From 0768ba7a75abfee607dca5079ef7259a50c239d1 Mon Sep 17 00:00:00 2001 From: "Mr. For Example" <62230687+MrForExample@users.noreply.github.com> Date: Tue, 29 Apr 2025 16:50:36 -0600 Subject: [PATCH 01/36] Integrated TripoSG plus Scribble model --- .../VAST-AI/TripoSG-scribble/.gitattributes | 35 + .../VAST-AI/TripoSG-scribble/README.md | 49 + .../preprocessor_config.json | 27 + .../image_encoder_dinov2/config.json | 60 + .../VAST-AI/TripoSG-scribble/model_index.json | 32 + .../scheduler/scheduler_config.json | 7 + .../TripoSG-scribble/text_encoder/config.json | 24 + .../TripoSG-scribble/tokenizer/merges.txt | 48895 +++++++++++++++ .../tokenizer/special_tokens_map.json | 30 + .../tokenizer/tokenizer_config.json | 31 + .../TripoSG-scribble/tokenizer/vocab.json | 49410 ++++++++++++++++ .../TripoSG-scribble/transformer/config.json | 11 + .../VAST-AI/TripoSG-scribble/vae/config.json | 14 + .../Diffusers/VAST-AI/TripoSG/.gitattributes | 36 + .../Diffusers/VAST-AI/TripoSG/README.md | 45 + .../preprocessor_config.json | 27 + .../TripoSG/image_encoder_dinov2/config.json | 60 + .../VAST-AI/TripoSG/model_index.json | 24 + .../TripoSG/scheduler/scheduler_config.json | 7 + .../VAST-AI/TripoSG/transformer/config.json | 9 + .../Diffusers/VAST-AI/TripoSG/vae/config.json | 14 + Gen_3D_Modules/TripoSG/LICENSE | 80 + Gen_3D_Modules/TripoSG/inference_utils.py | 479 + .../TripoSG/models/attention_processor.py | 420 + .../TripoSG/models/autoencoders/__init__.py | 1 + .../autoencoders/autoencoder_kl_triposg.py | 536 + .../TripoSG/models/autoencoders/vae.py | 69 + Gen_3D_Modules/TripoSG/models/embeddings.py | 96 + .../TripoSG/models/transformers/__init__.py | 61 + .../models/transformers/modeling_outputs.py | 8 + .../transformers/triposg_transformer.py | 775 + .../TripoSG/pipelines/pipeline_triposg.py | 329 + .../pipelines/pipeline_triposg_output.py | 17 + .../pipelines/pipeline_triposg_scribble.py | 343 + .../TripoSG/pipelines/pipeline_utils.py | 96 + Gen_3D_Modules/TripoSG/schedulers/__init__.py | 5 + .../schedulers/scheduling_rectified_flow.py | 327 + Gen_3D_Modules/TripoSG/utils/typing.py | 64 + .../TripoSG_Scribble_Model.json | 1 + _Example_Workflows/TripoSG_to_Mesh.json | 1 + mesh_processer/mesh_utils.py | 2 + nodes.py | 150 +- requirements.txt | 1 + 43 files changed, 102700 insertions(+), 8 deletions(-) create mode 100644 Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/.gitattributes create mode 100644 Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/README.md create mode 100644 Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/feature_extractor_dinov2/preprocessor_config.json create mode 100644 Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/image_encoder_dinov2/config.json create mode 100644 Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/model_index.json create mode 100644 Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/scheduler/scheduler_config.json create mode 100644 Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/text_encoder/config.json create mode 100644 Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/tokenizer/merges.txt create mode 100644 Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/tokenizer/special_tokens_map.json create mode 100644 Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/tokenizer/tokenizer_config.json create mode 100644 Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/tokenizer/vocab.json create mode 100644 Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/transformer/config.json create mode 100644 Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/vae/config.json create mode 100644 Checkpoints/Diffusers/VAST-AI/TripoSG/.gitattributes create mode 100644 Checkpoints/Diffusers/VAST-AI/TripoSG/README.md create mode 100644 Checkpoints/Diffusers/VAST-AI/TripoSG/feature_extractor_dinov2/preprocessor_config.json create mode 100644 Checkpoints/Diffusers/VAST-AI/TripoSG/image_encoder_dinov2/config.json create mode 100644 Checkpoints/Diffusers/VAST-AI/TripoSG/model_index.json create mode 100644 Checkpoints/Diffusers/VAST-AI/TripoSG/scheduler/scheduler_config.json create mode 100644 Checkpoints/Diffusers/VAST-AI/TripoSG/transformer/config.json create mode 100644 Checkpoints/Diffusers/VAST-AI/TripoSG/vae/config.json create mode 100644 Gen_3D_Modules/TripoSG/LICENSE create mode 100644 Gen_3D_Modules/TripoSG/inference_utils.py create mode 100644 Gen_3D_Modules/TripoSG/models/attention_processor.py create mode 100644 Gen_3D_Modules/TripoSG/models/autoencoders/__init__.py create mode 100644 Gen_3D_Modules/TripoSG/models/autoencoders/autoencoder_kl_triposg.py create mode 100644 Gen_3D_Modules/TripoSG/models/autoencoders/vae.py create mode 100644 Gen_3D_Modules/TripoSG/models/embeddings.py create mode 100644 Gen_3D_Modules/TripoSG/models/transformers/__init__.py create mode 100644 Gen_3D_Modules/TripoSG/models/transformers/modeling_outputs.py create mode 100644 Gen_3D_Modules/TripoSG/models/transformers/triposg_transformer.py create mode 100644 Gen_3D_Modules/TripoSG/pipelines/pipeline_triposg.py create mode 100644 Gen_3D_Modules/TripoSG/pipelines/pipeline_triposg_output.py create mode 100644 Gen_3D_Modules/TripoSG/pipelines/pipeline_triposg_scribble.py create mode 100644 Gen_3D_Modules/TripoSG/pipelines/pipeline_utils.py create mode 100644 Gen_3D_Modules/TripoSG/schedulers/__init__.py create mode 100644 Gen_3D_Modules/TripoSG/schedulers/scheduling_rectified_flow.py create mode 100644 Gen_3D_Modules/TripoSG/utils/typing.py create mode 100644 _Example_Workflows/TripoSG_Scribble_Model.json create mode 100644 _Example_Workflows/TripoSG_to_Mesh.json diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/.gitattributes b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/.gitattributes new file mode 100644 index 0000000..a6344aa --- /dev/null +++ b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs 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filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/README.md b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/README.md new file mode 100644 index 0000000..58a903f --- /dev/null +++ b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/README.md @@ -0,0 +1,49 @@ +--- +license: mit +pipeline_tag: image-to-3d +tags: +- triposg +- 3d-generation +- rectified-flow +--- +# TripoSG-scribble - Fast 3D Shape Prototyping with Scribble and Prompt + +TripoSG-scribble converts a scribble image and a text prompt to a 3D shape. TripoSG-scribble is a variant of TripoSG. TripoSG is a state-of-the-art image-to-3D generation foundation model that leverages large-scale rectified flow transformers to produce high-fidelity 3D shapes from single images. + +## Model Description + +### Model Architecture + +TripoSG utilizes a novel architecture combining: +- Rectified Flow (RF) based Transformer for stable, linear trajectory modeling +- Advanced VAE with SDF-based representation and hybrid geometric supervision +- Cross-attention mechanism for image feature condition +- 1.5B parameters operating on 2048 latent tokens + +For inference efficiency, TripoSG-scribble is different from TripoSG in: +- TripoSG-scribble is a CFG-distilled model and should be used with CFG=0 +- TripoSG-scribble is trained with 512 latent tokens + +## Intended Uses + +This model is designed for: +- Converting scribble image and text prompt to high-quality 3D meshes +- Creative and design applications +- Gaming and VFX asset creation +- Prototyping and visualization + +## Requirements + +- CUDA-capable GPU (>8GB VRAM) + +## Usage + +For detailed usage instructions, please visit our [GitHub repository](https://github.com/VAST-AI-Research/TripoSG). + +## About + +TripoSG-scribble is developed by [Tripo](https://www.tripo3d.ai), [VAST AI Research](https://github.com/orgs/VAST-AI-Research), pushing the boundaries of 3D Generative AI. +For more information: +- [GitHub Repository](https://github.com/VAST-AI-Research/TripoSG) +- [Paper](https://arxiv.org/abs/2502.06608) +- [Gradio Demo](https://huggingface.co/spaces/VAST-AI/TripoSG-scribble) \ No newline at end of file diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/feature_extractor_dinov2/preprocessor_config.json b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/feature_extractor_dinov2/preprocessor_config.json new file mode 100644 index 0000000..ff5b47c --- /dev/null +++ b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/feature_extractor_dinov2/preprocessor_config.json @@ -0,0 +1,27 @@ +{ + "crop_size": { + "height": 224, + "width": 224 + }, + "do_center_crop": true, + "do_convert_rgb": true, + "do_normalize": true, + "do_rescale": true, + "do_resize": true, + "image_mean": [ + 0.485, + 0.456, + 0.406 + ], + "image_processor_type": "BitImageProcessor", + "image_std": [ + 0.229, + 0.224, + 0.225 + ], + "resample": 3, + "rescale_factor": 0.00392156862745098, + "size": { + "shortest_edge": 256 + } +} diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/image_encoder_dinov2/config.json b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/image_encoder_dinov2/config.json new file mode 100644 index 0000000..0e2c2ac --- /dev/null +++ b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/image_encoder_dinov2/config.json @@ -0,0 +1,60 @@ +{ + "apply_layernorm": true, + "architectures": [ + "Dinov2Model" + ], + "attention_probs_dropout_prob": 0.0, + "drop_path_rate": 0.0, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.0, + "hidden_size": 1024, + "image_size": 518, + "initializer_range": 0.02, + "layer_norm_eps": 1e-06, + "layerscale_value": 1.0, + "mlp_ratio": 4, + "model_type": "dinov2", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 24, + "out_features": [ + "stage24" + ], + "out_indices": [ + 24 + ], + "patch_size": 14, + "qkv_bias": true, + "reshape_hidden_states": true, + "stage_names": [ + "stem", + "stage1", + "stage2", + "stage3", + "stage4", + "stage5", + "stage6", + "stage7", + "stage8", + "stage9", + "stage10", + "stage11", + "stage12", + "stage13", + "stage14", + "stage15", + "stage16", + "stage17", + "stage18", + "stage19", + "stage20", + "stage21", + "stage22", + "stage23", + "stage24" + ], + "torch_dtype": "float16", + "transformers_version": "4.50.3", + "use_mask_token": true, + "use_swiglu_ffn": false +} diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/model_index.json b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/model_index.json new file mode 100644 index 0000000..f80ab2f --- /dev/null +++ b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/model_index.json @@ -0,0 +1,32 @@ +{ + "_class_name": "TripoSGScribblePipeline", + "_diffusers_version": "0.33.1", + "feature_extractor_dinov2": [ + "transformers", + "BitImageProcessor" + ], + "image_encoder_dinov2": [ + "transformers", + "Dinov2Model" + ], + "scheduler": [ + "TripoSG.schedulers.scheduling_rectified_flow", + "RectifiedFlowScheduler" + ], + "text_encoder": [ + "transformers", + "CLIPTextModelWithProjection" + ], + "tokenizer": [ + "transformers", + "CLIPTokenizer" + ], + "transformer": [ + "TripoSG.models.transformers.triposg_transformer", + "TripoSGDiTModel" + ], + "vae": [ + "TripoSG.models.autoencoders.autoencoder_kl_triposg", + "TripoSGVAEModel" + ] +} diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/scheduler/scheduler_config.json b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/scheduler/scheduler_config.json new file mode 100644 index 0000000..8bc9f2b --- /dev/null +++ b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/scheduler/scheduler_config.json @@ -0,0 +1,7 @@ +{ + "_class_name": "RectifiedFlowScheduler", + "_diffusers_version": "0.33.1", + "num_train_timesteps": 1000, + "shift": 1, + "use_dynamic_shifting": false +} diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/text_encoder/config.json b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/text_encoder/config.json new file mode 100644 index 0000000..8f7a43f --- /dev/null +++ b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/text_encoder/config.json @@ -0,0 +1,24 @@ +{ + "architectures": [ + "CLIPTextModelWithProjection" + ], + "attention_dropout": 0.0, + "bos_token_id": 0, + "dropout": 0.0, + "eos_token_id": 2, + "hidden_act": "quick_gelu", + "hidden_size": 768, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 77, + "model_type": "clip_text_model", + "num_attention_heads": 12, + "num_hidden_layers": 12, + "pad_token_id": 1, + "projection_dim": 768, + "torch_dtype": "float16", + "transformers_version": "4.50.3", + "vocab_size": 49408 +} diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/tokenizer/merges.txt b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/tokenizer/merges.txt new file mode 100644 index 0000000..76e821f --- /dev/null +++ b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/tokenizer/merges.txt @@ -0,0 +1,48895 @@ +#version: 0.2 +i n +t h +a n +r e +a r +e r +th e +in g +o u +o n +s t +o r +e n +o n +a l +a t +e r +i t +i n +t o +r o +i s +l e +i c +a t +an d +e d +o f +c h +o r +e s +i l +e l +s t +a c +o m +a m +l o +a n +a y +s h +r i +l i +t i +f or +n e +ð Ł +r a +h a +d e +o l +v e +s i +u r +a l +s e +' s +u n +d i +b e +l a +w h +o o +d ay +e n +m a +n o +l e +t o +ou r +i r +g h +w it +i t +y o +a s +s p +th is +t s +at i +yo u +wit h +a d +i s +a b +l y +w e +th e +t e +a s +a g +v i +p p +s u +h o +m y +. . +b u +c om +s e +er s +m e +m e +al l +c on +m o +k e +g e +ou t +en t +c o +f e +v er +a r +f ro +a u +p o +c e +gh t +ar e +s s +fro m +c h +t r +ou n +on e +b y +d o +t h +w or +er e +k e +p ro +f or +d s +b o +t a +w e +g o +h e +t er +in g +d e +b e +ati on +m or +a y +e x +il l +p e +k s +s c +l u +f u +q u +v er +ðŁ ĺ +j u +m u +at e +an d +v e +k ing +m ar +o p +h i +.. . +p re +a d +r u +th at +j o +o f +c e +ne w +a m +a p +g re +s s +d u +no w +y e +t ing +y our +it y +n i +c i +p ar +g u +f i +a f +p er +t er +u p +s o +g i +on s +g r +g e +b r +p l +' t +m i +in e +we e +b i +u s +sh o +ha ve +to day +a v +m an +en t +ac k +ur e +ou r +â Ģ +c u +l d +lo o +i m +ic e +s om +f in +re d +re n +oo d +w as +ti on +p i +i r +th er +t y +p h +ar d +e c +! ! +m on +mor e +w ill +t ra +c an +c ol +p u +t e +w n +m b +s o +it i +ju st +n ing +h ere +t u +p a +p r +bu t +wh at +al ly +f ir +m in +c a +an t +s a +t ed +e v +m ent +f a +ge t +am e +ab out +g ra +no t +ha pp +ay s +m an +h is +ti me +li ke 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255, + "Ń": 511 +} diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/transformer/config.json b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/transformer/config.json new file mode 100644 index 0000000..bd1e3ba --- /dev/null +++ b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/transformer/config.json @@ -0,0 +1,11 @@ +{ + "_class_name": "TripoSGDiTModel", + "_diffusers_version": "0.33.1", + "cross_attention_2_dim": 1024, + "cross_attention_dim": 768, + "in_channels": 64, + "num_attention_heads": 16, + "num_layers": 21, + "use_cross_attention_2": true, + "width": 2048 +} diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/vae/config.json b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/vae/config.json new file mode 100644 index 0000000..b35bce7 --- /dev/null +++ b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/vae/config.json @@ -0,0 +1,14 @@ +{ + "_class_name": "TripoSGVAEModel", + "_diffusers_version": "0.33.1", + "embed_frequency": 8, + "embed_include_pi": false, + 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filter=lfs diff=lfs merge=lfs -text +*.tar.gz filter=lfs diff=lfs merge=lfs -text diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG/README.md b/Checkpoints/Diffusers/VAST-AI/TripoSG/README.md new file mode 100644 index 0000000..d48215b --- /dev/null +++ b/Checkpoints/Diffusers/VAST-AI/TripoSG/README.md @@ -0,0 +1,45 @@ +--- +license: mit +pipeline_tag: image-to-3d +tags: +- triposg +- 3d-generation +- rectified-flow +--- +# TripoSG - High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Models + +TripoSG is a state-of-the-art image-to-3D generation foundation model that leverages large-scale rectified flow transformers to produce high-fidelity 3D shapes from single images. + +## Model Description + +### Model Architecture + +TripoSG utilizes a novel architecture combining: +- Rectified Flow (RF) based Transformer for stable, linear trajectory modeling +- Advanced VAE with SDF-based representation and hybrid geometric supervision +- Cross-attention mechanism for image feature condition +- 1.5B parameters operating on 2048 latent tokens + +## Intended Uses + +This model is designed for: +- Converting single images to high-quality 3D meshes +- Creative and design applications +- Gaming and VFX asset creation +- Prototyping and visualization + +## Requirements + +- CUDA-capable GPU (>8GB VRAM) + +## Usage + +For detailed usage instructions, please visit our [GitHub repository](https://github.com/VAST-AI-Research/TripoSG). + +## About + +TripoSG is developed by [Tripo](https://www.tripo3d.ai), [VAST AI Research](https://github.com/orgs/VAST-AI-Research), pushing the boundaries of 3D Generative AI. +For more information: +- [GitHub Repository](https://github.com/VAST-AI-Research/TripoSG) +- [Paper](https://arxiv.org/abs/2502.06608) +- [Gradio Demo](https://huggingface.co/spaces/VAST-AI/TripoSG) \ No newline at end of file diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG/feature_extractor_dinov2/preprocessor_config.json b/Checkpoints/Diffusers/VAST-AI/TripoSG/feature_extractor_dinov2/preprocessor_config.json new file mode 100644 index 0000000..ff5b47c --- /dev/null +++ b/Checkpoints/Diffusers/VAST-AI/TripoSG/feature_extractor_dinov2/preprocessor_config.json @@ -0,0 +1,27 @@ +{ + "crop_size": { + "height": 224, + "width": 224 + }, + "do_center_crop": true, + "do_convert_rgb": true, + "do_normalize": true, + "do_rescale": true, + "do_resize": true, + "image_mean": [ + 0.485, + 0.456, + 0.406 + ], + "image_processor_type": "BitImageProcessor", + "image_std": [ + 0.229, + 0.224, + 0.225 + ], + "resample": 3, + "rescale_factor": 0.00392156862745098, + "size": { + "shortest_edge": 256 + } +} diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG/image_encoder_dinov2/config.json b/Checkpoints/Diffusers/VAST-AI/TripoSG/image_encoder_dinov2/config.json new file mode 100644 index 0000000..c3f240c --- /dev/null +++ b/Checkpoints/Diffusers/VAST-AI/TripoSG/image_encoder_dinov2/config.json @@ -0,0 +1,60 @@ +{ + "_name_or_path": "facebook/dinov2-large", + "apply_layernorm": true, + "architectures": [ + "Dinov2Model" + ], + "attention_probs_dropout_prob": 0.0, + "drop_path_rate": 0.0, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.0, + "hidden_size": 1024, + "image_size": 518, + "initializer_range": 0.02, + "layer_norm_eps": 1e-06, + "layerscale_value": 1.0, + "mlp_ratio": 4, + "model_type": "dinov2", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 24, + "out_features": [ + "stage24" + ], + "out_indices": [ + 24 + ], + "patch_size": 14, + "qkv_bias": true, + "reshape_hidden_states": true, + "stage_names": [ + "stem", + "stage1", + "stage2", + "stage3", + "stage4", + "stage5", + "stage6", + "stage7", + "stage8", + "stage9", + "stage10", + "stage11", + "stage12", + "stage13", + "stage14", + "stage15", + "stage16", + "stage17", + "stage18", + "stage19", + "stage20", + "stage21", + "stage22", + "stage23", + "stage24" + ], + "torch_dtype": "float32", + "transformers_version": "4.45.2", + "use_swiglu_ffn": false +} diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG/model_index.json b/Checkpoints/Diffusers/VAST-AI/TripoSG/model_index.json new file mode 100644 index 0000000..5d01bbd --- /dev/null +++ b/Checkpoints/Diffusers/VAST-AI/TripoSG/model_index.json @@ -0,0 +1,24 @@ +{ + "_class_name": "TripoSGPipeline", + "_diffusers_version": "0.30.3", + "feature_extractor_dinov2": [ + "transformers", + "BitImageProcessor" + ], + "image_encoder_dinov2": [ + "transformers", + "Dinov2Model" + ], + "scheduler": [ + "TripoSG.schedulers.scheduling_rectified_flow", + "RectifiedFlowScheduler" + ], + "transformer": [ + "TripoSG.models.transformers.triposg_transformer", + "TripoSGDiTModel" + ], + "vae": [ + "TripoSG.models.autoencoders.autoencoder_kl_triposg", + "TripoSGVAEModel" + ] +} diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG/scheduler/scheduler_config.json b/Checkpoints/Diffusers/VAST-AI/TripoSG/scheduler/scheduler_config.json new file mode 100644 index 0000000..da0fa53 --- /dev/null +++ b/Checkpoints/Diffusers/VAST-AI/TripoSG/scheduler/scheduler_config.json @@ -0,0 +1,7 @@ +{ + "_class_name": "RectifiedFlowScheduler", + "_diffusers_version": "0.30.3", + "num_train_timesteps": 1000, + "shift": 1, + "use_dynamic_shifting": false +} diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG/transformer/config.json b/Checkpoints/Diffusers/VAST-AI/TripoSG/transformer/config.json new file mode 100644 index 0000000..a21d98d --- /dev/null +++ b/Checkpoints/Diffusers/VAST-AI/TripoSG/transformer/config.json @@ -0,0 +1,9 @@ +{ + "_class_name": "TripoSGDiTModel", + "_diffusers_version": "0.30.3", + "cross_attention_dim": 1024, + "in_channels": 64, + "num_attention_heads": 16, + "num_layers": 21, + "width": 2048 +} diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG/vae/config.json b/Checkpoints/Diffusers/VAST-AI/TripoSG/vae/config.json