return vertices, faces - - -def parametrize_mesh(vertices: np.array, faces: np.array): - """ - Parametrize a mesh to a texture space, using xatlas. - - Args: - vertices (np.array): Vertices of the mesh. Shape (V, 3). - faces (np.array): Faces of the mesh. Shape (F, 3). - """ - - vmapping, indices, uvs = xatlas.parametrize(vertices, faces) - - vertices = vertices[vmapping] - faces = indices - - return vertices, faces, uvs - - -def bake_texture( - vertices: np.array, - faces: np.array, - uvs: np.array, - observations: List[np.array], - masks: List[np.array], - extrinsics: List[np.array], - intrinsics: List[np.array], - texture_size: int = 2048, - near: float = 0.1, - far: float = 10.0, - mode: Literal['fast', 'opt'] = 'opt', - lambda_tv: float = 1e-2, - verbose: bool = False, -): - """ - Bake texture to a mesh from multiple observations. - - Args: - vertices (np.array): Vertices of the mesh. Shape (V, 3). - faces (np.array): Faces of the mesh. Shape (F, 3). - uvs (np.array): UV coordinates of the mesh. Shape (V, 2). - observations (List[np.array]): List of observations. Each observation is a 2D image. Shape (H, W, 3). - masks (List[np.array]): List of masks. Each mask is a 2D image. Shape (H, W). - extrinsics (List[np.array]): List of extrinsics. Shape (4, 4). - intrinsics (List[np.array]): List of intrinsics. Shape (3, 3). - texture_size (int): Size of the texture. - near (float): Near plane of the camera. - far (float): Far plane of the camera. - mode (Literal['fast', 'opt']): Mode of texture baking. - lambda_tv (float): Weight of total variation loss in optimization. - verbose (bool): Whether to print progress. - """ - vertices = torch.tensor(vertices).cuda() - faces = torch.tensor(faces.astype(np.int32)).cuda() - uvs = torch.tensor(uvs).cuda() - observations = [torch.tensor(obs / 255.0).float().cuda() for obs in observations] - masks = [torch.tensor(m>0).bool().cuda() for m in masks] - views = [utils3d.torch.extrinsics_to_view(torch.tensor(extr).cuda()) for extr in extrinsics] - projections = [utils3d.torch.intrinsics_to_perspective(torch.tensor(intr).cuda(), near, far) for intr in intrinsics] - - steps = len(views) - comfy_pbar = comfy.utils.ProgressBar(steps) - - if mode == 'fast': - texture = torch.zeros((texture_size * texture_size, 3), dtype=torch.float32).cuda() - texture_weights = torch.zeros((texture_size * texture_size), dtype=torch.float32).cuda() - rastctx = utils3d.torch.RastContext(backend='cuda') - for i, (observation, view, projection) in enumerate(tqdm(zip(observations, views, projections), total=steps, disable=not verbose, desc='Texture baking (fast)')): - with torch.no_grad(): - rast = utils3d.torch.rasterize_triangle_faces( - rastctx, vertices[None], faces, observation.shape[1], observation.shape[0], uv=uvs[None], view=view, projection=projection - ) - uv_map = rast['uv'][0].detach().flip(0) - mask = rast['mask'][0].detach().bool() & masks[0] - - # nearest neighbor interpolation - uv_map = (uv_map * texture_size).floor().long() - obs = observation[mask] - uv_map = uv_map[mask] - idx = uv_map[:, 0] + (texture_size - uv_map[:, 1] - 1) * texture_size - texture = texture.scatter_add(0, idx.view(-1, 1).expand(-1, 3), obs) - texture_weights = texture_weights.scatter_add(0, idx, torch.ones((obs.shape[0]), dtype=torch.float32, device=texture.device)) - - comfy_pbar.update_absolute(i + 1) - - mask = texture_weights > 0 - texture[mask] /= texture_weights[mask][:, None] - texture = np.clip(texture.reshape(texture_size, texture_size, 3).cpu().numpy() * 255, 0, 255).astype(np.uint8) - - # inpaint - mask = (texture_weights == 0).cpu().numpy().astype(np.uint8).reshape(texture_size, texture_size) - texture = cv2.inpaint(texture, mask, 3, cv2.INPAINT_TELEA) - - elif mode == 'opt': - rastctx = utils3d.torch.RastContext(backend='cuda') - observations = [observations.flip(0) for observations in observations] - masks = [m.flip(0) for m in masks] - _uv = [] - _uv_dr = [] - for i, (observation, view, projection) in enumerate(tqdm(zip(observations, views, projections), total=steps, disable=not verbose, desc='Texture baking (opt): UV')): - with torch.no_grad(): - rast = utils3d.torch.rasterize_triangle_faces( - rastctx, vertices[None], faces, observation.shape[1], observation.shape[0], uv=uvs[None], view=view, projection=projection - ) - _uv.append(rast['uv'].detach()) - _uv_dr.append(rast['uv_dr'].detach()) - - comfy_pbar.update_absolute(i + 1) - - texture = torch.nn.Parameter(torch.zeros((1, texture_size, texture_size, 3), dtype=torch.float32).cuda()) - optimizer = torch.optim.Adam([texture], betas=(0.5, 0.9), lr=1e-2) - - def exp_anealing(optimizer, step, total_steps, start_lr, end_lr): - return start_lr * (end_lr / start_lr) ** (step / total_steps) - - def cosine_anealing(optimizer, step, total_steps, start_lr, end_lr): - return end_lr + 0.5 * (start_lr - end_lr) * (1 + np.cos(np.pi * step / total_steps)) - - def tv_loss(texture): - return torch.nn.functional.l1_loss(texture[:, :-1, :, :], texture[:, 1:, :, :]) + \ - torch.nn.functional.l1_loss(texture[:, :, :-1, :], texture[:, :, 1:, :]) - - total_steps = 2500 - comfy_pbar = comfy.utils.ProgressBar(total_steps) - with tqdm(total=total_steps, disable=not verbose, desc='Texture baking (opt): optimizing') as pbar: - for step in range(total_steps): - optimizer.zero_grad() - selected = np.random.randint(0, len(views)) - uv, uv_dr, observation, mask = _uv[selected], _uv_dr[selected], observations[selected], masks[selected] - render = dr.texture(texture, uv, uv_dr)[0] - loss = torch.nn.functional.l1_loss(render[mask], observation[mask]) - if lambda_tv > 0: - loss += lambda_tv * tv_loss(texture) - loss.backward() - optimizer.step() - # annealing - optimizer.param_groups[0]['lr'] = cosine_anealing(optimizer, step, total_steps, 1e-2, 1e-5) - pbar.set_postfix({'loss': loss.item()}) - pbar.update() - - comfy_pbar.update_absolute(step + 1) - - texture = np.clip(texture[0].flip(0).detach().cpu().numpy() * 255, 0, 255).astype(np.uint8) - mask = 1 - utils3d.torch.rasterize_triangle_faces( - rastctx, (uvs * 2 - 1)[None], faces, texture_size, texture_size - )['mask'][0].detach().cpu().numpy().astype(np.uint8) - texture = cv2.inpaint(texture, mask, 3, cv2.INPAINT_TELEA) - else: - raise ValueError(f'Unknown mode: {mode}') - - return texture - - -def finalize_mesh( - app_rep: Union[Strivec, Gaussian], - mesh: MeshExtractResult, - simplify: float = 0.95, - fill_holes: bool = True, - fill_holes_max_size: float = 0.04, - texture_size: int = 1024, - debug: bool = False, - verbose: bool = True, -): - """ - Convert a generated asset to a glb file. - - Args: - app_rep (Union[Strivec, Gaussian]): Appearance representation. - mesh (MeshExtractResult): Extracted mesh. - simplify (float): Ratio of faces to remove in simplification. - fill_holes (bool): Whether to fill holes in the mesh. - fill_holes_max_size (float): Maximum area of a hole to fill. - texture_size (int): Size of the texture. - debug (bool): Whether to print debug information. - verbose (bool): Whether to print progress. - """ - vertices = mesh.vertices.cpu().numpy() - faces = mesh.faces.cpu().numpy() - - # mesh postprocess - vertices, faces = postprocess_mesh( - vertices, faces, - simplify=simplify > 0, - simplify_ratio=simplify, - fill_holes=fill_holes, - fill_holes_max_hole_size=fill_holes_max_size, - fill_holes_max_hole_nbe=int(250 * np.sqrt(1-simplify)), - fill_holes_resolution=1024, - fill_holes_num_views=1000, - debug=debug, - verbose=verbose, - ) - - # parametrize mesh - vertices, faces, uvs = parametrize_mesh(vertices, faces) - - # bake texture - observations, extrinsics, intrinsics = render_multiview(app_rep, resolution=1024, nviews=100) - masks = [np.any(observation > 0, axis=-1) for observation in observations] - extrinsics = [extrinsics[i].cpu().numpy() for i in range(len(extrinsics))] - intrinsics = [intrinsics[i].cpu().numpy() for i in range(len(intrinsics))] - texture = bake_texture( - vertices, faces, uvs, - observations, masks, extrinsics, intrinsics, - texture_size=texture_size, mode='opt', - lambda_tv=0.01, - verbose=verbose - ) - texture = texture.astype(np.float32) / 255 - uvs[:, 1] = 1 - uvs[:, 1] - - # rotate mesh (from z-up to y-up) - vertices = vertices @ np.array([[1, 0, 0], [0, 0, -1], [0, 1, 0]]) - return vertices, faces, uvs, texture diff --git a/Gen_3D_Modules/TRELLIS/trellis_/utils/random_utils.py b/Gen_3D_Modules/TRELLIS/trellis_/utils/random_utils.py deleted file mode 100644 index 5b668c27..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/utils/random_utils.py +++ /dev/null @@ -1,30 +0,0 @@ -import numpy as np - -PRIMES = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53] - -def radical_inverse(base, n): - val = 0 - inv_base = 1.0 / base - inv_base_n = inv_base - while n > 0: - digit = n % base - val += digit * inv_base_n - n //= base - inv_base_n *= inv_base - return val - -def halton_sequence(dim, n): - return [radical_inverse(PRIMES[dim], n) for dim in range(dim)] - -def hammersley_sequence(dim, n, num_samples): - return [n / num_samples] + halton_sequence(dim - 1, n) - -def sphere_hammersley_sequence(n, num_samples, offset=(0, 0), remap=False): - u, v = hammersley_sequence(2, n, num_samples) - u += offset[0] / num_samples - v += offset[1] - if remap: - u = 2 * u if u < 0.25 else 2 / 3 * u + 1 / 3 - theta = np.arccos(1 - 2 * u) - np.pi / 2 - phi = v * 2 * np.pi - return [phi, theta] \ No newline at end of file diff --git a/Gen_3D_Modules/TRELLIS/trellis_/utils/render_utils.py b/Gen_3D_Modules/TRELLIS/trellis_/utils/render_utils.py deleted file mode 100644 index 8187c84f..00000000 --- a/Gen_3D_Modules/TRELLIS/trellis_/utils/render_utils.py +++ /dev/null @@ -1,116 +0,0 @@ -import torch -import numpy as np -from tqdm import tqdm -import utils3d -from PIL import Image - -from ..renderers import OctreeRenderer, GaussianRenderer, MeshRenderer -from ..representations import Octree, Gaussian, MeshExtractResult -from ..modules import sparse as sp -from .random_utils import sphere_hammersley_sequence - - -def yaw_pitch_r_fov_to_extrinsics_intrinsics(yaws, pitchs, rs, fovs): - is_list = isinstance(yaws, list) - if not is_list: - yaws = [yaws] - pitchs = [pitchs] - if not isinstance(rs, list): - rs = [rs] * len(yaws) - if not isinstance(fovs, list): - fovs = [fovs] * len(yaws) - extrinsics = [] - intrinsics = [] - for yaw, pitch, r, fov in zip(yaws, pitchs, rs, fovs): - fov = torch.deg2rad(torch.tensor(float(fov))).cuda() - yaw = torch.tensor(float(yaw)).cuda() - pitch = torch.tensor(float(pitch)).cuda() - orig = torch.tensor([ - torch.sin(yaw) * torch.cos(pitch), - torch.cos(yaw) * torch.cos(pitch), - torch.sin(pitch), - ]).cuda() * r - extr = utils3d.torch.extrinsics_look_at(orig, torch.tensor([0, 0, 0]).float().cuda(), torch.tensor([0, 0, 1]).float().cuda()) - intr = utils3d.torch.intrinsics_from_fov_xy(fov, fov) - extrinsics.append(extr) - intrinsics.append(intr) - if not is_list: - extrinsics = extrinsics[0] - intrinsics = intrinsics[0] - return extrinsics, intrinsics - - -def render_frames(sample, extrinsics, intrinsics, options={}, colors_overwrite=None, verbose=True, **kwargs): - if isinstance(sample, Octree): - renderer = OctreeRenderer() - renderer.rendering_options.resolution = options.get('resolution', 512) - renderer.rendering_options.near = options.get('near', 0.8) - renderer.rendering_options.far = options.get('far', 1.6) - renderer.rendering_options.bg_color = options.get('bg_color', (0, 0, 0)) - renderer.rendering_options.ssaa = options.get('ssaa', 4) - renderer.pipe.primitive = sample.primitive - elif isinstance(sample, Gaussian): - renderer = GaussianRenderer() - renderer.rendering_options.resolution = options.get('resolution', 512) - renderer.rendering_options.near = options.get('near', 0.8) - renderer.rendering_options.far = options.get('far', 1.6) - renderer.rendering_options.bg_color = options.get('bg_color', (0, 0, 0)) - renderer.rendering_options.ssaa = options.get('ssaa', 1) - renderer.pipe.kernel_size = kwargs.get('kernel_size', 0.1) - renderer.pipe.use_mip_gaussian = True - elif isinstance(sample, MeshExtractResult): - renderer = MeshRenderer() - renderer.rendering_options.resolution = options.get('resolution', 512) - renderer.rendering_options.near = options.get('near', 1) - renderer.rendering_options.far = options.get('far', 100) - renderer.rendering_options.ssaa = options.get('ssaa', 4) - else: - raise ValueError(f'Unsupported sample type: {type(sample)}') - - rets = {} - for j, (extr, intr) in tqdm(enumerate(zip(extrinsics, intrinsics)), desc='Rendering', disable=not verbose): - if not isinstance(sample, MeshExtractResult): - res = renderer.render(sample, extr, intr, colors_overwrite=colors_overwrite) - if 'color' not in rets: rets['color'] = [] - if 'depth' not in rets: rets['depth'] = [] - rets['color'].append(np.clip(res['color'].detach().cpu().numpy().transpose(1, 2, 0) * 255, 0, 255).astype(np.uint8)) - if 'percent_depth' in res: - rets['depth'].append(res['percent_depth'].detach().cpu().numpy()) - elif 'depth' in res: - rets['depth'].append(res['depth'].detach().cpu().numpy()) - else: - rets['depth'].append(None) - else: - res = renderer.render(sample, extr, intr) - if 'normal' not in rets: rets['normal'] = [] - rets['normal'].append(np.clip(res['normal'].detach().cpu().numpy().transpose(1, 2, 0) * 255, 0, 255).astype(np.uint8)) - return rets - - -def render_video(sample, resolution=512, bg_color=(0, 0, 0), num_frames=300, r=2, fov=40, **kwargs): - yaws = torch.linspace(0, 2 * 3.1415, num_frames) - pitch = 0.25 + 0.5 * torch.sin(torch.linspace(0, 2 * 3.1415, num_frames)) - yaws = yaws.tolist() - pitch = pitch.tolist() - extrinsics, intrinsics = yaw_pitch_r_fov_to_extrinsics_intrinsics(yaws, pitch, r, fov) - return render_frames(sample, extrinsics, intrinsics, {'resolution': resolution, 'bg_color': bg_color}, **kwargs) - - -def render_multiview(sample, resolution=512, nviews=30): - r = 2 - fov = 40 - cams = [sphere_hammersley_sequence(i, nviews) for i in range(nviews)] - yaws = [cam[0] for cam in cams] - pitchs = [cam[1] for cam in cams] - extrinsics, intrinsics = yaw_pitch_r_fov_to_extrinsics_intrinsics(yaws, pitchs, r, fov) - res = render_frames(sample, extrinsics, intrinsics, {'resolution': resolution, 'bg_color': (0, 0, 0)}) - return res['color'], extrinsics, intrinsics - - -def render_snapshot(samples, resolution=512, bg_color=(0, 0, 0), offset=(-16 / 180 * np.pi, 20 / 180 * np.pi), r=10, fov=8, **kwargs): - yaw = [0, np.pi/2, np.pi, 3*np.pi/2] - yaw_offset = offset[0] - yaw = [y + yaw_offset for y in yaw] - pitch = [offset[1] for _ in range(4)] - extrinsics, intrinsics = yaw_pitch_r_fov_to_extrinsics_intrinsics(yaw, pitch, r, fov) - return render_frames(samples, extrinsics, intrinsics, {'resolution': resolution, 'bg_color': bg_color}, **kwargs) diff --git a/nodes.py b/nodes.py index d99f7119..374332ea 100644 --- a/nodes.py +++ b/nodes.py @@ -239,39 +239,6 @@ def preview_mesh(self, mesh_file_path): ] return {"ui": {"previews": previews}, "result": ()} -class Preview_3DMesh2: - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "mesh": ("MESH", ), - }, - } - - OUTPUT_NODE = True - RETURN_TYPES = () - FUNCTION = "preview_mesh2" - CATEGORY = "Comfy3D/Visualize" - - def preview_mesh2(self, mesh): - - mesh_folder_path, filename = os.path.split(mesh_file_path) - - if not os.path.isabs(mesh_file_path): - mesh_file_path = os.path.join(comfy_paths.output_directory, mesh_folder_path) - - if not filename.lower().endswith(SUPPORTED_3D_EXTENSIONS): - cstr(f"[{self.