new file mode 100644 index 0000000..c5d72c0 --- /dev/null +++ b/Checkpoints/Diffusers/VAST-AI/TripoSG/vae/config.json @@ -0,0 +1,14 @@ +{ + "_class_name": "Tripo2VAEModel", + "_diffusers_version": "0.30.3", + "embed_frequency": 8, + "embed_include_pi": false, + "embedding_type": "frequency", + "in_channels": 3, + "latent_channels": 64, + "num_attention_heads": 8, + "num_layers_decoder": 16, + "num_layers_encoder": 8, + "width_decoder": 1024, + "width_encoder": 512 +} diff --git a/Gen_3D_Modules/TripoSG/LICENSE b/Gen_3D_Modules/TripoSG/LICENSE new file mode 100644 index 0000000..92e3953 --- /dev/null +++ b/Gen_3D_Modules/TripoSG/LICENSE @@ -0,0 +1,80 @@ +TENCENT HUNYUAN FLASHVDM COMMUNITY LICENSE AGREEMENT +Tencent Hunyuan FlashVDM Release Date: March 19, 2025 +THIS LICENSE AGREEMENT DOES NOT APPLY IN THE EUROPEAN UNION, UNITED KINGDOM AND SOUTH KORE AND IS EXPRESSLY LIMITED TO THE TERRITORY, AS DEFINED BELOW. +By clicking to agree or by using, reproducing, modifying, distributing, performing or displaying any portion or element of the Tencent 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For any use intended to discriminate against or harm individuals or groups based on protected characteristics or categories, online or offline social behavior or known or predicted personal or personality characteristics; +18. To intentionally exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm; +19. For military purposes; +20. To engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or other professional practices. \ No newline at end of file diff --git a/Gen_3D_Modules/TripoSG/inference_utils.py b/Gen_3D_Modules/TripoSG/inference_utils.py new file mode 100644 index 0000000..99f8a7e --- /dev/null +++ b/Gen_3D_Modules/TripoSG/inference_utils.py @@ -0,0 +1,479 @@ +import numpy as np +import torch +import torch.nn as nn +import scipy.ndimage +from skimage import measure +from einops import repeat +from diso import DiffDMC +import torch.nn.functional as F + +from TripoSG.utils.typing import * + +def generate_dense_grid_points_gpu(bbox_min: torch.Tensor, + bbox_max: torch.Tensor, + octree_depth: int, + indexing: str = "ij"): + length = bbox_max - bbox_min + num_cells = 2 ** octree_depth + device = bbox_min.device + + x = torch.linspace(bbox_min[0], bbox_max[0], int(num_cells), dtype=torch.float16, device=device) + y = torch.linspace(bbox_min[1], bbox_max[1], int(num_cells), dtype=torch.float16, device=device) + z = torch.linspace(bbox_min[2], bbox_max[2], int(num_cells), dtype=torch.float16, device=device) + + xs, ys, zs = torch.meshgrid(x, y, z, indexing=indexing) + xyz = torch.stack((xs, ys, zs), dim=-1) + xyz = xyz.view(-1, 3) + grid_size = [int(num_cells), int(num_cells), int(num_cells)] + + return xyz, grid_size, length + +def find_mesh_grid_coordinates_fast_gpu(occupancy_grid, n_limits=-1): + core_grid = occupancy_grid[1:-1, 1:-1, 1:-1] + occupied = core_grid > 0 + + neighbors_unoccupied = ( + (occupancy_grid[:-2, :-2, :-2] < 0) + | (occupancy_grid[:-2, :-2, 1:-1] < 0) + | (occupancy_grid[:-2, :-2, 2:] < 0) # x-1, y-1, z-1/0/1 + | (occupancy_grid[:-2, 1:-1, :-2] < 0) + | (occupancy_grid[:-2, 1:-1, 1:-1] < 0) + | (occupancy_grid[:-2, 1:-1, 2:] < 0) # x-1, y0, z-1/0/1 + | (occupancy_grid[:-2, 2:, :-2] < 0) + | (occupancy_grid[:-2, 2:, 1:-1] < 0) + | (occupancy_grid[:-2, 2:, 2:] < 0) # x-1, y+1, z-1/0/1 + | (occupancy_grid[1:-1, :-2, :-2] < 0) + | (occupancy_grid[1:-1, :-2, 1:-1] < 0) + | (occupancy_grid[1:-1, :-2, 2:] < 0) # x0, y-1, z-1/0/1 + | (occupancy_grid[1:-1, 1:-1, :-2] < 0) + | (occupancy_grid[1:-1, 1:-1, 2:] < 0) # x0, y0, z-1/1 + | (occupancy_grid[1:-1, 2:, :-2] < 0) + | (occupancy_grid[1:-1, 2:, 1:-1] < 0) + | (occupancy_grid[1:-1, 2:, 2:] < 0) # x0, y+1, z-1/0/1 + | (occupancy_grid[2:, :-2, :-2] < 0) + | (occupancy_grid[2:, :-2, 1:-1] < 0) + | (occupancy_grid[2:, :-2, 2:] < 0) # x+1, y-1, z-1/0/1 + | (occupancy_grid[2:, 1:-1, :-2] < 0) + | (occupancy_grid[2:, 1:-1, 1:-1] < 0) + | (occupancy_grid[2:, 1:-1, 2:] < 0) # x+1, y0, z-1/0/1 + | (occupancy_grid[2:, 2:, :-2] < 0) + | (occupancy_grid[2:, 2:, 1:-1] < 0) + | (occupancy_grid[2:, 2:, 2:] < 0) # x+1, y+1, z-1/0/1 + ) + core_mesh_coords = torch.nonzero(occupied & neighbors_unoccupied, as_tuple=False) + 1 + + if n_limits != -1 and core_mesh_coords.shape[0] > n_limits: + print(f"core mesh coords {core_mesh_coords.shape[0]} is too large, limited to {n_limits}") + ind = np.random.choice(core_mesh_coords.shape[0], n_limits, True) + core_mesh_coords = core_mesh_coords[ind] + + return core_mesh_coords + +def find_candidates_band(occupancy_grid: torch.Tensor, band_threshold: float, n_limits: int = -1) -> torch.Tensor: + """ + Returns the coordinates of all voxels in the occupancy_grid where |value| < band_threshold. + + Args: + occupancy_grid (torch.Tensor): A 3D tensor of SDF values. + band_threshold (float): The threshold below which |SDF| must be to include the voxel. + n_limits (int): Maximum number of points to return (-1 for no limit) + + Returns: + torch.Tensor: A 2D tensor of coordinates (N x 3) where each row is [x, y, z]. + """ + core_grid = occupancy_grid[1:-1, 1:-1, 1:-1] + # logits to sdf + core_grid = torch.sigmoid(core_grid) * 2 - 1 + # Create a boolean mask for all cells in the band + in_band = torch.abs(core_grid) < band_threshold + + # Get coordinates of all voxels in the band + core_mesh_coords = torch.nonzero(in_band, as_tuple=False) + 1 + + if n_limits != -1 and core_mesh_coords.shape[0] > n_limits: + print(f"core mesh coords {core_mesh_coords.shape[0]} is too large, limited to {n_limits}") + ind = np.random.choice(core_mesh_coords.shape[0], n_limits, True) + core_mesh_coords = core_mesh_coords[ind] + + return core_mesh_coords + +def expand_edge_region_fast(edge_coords, grid_size): + expanded_tensor = torch.zeros(grid_size, grid_size, grid_size, device='cuda', dtype=torch.float16, requires_grad=False) + expanded_tensor[edge_coords[:, 0], edge_coords[:, 1], edge_coords[:, 2]] = 1 + if grid_size < 512: + kernel_size = 5 + pooled_tensor = torch.nn.functional.max_pool3d(expanded_tensor.unsqueeze(0).unsqueeze(0), kernel_size=kernel_size, stride=1, padding=2).squeeze() + else: + kernel_size = 3 + pooled_tensor = torch.nn.functional.max_pool3d(expanded_tensor.unsqueeze(0).unsqueeze(0), kernel_size=kernel_size, stride=1, padding=1).squeeze() + expanded_coords_low_res = torch.nonzero(pooled_tensor, as_tuple=False).to(torch.int16) + + expanded_coords_high_res = torch.stack([ + torch.cat((expanded_coords_low_res[:, 0] * 2, expanded_coords_low_res[:, 0] * 2, expanded_coords_low_res[:, 0] * 2, expanded_coords_low_res[:, 0] * 2, expanded_coords_low_res[:, 0] * 2 + 1, expanded_coords_low_res[:, 0] * 2 + 1, expanded_coords_low_res[:, 0] * 2 + 1, expanded_coords_low_res[:, 0] * 2 + 1)), + torch.cat((expanded_coords_low_res[:, 1] * 2, expanded_coords_low_res[:, 1] * 2, expanded_coords_low_res[:, 1] * 2+1, expanded_coords_low_res[:, 1] * 2 + 1, expanded_coords_low_res[:, 1] * 2, expanded_coords_low_res[:, 1] * 2, expanded_coords_low_res[:, 1] * 2 + 1, expanded_coords_low_res[:, 1] * 2 + 1)), + torch.cat((expanded_coords_low_res[:, 2] * 2, expanded_coords_low_res[:, 2] * 2+1, expanded_coords_low_res[:, 2] * 2, expanded_coords_low_res[:, 2] * 2 + 1, expanded_coords_low_res[:, 2] * 2, expanded_coords_low_res[:, 2] * 2+1, expanded_coords_low_res[:, 2] * 2, expanded_coords_low_res[:, 2] * 2 + 1)) + ], dim=1) + + return expanded_coords_high_res + +def zoom_block(block, scale_factor, order=3): + block = block.astype(np.float32) + return scipy.ndimage.zoom(block, scale_factor, order=order) + +def parallel_zoom(occupancy_grid, scale_factor): + result = torch.nn.functional.interpolate(occupancy_grid.unsqueeze(0).unsqueeze(0), scale_factor=scale_factor) + return result.squeeze(0).squeeze(0) + + +@torch.no_grad() +def hierarchical_extract_geometry(geometric_func: Callable, + device: torch.device, + bounds: Union[Tuple[float], List[float], float] = (-1.25, -1.25, -1.25, 1.25, 1.25, 1.25), + dense_octree_depth: int = 8, + hierarchical_octree_depth: int = 9, + ): + """ + + Args: + geometric_func: + device: + bounds: + dense_octree_depth: + hierarchical_octree_depth: + Returns: + + """ + if isinstance(bounds, float): + bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds] + + bbox_min = torch.tensor(bounds[0:3]).to(device) + bbox_max = torch.tensor(bounds[3:6]).to(device) + bbox_size = bbox_max - bbox_min + + xyz_samples, grid_size, length = generate_dense_grid_points_gpu( + bbox_min=bbox_min, + bbox_max=bbox_max, + octree_depth=dense_octree_depth, + indexing="ij" + ) + + print(f'step 1 query num: {xyz_samples.shape[0]}') + grid_logits = geometric_func(xyz_samples.unsqueeze(0)).to(torch.float16).view(grid_size[0], grid_size[1], grid_size[2]) + # print(f'step 1 grid_logits shape: {grid_logits.shape}') + for i in range(hierarchical_octree_depth - dense_octree_depth): + curr_octree_depth = dense_octree_depth + i + 1 + # upsample + grid_size = 2**curr_octree_depth + normalize_offset = grid_size / 2 + high_res_occupancy = parallel_zoom(grid_logits, 2) + + band_threshold = 1.0 + edge_coords = find_candidates_band(grid_logits, band_threshold) + expanded_coords = expand_edge_region_fast(edge_coords, grid_size=int(grid_size/2)).to(torch.float16) + print(f'step {i+2} query num: {len(expanded_coords)}') + expanded_coords_norm = (expanded_coords - normalize_offset) * (abs(bounds[0]) / normalize_offset) + + all_logits = None + + all_logits = geometric_func(expanded_coords_norm.unsqueeze(0)).to(torch.float16) + all_logits = torch.cat([expanded_coords_norm, all_logits[0]], dim=1) + # print("all logits shape = ", all_logits.shape) + + indices = all_logits[..., :3] + indices = indices * (normalize_offset / abs(bounds[0])) + normalize_offset + indices = indices.type(torch.IntTensor) + values = all_logits[:, 3] + # breakpoint() + high_res_occupancy[indices[:, 0], indices[:, 1], indices[:, 2]] = values + grid_logits = high_res_occupancy + torch.cuda.empty_cache() + mesh_v_f = [] + try: + print("final grids shape = ", grid_logits.shape) + vertices, faces, normals, _ = measure.marching_cubes(grid_logits.float().cpu().numpy(), 0, method="lewiner") + vertices = vertices / (2**hierarchical_octree_depth) * bbox_size.cpu().numpy() + bbox_min.cpu().numpy() + mesh_v_f = (vertices.astype(np.float32), np.ascontiguousarray(faces)) + except Exception as e: + print(e) + torch.cuda.empty_cache() + mesh_v_f = (None, None) + + return [mesh_v_f] + +def extract_near_surface_volume_fn(input_tensor: torch.Tensor, alpha: float): + """ + Args: + input_tensor: shape [D, D, D], torch.float16 + alpha: isosurface offset + Returns: + mask: shape [D, D, D], torch.int32 + """ + device = input_tensor.device + D = input_tensor.shape[0] + signed_val = 0.0 + + # add isosurface offset and exclude invalid value + val = input_tensor + alpha + valid_mask = val > -9000 + + # obtain neighbors + def get_neighbor(t, shift, axis): + if shift == 0: + return t.clone() + + pad_dims = [0, 0, 0, 0, 0, 0] # [x_front，x_back，y_front，y_back，z_front，z_back] + + if axis == 0: # x axis + pad_idx = 0 if shift > 0 else 1 + pad_dims[pad_idx] = abs(shift) + elif axis == 1: # y axis + pad_idx = 2 if shift > 0 else 3 + pad_dims[pad_idx] = abs(shift) + elif axis == 2: # z axis + pad_idx = 4 if shift > 0 else 5 + pad_dims[pad_idx] = abs(shift) + + # Apply padding with replication at boundaries + padded = F.pad(t.unsqueeze(0).unsqueeze(0), pad_dims[::-1], mode='replicate') + + # Create dynamic slicing indices + slice_dims = [slice(None)] * 3 + if axis == 0: # x axis + if shift > 0: + slice_dims[0] = slice(shift, None) + else: + slice_dims[0] = slice(None, shift) + elif axis == 1: # y axis + if shift > 0: + slice_dims[1] = slice(shift, None) + else: + slice_dims[1] = slice(None, shift) + elif axis == 2: # z axis + if shift > 0: + slice_dims[2] = slice(shift, None) + else: + slice_dims[2] = slice(None, shift) + + # Apply slicing and restore dimensions + padded = padded.squeeze(0).squeeze(0) + sliced = padded[slice_dims] + return sliced + + # Get neighbors in all directions + left = get_neighbor(val, 1, axis=0) # x axis + right = get_neighbor(val, -1, axis=0) + back = get_neighbor(val, 1, axis=1) # y axis + front = get_neighbor(val, -1, axis=1) + down = get_neighbor(val, 1, axis=2) # z axis + up = get_neighbor(val, -1, axis=2) + + # Handle invalid boundary values + def safe_where(neighbor): + return torch.where(neighbor > -9000, neighbor, val) + + left = safe_where(left) + right = safe_where(right) + back = safe_where(back) + front = safe_where(front) + down = safe_where(down) + up = safe_where(up) + + # Calculate sign consistency + sign = torch.sign(val.to(torch.float32)) + neighbors_sign = torch.stack([ + torch.sign(left.to(torch.float32)), + torch.sign(right.to(torch.float32)), + torch.sign(back.to(torch.float32)), + torch.sign(front.to(torch.float32)), + torch.sign(down.to(torch.float32)), + torch.sign(up.to(torch.float32)) + ], dim=0) + + # Check if all signs are consistent + same_sign = torch.all(neighbors_sign == sign, dim=0) + + # Generate final mask + mask = (~same_sign).to(torch.int32) + return mask * valid_mask.to(torch.int32) + + +def generate_dense_grid_points_2( + bbox_min: np.ndarray, + bbox_max: np.ndarray, + octree_resolution: int, + indexing: str = "ij", +): + length = bbox_max - bbox_min + num_cells = octree_resolution + + x = np.linspace(bbox_min[0], bbox_max[0], int(num_cells) + 1, dtype=np.float32) + y = np.linspace(bbox_min[1], bbox_max[1], int(num_cells) + 1, dtype=np.float32) + z = np.linspace(bbox_min[2], bbox_max[2], int(num_cells) + 1, dtype=np.float32) + [xs, ys, zs] = np.meshgrid(x, y, z, indexing=indexing) + xyz = np.stack((xs, ys, zs), axis=-1) + grid_size = [int(num_cells) + 1, int(num_cells) + 1, int(num_cells) + 1] + + return xyz, grid_size, length + +@torch.no_grad() +def flash_extract_geometry( + latents: torch.FloatTensor, + vae: Callable, + bounds: Union[Tuple[float], List[float], float] = 1.01, + num_chunks: int = 10000, + mc_level: float = 0.0, + octree_depth: int = 9, + min_resolution: int = 63, + mini_grid_num: int = 4, + **kwargs, +): + geo_decoder = vae.decoder + device = latents.device + dtype = latents.dtype + # resolution to depth + octree_resolution = 2 ** octree_depth + resolutions = [] + if octree_resolution < min_resolution: + resolutions.append(octree_resolution) + while octree_resolution >= min_resolution: + resolutions.append(octree_resolution) + octree_resolution = octree_resolution // 2 + resolutions.reverse() + resolutions[0] = round(resolutions[0] / mini_grid_num) * mini_grid_num - 1 + for i, resolution in enumerate(resolutions[1:]): + resolutions[i + 1] = resolutions[0] * 2 ** (i + 1) + + + # 1. generate query points + if isinstance(bounds, float): + bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds] + bbox_min = np.array(bounds[0:3]) + bbox_max = np.array(bounds[3:6]) + bbox_size = bbox_max - bbox_min + + xyz_samples, grid_size, length = generate_dense_grid_points_2( + bbox_min=bbox_min, + bbox_max=bbox_max, + octree_resolution=resolutions[0], + indexing="ij" + ) + + dilate = nn.Conv3d(1, 1, 3, padding=1, bias=False, device=device, dtype=dtype) + dilate.weight = torch.nn.Parameter(torch.ones(dilate.weight.shape, dtype=dtype, device=device)) + + grid_size = np.array(grid_size) + + # 2. latents to 3d volume + xyz_samples = torch.from_numpy(xyz_samples).to(device, dtype=dtype) + batch_size = latents.shape[0] + mini_grid_size = xyz_samples.shape[0] // mini_grid_num + xyz_samples = xyz_samples.view( + mini_grid_num, mini_grid_size, + mini_grid_num, mini_grid_size, + mini_grid_num, mini_grid_size, 3 + ).permute( + 0, 2, 4, 1, 3, 5, 6 + ).reshape( + -1, mini_grid_size * mini_grid_size * mini_grid_size, 3 + ) + batch_logits = [] + num_batchs = max(num_chunks // xyz_samples.shape[1], 1) + for start in range(0, xyz_samples.shape[0], num_batchs): + queries = xyz_samples[start: start + num_batchs, :] + batch = queries.shape[0] + batch_latents = repeat(latents.squeeze(0), "p c -> b p c", b=batch) + # geo_decoder.set_topk(True) + geo_decoder.set_topk(False) + logits = vae.decode(batch_latents, queries).sample + batch_logits.append(logits) + grid_logits = torch.cat(batch_logits, dim=0).reshape( + mini_grid_num, mini_grid_num, mini_grid_num, + mini_grid_size, mini_grid_size, + mini_grid_size + ).permute(0, 3, 1, 4, 2, 5).contiguous().view( + (batch_size, grid_size[0], grid_size[1], grid_size[2]) + ) + + for octree_depth_now in resolutions[1:]: + grid_size = np.array([octree_depth_now + 1] * 3) + resolution = bbox_size / octree_depth_now + next_index = torch.zeros(tuple(grid_size), dtype=dtype, device=device) + next_logits = torch.full(next_index.shape, -10000., dtype=dtype, device=device) + curr_points = extract_near_surface_volume_fn(grid_logits.squeeze(0), mc_level) + curr_points += grid_logits.squeeze(0).abs() < 0.95 + + if octree_depth_now == resolutions[-1]: + expand_num = 0 + else: + expand_num = 1 + for i in range(expand_num): + curr_points = dilate(curr_points.unsqueeze(0).to(dtype)).squeeze(0) + curr_points = dilate(curr_points.unsqueeze(0).to(dtype)).squeeze(0) + (cidx_x, cidx_y, cidx_z) = torch.where(curr_points > 0) + + next_index[cidx_x * 2, cidx_y * 2, cidx_z * 2] = 1 + for i in range(2 - expand_num): + next_index = dilate(next_index.unsqueeze(0)).squeeze(0) + nidx = torch.where(next_index > 0) + + next_points = torch.stack(nidx, dim=1) + next_points = (next_points * torch.tensor(resolution, dtype=torch.float32, device=device) + + torch.tensor(bbox_min, dtype=torch.float32, device=device)) + + query_grid_num = 6 + min_val = next_points.min(axis=0).values + max_val = next_points.max(axis=0).values + vol_queries_index = (next_points - min_val) / (max_val - min_val) * (query_grid_num - 0.001) + index = torch.floor(vol_queries_index).long() + index = index[..., 0] * (query_grid_num ** 2) + index[..., 1] * query_grid_num + index[..., 2] + index = index.sort() + next_points = next_points[index.indices].unsqueeze(0).contiguous() + unique_values = torch.unique(index.values, return_counts=True) + grid_logits = torch.zeros((next_points.shape[1]), dtype=latents.dtype, device=latents.device) + input_grid = [[], []] + logits_grid_list = [] + start_num = 0 + sum_num = 0 + for grid_index, count in zip(unique_values[0].cpu().tolist(), unique_values[1].cpu().tolist()): + if sum_num + count < num_chunks or sum_num == 0: + sum_num += count + input_grid[0].append(grid_index) + input_grid[1].append(count) + else: + # geo_decoder.set_topk(input_grid) + geo_decoder.set_topk(False) + logits_grid = vae.decode(latents,next_points[:, start_num:start_num + sum_num]).sample + start_num = start_num + sum_num + logits_grid_list.append(logits_grid) + input_grid = [[grid_index], [count]] + sum_num = count + if sum_num > 0: + # geo_decoder.set_topk(input_grid) + geo_decoder.set_topk(False) + logits_grid = vae.decode(latents,next_points[:, start_num:start_num + sum_num]).sample + logits_grid_list.append(logits_grid) + logits_grid = torch.cat(logits_grid_list, dim=1) + grid_logits[index.indices] = logits_grid.squeeze(0).squeeze(-1) + next_logits[nidx] = grid_logits + grid_logits = next_logits.unsqueeze(0) + + grid_logits[grid_logits == -10000.] = float('nan') + torch.cuda.empty_cache() + mesh_v_f = [] + grid_logits = grid_logits[0] + try: + print("final grids shape = ", grid_logits.shape) + dmc = DiffDMC(dtype=torch.float32).to(grid_logits.device) + sdf = -grid_logits / octree_resolution + sdf = sdf.to(torch.float32).contiguous() + vertices, faces = dmc(sdf, deform=None, return_quads=False, normalize=False) + vertices = vertices.detach().cpu().numpy() + faces = faces.detach().cpu().numpy()[:, ::-1] + vertices = vertices / (2 ** octree_depth) * bbox_size + bbox_min + mesh_v_f = (vertices.astype(np.float32), np.ascontiguousarray(faces)) + except Exception as e: + print(e) + torch.cuda.empty_cache() + mesh_v_f = (None, None) + + return [mesh_v_f] \ No newline at end of file diff --git a/Gen_3D_Modules/TripoSG/models/attention_processor.py b/Gen_3D_Modules/TripoSG/models/attention_processor.py new file mode 100644 index 0000000..79379d6 --- /dev/null +++ b/Gen_3D_Modules/TripoSG/models/attention_processor.py @@ -0,0 +1,420 @@ +from typing import Callable, List, Optional, Tuple, Union + +import torch +import torch.nn.functional as F +from diffusers.models.attention_processor import Attention +from diffusers.models.embeddings import apply_rotary_emb +from diffusers.utils import logging +from einops import rearrange + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +class FlashTripoSGAttnProcessor2_0: + r""" + Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is + used in the Tripo2DiT model. It applies a s normalization layer and rotary embedding on query and key vector. + """ + + def __init__(self, topk=True): + if not hasattr(F, "scaled_dot_product_attention"): + raise ImportError( + "AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0." + ) + self.topk = topk + + def qkv(self, attn, q, k, v, attn_mask, dropout_p, is_causal): + if k.shape[-2] == 3072: + topk = 1024 + elif k.shape[-2] == 512: + topk = 256 + else: + topk = k.shape[-2] // 3 + + if self.topk is True: + q1 = q[:, :, ::100, :] + sim = q1 @ k.transpose(-1, -2) + sim = torch.mean(sim, -2) + topk_ind = torch.topk(sim, dim=-1, k=topk).indices.squeeze(-2).unsqueeze(-1) + topk_ind = topk_ind.expand(-1, -1, -1, v.shape[-1]) + v0 = torch.gather(v, dim=-2, index=topk_ind) + k0 = torch.gather(k, dim=-2, index=topk_ind) + out = F.scaled_dot_product_attention(q, k0, v0) + elif self.topk is False: + out = F.scaled_dot_product_attention(q, k, v) + else: + idx, counts = self.topk + start = 0 + outs = [] + for grid_coord, count in zip(idx, counts): + end = start + count + q_chunk = q[:, :, start:end, :] + q1 = q_chunk[:, :, ::50, :] + sim = q1 @ k.transpose(-1, -2) + sim = torch.mean(sim, -2) + topk_ind = torch.topk(sim, dim=-1, k=topk).indices.squeeze(-2).unsqueeze(-1) + topk_ind = topk_ind.expand(-1, -1, -1, v.shape[-1]) + v0 = torch.gather(v, dim=-2, index=topk_ind) + k0 = torch.gather(k, dim=-2, index=topk_ind) + out = F.scaled_dot_product_attention(q_chunk, k0, v0) + outs.append(out) + start += count + out = torch.cat(outs, dim=-2) + self.topk = False + return out + + def __call__( + self, + attn: Attention, + hidden_states: torch.Tensor, + encoder_hidden_states: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + temb: Optional[torch.Tensor] = None, + image_rotary_emb: Optional[torch.Tensor] = None, + **kwargs, + ) -> torch.Tensor: + residual = hidden_states + if attn.spatial_norm is not None: + hidden_states = attn.spatial_norm(hidden_states, temb) + + input_ndim = hidden_states.ndim + + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view( + batch_size, channel, height * width + ).transpose(1, 2) + + batch_size, sequence_length, _ = ( + hidden_states.shape + if encoder_hidden_states is None + else encoder_hidden_states.shape + ) + + if attention_mask is not None: + attention_mask = attn.prepare_attention_mask( + attention_mask, sequence_length, batch_size + ) + # scaled_dot_product_attention expects attention_mask shape to be + # (batch, heads, source_length, target_length) + attention_mask = attention_mask.view( + batch_size, attn.heads, -1, attention_mask.shape[-1] + ) + + if attn.group_norm is not None: + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose( + 1, 2 + ) + + query = attn.to_q(hidden_states) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states( + encoder_hidden_states + ) + + key = attn.to_k(encoder_hidden_states) + value = attn.to_v(encoder_hidden_states) + + # NOTE that tripo2 split heads first then split qkv or kv, like .view(..., attn.heads, 3, dim) + # instead of .view(..., 3, attn.heads, dim). So we need to re-split here. + if not attn.is_cross_attention: + qkv = torch.cat((query, key, value), dim=-1) + split_size = qkv.shape[-1] // attn.heads // 3 + qkv = qkv.view(batch_size, -1, attn.heads, split_size * 3) + query, key, value = torch.split(qkv, split_size, dim=-1) + else: + kv = torch.cat((key, value), dim=-1) + split_size = kv.shape[-1] // attn.heads // 2 + kv = kv.view(batch_size, -1, attn.heads, split_size * 2) + key, value = torch.split(kv, split_size, dim=-1) + + head_dim = key.shape[-1] + + query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + + key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + + if attn.norm_q is not None: + query = attn.norm_q(query) + if attn.norm_k is not None: + key = attn.norm_k(key) + + # Apply RoPE if needed + if image_rotary_emb is not None: + query = apply_rotary_emb(query, image_rotary_emb) + if not attn.is_cross_attention: + key = apply_rotary_emb(key, image_rotary_emb) + + # flashvdm topk + hidden_states = self.qkv(attn, query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False) + + hidden_states = hidden_states.transpose(1, 2).reshape( + batch_size, -1, attn.heads * head_dim + ) + hidden_states = hidden_states.to(query.dtype) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape( + batch_size, channel, height, width + ) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states + +class TripoSGAttnProcessor2_0: + r""" + Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is + used in the TripoSG model. It applies a s normalization layer and rotary embedding on query and key vector. + """ + + def __init__(self): + if not hasattr(F, "scaled_dot_product_attention"): + raise ImportError( + "AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0." + ) + + def __call__( + self, + attn: Attention, + hidden_states: torch.Tensor, + encoder_hidden_states: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + temb: Optional[torch.Tensor] = None, + image_rotary_emb: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + residual = hidden_states + if attn.spatial_norm is not None: + hidden_states = attn.spatial_norm(hidden_states, temb) + + input_ndim = hidden_states.ndim + + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view( + batch_size, channel, height * width + ).transpose(1, 2) + + batch_size, sequence_length, _ = ( + hidden_states.shape + if encoder_hidden_states is None + else encoder_hidden_states.shape + ) + + if attention_mask is not None: + attention_mask = attn.prepare_attention_mask( + attention_mask, sequence_length, batch_size + ) + # scaled_dot_product_attention expects attention_mask shape to be + # (batch, heads, source_length, target_length) + attention_mask = attention_mask.view( + batch_size, attn.heads, -1, attention_mask.shape[-1] + ) + + if attn.group_norm is not None: + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose( + 1, 2 + ) + + query = attn.to_q(hidden_states) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states( + encoder_hidden_states + ) + + key = attn.to_k(encoder_hidden_states) + value = attn.to_v(encoder_hidden_states) + + # NOTE that pre-trained models split heads first then split qkv or kv, like .view(..., attn.heads, 3, dim) + # instead of .view(..., 3, attn.heads, dim). So we need to re-split here. + if not attn.is_cross_attention: + qkv = torch.cat((query, key, value), dim=-1) + split_size = qkv.shape[-1] // attn.heads // 3 + qkv = qkv.view(batch_size, -1, attn.heads, split_size * 3) + query, key, value = torch.split(qkv, split_size, dim=-1) + else: + kv = torch.cat((key, value), dim=-1) + split_size = kv.shape[-1] // attn.heads // 2 + kv = kv.view(batch_size, -1, attn.heads, split_size * 2) + key, value = torch.split(kv, split_size, dim=-1) + + head_dim = key.shape[-1] + + query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + + key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + + if attn.norm_q is not None: + query = attn.norm_q(query) + if attn.norm_k is not None: + key = attn.norm_k(key) + + # Apply RoPE if needed + if image_rotary_emb is not None: + query = apply_rotary_emb(query, image_rotary_emb) + if not attn.is_cross_attention: + key = apply_rotary_emb(key, image_rotary_emb) + + # the output of sdp = (batch, num_heads, seq_len, head_dim) + # TODO: add support for attn.scale when we move to Torch 2.1 + hidden_states = F.scaled_dot_product_attention( + query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False + ) + + hidden_states = hidden_states.transpose(1, 2).reshape( + batch_size, -1, attn.heads * head_dim + ) + hidden_states = hidden_states.to(query.dtype) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape( + batch_size, channel, height, width + ) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states + + +class FusedTripoSGAttnProcessor2_0: + r""" + Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0) with fused + projection layers. This is used in the HunyuanDiT model. It applies a s normalization layer and rotary embedding on + query and key vector. + """ + + def __init__(self): + if not hasattr(F, "scaled_dot_product_attention"): + raise ImportError( + "FusedTripoSGAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0." + ) + + def __call__( + self, + attn: Attention, + hidden_states: torch.Tensor, + encoder_hidden_states: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + temb: Optional[torch.Tensor] = None, + image_rotary_emb: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + residual = hidden_states + if attn.spatial_norm is not None: + hidden_states = attn.spatial_norm(hidden_states, temb) + + input_ndim = hidden_states.ndim + + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view( + batch_size, channel, height * width + ).transpose(1, 2) + + batch_size, sequence_length, _ = ( + hidden_states.shape + if encoder_hidden_states is None + else encoder_hidden_states.shape + ) + + if attention_mask is not None: + attention_mask = attn.prepare_attention_mask( + attention_mask, sequence_length, batch_size + ) + # scaled_dot_product_attention expects attention_mask shape to be + # (batch, heads, source_length, target_length) + attention_mask = attention_mask.view( + batch_size, attn.heads, -1, attention_mask.shape[-1] + ) + + if attn.group_norm is not None: + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose( + 1, 2 + ) + + # NOTE that pre-trained split heads first, then split qkv + if encoder_hidden_states is None: + qkv = attn.to_qkv(hidden_states) + split_size = qkv.shape[-1] // attn.heads // 3 + qkv = qkv.view(batch_size, -1, attn.heads, split_size * 3) + query, key, value = torch.split(qkv, split_size, dim=-1) + else: + if attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states( + encoder_hidden_states + ) + query = attn.to_q(hidden_states) + + kv = attn.to_kv(encoder_hidden_states) + split_size = kv.shape[-1] // attn.heads // 2 + kv = kv.view(batch_size, -1, attn.heads, split_size * 2) + key, value = torch.split(kv, split_size, dim=-1) + + head_dim = key.shape[-1] + + query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + + if attn.norm_q is not None: + query = attn.norm_q(query) + if attn.norm_k is not None: + key = attn.norm_k(key) + + # Apply RoPE if needed + if image_rotary_emb is not None: + query = apply_rotary_emb(query, image_rotary_emb) + if not attn.is_cross_attention: + key = apply_rotary_emb(key, image_rotary_emb) + + # the output of sdp = (batch, num_heads, seq_len, head_dim) + # TODO: add support for attn.scale when we move to Torch 2.1 + hidden_states = F.scaled_dot_product_attention( + query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False + ) + + hidden_states = hidden_states.transpose(1, 2).reshape( + batch_size, -1, attn.heads * head_dim + ) + hidden_states = hidden_states.to(query.dtype) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape( + batch_size, channel, height, width + ) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states diff --git a/Gen_3D_Modules/TripoSG/models/autoencoders/__init__.py b/Gen_3D_Modules/TripoSG/models/autoencoders/__init__.py new file mode 100644 index 0000000..64a9ea2 --- /dev/null +++ b/Gen_3D_Modules/TripoSG/models/autoencoders/__init__.py @@ -0,0 +1 @@ +from .autoencoder_kl_triposg import TripoSGVAEModel diff --git a/Gen_3D_Modules/TripoSG/models/autoencoders/autoencoder_kl_triposg.py b/Gen_3D_Modules/TripoSG/models/autoencoders/autoencoder_kl_triposg.py new file mode 100644 index 0000000..f7ec6cd --- /dev/null +++ b/Gen_3D_Modules/TripoSG/models/autoencoders/autoencoder_kl_triposg.py @@ -0,0 +1,536 @@ +from typing import Dict, List, Optional, Tuple, Union + +import numpy as np +import torch +import torch.nn as nn +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.models.attention_processor import Attention, AttentionProcessor +from diffusers.models.autoencoders.vae import DecoderOutput +from diffusers.models.modeling_outputs import AutoencoderKLOutput +from diffusers.models.modeling_utils import ModelMixin +from diffusers.models.normalization import FP32LayerNorm, LayerNorm +from diffusers.utils import logging +from diffusers.utils.accelerate_utils import apply_forward_hook +from einops import repeat +# from torch_cluster import fps +from tqdm import tqdm + +from ..attention_processor import FusedTripoSGAttnProcessor2_0, TripoSGAttnProcessor2_0, FlashTripoSGAttnProcessor2_0 +from ..embeddings import FrequencyPositionalEmbedding +from ..transformers.triposg_transformer import DiTBlock +from .vae import DiagonalGaussianDistribution + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +class TripoSGEncoder(nn.Module): + def __init__( + self, + in_channels: int = 3, + dim: int = 512, + num_attention_heads: int = 8, + num_layers: int = 8, + ): + super().__init__() + + self.proj_in = nn.Linear(in_channels, dim, bias=True) + + self.blocks = nn.ModuleList( + [ + DiTBlock( + dim=dim, + num_attention_heads=num_attention_heads, + use_self_attention=False, + use_cross_attention=True, + cross_attention_dim=dim, + cross_attention_norm_type="layer_norm", + activation_fn="gelu", + norm_type="fp32_layer_norm", + norm_eps=1e-5, + qk_norm=False, + qkv_bias=False, + ) # cross attention + ] + + [ + DiTBlock( + dim=dim, + num_attention_heads=num_attention_heads, + use_self_attention=True, + self_attention_norm_type="fp32_layer_norm", + use_cross_attention=False, + activation_fn="gelu", + norm_type="fp32_layer_norm", + norm_eps=1e-5, + qk_norm=False, + qkv_bias=False, + ) + for _ in range(num_layers) # self attention + ] + ) + + self.norm_out = LayerNorm(dim) + + def forward(self, sample_1: torch.Tensor, sample_2: torch.Tensor): + hidden_states = self.proj_in(sample_1) + encoder_hidden_states = self.proj_in(sample_2) + + for layer, block in enumerate(self.blocks): + if layer == 0: + hidden_states = block( + hidden_states, encoder_hidden_states=encoder_hidden_states + ) + else: + hidden_states = block(hidden_states) + + hidden_states = self.norm_out(hidden_states) + + return hidden_states + + +class TripoSGDecoder(nn.Module): + def __init__( + self, + in_channels: int = 3, + out_channels: int = 1, + dim: int = 512, + num_attention_heads: int = 8, + num_layers: int = 16, + grad_type: str = "analytical", + grad_interval: float = 0.001, + ): + super().__init__() + + if grad_type not in ["numerical", "analytical"]: + raise ValueError(f"grad_type must be one of ['numerical', 'analytical']") + self.grad_type = grad_type + self.grad_interval = grad_interval + + self.blocks = nn.ModuleList( + [ + DiTBlock( + dim=dim, + num_attention_heads=num_attention_heads, + use_self_attention=True, + self_attention_norm_type="fp32_layer_norm", + use_cross_attention=False, + activation_fn="gelu", + norm_type="fp32_layer_norm", + norm_eps=1e-5, + qk_norm=False, + qkv_bias=False, + ) + for _ in range(num_layers) # self attention + ] + + [ + DiTBlock( + dim=dim, + num_attention_heads=num_attention_heads, + use_self_attention=False, + use_cross_attention=True, + cross_attention_dim=dim, + cross_attention_norm_type="layer_norm", + activation_fn="gelu", + norm_type="fp32_layer_norm", + norm_eps=1e-5, + qk_norm=False, + qkv_bias=False, + ) # cross attention + ] + ) + + self.proj_query = nn.Linear(in_channels, dim, bias=True) + + self.norm_out = LayerNorm(dim) + self.proj_out = nn.Linear(dim, out_channels, bias=True) + + def set_topk(self, topk): + self.blocks[-1].set_topk(topk) + + def set_flash_processor(self, processor): + self.blocks[-1].set_flash_processor(processor) + + def query_geometry( + self, + model_fn: callable, + queries: torch.Tensor, + sample: torch.Tensor, + grad: bool = False, + ): + logits = model_fn(queries, sample) + if grad: + with torch.autocast(device_type="cuda", dtype=torch.float32): + if self.grad_type == "numerical": + interval = self.grad_interval + grad_value = [] + for offset in [ + (interval, 0, 0), + (0, interval, 0), + (0, 0, interval), + ]: + offset_tensor = torch.tensor(offset, device=queries.device)[ + None, : + ] + res_p = model_fn(queries + offset_tensor, sample)[..., 0] + res_n = model_fn(queries - offset_tensor, sample)[..., 0] + grad_value.append((res_p - res_n) / (2 * interval)) + grad_value = torch.stack(grad_value, dim=-1) + else: + queries_d = torch.clone(queries) + queries_d.requires_grad = True + with torch.enable_grad(): + res_d = model_fn(queries_d, sample) + grad_value = torch.autograd.grad( + res_d, + [queries_d], + grad_outputs=torch.ones_like(res_d), + create_graph=self.training, + )[0] + else: + grad_value = None + + return logits, grad_value + + def forward( + self, + sample: torch.Tensor, + queries: torch.Tensor, + kv_cache: Optional[torch.Tensor] = None, + ): + if kv_cache is None: + hidden_states = sample + for _, block in enumerate(self.blocks[:-1]): + hidden_states = block(hidden_states) + kv_cache = hidden_states + + # query grid logits by cross attention + def query_fn(q, kv): + q = self.proj_query(q) + l = self.blocks[-1](q, encoder_hidden_states=kv) + return self.proj_out(self.norm_out(l)) + + logits, grad = self.query_geometry( + query_fn, queries, kv_cache, grad=self.training + ) + logits = logits * -1 if not isinstance(logits, Tuple) else logits[0] * -1 + + return logits, kv_cache + + +class TripoSGVAEModel(ModelMixin, ConfigMixin): + @register_to_config + def __init__( + self, + in_channels: int = 3, # NOTE xyz instead of feature dim + latent_channels: int = 64, + num_attention_heads: int = 8, + width_encoder: int = 512, + width_decoder: int = 1024, + num_layers_encoder: int = 8, + num_layers_decoder: int = 16, + embedding_type: str = "frequency", + embed_frequency: int = 8, + embed_include_pi: bool = False, + ): + super().__init__() + + self.out_channels = 1 + + if embedding_type == "frequency": + self.embedder = FrequencyPositionalEmbedding( + num_freqs=embed_frequency, + logspace=True, + input_dim=in_channels, + include_pi=embed_include_pi, + ) + else: + raise NotImplementedError( + f"Embedding type {embedding_type} is not supported." + ) + + self.encoder = TripoSGEncoder( + in_channels=in_channels + self.embedder.out_dim, + dim=width_encoder, + num_attention_heads=num_attention_heads, + num_layers=num_layers_encoder, + ) + self.decoder = TripoSGDecoder( + in_channels=self.embedder.out_dim, + out_channels=self.out_channels, + dim=width_decoder, + num_attention_heads=num_attention_heads, + num_layers=num_layers_decoder, + ) + + self.quant = nn.Linear(width_encoder, latent_channels * 2, bias=True) + self.post_quant = nn.Linear(latent_channels, width_decoder, bias=True) + + self.use_slicing = False + self.slicing_length = 1 + + def set_flash_decoder(self): + self.decoder.set_flash_processor(FlashTripoSGAttnProcessor2_0()) + + # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedTripoSGAttnProcessor2_0 + def fuse_qkv_projections(self): + """ + Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value) + are fused. For cross-attention modules, key and value projection matrices are fused. + + + + This API is 🧪 experimental. + + + """ + self.original_attn_processors = None + + for _, attn_processor in self.attn_processors.items(): + if "Added" in str(attn_processor.__class__.