__class__.__name__}] File name {filename} does not end with supported 3D file extensions: {SUPPORTED_3D_EXTENSIONS}").error.print() - mesh_file_path = "" - - previews = [ - { - "filepath": mesh_file_path, - } - ] - return {"ui": {"previews": previews}, "result": ()} - class Load_3D_Mesh: @classmethod @@ -3992,13 +3959,13 @@ def INPUT_TYPES(cls): "reference_image": ("IMAGE",), "reference_mask": ("MASK",), "seed": ("INT", {"default": 1, "min": 0, "max": 0xffffffffffffffff}), - "sparse_structure_guidance_scale": ("FLOAT", {"default": 7.5, "min": 0.0, "step": 0.01}), + "sparse_structure_guidance_scale": ("FLOAT", {"default": 7.5, "min": 0.0, "step": 0.1}), "sparse_structure_sample_steps": ("INT", {"default": 12, "min": 1}), - "structured_latent_guidance_scale": ("FLOAT", {"default": 3.0, "min": 0.0, "step": 0.01}), + "structured_latent_guidance_scale": ("FLOAT", {"default": 3.0, "min": 0.0, "step": 0.1}), "structured_latent_sample_steps": ("INT", {"default": 12, "min": 1}), - "texture_size": ("INT", {"default": 1024, "min": 256, "max": 4096, "step": 256}), + "texture_size": ("INT", {"default": 1024, "min": 64, "max": 4096, "step": 64}), "render_resolution": ("INT", {"default": 1024, "min": 256, "max": 4096, "step": 256}), - "simplify_ratio": ("FLOAT", {"default": 0.95, "min": 0.80, "max": 0.99, "step": 0.01}), + "simplify_ratio": ("FLOAT", {"default": 0.95, "min": 0.80, "max": 0.99999, "step": 0.01, "round": False}), } } @@ -4028,23 +3995,41 @@ def run_model( render_resolution, simplify_ratio, ): - single_image = torch_imgs_to_pils(reference_image, reference_mask)[0] + + + images = torch_imgs_to_pils(reference_image, reference_mask) with torch.inference_mode(False): - outputs = trellis_pipe.run( - single_image, - # Optional parameters - seed=seed, - formats=["gaussian", "mesh"], - sparse_structure_sampler_params={ - "cfg_strength": sparse_structure_guidance_scale, - "steps": sparse_structure_sample_steps, - }, - slat_sampler_params={ - "cfg_strength": structured_latent_guidance_scale, - "steps": structured_latent_sample_steps, - }, - ) + if (len(images) == 1): + outputs = trellis_pipe.run( + images[0], + # Optional parameters + seed=seed, + formats=["gaussian", "mesh"], + sparse_structure_sampler_params={ + "cfg_strength": sparse_structure_guidance_scale, + "steps": sparse_structure_sample_steps, + }, + slat_sampler_params={ + "cfg_strength": structured_latent_guidance_scale, + "steps": structured_latent_sample_steps, + }, + ) + else: + outputs = trellis_pipe.run_multi_image( + images, + # Optional parameters + seed=seed, + formats=["gaussian", "mesh"], + sparse_structure_sampler_params={ + "cfg_strength": sparse_structure_guidance_scale, + "steps": sparse_structure_sample_steps, + }, + slat_sampler_params={ + "cfg_strength": structured_latent_guidance_scale, + "steps": structured_latent_sample_steps, + }, + ) ## GLB files can be extracted from the outputs #vertices, faces, uvs, texture = postprocessing_utils.finalize_mesh( @@ -4088,9 +4073,6 @@ def run_model( verbose=False ) - #texture = np.transpose(texture / 255.0, (2,0,1)) - #texture_raw = texture - vertices = vertices @ np.array([[1, 0, 0], [0, 0, -1], [0, 1, 0]]) texture_0 = torch.flip(torch.tensor(torch.from_numpy(texture / 255.0).unsqueeze(0)), (1,))

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