__name__): + raise ValueError( + "`fuse_qkv_projections()` is not supported for models having added KV projections." + ) + + self.original_attn_processors = self.attn_processors + + for module in self.modules(): + if isinstance(module, Attention): + module.fuse_projections(fuse=True) + + self.set_attn_processor(FusedTripoSGAttnProcessor2_0()) + + # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections + def unfuse_qkv_projections(self): + """Disables the fused QKV projection if enabled. + + + + This API is 🧪 experimental. + + + + """ + if self.original_attn_processors is not None: + self.set_attn_processor(self.original_attn_processors) + + @property + # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors + def attn_processors(self) -> Dict[str, AttentionProcessor]: + r""" + Returns: + `dict` of attention processors: A dictionary containing all attention processors used in the model with + indexed by its weight name. + """ + # set recursively + processors = {} + + def fn_recursive_add_processors( + name: str, + module: torch.nn.Module, + processors: Dict[str, AttentionProcessor], + ): + if hasattr(module, "get_processor"): + processors[f"{name}.processor"] = module.get_processor() + + for sub_name, child in module.named_children(): + fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) + + return processors + + for name, module in self.named_children(): + fn_recursive_add_processors(name, module, processors) + + return processors + + # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor + def set_attn_processor( + self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]] + ): + r""" + Sets the attention processor to use to compute attention. + + Parameters: + processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): + The instantiated processor class or a dictionary of processor classes that will be set as the processor + for **all** `Attention` layers. + + If `processor` is a dict, the key needs to define the path to the corresponding cross attention + processor. This is strongly recommended when setting trainable attention processors. + + """ + count = len(self.attn_processors.keys()) + + if isinstance(processor, dict) and len(processor) != count: + raise ValueError( + f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" + f" number of attention layers: {count}. Please make sure to pass {count} processor classes." + ) + + def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): + if hasattr(module, "set_processor"): + if not isinstance(processor, dict): + module.set_processor(processor) + else: + module.set_processor(processor.pop(f"{name}.processor")) + + for sub_name, child in module.named_children(): + fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) + + for name, module in self.named_children(): + fn_recursive_attn_processor(name, module, processor) + + def set_default_attn_processor(self): + """ + Disables custom attention processors and sets the default attention implementation. + """ + self.set_attn_processor(TripoSGAttnProcessor2_0()) + + def enable_slicing(self, slicing_length: int = 1) -> None: + r""" + Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to + compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. + """ + self.use_slicing = True + self.slicing_length = slicing_length + + def disable_slicing(self) -> None: + r""" + Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing + decoding in one step. + """ + self.use_slicing = False + + def _sample_features( + self, x: torch.Tensor, num_tokens: int = 2048, seed: Optional[int] = None + ): + """ + Sample points from features of the input point cloud. + + Args: + x (torch.Tensor): The input point cloud. shape: (B, N, C) + num_tokens (int, optional): The number of points to sample. Defaults to 2048. + seed (Optional[int], optional): The random seed. Defaults to None. + """ + rng = np.random.default_rng(seed) + indices = rng.choice( + x.shape[1], num_tokens * 4, replace=num_tokens * 4 > x.shape[1] + ) + selected_points = x[:, indices] + + batch_size, num_points, num_channels = selected_points.shape + flattened_points = selected_points.view(batch_size * num_points, num_channels) + batch_indices = ( + torch.arange(batch_size).to(x.device).repeat_interleave(num_points) + ) + + # fps sampling + sampling_ratio = 1.0 / 4 + sampled_indices = fps( + flattened_points[:, :3], + batch_indices, + ratio=sampling_ratio, + random_start=self.training, + ) + sampled_points = flattened_points[sampled_indices].view( + batch_size, -1, num_channels + ) + + return sampled_points + + def _encode( + self, x: torch.Tensor, num_tokens: int = 2048, seed: Optional[int] = None + ): + position_channels = self.config.in_channels + positions, features = x[..., :position_channels], x[..., position_channels:] + x_kv = torch.cat([self.embedder(positions), features], dim=-1) + + sampled_x = self._sample_features(x, num_tokens, seed) + positions, features = ( + sampled_x[..., :position_channels], + sampled_x[..., position_channels:], + ) + x_q = torch.cat([self.embedder(positions), features], dim=-1) + + x = self.encoder(x_q, x_kv) + + x = self.quant(x) + + return x + + @apply_forward_hook + def encode( + self, x: torch.Tensor, return_dict: bool = True, **kwargs + ) -> Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]: + """ + Encode a batch of point features into latents. + """ + if self.use_slicing and x.shape[0] > 1: + encoded_slices = [ + self._encode(x_slice, **kwargs) + for x_slice in x.split(self.slicing_length) + ] + h = torch.cat(encoded_slices) + else: + h = self._encode(x, **kwargs) + + posterior = DiagonalGaussianDistribution(h, feature_dim=-1) + + if not return_dict: + return (posterior,) + return AutoencoderKLOutput(latent_dist=posterior) + + def _decode( + self, + z: torch.Tensor, + sampled_points: torch.Tensor, + num_chunks: int = 50000, + to_cpu: bool = False, + return_dict: bool = True, + ) -> Union[DecoderOutput, torch.Tensor]: + xyz_samples = sampled_points + + z = self.post_quant(z) + + num_points = xyz_samples.shape[1] + kv_cache = None + dec = [] + + for i in range(0, num_points, num_chunks): + queries = xyz_samples[:, i : i + num_chunks, :].to(z.device, dtype=z.dtype) + queries = self.embedder(queries) + + z_, kv_cache = self.decoder(z, queries, kv_cache) + dec.append(z_ if not to_cpu else z_.cpu()) + + z = torch.cat(dec, dim=1) + + if not return_dict: + return (z,) + + return DecoderOutput(sample=z) + + @apply_forward_hook + def decode( + self, + z: torch.Tensor, + sampled_points: torch.Tensor, + return_dict: bool = True, + **kwargs, + ) -> Union[DecoderOutput, torch.Tensor]: + if self.use_slicing and z.shape[0] > 1: + decoded_slices = [ + self._decode(z_slice, p_slice, **kwargs).sample + for z_slice, p_slice in zip( + z.split(self.slicing_length), + sampled_points.split(self.slicing_length), + ) + ] + decoded = torch.cat(decoded_slices) + else: + decoded = self._decode(z, sampled_points, **kwargs).sample + + if not return_dict: + return (decoded,) + return DecoderOutput(sample=decoded) + + def forward(self, x: torch.Tensor): + pass diff --git a/Gen_3D_Modules/TripoSG/models/autoencoders/vae.py b/Gen_3D_Modules/TripoSG/models/autoencoders/vae.py new file mode 100644 index 0000000..01aae60 --- /dev/null +++ b/Gen_3D_Modules/TripoSG/models/autoencoders/vae.py @@ -0,0 +1,69 @@ +from typing import Optional, Tuple + +import numpy as np +import torch +from diffusers.utils.torch_utils import randn_tensor + + +class DiagonalGaussianDistribution(object): + def __init__( + self, + parameters: torch.Tensor, + deterministic: bool = False, + feature_dim: int = 1, + ): + self.parameters = parameters + self.feature_dim = feature_dim + self.mean, self.logvar = torch.chunk(parameters, 2, dim=feature_dim) + self.logvar = torch.clamp(self.logvar, -30.0, 20.0) + self.deterministic = deterministic + self.std = torch.exp(0.5 * self.logvar) + self.var = torch.exp(self.logvar) + if self.deterministic: + self.var = self.std = torch.zeros_like( + self.mean, device=self.parameters.device, dtype=self.parameters.dtype + ) + + def sample(self, generator: Optional[torch.Generator] = None) -> torch.Tensor: + # make sure sample is on the same device as the parameters and has same dtype + sample = randn_tensor( + self.mean.shape, + generator=generator, + device=self.parameters.device, + dtype=self.parameters.dtype, + ) + x = self.mean + self.std * sample + return x + + def kl(self, other: "DiagonalGaussianDistribution" = None) -> torch.Tensor: + if self.deterministic: + return torch.Tensor([0.0]) + else: + if other is None: + return 0.5 * torch.sum( + torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, + dim=[1, 2, 3], + ) + else: + return 0.5 * torch.sum( + torch.pow(self.mean - other.mean, 2) / other.var + + self.var / other.var + - 1.0 + - self.logvar + + other.logvar, + dim=[1, 2, 3], + ) + + def nll( + self, sample: torch.Tensor, dims: Tuple[int, ...] = [1, 2, 3] + ) -> torch.Tensor: + if self.deterministic: + return torch.Tensor([0.0]) + logtwopi = np.log(2.0 * np.pi) + return 0.5 * torch.sum( + logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, + dim=dims, + ) + + def mode(self) -> torch.Tensor: + return self.mean diff --git a/Gen_3D_Modules/TripoSG/models/embeddings.py b/Gen_3D_Modules/TripoSG/models/embeddings.py new file mode 100644 index 0000000..1fdb5ea --- /dev/null +++ b/Gen_3D_Modules/TripoSG/models/embeddings.py @@ -0,0 +1,96 @@ +import torch +import torch.nn as nn + + +class FrequencyPositionalEmbedding(nn.Module): + """The sin/cosine positional embedding. Given an input tensor `x` of shape [n_batch, ..., c_dim], it converts + each feature dimension of `x[..., i]` into: + [ + sin(x[..., i]), + sin(f_1*x[..., i]), + sin(f_2*x[..., i]), + ... + sin(f_N * x[..., i]), + cos(x[..., i]), + cos(f_1*x[..., i]), + cos(f_2*x[..., i]), + ... + cos(f_N * x[..., i]), + x[..., i] # only present if include_input is True. + ], here f_i is the frequency. + + Denote the space is [0 / num_freqs, 1 / num_freqs, 2 / num_freqs, 3 / num_freqs, ..., (num_freqs - 1) / num_freqs]. + If logspace is True, then the frequency f_i is [2^(0 / num_freqs), ..., 2^(i / num_freqs), ...]; + Otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1)]. + + Args: + num_freqs (int): the number of frequencies, default is 6; + logspace (bool): If logspace is True, then the frequency f_i is [..., 2^(i / num_freqs), ...], + otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1)]; + input_dim (int): the input dimension, default is 3; + include_input (bool): include the input tensor or not, default is True. + + Attributes: + frequencies (torch.Tensor): If logspace is True, then the frequency f_i is [..., 2^(i / num_freqs), ...], + otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1); + + out_dim (int): the embedding size, if include_input is True, it is input_dim * (num_freqs * 2 + 1), + otherwise, it is input_dim * num_freqs * 2. + + """ + + def __init__( + self, + num_freqs: int = 6, + logspace: bool = True, + input_dim: int = 3, + include_input: bool = True, + include_pi: bool = True, + ) -> None: + """The initialization""" + + super().__init__() + + if logspace: + frequencies = 2.0 ** torch.arange(num_freqs, dtype=torch.float32) + else: + frequencies = torch.linspace( + 1.0, 2.0 ** (num_freqs - 1), num_freqs, dtype=torch.float32 + ) + + if include_pi: + frequencies *= torch.pi + + self.register_buffer("frequencies", frequencies, persistent=False) + self.include_input = include_input + self.num_freqs = num_freqs + + self.out_dim = self.get_dims(input_dim) + + def get_dims(self, input_dim): + temp = 1 if self.include_input or self.num_freqs == 0 else 0 + out_dim = input_dim * (self.num_freqs * 2 + temp) + + return out_dim + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Forward process. + + Args: + x: tensor of shape [..., dim] + + Returns: + embedding: an embedding of `x` of shape [..., dim * (num_freqs * 2 + temp)] + where temp is 1 if include_input is True and 0 otherwise. + """ + + if self.num_freqs > 0: + embed = (x[..., None].contiguous() * self.frequencies).view( + *x.shape[:-1], -1 + ) + if self.include_input: + return torch.cat((x, embed.sin(), embed.cos()), dim=-1) + else: + return torch.cat((embed.sin(), embed.cos()), dim=-1) + else: + return x diff --git a/Gen_3D_Modules/TripoSG/models/transformers/__init__.py b/Gen_3D_Modules/TripoSG/models/transformers/__init__.py new file mode 100644 index 0000000..952297c --- /dev/null +++ b/Gen_3D_Modules/TripoSG/models/transformers/__init__.py @@ -0,0 +1,61 @@ +from typing import Callable, Optional + +from .triposg_transformer import TripoSGDiTModel + + +def default_set_attn_proc_func( + name: str, + hidden_size: int, + cross_attention_dim: Optional[int], + ori_attn_proc: object, +) -> object: + return ori_attn_proc + + +def set_transformer_attn_processor( + transformer: TripoSGDiTModel, + set_self_attn_proc_func: Callable = default_set_attn_proc_func, + set_cross_attn_1_proc_func: Callable = default_set_attn_proc_func, + set_cross_attn_2_proc_func: Callable = default_set_attn_proc_func, + set_self_attn_module_names: Optional[list[str]] = None, + set_cross_attn_1_module_names: Optional[list[str]] = None, + set_cross_attn_2_module_names: Optional[list[str]] = None, +) -> None: + do_set_processor = lambda name, module_names: ( + any([name.startswith(module_name) for module_name in module_names]) + if module_names is not None + else True + ) # prefix match + + attn_procs = {} + for name, attn_processor in transformer.attn_processors.items(): + hidden_size = transformer.config.width + if name.endswith("attn1.processor"): + # self attention + attn_procs[name] = ( + set_self_attn_proc_func(name, hidden_size, None, attn_processor) + if do_set_processor(name, set_self_attn_module_names) + else attn_processor + ) + elif name.endswith("attn2.processor"): + # cross attention + cross_attention_dim = transformer.config.cross_attention_dim + attn_procs[name] = ( + set_cross_attn_1_proc_func( + name, hidden_size, cross_attention_dim, attn_processor + ) + if do_set_processor(name, set_cross_attn_1_module_names) + else attn_processor + ) + elif name.endswith("attn2_2.processor"): + # cross attention 2 + cross_attention_dim = transformer.config.cross_attention_2_dim + attn_procs[name] = ( + set_cross_attn_2_proc_func( + name, hidden_size, cross_attention_dim, attn_processor + ) + if do_set_processor(name, set_cross_attn_2_module_names) + else attn_processor + ) + + transformer.set_attn_processor(attn_procs) diff --git a/Gen_3D_Modules/TripoSG/models/transformers/modeling_outputs.py b/Gen_3D_Modules/TripoSG/models/transformers/modeling_outputs.py new file mode 100644 index 0000000..0928fa0 --- /dev/null +++ b/Gen_3D_Modules/TripoSG/models/transformers/modeling_outputs.py @@ -0,0 +1,8 @@ +from dataclasses import dataclass + +import torch + + +@dataclass +class Transformer1DModelOutput: + sample: torch.FloatTensor diff --git a/Gen_3D_Modules/TripoSG/models/transformers/triposg_transformer.py b/Gen_3D_Modules/TripoSG/models/transformers/triposg_transformer.py new file mode 100644 index 0000000..5676a11 --- /dev/null +++ b/Gen_3D_Modules/TripoSG/models/transformers/triposg_transformer.py @@ -0,0 +1,775 @@ +# Copyright (c) 2025 VAST-AI-Research and contributors + +# This code is based on Tencent HunyuanDiT (https://huggingface.co/Tencent-Hunyuan/HunyuanDiT), +# which is licensed under the Tencent Hunyuan Community License Agreement. +# Portions of this code are copied or adapted from HunyuanDiT. +# See the original license below: + +# ---- Start of Tencent Hunyuan Community License Agreement ---- + +# TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT +# Tencent Hunyuan DiT Release Date: 14 May 2024 +# THIS LICENSE AGREEMENT DOES NOT APPLY IN THE EUROPEAN UNION AND IS EXPRESSLY LIMITED TO THE TERRITORY, AS DEFINED BELOW. +# By clicking to agree or by using, reproducing, modifying, distributing, performing or displaying any portion or element of the Tencent Hunyuan Works, including via any Hosted Service, You will be deemed to have recognized and accepted the content of this Agreement, which is effective immediately. +# 1. 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For any use intended to discriminate against or harm individuals or groups based on protected characteristics or categories, online or offline social behavior or known or predicted personal or personality characteristics; +# 18. To intentionally exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm; +# 19. For military purposes; +# 20. To engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or other professional practices. + +# ---- End of Tencent Hunyuan Community License Agreement ---- + +# Please note that the use of this code is subject to the terms and conditions +# of the Tencent Hunyuan Community License Agreement, including the Acceptable Use Policy. + +from typing import Any, Dict, Optional, Tuple, Union + +import torch +import torch.utils.checkpoint +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.loaders import PeftAdapterMixin +from diffusers.models.attention import FeedForward +from diffusers.models.attention_processor import Attention, AttentionProcessor +from diffusers.models.embeddings import ( + GaussianFourierProjection, + TimestepEmbedding, + Timesteps, +) +from diffusers.models.modeling_utils import ModelMixin +from diffusers.models.normalization import ( + AdaLayerNormContinuous, + FP32LayerNorm, + LayerNorm, +) +from diffusers.utils import ( + USE_PEFT_BACKEND, + is_torch_version, + logging, + scale_lora_layers, + unscale_lora_layers, +) +from diffusers.utils.torch_utils import maybe_allow_in_graph +from torch import nn + +from ..attention_processor import FusedTripoSGAttnProcessor2_0, TripoSGAttnProcessor2_0 +from .modeling_outputs import Transformer1DModelOutput + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +@maybe_allow_in_graph +class DiTBlock(nn.Module): + r""" + Transformer block used in Hunyuan-DiT model (https://github.com/Tencent/HunyuanDiT). Allow skip connection and + QKNorm + + Parameters: + dim (`int`): + The number of channels in the input and output. + num_attention_heads (`int`): + The number of headsto use for multi-head attention. + cross_attention_dim (`int`,*optional*): + The size of the encoder_hidden_states vector for cross attention. + dropout(`float`, *optional*, defaults to 0.0): + The dropout probability to use. + activation_fn (`str`,*optional*, defaults to `"geglu"`): + Activation function to be used in feed-forward. . + norm_elementwise_affine (`bool`, *optional*, defaults to `True`): + Whether to use learnable elementwise affine parameters for normalization. + norm_eps (`float`, *optional*, defaults to 1e-6): + A small constant added to the denominator in normalization layers to prevent division by zero. + final_dropout (`bool` *optional*, defaults to False): + Whether to apply a final dropout after the last feed-forward layer. + ff_inner_dim (`int`, *optional*): + The size of the hidden layer in the feed-forward block. Defaults to `None`. + ff_bias (`bool`, *optional*, defaults to `True`): + Whether to use bias in the feed-forward block. + skip (`bool`, *optional*, defaults to `False`): + Whether to use skip connection. Defaults to `False` for down-blocks and mid-blocks. + qk_norm (`bool`, *optional*, defaults to `True`): + Whether to use normalization in QK calculation. Defaults to `True`. + """ + + def __init__( + self, + dim: int, + num_attention_heads: int, + use_self_attention: bool = True, + self_attention_norm_type: Optional[str] = None, + use_cross_attention: bool = True, # ada layer norm + cross_attention_dim: Optional[int] = None, + cross_attention_norm_type: Optional[str] = "fp32_layer_norm", + use_cross_attention_2: bool = False, + cross_attention_2_dim: Optional[int] = None, + cross_attention_2_norm_type: Optional[str] = None, + dropout=0.0, + activation_fn: str = "gelu", + norm_type: str = "fp32_layer_norm", # TODO + norm_elementwise_affine: bool = True, + norm_eps: float = 1e-5, + final_dropout: bool = False, + ff_inner_dim: Optional[int] = None, # int(dim * 4) if None + ff_bias: bool = True, + skip: bool = False, + skip_concat_front: bool = False, # [x, skip] or [skip, x] + skip_norm_last: bool = False, # this is an error + qk_norm: bool = True, + qkv_bias: bool = True, + ): + super().__init__() + + self.use_self_attention = use_self_attention + self.use_cross_attention = use_cross_attention + self.use_cross_attention_2 = use_cross_attention_2 + self.skip_concat_front = skip_concat_front + self.skip_norm_last = skip_norm_last + # Define 3 blocks. Each block has its own normalization layer. + # NOTE: when new version comes, check norm2 and norm 3 + # 1. Self-Attn + if use_self_attention: + if ( + self_attention_norm_type == "fp32_layer_norm" + or self_attention_norm_type is None + ): + self.norm1 = FP32LayerNorm(dim, norm_eps, norm_elementwise_affine) + else: + raise NotImplementedError + + self.attn1 = Attention( + query_dim=dim, + cross_attention_dim=None, + dim_head=dim // num_attention_heads, + heads=num_attention_heads, + qk_norm="rms_norm" if qk_norm else None, + eps=1e-6, + bias=qkv_bias, + processor=TripoSGAttnProcessor2_0(), + ) + + # 2. Cross-Attn + if use_cross_attention: + assert cross_attention_dim is not None + + self.norm2 = FP32LayerNorm(dim, norm_eps, norm_elementwise_affine) + + self.attn2 = Attention( + query_dim=dim, + cross_attention_dim=cross_attention_dim, + dim_head=dim // num_attention_heads, + heads=num_attention_heads, + qk_norm="rms_norm" if qk_norm else None, + cross_attention_norm=cross_attention_norm_type, + eps=1e-6, + bias=qkv_bias, + processor=TripoSGAttnProcessor2_0(), + ) + + if use_cross_attention_2: + assert cross_attention_2_dim is not None + + self.norm2_2 = FP32LayerNorm(dim, norm_eps, norm_elementwise_affine) + + self.attn2_2 = Attention( + query_dim=dim, + cross_attention_dim=cross_attention_2_dim, + dim_head=dim // num_attention_heads, + heads=num_attention_heads, + qk_norm="rms_norm" if qk_norm else None, + cross_attention_norm=cross_attention_2_norm_type, + eps=1e-6, + bias=qkv_bias, + processor=TripoSGAttnProcessor2_0(), + ) + + # 3. Feed-forward + self.norm3 = FP32LayerNorm(dim, norm_eps, norm_elementwise_affine) + + self.ff = FeedForward( + dim, + dropout=dropout, ### 0.0 + activation_fn=activation_fn, ### approx GeLU + final_dropout=final_dropout, ### 0.0 + inner_dim=ff_inner_dim, ### int(dim * mlp_ratio) + bias=ff_bias, + ) + + # 4. Skip Connection + if skip: + self.skip_norm = FP32LayerNorm(dim, norm_eps, elementwise_affine=True) + self.skip_linear = nn.Linear(2 * dim, dim) + else: + self.skip_linear = None + + # let chunk size default to None + self._chunk_size = None + self._chunk_dim = 0 + + def set_topk(self, topk): + self.flash_processor.topk = topk + + def set_flash_processor(self, flash_processor): + self.flash_processor = flash_processor + self.attn2.processor = self.flash_processor + + # Copied from diffusers.models.attention.BasicTransformerBlock.set_chunk_feed_forward + def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0): + # Sets chunk feed-forward + self._chunk_size = chunk_size + self._chunk_dim = dim + + def forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_hidden_states_2: Optional[torch.Tensor] = None, + temb: Optional[torch.Tensor] = None, + image_rotary_emb: Optional[torch.Tensor] = None, + skip: Optional[torch.Tensor] = None, + attention_kwargs: Optional[Dict[str, Any]] = None, + ) -> torch.Tensor: + # Prepare attention kwargs + attention_kwargs = attention_kwargs.copy() if attention_kwargs is not None else {} + cross_attention_scale = attention_kwargs.pop("cross_attention_scale", 1.0) + cross_attention_2_scale = attention_kwargs.pop("cross_attention_2_scale", 1.0) + + # Notice that normalization is always applied before the real computation in the following blocks. + # 0. Long Skip Connection + if self.skip_linear is not None: + cat = torch.cat( + ( + [skip, hidden_states] + if self.skip_concat_front + else [hidden_states, skip] + ), + dim=-1, + ) + if self.skip_norm_last: + # don't do this + hidden_states = self.skip_linear(cat) + hidden_states = self.skip_norm(hidden_states) + else: + cat = self.skip_norm(cat) + hidden_states = self.skip_linear(cat) + + # 1. Self-Attention + if self.use_self_attention: + norm_hidden_states = self.norm1(hidden_states) + attn_output = self.attn1( + norm_hidden_states, + image_rotary_emb=image_rotary_emb, + **attention_kwargs, + ) + hidden_states = hidden_states + attn_output + + # 2. Cross-Attention + if self.use_cross_attention: + if self.use_cross_attention_2: + hidden_states = ( + hidden_states + + self.attn2( + self.norm2(hidden_states), + encoder_hidden_states=encoder_hidden_states, + image_rotary_emb=image_rotary_emb, + **attention_kwargs, + ) * cross_attention_scale + + self.attn2_2( + self.norm2_2(hidden_states), + encoder_hidden_states=encoder_hidden_states_2, + image_rotary_emb=image_rotary_emb, + **attention_kwargs, + ) * cross_attention_2_scale + ) + else: + hidden_states = hidden_states + self.attn2( + self.norm2(hidden_states), + encoder_hidden_states=encoder_hidden_states, + image_rotary_emb=image_rotary_emb, + **attention_kwargs, + ) * cross_attention_scale + + # FFN Layer ### TODO: switch norm2 and norm3 in the state dict + mlp_inputs = self.norm3(hidden_states) + hidden_states = hidden_states + self.ff(mlp_inputs) + + return hidden_states + + +class TripoSGDiTModel(ModelMixin, ConfigMixin, PeftAdapterMixin): + """ + TripoSG: Diffusion model with a Transformer backbone. + + Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers. + + Parameters: + num_attention_heads (`int`, *optional*, defaults to 16): + The number of heads to use for multi-head attention. + attention_head_dim (`int`, *optional*, defaults to 88): + The number of channels in each head. + in_channels (`int`, *optional*): + The number of channels in the input and output (specify if the input is **continuous**). + patch_size (`int`, *optional*): + The size of the patch to use for the input. + activation_fn (`str`, *optional*, defaults to `"geglu"`): + Activation function to use in feed-forward. + sample_size (`int`, *optional*): + The width of the latent images. This is fixed during training since it is used to learn a number of + position embeddings. + dropout (`float`, *optional*, defaults to 0.0): + The dropout probability to use. + cross_attention_dim (`int`, *optional*): + The number of dimension in the clip text embedding. + hidden_size (`int`, *optional*): + The size of hidden layer in the conditioning embedding layers. + num_layers (`int`, *optional*, defaults to 1): + The number of layers of Transformer blocks to use. + mlp_ratio (`float`, *optional*, defaults to 4.0): + The ratio of the hidden layer size to the input size. + learn_sigma (`bool`, *optional*, defaults to `True`): + Whether to predict variance. + cross_attention_dim_t5 (`int`, *optional*): + The number dimensions in t5 text embedding. + pooled_projection_dim (`int`, *optional*): + The size of the pooled projection. + text_len (`int`, *optional*): + The length of the clip text embedding. + text_len_t5 (`int`, *optional*): + The length of the T5 text embedding. + use_style_cond_and_image_meta_size (`bool`, *optional*): + Whether or not to use style condition and image meta size. True for version <=1.1, False for version >= 1.2 + """ + + _supports_gradient_checkpointing = True + + @register_to_config + def __init__( + self, + num_attention_heads: int = 16, + width: int = 2048, + in_channels: int = 64, + num_layers: int = 21, + cross_attention_dim: int = 1024, + use_cross_attention_2: bool = False, + cross_attention_2_dim: Optional[int] = None + ): + super().__init__() + self.out_channels = in_channels + self.num_heads = num_attention_heads + self.inner_dim = width + self.mlp_ratio = 4.0 + + time_embed_dim, timestep_input_dim = self._set_time_proj( + "positional", + inner_dim=self.inner_dim, + flip_sin_to_cos=False, + freq_shift=0, + time_embedding_dim=None, + ) + self.time_proj = TimestepEmbedding( + timestep_input_dim, time_embed_dim, act_fn="gelu", out_dim=self.inner_dim + ) + self.proj_in = nn.Linear(self.config.in_channels, self.inner_dim, bias=True) + + self.blocks = nn.ModuleList( + [ + DiTBlock( + dim=self.inner_dim, + num_attention_heads=self.config.num_attention_heads, + use_self_attention=True, + self_attention_norm_type="fp32_layer_norm", + use_cross_attention=True, + cross_attention_dim=cross_attention_dim, + cross_attention_norm_type=None, + use_cross_attention_2=use_cross_attention_2, + cross_attention_2_dim=cross_attention_2_dim, + cross_attention_2_norm_type=None, + activation_fn="gelu", + norm_type="fp32_layer_norm", # TODO + norm_eps=1e-5, + ff_inner_dim=int(self.inner_dim * self.mlp_ratio), + skip=layer > num_layers // 2, + skip_concat_front=True, + skip_norm_last=True, # this is an error + qk_norm=True, # See http://arxiv.org/abs/2302.05442 for details. + qkv_bias=False, + ) + for layer in range(num_layers) + ] + ) + + self.norm_out = LayerNorm(self.inner_dim) + self.proj_out = nn.Linear(self.inner_dim, self.out_channels, bias=True) + + self.gradient_checkpointing = False + + def _set_gradient_checkpointing(self, module, value=False): + self.gradient_checkpointing = value + + def _set_time_proj( + self, + time_embedding_type: str, + inner_dim: int, + flip_sin_to_cos: bool, + freq_shift: float, + time_embedding_dim: int, + ) -> Tuple[int, int]: + if time_embedding_type == "fourier": + time_embed_dim = time_embedding_dim or inner_dim * 2 + if time_embed_dim % 2 != 0: + raise ValueError( + f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}." + ) + self.time_embed = GaussianFourierProjection( + time_embed_dim // 2, + set_W_to_weight=False, + log=False, + flip_sin_to_cos=flip_sin_to_cos, + ) + timestep_input_dim = time_embed_dim + elif time_embedding_type == "positional": + time_embed_dim = time_embedding_dim or inner_dim * 4 + + self.time_embed = Timesteps(inner_dim, flip_sin_to_cos, freq_shift) + timestep_input_dim = inner_dim + else: + raise ValueError( + f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`." + ) + + return time_embed_dim, timestep_input_dim + + # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedTripoSGAttnProcessor2_0 + def fuse_qkv_projections(self): + """ + Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value) + are fused. For cross-attention modules, key and value projection matrices are fused. + + + + This API is 🧪 experimental. + + + """ + self.original_attn_processors = None + + for _, attn_processor in self.attn_processors.items(): + if "Added" in str(attn_processor.__class__.__name__): + raise ValueError( + "`fuse_qkv_projections()` is not supported for models having added KV projections." + ) + + self.original_attn_processors = self.attn_processors + + for module in self.modules(): + if isinstance(module, Attention): + module.fuse_projections(fuse=True) + + self.set_attn_processor(FusedTripoSGAttnProcessor2_0()) + + # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections + def unfuse_qkv_projections(self): + """Disables the fused QKV projection if enabled. + + + + This API is 🧪 experimental. + + + + """ + if self.original_attn_processors is not None: + self.set_attn_processor(self.original_attn_processors) + + @property + # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors + def attn_processors(self) -> Dict[str, AttentionProcessor]: + r""" + Returns: + `dict` of attention processors: A dictionary containing all attention processors used in the model with + indexed by its weight name. + """ + # set recursively + processors = {} + + def fn_recursive_add_processors( + name: str, + module: torch.nn.Module, + processors: Dict[str, AttentionProcessor], + ): + if hasattr(module, "get_processor"): + processors[f"{name}.processor"] = module.get_processor() + + for sub_name, child in module.named_children(): + fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) + + return processors + + for name, module in self.named_children(): + fn_recursive_add_processors(name, module, processors) + + return processors + + # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor + def set_attn_processor( + self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]] + ): + r""" + Sets the attention processor to use to compute attention. + + Parameters: + processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): + The instantiated processor class or a dictionary of processor classes that will be set as the processor + for **all** `Attention` layers. + + If `processor` is a dict, the key needs to define the path to the corresponding cross attention + processor. This is strongly recommended when setting trainable attention processors. + + """ + count = len(self.attn_processors.keys()) + + if isinstance(processor, dict) and len(processor) != count: + raise ValueError( + f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" + f" number of attention layers: {count}. Please make sure to pass {count} processor classes." + ) + + def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): + if hasattr(module, "set_processor"): + if not isinstance(processor, dict): + module.set_processor(processor) + else: + module.set_processor(processor.pop(f"{name}.processor")) + + for sub_name, child in module.named_children(): + fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) + + for name, module in self.named_children(): + fn_recursive_attn_processor(name, module, processor) + + def set_default_attn_processor(self): + """ + Disables custom attention processors and sets the default attention implementation. + """ + self.set_attn_processor(TripoSGAttnProcessor2_0()) + + def forward( + self, + hidden_states: Optional[torch.Tensor], + timestep: Union[int, float, torch.LongTensor], + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_hidden_states_2: Optional[torch.Tensor] = None, + image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, + attention_kwargs: Optional[Dict[str, Any]] = None, + return_dict: bool = True, + ): + """ + The [`HunyuanDiT2DModel`] forward method. + + Args: + hidden_states (`torch.Tensor` of shape `(batch size, dim, height, width)`): + The input tensor. + timestep ( `torch.LongTensor`, *optional*): + Used to indicate denoising step. + encoder_hidden_states ( `torch.Tensor` of shape `(batch size, sequence len, embed dims)`, *optional*): + Conditional embeddings for cross attention layer. + return_dict: bool + Whether to return a dictionary. + """ + + if attention_kwargs is not None: + attention_kwargs = attention_kwargs.copy() + lora_scale = attention_kwargs.pop("scale", 1.0) + else: + lora_scale = 1.0 + + if USE_PEFT_BACKEND: + # weight the lora layers by setting `lora_scale` for each PEFT layer + scale_lora_layers(self, lora_scale) + else: + if ( + attention_kwargs is not None + and attention_kwargs.get("scale", None) is not None + ): + logger.warning( + "Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective." + ) + + _, N, _ = hidden_states.shape + + temb = self.time_embed(timestep).to(hidden_states.dtype) + temb = self.time_proj(temb) + temb = temb.unsqueeze(dim=1) # unsqueeze to concat with hidden_states + + hidden_states = self.proj_in(hidden_states) + + # N + 1 token + hidden_states = torch.cat([temb, hidden_states], dim=1) + + skips = [] + for layer, block in enumerate(self.blocks): + skip = None if layer <= self.config.num_layers // 2 else skips.pop() + + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = ( + {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + ) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + hidden_states, + encoder_hidden_states, + encoder_hidden_states_2, + temb, + image_rotary_emb, + skip, + attention_kwargs, + **ckpt_kwargs, + ) + else: + hidden_states = block( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + encoder_hidden_states_2=encoder_hidden_states_2, + temb=temb, + image_rotary_emb=image_rotary_emb, + skip=skip, + attention_kwargs=attention_kwargs, + ) # (N, L, D) + + if layer < self.config.num_layers // 2: + skips.append(hidden_states) + + # final layer + hidden_states = self.norm_out(hidden_states) + hidden_states = hidden_states[:, -N:] + hidden_states = self.proj_out(hidden_states) + + if USE_PEFT_BACKEND: + # remove `lora_scale` from each PEFT layer + unscale_lora_layers(self, lora_scale) + + if not return_dict: + return (hidden_states,) + + return Transformer1DModelOutput(sample=hidden_states) + + # Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking + def enable_forward_chunking( + self, chunk_size: Optional[int] = None, dim: int = 0 + ) -> None: + """ + Sets the attention processor to use [feed forward + chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers). + + Parameters: + chunk_size (`int`, *optional*): + The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually + over each tensor of dim=`dim`. + dim (`int`, *optional*, defaults to `0`): + The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch) + or dim=1 (sequence length). + """ + if dim not in [0, 1]: + raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}") + + # By default chunk size is 1 + chunk_size = chunk_size or 1 + + def fn_recursive_feed_forward( + module: torch.nn.Module, chunk_size: int, dim: int + ): + if hasattr(module, "set_chunk_feed_forward"): + module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim) + + for child in module.children(): + fn_recursive_feed_forward(child, chunk_size, dim) + + for module in self.children(): + fn_recursive_feed_forward(module, chunk_size, dim) + + # Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.disable_forward_chunking + def disable_forward_chunking(self): + def fn_recursive_feed_forward( + module: torch.nn.Module, chunk_size: int, dim: int + ): + if hasattr(module, "set_chunk_feed_forward"): + module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim) + + for child in module.children(): + fn_recursive_feed_forward(child, chunk_size, dim) + + for module in self.children(): + fn_recursive_feed_forward(module, None, 0) diff --git a/Gen_3D_Modules/TripoSG/pipelines/pipeline_triposg.py b/Gen_3D_Modules/TripoSG/pipelines/pipeline_triposg.py new file mode 100644 index 0000000..9cd8ebe --- /dev/null +++ b/Gen_3D_Modules/TripoSG/pipelines/pipeline_triposg.py @@ -0,0 +1,329 @@ +import inspect +import math +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + +import comfy.utils + +import numpy as np +import PIL +import PIL.Image +import torch +import trimesh +from diffusers.image_processor import PipelineImageInput +from diffusers.pipelines.pipeline_utils import DiffusionPipeline +from diffusers.schedulers import FlowMatchEulerDiscreteScheduler +from diffusers.utils import logging +from diffusers.utils.torch_utils import randn_tensor +from transformers import ( + BitImageProcessor, + Dinov2Model, +) +from ..inference_utils import hierarchical_extract_geometry, flash_extract_geometry + +from ..models.autoencoders import TripoSGVAEModel +from ..models.transformers import TripoSGDiTModel +from .pipeline_triposg_output import TripoSGPipelineOutput +from .pipeline_utils import TransformerDiffusionMixin + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps +def retrieve_timesteps( + scheduler, + num_inference_steps: Optional[int] = None, + device: Optional[Union[str, torch.device]] = None, + timesteps: Optional[List[int]] = None, + sigmas: Optional[List[float]] = None, + **kwargs, +): + """ + Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles + custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`. + + Args: + scheduler (`SchedulerMixin`): + The scheduler to get timesteps from. + num_inference_steps (`int`): + The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps` + must be `None`. + device (`str` or `torch.device`, *optional*): + The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. + timesteps (`List[int]`, *optional*): + Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed, + `num_inference_steps` and `sigmas` must be `None`. + sigmas (`List[float]`, *optional*): + Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed, + `num_inference_steps` and `timesteps` must be `None`. + + Returns: + `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the + second element is the number of inference steps. + """ + if timesteps is not None and sigmas is not None: + raise ValueError( + "Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values" + ) + if timesteps is not None: + accepts_timesteps = "timesteps" in set( + inspect.signature(scheduler.set_timesteps).parameters.keys() + ) + if not accepts_timesteps: + raise ValueError( + f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" + f" timestep schedules. Please check whether you are using the correct scheduler." + ) + scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) + timesteps = scheduler.timesteps + num_inference_steps = len(timesteps) + elif sigmas is not None: + accept_sigmas = "sigmas" in set( + inspect.signature(scheduler.set_timesteps).parameters.keys() + ) + if not accept_sigmas: + raise ValueError( + f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" + f" sigmas schedules. Please check whether you are using the correct scheduler." + ) + scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs) + timesteps = scheduler.timesteps + num_inference_steps = len(timesteps) + else: + scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) + timesteps = scheduler.timesteps + return timesteps, num_inference_steps + + +class TripoSGPipeline(DiffusionPipeline, TransformerDiffusionMixin): + """ + Pipeline for image-to-3D generation. + """ + + def __init__( + self, + vae: TripoSGVAEModel, + transformer: TripoSGDiTModel, + scheduler: FlowMatchEulerDiscreteScheduler, + image_encoder_dinov2: Dinov2Model, + feature_extractor_dinov2: BitImageProcessor, + ): + super().__init__() + + self.register_modules( + vae=vae, + transformer=transformer, + scheduler=scheduler, + image_encoder_dinov2=image_encoder_dinov2, + feature_extractor_dinov2=feature_extractor_dinov2, + ) + + @property + def guidance_scale(self): + return self._guidance_scale + + @property + def do_classifier_free_guidance(self): + return self._guidance_scale > 1 + + @property + def num_timesteps(self): + return self._num_timesteps + + @property + def attention_kwargs(self): + return self._attention_kwargs + + @property + def decode_progressive(self): + return self._decode_progressive + + def encode_image(self, image, device, num_images_per_prompt): + dtype = next(self.image_encoder_dinov2.parameters()).dtype + + if not isinstance(image, torch.Tensor): + image = self.feature_extractor_dinov2(image, return_tensors="pt").pixel_values + + image = image.to(device=device, dtype=dtype) + image_embeds = self.image_encoder_dinov2(image).last_hidden_state + image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0) + uncond_image_embeds = torch.zeros_like(image_embeds) + + return image_embeds, uncond_image_embeds + + def prepare_latents( + self, + batch_size, + num_tokens, + num_channels_latents, + dtype, + device, + generator, + latents: Optional[torch.Tensor] = None, + ): + if latents is not None: + return latents.to(device=device, dtype=dtype) + + shape = (batch_size, num_tokens, num_channels_latents) + + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + + return latents + + + @torch.no_grad() + def __call__( + self, + image: PipelineImageInput, + num_inference_steps: int = 50, + num_tokens: int = 2048, + timesteps: List[int] = None, + guidance_scale: float = 7.0, + num_shapes_per_prompt: int = 1, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.FloatTensor] = None, + attention_kwargs: Optional[Dict[str, Any]] = None, + callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, + callback_on_step_end_tensor_inputs: List[str] = ["latents"], + bounds: Union[Tuple[float], List[float], float] = (-1.005, -1.005, -1.005, 1.005, 1.005, 1.005), + dense_octree_depth: int = 8, + hierarchical_octree_depth: int = 9, + flash_octree_depth: int = 9, + use_flash_decoder: bool = True, + return_dict: bool = True, + ): + # 1. Define call parameters + self._guidance_scale = guidance_scale + self._attention_kwargs = attention_kwargs + + # 2. Define call parameters + if isinstance(image, PIL.Image.Image): + batch_size = 1 + elif isinstance(image, list): + batch_size = len(image) + elif isinstance(image, torch.Tensor): + batch_size = image.shape[0] + else: + raise ValueError("Invalid input type for image") + + device = self._execution_device + + # 3. Encode condition + image_embeds, negative_image_embeds = self.encode_image( + image, device, num_shapes_per_prompt + ) + + if self.do_classifier_free_guidance: + image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0) + + # 4. Prepare timesteps + timesteps, num_inference_steps = retrieve_timesteps( + self.scheduler, num_inference_steps, device, timesteps + ) + num_warmup_steps = max( + len(timesteps) - num_inference_steps * self.scheduler.order, 0 + ) + self._num_timesteps = len(timesteps) + + # 5. Prepare latent variables + num_channels_latents = self.transformer.config.in_channels + latents = self.prepare_latents( + batch_size * num_shapes_per_prompt, + num_tokens, + num_channels_latents, + image_embeds.dtype, + device, + generator, + latents, + ) + + # 6. Denoising loop + comfy_pbar = comfy.utils.ProgressBar(num_inference_steps) + with self.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(timesteps): + + # expand the latents if we are doing classifier free guidance + latent_model_input = ( + torch.cat([latents] * 2) + if self.do_classifier_free_guidance + else latents + ) + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + timestep = t.expand(latent_model_input.shape[0]) + + noise_pred = self.transformer( + latent_model_input, + timestep, + encoder_hidden_states=image_embeds, + attention_kwargs=attention_kwargs, + return_dict=False, + )[0] + + # perform guidance + if self.do_classifier_free_guidance: + noise_pred_uncond, noise_pred_image = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + self.guidance_scale * ( + noise_pred_image - noise_pred_uncond + ) + + # compute the previous noisy sample x_t -> x_t-1 + latents_dtype = latents.dtype + latents = self.scheduler.step( + noise_pred, t, latents, return_dict=False + )[0] + + if latents.dtype != latents_dtype: + if torch.backends.mps.is_available(): + # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272 + latents = latents.to(latents_dtype) + + if callback_on_step_end is not None: + callback_kwargs = {} + for k in callback_on_step_end_tensor_inputs: + callback_kwargs[k] = locals()[k] + callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) + + latents = callback_outputs.pop("latents", latents) + + # call the callback, if provided + if i == len(timesteps) - 1 or ( + (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0 + ): + progress_bar.update() + + comfy_pbar.update_absolute(i + 1) + + + # 7. decoder mesh + if not use_flash_decoder: + geometric_func = lambda x: self.vae.decode(latents, sampled_points=x).sample + output = hierarchical_extract_geometry( + geometric_func, + device, + bounds=bounds, + dense_octree_depth=dense_octree_depth, + hierarchical_octree_depth=hierarchical_octree_depth, + ) + else: + self.vae.set_flash_decoder() + output = flash_extract_geometry( + latents, + self.vae, + bounds=bounds, + octree_depth=flash_octree_depth, + ) + meshes = [trimesh.Trimesh(mesh_v_f[0].astype(np.float32), mesh_v_f[1]) for mesh_v_f in output] + + # Offload all models + self.maybe_free_model_hooks() + + if not return_dict: + return (output, meshes) + + return TripoSGPipelineOutput(samples=output, meshes=meshes) + diff --git a/Gen_3D_Modules/TripoSG/pipelines/pipeline_triposg_output.py b/Gen_3D_Modules/TripoSG/pipelines/pipeline_triposg_output.py new file mode 100644 index 0000000..bc4c7e0 --- /dev/null +++ b/Gen_3D_Modules/TripoSG/pipelines/pipeline_triposg_output.py @@ -0,0 +1,17 @@ +from dataclasses import dataclass +from typing import List, Optional, Union + +import numpy as np +import torch +import trimesh +from diffusers.utils import BaseOutput + + +@dataclass +class TripoSGPipelineOutput(BaseOutput): + r""" + Output class for ShapeDiff pipelines. + """ + + samples: torch.Tensor + meshes: List[trimesh.Trimesh] diff --git a/Gen_3D_Modules/TripoSG/pipelines/pipeline_triposg_scribble.py b/Gen_3D_Modules/TripoSG/pipelines/pipeline_triposg_scribble.py new file mode 100644 index 0000000..9acf745 --- /dev/null +++ b/Gen_3D_Modules/TripoSG/pipelines/pipeline_triposg_scribble.py @@ -0,0 +1,343 @@ +import inspect +import math +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + +import comfy.utils + +import numpy as np +import PIL +import PIL.Image +import torch +import trimesh +from diffusers.image_processor import PipelineImageInput +from diffusers.pipelines.pipeline_utils import DiffusionPipeline +from diffusers.schedulers import FlowMatchEulerDiscreteScheduler # not sure +from diffusers.utils import logging +from diffusers.utils.torch_utils import randn_tensor +from transformers import ( + BitImageProcessor, + CLIPTokenizer, + CLIPTextModelWithProjection, + Dinov2Model, +) +from ..inference_utils import hierarchical_extract_geometry, flash_extract_geometry + +from ..models.autoencoders import TripoSGVAEModel +from ..models.transformers import TripoSGDiTModel +from .pipeline_triposg_output import TripoSGPipelineOutput +from .pipeline_utils import TransformerDiffusionMixin + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps +def retrieve_timesteps( + scheduler, + num_inference_steps: Optional[int] = None, + device: Optional[Union[str, torch.device]] = None, + timesteps: Optional[List[int]] = None, + sigmas: Optional[List[float]] = None, + **kwargs, +): + """ + Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles + custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`. + + Args: + scheduler (`SchedulerMixin`): + The scheduler to get timesteps from. + num_inference_steps (`int`): + The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps` + must be `None`. + device (`str` or `torch.device`, *optional*): + The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. + timesteps (`List[int]`, *optional*): + Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed, + `num_inference_steps` and `sigmas` must be `None`. + sigmas (`List[float]`, *optional*): + Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed, + `num_inference_steps` and `timesteps` must be `None`. + + Returns: + `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the + second element is the number of inference steps. + """ + if timesteps is not None and sigmas is not None: + raise ValueError( + "Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values" + ) + if timesteps is not None: + accepts_timesteps = "timesteps" in set( + inspect.signature(scheduler.set_timesteps).parameters.keys() + ) + if not accepts_timesteps: + raise ValueError( + f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" + f" timestep schedules. Please check whether you are using the correct scheduler." + ) + scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) + timesteps = scheduler.timesteps + num_inference_steps = len(timesteps) + elif sigmas is not None: + accept_sigmas = "sigmas" in set( + inspect.signature(scheduler.set_timesteps).parameters.keys() + ) + if not accept_sigmas: + raise ValueError( + f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" + f" sigmas schedules. Please check whether you are using the correct scheduler." + ) + scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs) + timesteps = scheduler.timesteps + num_inference_steps = len(timesteps) + else: + scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) + timesteps = scheduler.timesteps + return timesteps, num_inference_steps + + +class TripoSGScribblePipeline(DiffusionPipeline, TransformerDiffusionMixin): + """ + Pipeline for (scribble and text)-to-3D generation. + """ + + def __init__( + self, + vae: TripoSGVAEModel, + transformer: TripoSGDiTModel, + scheduler: FlowMatchEulerDiscreteScheduler, + tokenizer: CLIPTokenizer, + text_encoder: CLIPTextModelWithProjection, + image_encoder_dinov2: Dinov2Model, + feature_extractor_dinov2: BitImageProcessor, + ): + super().__init__() + + self.register_modules( + vae=vae, + transformer=transformer, + scheduler=scheduler, + tokenizer=tokenizer, + text_encoder=text_encoder, + image_encoder_dinov2=image_encoder_dinov2, + feature_extractor_dinov2=feature_extractor_dinov2 + ) + + @property + def guidance_scale(self): + return self._guidance_scale + + @property + def do_classifier_free_guidance(self): + return self._guidance_scale > 1 + + @property + def num_timesteps(self): + return self._num_timesteps + + @property + def attention_kwargs(self): + return self._attention_kwargs + + def encode_text(self, prompt, device, num_shapes_per_prompt): + input_ids = self.tokenizer( + [prompt], max_length=self.tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt" + )['input_ids'].to(device) + text_embeds = self.text_encoder(input_ids).last_hidden_state + text_embeds = text_embeds.repeat_interleave(num_shapes_per_prompt, dim=0) + uncond_text_embeds = torch.zeros_like(text_embeds) + return text_embeds, uncond_text_embeds + + def encode_image(self, image, device, num_shapes_per_prompt): + dtype = next(self.image_encoder_dinov2.parameters()).dtype + + if not isinstance(image, torch.Tensor): + image = self.feature_extractor_dinov2(image, return_tensors="pt").pixel_values + + image = image.to(device=device, dtype=dtype) + image_embeds = self.image_encoder_dinov2(image).last_hidden_state + image_embeds = image_embeds.repeat_interleave(num_shapes_per_prompt, dim=0) + uncond_image_embeds = torch.zeros_like(image_embeds) + + return image_embeds, uncond_image_embeds + + def prepare_latents( + self, + batch_size, + num_tokens, + num_channels_latents, + dtype, + device, + generator, + latents: Optional[torch.Tensor] = None, + ): + if latents is not None: + return latents.to(device=device, dtype=dtype) + + shape = (batch_size, num_tokens, num_channels_latents) + + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + + return latents + + @torch.no_grad() + def __call__( + self, + image: PipelineImageInput, + prompt: str, + num_tokens: int = 512, + num_inference_steps: int = 50, + timesteps: List[int] = None, + guidance_scale: float = 7.0, + num_shapes_per_prompt: int = 1, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.FloatTensor] = None, + attention_kwargs: Optional[Dict[str, Any]] = None, + callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, + callback_on_step_end_tensor_inputs: List[str] = ["latents"], + bounds: Union[Tuple[float], List[float], float] = (-1.005, -1.005, -1.005, 1.005, 1.005, 1.005), + dense_octree_depth: int = 8, + hierarchical_octree_depth: int = 9, + flash_octree_depth: int = 9, + use_flash_decoder: bool = True, + return_dict: bool = True, + ): + self._guidance_scale = guidance_scale + self._attention_kwargs = attention_kwargs + + # 2. Define call parameters + if isinstance(image, PIL.Image.Image): + batch_size = 1 + elif isinstance(image, list): + batch_size = len(image) + elif isinstance(image, torch.Tensor): + batch_size = image.shape[0] + else: + raise ValueError("Invalid input type for image") + + device = self._execution_device + + # 3. Encode condition + text_embeds, negative_text_embeds = self.encode_text( + prompt, device, num_shapes_per_prompt + ) + + image_embeds, negative_image_embeds = self.encode_image( + image, device, num_shapes_per_prompt + ) + + if self.do_classifier_free_guidance: + text_embeds = torch.cat([negative_text_embeds, text_embeds], dim=0) + image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0) + + # 4. Prepare timesteps + timesteps, num_inference_steps = retrieve_timesteps( + self.scheduler, num_inference_steps, device, timesteps + ) + num_warmup_steps = max( + len(timesteps) - num_inference_steps * self.scheduler.order, 0 + ) + self._num_timesteps = len(timesteps) + + # 5. Prepare latent variables + num_channels_latents = self.transformer.config.in_channels + latents = self.prepare_latents( + batch_size * num_shapes_per_prompt, + num_tokens, + num_channels_latents, + image_embeds.dtype, + device, + generator, + latents, + ) + + # 6. Denoising loop + comfy_pbar = comfy.utils.ProgressBar(num_inference_steps) + with self.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(timesteps): + + # expand the latents if we are doing classifier free guidance + latent_model_input = ( + torch.cat([latents] * 2) + if self.do_classifier_free_guidance + else latents + ) + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + timestep = t.expand(latent_model_input.shape[0]) + + noise_pred = self.transformer( + latent_model_input, + timestep.to(dtype=latents.dtype), + encoder_hidden_states=text_embeds, + encoder_hidden_states_2=image_embeds, + attention_kwargs=attention_kwargs, + return_dict=False, + )[0] + + # perform guidance + if self.do_classifier_free_guidance: + noise_pred_uncond, noise_pred_image = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + self.guidance_scale * ( + noise_pred_image - noise_pred_uncond + ) + + # compute the previous noisy sample x_t -> x_t-1 + latents_dtype = latents.dtype + latents = self.scheduler.step( + noise_pred, t, latents, return_dict=False + )[0] + + if latents.dtype != latents_dtype: + if torch.backends.mps.is_available(): + # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272 + latents = latents.to(latents_dtype) + + if callback_on_step_end is not None: + callback_kwargs = {} + for k in callback_on_step_end_tensor_inputs: + callback_kwargs[k] = locals()[k] + callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) + + latents = callback_outputs.pop("latents", latents) + + # call the callback, if provided + if i == len(timesteps) - 1 or ( + (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0 + ): + progress_bar.update() + + comfy_pbar.update_absolute(i + 1) + + # 7. decoder mesh + if not use_flash_decoder: + geometric_func = lambda x: self.vae.decode(latents, sampled_points=x).sample + output = hierarchical_extract_geometry( + geometric_func, + device, + bounds=bounds, + dense_octree_depth=dense_octree_depth, + hierarchical_octree_depth=hierarchical_octree_depth, + ) + else: + self.vae.set_flash_decoder() + output = flash_extract_geometry( + latents, + self.vae, + bounds=bounds, + octree_depth=flash_octree_depth, + ) + meshes = [trimesh.Trimesh(mesh_v_f[0].astype(np.float32), mesh_v_f[1]) for mesh_v_f in output] + + # Offload all models + self.maybe_free_model_hooks() + + if not return_dict: + return (output, meshes) + + return TripoSGPipelineOutput(samples=output, meshes=meshes) diff --git a/Gen_3D_Modules/TripoSG/pipelines/pipeline_utils.py b/Gen_3D_Modules/TripoSG/pipelines/pipeline_utils.py new file mode 100644 index 0000000..a2dc441 --- /dev/null +++ b/Gen_3D_Modules/TripoSG/pipelines/pipeline_utils.py @@ -0,0 +1,96 @@ +from diffusers.utils import logging + +logger = logging.get_logger(__name__) + + +class TransformerDiffusionMixin: + r""" + Helper for DiffusionPipeline with vae and transformer.(mainly for DIT) + """ + + def enable_vae_slicing(self): + r""" + Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to + compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. + """ + self.vae.enable_slicing() + + def disable_vae_slicing(self): + r""" + Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to + computing decoding in one step. + """ + self.vae.disable_slicing() + + def enable_vae_tiling(self): + r""" + Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to + compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow + processing larger images. + """ + self.vae.enable_tiling() + + def disable_vae_tiling(self): + r""" + Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to + computing decoding in one step. + """ + self.vae.disable_tiling() + + def fuse_qkv_projections(self, transformer: bool = True, vae: bool = True): + """ + Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value) + are fused. For cross-attention modules, key and value projection matrices are fused. + + + + This API is 🧪 experimental. + + + + Args: + transformer (`bool`, defaults to `True`): To apply fusion on the Transformer. + vae (`bool`, defaults to `True`): To apply fusion on the VAE. + """ + self.fusing_transformer = False + self.fusing_vae = False + + if transformer: + self.fusing_transformer = True + self.transformer.fuse_qkv_projections() + + if vae: + self.fusing_vae = True + self.vae.fuse_qkv_projections() + + def unfuse_qkv_projections(self, transformer: bool = True, vae: bool = True): + """Disable QKV projection fusion if enabled. + + + + This API is 🧪 experimental. + + + + Args: + transformer (`bool`, defaults to `True`): To apply fusion on the Transformer. + vae (`bool`, defaults to `True`): To apply fusion on the VAE. + + """ + if transformer: + if not self.fusing_transformer: + logger.warning( + "The UNet was not initially fused for QKV projections. Doing nothing." + ) + else: + self.transformer.unfuse_qkv_projections() + self.fusing_transformer = False + + if vae: + if not self.fusing_vae: + logger.warning( + "The VAE was not initially fused for QKV projections. Doing nothing." + ) + else: + self.vae.unfuse_qkv_projections() + self.fusing_vae = False diff --git a/Gen_3D_Modules/TripoSG/schedulers/__init__.py b/Gen_3D_Modules/TripoSG/schedulers/__init__.py new file mode 100644 index 0000000..619c331 --- /dev/null +++ b/Gen_3D_Modules/TripoSG/schedulers/__init__.py @@ -0,0 +1,5 @@ +from .scheduling_rectified_flow import ( + RectifiedFlowScheduler, + compute_density_for_timestep_sampling, + compute_loss_weighting, +) diff --git a/Gen_3D_Modules/TripoSG/schedulers/scheduling_rectified_flow.py b/Gen_3D_Modules/TripoSG/schedulers/scheduling_rectified_flow.py new file mode 100644 index 0000000..d97c1bb --- /dev/null +++ b/Gen_3D_Modules/TripoSG/schedulers/scheduling_rectified_flow.py @@ -0,0 +1,327 @@ +""" +Adapted from https://github.com/huggingface/diffusers/blob/v0.30.3/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py. +""" + +import math +from dataclasses import dataclass +from typing import List, Optional, Tuple, Union + +import numpy as np +import torch +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.schedulers.scheduling_utils import SchedulerMixin +from diffusers.utils import BaseOutput, logging +from torch.distributions import LogisticNormal + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +# TODO: may move to training_utils.py +def compute_density_for_timestep_sampling( + weighting_scheme: str, + batch_size: int, + logit_mean: float = 0.0, + logit_std: float = 1.0, + mode_scale: float = None, +): + if weighting_scheme == "logit_normal": + # See 3.1 in the SD3 paper ($rf/lognorm(0.00,1.00)$). + u = torch.normal( + mean=logit_mean, std=logit_std, size=(batch_size,), device="cpu" + ) + u = torch.nn.functional.sigmoid(u) + elif weighting_scheme == "logit_normal_dist": + u = ( + LogisticNormal(loc=logit_mean, scale=logit_std) + .sample((batch_size,))[:, 0] + .to("cpu") + ) + elif weighting_scheme == "mode": + u = torch.rand(size=(batch_size,), device="cpu") + u = 1 - u - mode_scale * (torch.cos(math.pi * u / 2) ** 2 - 1 + u) + else: + u = torch.rand(size=(batch_size,), device="cpu") + return u + + +def compute_loss_weighting(weighting_scheme: str, sigmas=None): + """ + Computes loss weighting scheme for SD3 training. + + Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528. + + SD3 paper reference: https://arxiv.org/abs/2403.03206v1. + """ + if weighting_scheme == "sigma_sqrt": + weighting = (sigmas**-2.0).float() + elif weighting_scheme == "cosmap": + bot = 1 - 2 * sigmas + 2 * sigmas**2 + weighting = 2 / (math.pi * bot) + else: + weighting = torch.ones_like(sigmas) + return weighting + + +@dataclass +class RectifiedFlowSchedulerOutput(BaseOutput): + """ + Output class for the scheduler's `step` function output. + + Args: + prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): + Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the + denoising loop. + """ + + prev_sample: torch.FloatTensor + + +class RectifiedFlowScheduler(SchedulerMixin, ConfigMixin): + """ + The rectified flow scheduler is a scheduler that is used to propagate the diffusion process in the rectified flow. + + This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic + methods the library implements for all schedulers such as loading and saving. + + Args: + num_train_timesteps (`int`, defaults to 1000): + The number of diffusion steps to train the model. + timestep_spacing (`str`, defaults to `"linspace"`): + The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and + Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information. + shift (`float`, defaults to 1.0): + The shift value for the timestep schedule. + """ + + _compatibles = [] + order = 1 + + @register_to_config + def __init__( + self, + num_train_timesteps: int = 1000, + shift: float = 1.0, + use_dynamic_shifting: bool = False, + ): + # pre-compute timesteps and sigmas; no use in fact + # NOTE that shape diffusion sample timesteps randomly or in a distribution, + # instead of sampling from the pre-defined linspace + timesteps = np.array( + [ + (1.0 - i / num_train_timesteps) * num_train_timesteps + for i in range(num_train_timesteps) + ] + ) + timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32) + + sigmas = timesteps / num_train_timesteps + if not use_dynamic_shifting: + # when use_dynamic_shifting is True, we apply the timestep shifting on the fly based on the image resolution + sigmas = self.time_shift(sigmas) + + self.timesteps = sigmas * num_train_timesteps + + self._step_index = None + self._begin_index = None + + self.sigmas = sigmas.to("cpu") # to avoid too much CPU/GPU communication + + @property + def step_index(self): + """ + The index counter for current timestep. It will increase 1 after each scheduler step. + """ + return self._step_index + + @property + def begin_index(self): + """ + The index for the first timestep. It should be set from pipeline with `set_begin_index` method. + """ + return self._begin_index + + # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index + def set_begin_index(self, begin_index: int = 0): + """ + Sets the begin index for the scheduler. This function should be run from pipeline before the inference. + + Args: + begin_index (`int`): + The begin index for the scheduler. + """ + self._begin_index = begin_index + + def _sigma_to_t(self, sigma): + return sigma * self.config.num_train_timesteps + + def _t_to_sigma(self, timestep): + return timestep / self.config.num_train_timesteps + + def time_shift_dynamic(self, mu: float, sigma: float, t: torch.Tensor): + return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma) + + def time_shift(self, t: torch.Tensor): + return self.config.shift * t / (1 + (self.config.shift - 1) * t) + + def set_timesteps( + self, + num_inference_steps: int = None, + device: Union[str, torch.device] = None, + sigmas: Optional[List[float]] = None, + mu: Optional[float] = None, + ): + """ + Sets the discrete timesteps used for the diffusion chain (to be run before inference). + + Args: + num_inference_steps (`int`): + The number of diffusion steps used when generating samples with a pre-trained model. + device (`str` or `torch.device`, *optional*): + The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. + """ + + if self.config.use_dynamic_shifting and mu is None: + raise ValueError( + " you have to pass a value for `mu` when `use_dynamic_shifting` is set to be `True`" + ) + + if sigmas is None: + self.num_inference_steps = num_inference_steps + timesteps = np.array( + [ + (1.0 - i / num_inference_steps) * self.config.num_train_timesteps + for i in range(num_inference_steps) + ] + ) # different from the original code in SD3 + sigmas = timesteps / self.config.num_train_timesteps + + if self.config.use_dynamic_shifting: + sigmas = self.time_shift_dynamic(mu, 1.0, sigmas) + else: + sigmas = self.config.shift * sigmas / (1 + (self.config.shift - 1) * sigmas) + + sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32, device=device) + timesteps = sigmas * self.config.num_train_timesteps + + self.timesteps = timesteps.to(device=device) + self.sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)]) + + self._step_index = None + self._begin_index = None + + def index_for_timestep(self, timestep, schedule_timesteps=None): + if schedule_timesteps is None: + schedule_timesteps = self.timesteps + + indices = (schedule_timesteps == timestep).nonzero() + + # The sigma index that is taken for the **very** first `step` + # is always the second index (or the last index if there is only 1) + # This way we can ensure we don't accidentally skip a sigma in + # case we start in the middle of the denoising schedule (e.g. for image-to-image) + pos = 1 if len(indices) > 1 else 0 + + return indices[pos].item() + + def _init_step_index(self, timestep): + if self.begin_index is None: + if isinstance(timestep, torch.Tensor): + timestep = timestep.to(self.timesteps.device) + self._step_index = self.index_for_timestep(timestep) + else: + self._step_index = self._begin_index + + def step( + self, + model_output: torch.FloatTensor, + timestep: Union[float, torch.FloatTensor], + sample: torch.FloatTensor, + s_churn: float = 0.0, + s_tmin: float = 0.0, + s_tmax: float = float("inf"), + s_noise: float = 1.0, + generator: Optional[torch.Generator] = None, + return_dict: bool = True, + ) -> Union[RectifiedFlowSchedulerOutput, Tuple]: + """ + Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion + process from the learned model outputs (most often the predicted noise). + + Args: + model_output (`torch.FloatTensor`): + The direct output from learned diffusion model. + timestep (`float`): + The current discrete timestep in the diffusion chain. + sample (`torch.FloatTensor`): + A current instance of a sample created by the diffusion process. + s_churn (`float`): + s_tmin (`float`): + s_tmax (`float`): + s_noise (`float`, defaults to 1.0): + Scaling factor for noise added to the sample. + generator (`torch.Generator`, *optional*): + A random number generator. + return_dict (`bool`): + Whether or not to return a [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or + tuple. + + Returns: + [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or `tuple`: + If return_dict is `True`, [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] is + returned, otherwise a tuple is returned where the first element is the sample tensor. + """ + + if ( + isinstance(timestep, int) + or isinstance(timestep, torch.IntTensor) + or isinstance(timestep, torch.LongTensor) + ): + raise ValueError( + ( + "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to" + " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass" + " one of the `scheduler.timesteps` as a timestep." + ), + ) + + if self.step_index is None: + self._init_step_index(timestep) + + # Upcast to avoid precision issues when computing prev_sample + sample = sample.to(torch.float32) + + sigma = self.sigmas[self.step_index] + sigma_next = self.sigmas[self.step_index + 1] + + # Here different directions are used for the flow matching + prev_sample = sample + (sigma - sigma_next) * model_output + + # Cast sample back to model compatible dtype + prev_sample = prev_sample.to(model_output.dtype) + + # upon completion increase step index by one + self._step_index += 1 + + if not return_dict: + return (prev_sample,) + + return RectifiedFlowSchedulerOutput(prev_sample=prev_sample) + + def scale_noise( + self, + original_samples: torch.Tensor, + noise: torch.Tensor, + timesteps: torch.IntTensor, + ) -> torch.Tensor: + """ + Forward function for the noise scaling in the flow matching. + """ + sigmas = self._t_to_sigma(timesteps.to(dtype=torch.float32)) + + while len(sigmas.shape) < len(original_samples.shape): + sigmas = sigmas.unsqueeze(-1) + + return (1.0 - sigmas) * original_samples + sigmas * noise + + def __len__(self): + return self.config.num_train_timesteps diff --git a/Gen_3D_Modules/TripoSG/utils/typing.py b/Gen_3D_Modules/TripoSG/utils/typing.py new file mode 100644 index 0000000..8a84e73 --- /dev/null +++ b/Gen_3D_Modules/TripoSG/utils/typing.py @@ -0,0 +1,64 @@ +""" +This module contains type annotations for the project, using +1. Python type hints (https://docs.python.org/3/library/typing.html) for Python objects +2. jaxtyping (https://github.com/google/jaxtyping/blob/main/API.md) for PyTorch tensors + +Two types of typing checking can be used: +1. Static type checking with mypy (install with pip and enabled as the default linter in VSCode) +2. Runtime type checking with typeguard (install with pip and triggered at runtime, mainly for tensor dtype and shape checking) +""" + +# Basic types +from typing import ( + Any, + Callable, + Dict, + Iterable, + List, + Literal, + NamedTuple, + NewType, + Optional, + Sized, + Tuple, + Type, + TypedDict, + TypeVar, + Union, +) + +# Tensor dtype +# for jaxtyping usage, see https://github.com/google/jaxtyping/blob/main/API.md +from jaxtyping import Bool, Complex, Float, Inexact, Int, Integer, Num, Shaped, UInt + +# Config type +from omegaconf import DictConfig, ListConfig + +# PyTorch Tensor type +from torch import Tensor + +# Runtime type checking decorator +from typeguard import typechecked as typechecker + + +# Custom types +class FuncArgs(TypedDict): + """Type for instantiating a function with keyword arguments""" + + name: str + kwargs: Dict[str, Any] + + @staticmethod + def validate(variable): + necessary_keys = ["name", "kwargs"] + for key in necessary_keys: + assert key in variable, 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name for S&R":"[Comfy3D] Load Diffusers Pipeline"},"widgets_values":["TripoSGPipeline","VAST-AI/TripoSG","",false,"",true]},{"id":19,"type":"PreviewImage","pos":[1032.9256591796875,431.04876708984375],"size":[259.2801818847656,478.1733093261719],"flags":{},"order":4,"mode":0,"inputs":[{"localized_name":"images","name":"images","type":"IMAGE","link":28}],"outputs":[],"properties":{"Node name for S&R":"PreviewImage"},"widgets_values":[]}],"links":[[1,2,1,1,0,"MASK"],[9,5,0,10,0,"DIFFUSERS_PIPE"],[11,10,0,11,0,"MESH"],[12,11,0,13,0,"MESH"],[13,13,0,12,0,"STRING"],[14,10,0,14,0,"MESH"],[15,14,0,15,0,"STRING"],[17,4,0,17,0,"IMAGE"],[22,2,0,18,0,"IMAGE"],[24,1,0,18,1,"MASK"],[25,18,0,4,0,"IMAGE"],[26,18,1,4,1,"MASK"],[27,4,0,10,1,"IMAGE"],[28,18,0,19,0,"IMAGE"]],"groups":[],"config":{},"extra":{"ds":{"scale":0.8264462809917354,"offset":[41.134269747098244,8.720844783035172]},"frontendVersion":"1.17.11"},"version":0.4} \ No newline at end of file diff --git a/mesh_processer/mesh_utils.py b/mesh_processer/mesh_utils.py index a70b61b..c3799df 100644 --- a/mesh_processer/mesh_utils.py +++ b/mesh_processer/mesh_utils.py @@ -242,6 +242,8 @@ def decimate_mesh( m = pml.Mesh(verts, faces) ms = pml.MeshSet() ms.add_mesh(m, "mesh") # will copy! + + ms.meshing_merge_close_vertices() # filters # ms.meshing_decimation_clustering(threshold=pml.PercentageValue(1)) diff --git a/nodes.py b/nodes.py index b527f33..f75a9ce 100644 --- a/nodes.py +++ b/nodes.py @@ -99,6 +99,8 @@ from Hunyuan3D_V2.hy3dgen.shapegen import FaceReducer, FloaterRemover, Degenerat from Hunyuan3D_V2.hy3dgen.texgen import Hunyuan3DPaintPipeline from TRELLIS.trellis.pipelines import TrellisImageTo3DPipeline from TRELLIS.trellis.utils import postprocessing_utils +from TripoSG.pipelines.pipeline_triposg import TripoSGPipeline +from TripoSG.pipelines.pipeline_triposg_scribble import TripoSGScribblePipeline os.environ['SPCONV_ALGO'] = 'native' @@ -126,6 +128,8 @@ DIFFUSERS_PIPE_DICT = OrderedDict([ ("Hunyuan3DMVDLitePipeline", Hunyuan3D_MVD_Lite_Pipeline), ("Hunyuan3DDiTFlowMatchingPipeline", Hunyuan3DDiTFlowMatchingPipeline), ("Hunyuan3DPaintPipeline", Hunyuan3DPaintPipeline), + ("TripoSGPipeline", TripoSGPipeline), + ("TripoSGScribblePipeline", TripoSGScribblePipeline), ]) DIFFUSERS_SCHEDULER_DICT = OrderedDict([ @@ -632,11 +636,9 @@ class Decimate_Mesh: CATEGORY = "Comfy3D/Preprocessor" def process_mesh(self, mesh, target, remesh, optimalplacement): - - vertices, faces = decimate_mesh(mesh.v, mesh.f, target, remesh, optimalplacement) - mesh.v = vertices - mesh.f = faces - + vertices, faces = decimate_mesh(mesh.v.detach().cpu().numpy(), mesh.f.detach().cpu().numpy(), target, remesh, optimalplacement) + mesh.v, mesh.f = torch.from_numpy(vertices).to(DEVICE), torch.from_numpy(faces).to(torch.int64).to(DEVICE) + mesh.auto_normal() return (mesh,) class Switch_3DGS_Axis: @@ -1486,6 +1488,7 @@ class Load_Diffusers_Pipeline: }, "optional": { "checkpoint_sub_dir": ("STRING", {"default": "", "multiline": False}), + "force_disable_xformers": ("BOOLEAN", {"default": False}), } } @@ -1498,7 +1501,7 @@ class Load_Diffusers_Pipeline: FUNCTION = "load_diffusers_pipe" CATEGORY = "Comfy3D/Import|Export" - def load_diffusers_pipe(self, diffusers_pipeline_name, repo_id, custom_pipeline, force_download, checkpoint_sub_dir=""): + def load_diffusers_pipe(self, diffusers_pipeline_name, repo_id, custom_pipeline, force_download, checkpoint_sub_dir="", force_disable_xformers=False): # resume download pretrained checkpoint ckpt_download_dir = os.path.join(CKPT_DIFFUSERS_PATH, repo_id) @@ -1515,9 +1518,9 @@ class Load_Diffusers_Pipeline: ckpt_path, torch_dtype=WEIGHT_DTYPE, custom_pipeline=custom_pipeline, - ).to(DEVICE) + ).to(DEVICE, WEIGHT_DTYPE) - if hasattr(pipe, 'enable_xformers_memory_efficient_attention'): + if hasattr(pipe, 'enable_xformers_memory_efficient_attention') and not force_disable_xformers: pipe.enable_xformers_memory_efficient_attention() return (pipe, ) @@ -4117,6 +4120,137 @@ class Trellis_Structured_3D_Latents_Models: mesh.auto_normal() return (mesh,) + +class TripoSG_I23D_Model: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "tsg_pipe": ("DIFFUSERS_PIPE",), + "reference_image": ("IMAGE",), + "seed": ("INT", {"default": 42, "min": 0, "max": 0xffffffffffffffff}), + "guidance_scale": ("FLOAT", {"default": 7.0, "min": 0.0, "step": 0.01}), + "num_inference_steps": ("INT", {"default": 50, "min": 1}), + "use_flash_decoder": ("BOOLEAN", {"default": True}), + "flash_octree_depth": ("INT", {"default": 9, "min": 1}), + "hierarchical_octree_depth": ("INT", {"default": 9, "min": 1}), + "dense_octree_depth": ("INT", {"default": 8, "min": 1}), + } + } + + RETURN_TYPES = ( + "MESH", + ) + RETURN_NAMES = ( + "mesh", + ) + FUNCTION = "run_model" + CATEGORY = "Comfy3D/Algorithm" + + @torch.no_grad() + def run_model( + self, + tsg_pipe, + reference_image, # [1, H, W, 3] + seed, + guidance_scale, + num_inference_steps, + use_flash_decoder, + flash_octree_depth, + hierarchical_octree_depth, + dense_octree_depth, + ): + + single_image = torch_imgs_to_pils(reference_image)[0] + + with torch.inference_mode(False): + outputs = tsg_pipe( + image=single_image, + generator=torch.Generator(device=DEVICE).manual_seed(seed), + num_inference_steps=num_inference_steps, + guidance_scale=guidance_scale, + use_flash_decoder=use_flash_decoder, + flash_octree_depth=flash_octree_depth, + hierarchical_octree_depth=hierarchical_octree_depth, + dense_octree_depth=dense_octree_depth, + ).samples[0] + + vertices, faces = torch.from_numpy(outputs[0].astype(np.float32)).to(DEVICE), torch.from_numpy(np.ascontiguousarray(outputs[1])).to(torch.int64).to(DEVICE) + mesh = Mesh(v=vertices, f=faces, device=DEVICE) + mesh.auto_normal() + + return (mesh,) + +class TripoSG_Scribble_Model: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "tsg_scribble_pipe": ("DIFFUSERS_PIPE",), + "scribble_image": ("IMAGE",), + "prompt": ("STRING", { + "default": "3D assets", + "multiline": True + }), + "seed": ("INT", {"default": 42, "min": 0, "max": 0xffffffffffffffff}), + "num_inference_steps": ("INT", {"default": 16, "min": 1}), + "scribble_confidence": ("FLOAT", {"default": 0.4, "min": 0.0, "step": 0.01}), + "prompt_confidence": ("FLOAT", {"default": 1.0, "min": 0.0, "step": 0.01}), + "use_flash_decoder": ("BOOLEAN", {"default": False}), + "flash_octree_depth": ("INT", {"default": 8, "min": 1}), + "hierarchical_octree_depth": ("INT", {"default": 8, "min": 1}), + "dense_octree_depth": ("INT", {"default": 8, "min": 1}), + } + } + + RETURN_TYPES = ( + "MESH", + ) + RETURN_NAMES = ( + "mesh", + ) + FUNCTION = "run_model" + CATEGORY = "Comfy3D/Algorithm" + + @torch.no_grad() + def run_model( + self, + tsg_scribble_pipe, + scribble_image, # [1, H, W, 3] + prompt, + seed, + num_inference_steps, + scribble_confidence, + prompt_confidence, + use_flash_decoder, + flash_octree_depth, + hierarchical_octree_depth, + dense_octree_depth, + ): + + single_image = torch_imgs_to_pils(scribble_image)[0] + + with torch.inference_mode(False): + outputs = tsg_scribble_pipe( + image=single_image, + prompt=prompt, + generator=torch.Generator(device=DEVICE).manual_seed(seed), + num_inference_steps=num_inference_steps, + guidance_scale=0, # this is a CFG-distilled model + attention_kwargs={"cross_attention_scale": prompt_confidence, "cross_attention_2_scale": scribble_confidence}, + use_flash_decoder=use_flash_decoder, + flash_octree_depth=flash_octree_depth, # there're some boundary problems when using flash decoder with this model + hierarchical_octree_depth=hierarchical_octree_depth, + dense_octree_depth=dense_octree_depth, + ).samples[0] + + vertices, faces = torch.from_numpy(outputs[0].astype(np.float32)).to(DEVICE), torch.from_numpy(np.ascontiguousarray(outputs[1])).to(torch.int64).to(DEVICE) + mesh = Mesh(v=vertices, f=faces.to(torch.int64), device=DEVICE) + mesh.auto_normal() + + return (mesh,) + + diff --git a/requirements.txt b/requirements.txt index 4f1293e..c8b4234 100644 --- a/requirements.txt +++ b/requirements.txt @@ -48,6 +48,7 @@ libigl pyvista pymeshfix igraph +diso # configs & extra torchtyping>=0.1.5 From a29213fecc849dbfc8ae4d0148e9b59a484da86e Mon Sep 17 00:00:00 2001 From: "Mr. For Example" <62230687+MrForExample@users.noreply.github.com> Date: Tue, 29 Apr 2025 16:53:46 -0600 Subject: [PATCH 02/36] Update README.md --- README.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 1034e39..4409819 100644 --- a/README.md +++ b/README.md @@ -43,9 +43,13 @@ Your ComfyUI Root Directory\python_embeded\python.exe install.py ## Features: -- For use cases please check out [Example Workflows](./_Example_Workflows/). [**Last update: 22/January/2025**] +- For use cases please check out [Example Workflows](./_Example_Workflows/). [**Last update: 29/April/2025**] - **Note:** you need to put [Example Inputs Files & Folders](_Example_Workflows/_Example_Inputs_Files/) under ComfyUI Root Directory\ComfyUI\input folder before you can run the example workflow +- **TripoSG**: [VAST-AI-Research/TripoSG](https://github.com/VAST-AI-Research/TripoSG) + - Single image (Reference or Scribble) to 3D Mesh + - Model weights: https://huggingface.co/VAST-AI/TripoSG, https://huggingface.co/VAST-AI/TripoSG-scribble + - **TRELLIS**: [microsoft/TRELLIS](https://github.com/microsoft/TRELLIS) - Single image to 3D Mesh with RGB texture - Model weights: https://huggingface.co/JeffreyXiang/TRELLIS-image-large From 2c629522408180cff31f87b492c848d94948a811 Mon Sep 17 00:00:00 2001 From: "Mr. For Example" <62230687+MrForExample@users.noreply.github.com> Date: Tue, 29 Apr 2025 16:55:38 -0600 Subject: [PATCH 03/36] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 4409819..f6fe816 100644 --- a/README.md +++ b/README.md @@ -50,6 +50,8 @@ Your ComfyUI Root Directory\python_embeded\python.exe install.py - Single image (Reference or Scribble) to 3D Mesh - Model weights: https://huggingface.co/VAST-AI/TripoSG, https://huggingface.co/VAST-AI/TripoSG-scribble + + - **TRELLIS**: [microsoft/TRELLIS](https://github.com/microsoft/TRELLIS) - Single image to 3D Mesh with RGB texture - Model weights: https://huggingface.co/JeffreyXiang/TRELLIS-image-large From 8aeeeecae53fc0d88c38b49788356d1428423f01 Mon Sep 17 00:00:00 2001 From: Denis Kochetov Date: Wed, 7 May 2025 16:57:08 +0000 Subject: [PATCH 04/36] feat: add Hunyuan3D-V2 Turbo, Fast, Mini models (MV not implemented yet) --- .../Diffusers/tencent/Hunyuan3D-2/README.md | 224 - .../hunyuan3d-dit-v2-0-fast/config.yaml | 69 + .../hunyuan3d-dit-v2-0-turbo/config.yaml | 70 + .../hunyuan3d-paint-v2-0-turbo/.gitattributes | 35 + .../hunyuan3d-paint-v2-0-turbo/README.md | 53 + .../preprocessor_config.json | 20 + .../image_encoder/config.json | 23 + .../image_encoder/preprocessor_config.json | 27 + .../model_index.json | 37 + .../scheduler/scheduler_config.json | 15 + .../text_encoder/config.json | 25 + .../tokenizer/merges.txt | 48895 +++++++++++++++ .../tokenizer/special_tokens_map.json | 24 + .../tokenizer/tokenizer_config.json | 34 + .../tokenizer/vocab.json | 49410 ++++++++++++++++ .../unet/config.json | 45 + .../unet/modules.py | 610 + .../vae/config.json | 29 + .../hunyuan3d-vae-v2-0-turbo/config.yaml | 15 + .../hunyuan3d-vae-v2-0/config.yaml | 15 + .../hunyuan3d-dit-v2-mini-fast/config.yaml | 69 + .../hunyuan3d-dit-v2-mini-turbo/config.yaml | 70 + .../hunyuan3d-dit-v2-mini/config.yaml | 69 + .../hunyuan3d-dit-v2-mv-turbo/config.yaml | 70 + Gen_3D_Modules/Hunyuan3D_V2/.gitignore | 169 + Gen_3D_Modules/Hunyuan3D_V2/LICENSE | 80 + Gen_3D_Modules/Hunyuan3D_V2/NOTICE | 214 + Gen_3D_Modules/Hunyuan3D_V2/__init__.py | 23 + Gen_3D_Modules/Hunyuan3D_V2/assets/1.glb | Bin 0 -> 720764 bytes 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create mode 100644 Gen_3D_Modules/Hunyuan3D_V2/requirements.txt create mode 100644 Gen_3D_Modules/Hunyuan3D_V2/setup.py create mode 100644 _Example_Workflows/Hunyuan3D_V2_Turbo.json diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/README.md b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/README.md deleted file mode 100644 index 51ebc3e..0000000 --- a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/README.md +++ /dev/null @@ -1,224 +0,0 @@ ---- -library_name: hunyuan3d-2.0 -license: other -license_name: tencent-hunyuan-community -license_link: https://huggingface.co/tencent/Hunyuan3D-2/blob/main/LICENSE.txt -language: - - en - - zh -tags: - - image-to-3d - - text-to-3d -pipeline_tag: image-to-3d ---- - -

- -

- -
- - - - - - -
- - -[//]: # ( ) - -[//]: # ( ) - -[//]: # ( PyPI - Downloads) - -
-

-“ Living out everyone’s imagination on creating and manipulating 3D assets.” -

- -This repository contains the models of the paper [Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation](https://huggingface.co/papers/2501.12202). -For code and more details on how to use it, refer to the [Github repository](https://github.com/Tencent/Hunyuan3D-2). - -## 🔥 News - -- Jan 21, 2025: 💬 Release [Hunyuan3D 2.0](https://huggingface.co/spaces/tencent/Hunyuan3D-2). Please give it a try! - -## **Abstract** - -We present Hunyuan3D 2.0, an advanced large-scale 3D synthesis system for generating high-resolution textured 3D assets. -This system includes two foundation components: a large-scale shape generation model - Hunyuan3D-DiT, and a large-scale -texture synthesis model - Hunyuan3D-Paint. -The shape generative model, built on a scalable flow-based diffusion transformer, aims to create geometry that properly -aligns with a given condition image, laying a solid foundation for downstream applications. -The texture synthesis model, benefiting from strong geometric and diffusion priors, produces high-resolution and vibrant -texture maps for either generated or hand-crafted meshes. -Furthermore, we build Hunyuan3D-Studio - a versatile, user-friendly production platform that simplifies the re-creation -process of 3D assets. It allows both professional and amateur users to manipulate or even animate their meshes -efficiently. -We systematically evaluate our models, showing that Hunyuan3D 2.0 outperforms previous state-of-the-art models, -including the open-source models and closed-source models in geometry details, condition alignment, texture quality, and -e.t.c. - -

- -

- -## ☯️ **Hunyuan3D 2.0** - -### Architecture - -Hunyuan3D 2.0 features a two-stage generation pipeline, starting with the creation of a bare mesh, followed by the -synthesis of a texture map for that mesh. This strategy is effective for decoupling the difficulties of shape and -texture generation and also provides flexibility for texturing either generated or handcrafted meshes. - -

- -

- -### Performance - -We have evaluated Hunyuan3D 2.0 with other open-source as well as close-source 3d-generation methods. -The numerical results indicate that Hunyuan3D 2.0 surpasses all baselines in the quality of generated textured 3D assets -and the condition following ability. - -| Model | CMMD(⬇) | FID_CLIP(⬇) | FID(⬇) | CLIP-score(⬆) | -|-------------------------|-----------|-------------|-------------|---------------| -| Top Open-source Model1 | 3.591 | 54.639 | 289.287 | 0.787 | -| Top Close-source Model1 | 3.600 | 55.866 | 305.922 | 0.779 | -| Top Close-source Model2 | 3.368 | 49.744 | 294.628 | 0.806 | -| Top Close-source Model3 | 3.218 | 51.574 | 295.691 | 0.799 | -| Hunyuan3D 2.0 | **3.193** | **49.165** | **282.429** | **0.809** | - -Generation results of Hunyuan3D 2.0: -

- - -

- -### Pretrained Models - -| Model | Date | Huggingface | -|----------------------|------------|--------------------------------------------------------| -| Hunyuan3D-DiT-v2-0 | 2025-01-21 | [Download](https://huggingface.co/tencent/Hunyuan3D-2) | -| Hunyuan3D-Paint-v2-0 | 2025-01-21 | [Download](https://huggingface.co/tencent/Hunyuan3D-2) | -| Hunyuan3D-Delight-v2-0 | 2025-01-21 | [Download](https://huggingface.co/tencent/Hunyuan3D-2/tree/main/hunyuan3d-delight-v2-0) | - -## 🤗 Get Started with Hunyuan3D 2.0 - -You may follow the next steps to use Hunyuan3D 2.0 via code or the Gradio App. - -### Install Requirements - -Please install Pytorch via the [official](https://pytorch.org/) site. Then install the other requirements via - -```bash -pip install -r requirements.txt -# for texture -cd hy3dgen/texgen/custom_rasterizer -python3 setup.py install -cd ../../.. -cd hy3dgen/texgen/differentiable_renderer -bash compile_mesh_painter.sh OR python3 setup.py install (on Windows) -``` - -### API Usage - -We designed a diffusers-like API to use our shape generation model - Hunyuan3D-DiT and texture synthesis model - -Hunyuan3D-Paint. - -You could assess **Hunyuan3D-DiT** via: - -```python -from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline - -pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained('tencent/Hunyuan3D-2') -mesh = pipeline(image='assets/demo.png')[0] -``` - -The output mesh is a [trimesh object](https://trimesh.org/trimesh.html), which you could save to glb/obj (or other -format) file. - -For **Hunyuan3D-Paint**, do the following: - -```python -from hy3dgen.texgen import Hunyuan3DPaintPipeline -from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline - -# let's generate a mesh first -pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained('tencent/Hunyuan3D-2') -mesh = pipeline(image='assets/demo.png')[0] - -pipeline = Hunyuan3DPaintPipeline.from_pretrained('tencent/Hunyuan3D-2') -mesh = pipeline(mesh, image='assets/demo.png') -``` - -Please visit [minimal_demo.py](minimal_demo.py) for more advanced usage, such as **text to 3D** and **texture generation -for handcrafted mesh**. - -### Gradio App - -You could also host a [Gradio](https://www.gradio.app/) App in your own computer via: - -```bash -pip3 install gradio==3.39.0 -python3 gradio_app.py -``` - -Don't forget to visit [Hunyuan3D](https://3d.hunyuan.tencent.com) for quick use, if you don't want to host yourself. - -## 📑 Open-Source Plan - -- [x] Inference Code -- [x] Model Checkpoints -- [x] Technical Report -- [ ] ComfyUI -- [ ] TensorRT Version - -## 🔗 BibTeX - -If you found this repository helpful, please cite our report: - -```bibtex -@misc{hunyuan3d22025tencent, - title={Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation}, - author={Tencent Hunyuan3D Team}, - year={2025}, - eprint={2501.12202}, - archivePrefix={arXiv}, - primaryClass={cs.CV} -} - -@misc{yang2024tencent, - title={Tencent Hunyuan3D-1.0: A Unified Framework for Text-to-3D and Image-to-3D Generation}, - author={Tencent Hunyuan3D Team}, - year={2024}, - eprint={2411.02293}, - archivePrefix={arXiv}, - primaryClass={cs.CV} -} -``` - -## Community Resources - -Thanks for the contributions of community members, here we have these great extensions of Hunyuan3D 2.0: - -- [ComfyUI-Hunyuan3DWrapper](https://github.com/kijai/ComfyUI-Hunyuan3DWrapper) -- [Hunyuan3D-2-for-windows](https://github.com/sdbds/Hunyuan3D-2-for-windows) -- [📦 A bundle for running on Windows | 整合包](https://github.com/YanWenKun/Comfy3D-WinPortable/releases/tag/r8-hunyuan3d2) - -## Acknowledgements - -We would like to thank the contributors to -the [DINOv2](https://github.com/facebookresearch/dinov2), [Stable Diffusion](https://github.com/Stability-AI/stablediffusion), [FLUX](https://github.com/black-forest-labs/flux), [diffusers](https://github.com/huggingface/diffusers) -and [HuggingFace](https://huggingface.co) repositories, for their open research and exploration. - -## Star History - - - - - - Star History Chart - - \ No newline at end of file diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-dit-v2-0-fast/config.yaml b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-dit-v2-0-fast/config.yaml new file mode 100644 index 0000000..7f52d46 --- /dev/null +++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-dit-v2-0-fast/config.yaml @@ -0,0 +1,69 @@ +model: + target: Hunyuan3D_V2.hy3dgen.shapegen.models.Hunyuan3DDiT + params: + in_channels: 64 + context_in_dim: 1536 + hidden_size: 1024 + mlp_ratio: 4.0 + num_heads: 16 + depth: 16 + depth_single_blocks: 32 + axes_dim: [ 64 ] + theta: 10000 + qkv_bias: true + guidance_embed: true + +vae: + target: Hunyuan3D_V2.hy3dgen.shapegen.models.ShapeVAE + params: + num_latents: 3072 + embed_dim: 64 + num_freqs: 8 + include_pi: false + heads: 16 + width: 1024 + num_decoder_layers: 16 + qkv_bias: false + qk_norm: true + scale_factor: 0.9990943042622529 + +conditioner: + target: Hunyuan3D_V2.hy3dgen.shapegen.models.SingleImageEncoder + params: + main_image_encoder: + type: DinoImageEncoder # dino giant + kwargs: + config: + attention_probs_dropout_prob: 0.0 + drop_path_rate: 0.0 + hidden_act: gelu + hidden_dropout_prob: 0.0 + hidden_size: 1536 + image_size: 518 + initializer_range: 0.02 + layer_norm_eps: 1.e-6 + layerscale_value: 1.0 + mlp_ratio: 4 + model_type: dinov2 + num_attention_heads: 24 + num_channels: 3 + num_hidden_layers: 40 + patch_size: 14 + qkv_bias: true + torch_dtype: float32 + use_swiglu_ffn: true + image_size: 518 + +scheduler: + target: Hunyuan3D_V2.hy3dgen.shapegen.schedulers.FlowMatchEulerDiscreteScheduler + params: + num_train_timesteps: 1000 + +image_processor: + target: Hunyuan3D_V2.hy3dgen.shapegen.preprocessors.ImageProcessorV2 + params: + size: 512 + border_ratio: 0.15 + +pipeline: + target: Hunyuan3D_V2.hy3dgen.shapegen.pipelines.Hunyuan3DDiTFlowMatchingPipeline diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-dit-v2-0-turbo/config.yaml b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-dit-v2-0-turbo/config.yaml new file mode 100644 index 0000000..8db19f9 --- /dev/null +++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-dit-v2-0-turbo/config.yaml @@ -0,0 +1,70 @@ +model: + target: Hunyuan3D_V2.hy3dgen.shapegen.models.Hunyuan3DDiT + params: + in_channels: 64 + context_in_dim: 1536 + hidden_size: 1024 + mlp_ratio: 4.0 + num_heads: 16 + depth: 16 + depth_single_blocks: 32 + axes_dim: [ 64 ] + theta: 10000 + qkv_bias: true + guidance_embed: true + +vae: + target: Hunyuan3D_V2.hy3dgen.shapegen.models.ShapeVAE + params: + num_latents: 3072 + embed_dim: 64 + num_freqs: 8 + include_pi: false + heads: 16 + width: 1024 + num_decoder_layers: 16 + qkv_bias: false + qk_norm: true + scale_factor: 0.9990943042622529 + +conditioner: + target: Hunyuan3D_V2.hy3dgen.shapegen.models.SingleImageEncoder + params: + main_image_encoder: + type: DinoImageEncoder # dino giant + kwargs: + config: + attention_probs_dropout_prob: 0.0 + drop_path_rate: 0.0 + hidden_act: gelu + hidden_dropout_prob: 0.0 + hidden_size: 1536 + image_size: 518 + initializer_range: 0.02 + layer_norm_eps: 1.e-6 + layerscale_value: 1.0 + mlp_ratio: 4 + model_type: dinov2 + num_attention_heads: 24 + num_channels: 3 + num_hidden_layers: 40 + patch_size: 14 + qkv_bias: true + torch_dtype: float32 + use_swiglu_ffn: true + image_size: 518 + +scheduler: + target: Hunyuan3D_V2.hy3dgen.shapegen.schedulers.ConsistencyFlowMatchEulerDiscreteScheduler + params: + num_train_timesteps: 1000 + pcm_timesteps: 100 + +image_processor: + target: Hunyuan3D_V2.hy3dgen.shapegen.preprocessors.ImageProcessorV2 + params: + size: 512 + border_ratio: 0.15 + +pipeline: + target: Hunyuan3D_V2.hy3dgen.shapegen.pipelines.Hunyuan3DDiTFlowMatchingPipeline diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/.gitattributes b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/.gitattributes new file mode 100644 index 0000000..a6344aa --- /dev/null +++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/README.md b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/README.md new file mode 100644 index 0000000..ced35bb --- /dev/null +++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/README.md @@ -0,0 +1,53 @@ +--- +license: openrail++ +tags: +- stable-diffusion +- text-to-image +--- + +# SD v2.1-base with Zero Terminal SNR (LAION Aesthetic 6+) + +This model is used in [Diffusion Model with Perceptual Loss](https://arxiv.org/abs/2401.00110) paper as the MSE baseline. + +This model is trained using zero terminal SNR schedule following [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/abs/2305.08891) paper on LAION aesthetic 6+ data. + +This model is finetuned from [stabilityai/stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base). + +This model is meant for research demonstration, not for production use. + +## Usage + +```python +from diffusers import StableDiffusionPipeline +prompt = "A young girl smiling" +pipe = StableDiffusionPipeline.from_pretrained("ByteDance/sd2.1-base-zsnr-laionaes6").to("cuda") +pipe(prompt, guidance_scale=7.5, guidance_rescale=0.7).images[0].save("out.jpg") +``` + +## Related Models + +* [bytedance/sd2.1-base-zsnr-laionaes5](https://huggingface.co/ByteDance/sd2.1-base-zsnr-laionaes5) +* [bytedance/sd2.1-base-zsnr-laionaes6](https://huggingface.co/ByteDance/sd2.1-base-zsnr-laionaes6) +* [bytedance/sd2.1-base-zsnr-laionaes6-perceptual](https://huggingface.co/ByteDance/sd2.1-base-zsnr-laionaes6-perceptual) + + +## Cite as +``` +@misc{lin2024diffusion, + title={Diffusion Model with Perceptual Loss}, + author={Shanchuan Lin and Xiao Yang}, + year={2024}, + eprint={2401.00110}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} + +@misc{lin2023common, + title={Common Diffusion Noise Schedules and Sample Steps are Flawed}, + author={Shanchuan Lin and Bingchen Liu and Jiashi Li and Xiao Yang}, + year={2023}, + eprint={2305.08891}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` \ No newline at end of file diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/feature_extractor/preprocessor_config.json b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/feature_extractor/preprocessor_config.json new file mode 100644 index 0000000..5294955 --- /dev/null +++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/feature_extractor/preprocessor_config.json @@ -0,0 +1,20 @@ +{ + "crop_size": 224, + "do_center_crop": true, + "do_convert_rgb": true, + "do_normalize": true, + "do_resize": true, + "feature_extractor_type": "CLIPFeatureExtractor", + "image_mean": [ + 0.48145466, + 0.4578275, + 0.40821073 + ], + "image_std": [ + 0.26862954, + 0.26130258, + 0.27577711 + ], + "resample": 3, + "size": 224 +} diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/image_encoder/config.json b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/image_encoder/config.json new file mode 100644 index 0000000..5e55ea1 --- /dev/null +++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/image_encoder/config.json @@ -0,0 +1,23 @@ +{ + "_name_or_path": "D:\\.cache\\huggingface\\hub\\models--sudo-ai--zero123plus-v1.1\\snapshots\\36df7de980afd15f80b2e1a4e9a920d7020e2654\\vision_encoder", + "architectures": [ + "CLIPVisionModelWithProjection" + ], + "attention_dropout": 0.0, + "dropout": 0.0, + "hidden_act": "gelu", + "hidden_size": 1280, + "image_size": 224, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 5120, + "layer_norm_eps": 1e-05, + "model_type": "clip_vision_model", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 32, + "patch_size": 14, + "projection_dim": 1024, + "torch_dtype": "float16", + "transformers_version": "4.36.0" +} diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/image_encoder/preprocessor_config.json b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/image_encoder/preprocessor_config.json new file mode 100644 index 0000000..fb1692f --- /dev/null +++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/image_encoder/preprocessor_config.json @@ -0,0 +1,27 @@ +{ + "crop_size": { + "height": 224, + "width": 224 + }, + "do_center_crop": true, + "do_convert_rgb": true, + "do_normalize": true, + "do_rescale": true, + "do_resize": true, + "image_mean": [ + 0.48145466, + 0.4578275, + 0.40821073 + ], + "image_processor_type": "CLIPImageProcessor", + "image_std": [ + 0.26862954, + 0.26130258, + 0.27577711 + ], + "resample": 3, + "rescale_factor": 0.00392156862745098, + "size": { + "shortest_edge": 224 + } +} diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/model_index.json b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/model_index.json new file mode 100644 index 0000000..01d2c53 --- /dev/null +++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/model_index.json @@ -0,0 +1,37 @@ +{ + "_class_name": "StableDiffusionPipeline", + "_diffusers_version": "0.23.1", + "feature_extractor": [ + "transformers", + "CLIPImageProcessor" + ], + "requires_safety_checker": false, + "safety_checker": [ + null, + null + ], + "scheduler": [ + "diffusers", + "DDIMScheduler" + ], + "text_encoder": [ + "transformers", + "CLIPTextModel" + ], + "tokenizer": [ + "transformers", + "CLIPTokenizer" + ], + "image_encoder": [ + "transformers", + "CLIPVisionModelWithProjection" + ], + "unet": [ + "modules", + "UNet2p5DConditionModel" + ], + "vae": [ + "diffusers", + "AutoencoderKL" + ] +} diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/scheduler/scheduler_config.json b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/scheduler/scheduler_config.json new file mode 100644 index 0000000..e2cca4b --- /dev/null +++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/scheduler/scheduler_config.json @@ -0,0 +1,15 @@ +{ + "_class_name": "DDIMScheduler", + "_diffusers_version": "0.23.1", + "beta_end": 0.012, + "beta_schedule": "scaled_linear", + "beta_start": 0.00085, + "clip_sample": false, + "num_train_timesteps": 1000, + "prediction_type": "v_prediction", + "set_alpha_to_one": true, + "steps_offset": 1, + "trained_betas": null, + "timestep_spacing": "trailing", + "rescale_betas_zero_snr": true +} diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/text_encoder/config.json b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/text_encoder/config.json new file mode 100644 index 0000000..f9ac7c0 --- /dev/null +++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/text_encoder/config.json @@ -0,0 +1,25 @@ +{ + "_name_or_path": "stabilityai/stable-diffusion-2", + "architectures": [ + "CLIPTextModel" + ], + "attention_dropout": 0.0, + "bos_token_id": 0, + "dropout": 0.0, + "eos_token_id": 2, + "hidden_act": "gelu", + "hidden_size": 1024, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 4096, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 77, + "model_type": "clip_text_model", + "num_attention_heads": 16, + "num_hidden_layers": 23, + "pad_token_id": 1, + "projection_dim": 512, + "torch_dtype": "float32", + "transformers_version": "4.25.0.dev0", + "vocab_size": 49408 +} diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/tokenizer/merges.txt b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/tokenizer/merges.txt new file mode 100644 index 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255, + "Ń": 511 +} diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/unet/config.json b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/unet/config.json new file mode 100644 index 0000000..0fa07f9 --- /dev/null +++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/unet/config.json @@ -0,0 +1,45 @@ +{ + "_class_name": "UNet2DConditionModel", + "_diffusers_version": "0.10.0.dev0", + "act_fn": "silu", + "attention_head_dim": [ + 5, + 10, + 20, + 20 + ], + "block_out_channels": [ + 320, + 640, + 1280, + 1280 + ], + "center_input_sample": false, + "cross_attention_dim": 1024, + "down_block_types": [ + "CrossAttnDownBlock2D", + "CrossAttnDownBlock2D", + "CrossAttnDownBlock2D", + "DownBlock2D" + ], + "downsample_padding": 1, + "dual_cross_attention": false, + "flip_sin_to_cos": true, + "freq_shift": 0, + "in_channels": 4, + "layers_per_block": 2, + "mid_block_scale_factor": 1, + "norm_eps": 1e-05, + "norm_num_groups": 32, + "num_class_embeds": null, + "only_cross_attention": false, + "out_channels": 4, + "sample_size": 64, + "up_block_types": [ + "UpBlock2D", + "CrossAttnUpBlock2D", + "CrossAttnUpBlock2D", + "CrossAttnUpBlock2D" + ], + "use_linear_projection": true +} diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/unet/modules.py b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/unet/modules.py new file mode 100644 index 0000000..36b4594 --- /dev/null +++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/unet/modules.py @@ -0,0 +1,610 @@ +# Open Source Model Licensed under the Apache License Version 2.0 +# and Other Licenses of the Third-Party Components therein: +# The below Model in this distribution may have been modified by THL A29 Limited +# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited. + +# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. +# The below software and/or models in this distribution may have been +# modified by THL A29 Limited ("Tencent Modifications"). +# All Tencent Modifications are Copyright (C) THL A29 Limited. + +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +import copy +import json +import os +from typing import Any, Dict, List, Optional, Tuple, Union + +import torch +import torch.nn as nn +import torch.nn.functional as F +from diffusers.models import UNet2DConditionModel +from diffusers.models.attention_processor import Attention +from diffusers.models.transformers.transformer_2d import BasicTransformerBlock +from einops import rearrange + + +def _chunked_feed_forward(ff: nn.Module, hidden_states: torch.Tensor, chunk_dim: int, chunk_size: int): + # "feed_forward_chunk_size" can be used to save memory + if hidden_states.shape[chunk_dim] % chunk_size != 0: + raise ValueError( + f"`hidden_states` dimension to be chunked: {hidden_states.shape[chunk_dim]}" + f"has to be divisible by chunk size: {chunk_size}." + f" Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`." + ) + + num_chunks = hidden_states.shape[chunk_dim] // chunk_size + ff_output = torch.cat( + [ff(hid_slice) for hid_slice in hidden_states.chunk(num_chunks, dim=chunk_dim)], + dim=chunk_dim, + ) + return ff_output + + +class Basic2p5DTransformerBlock(torch.nn.Module): + def __init__(self, transformer: BasicTransformerBlock, layer_name, use_ma=True, use_ra=True, is_turbo=False) -> None: + super().__init__() + self.transformer = transformer + self.layer_name = layer_name + self.use_ma = use_ma + self.use_ra = use_ra + self.is_turbo = is_turbo + + # multiview attn + if self.use_ma: + self.attn_multiview = Attention( + query_dim=self.dim, + heads=self.num_attention_heads, + dim_head=self.attention_head_dim, + dropout=self.dropout, + bias=self.attention_bias, + cross_attention_dim=None, + upcast_attention=self.attn1.upcast_attention, + out_bias=True, + ) + + # ref attn + if self.use_ra: + self.attn_refview = Attention( + query_dim=self.dim, + heads=self.num_attention_heads, + dim_head=self.attention_head_dim, + dropout=self.dropout, + bias=self.attention_bias, + cross_attention_dim=None, + upcast_attention=self.attn1.upcast_attention, + out_bias=True, + ) + if self.is_turbo: + self._initialize_attn_weights() + + def _initialize_attn_weights(self): + + if self.use_ma: + self.attn_multiview.load_state_dict(self.attn1.state_dict()) + with torch.no_grad(): + for layer in self.attn_multiview.to_out: + for param in layer.parameters(): + param.zero_() + if self.use_ra: + self.attn_refview.load_state_dict(self.attn1.state_dict()) + with torch.no_grad(): + for layer in self.attn_refview.to_out: + for param in layer.parameters(): + param.zero_() + + def __getattr__(self, name: str): + try: + return super().__getattr__(name) + except AttributeError: + return getattr(self.transformer, name) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + timestep: Optional[torch.LongTensor] = None, + cross_attention_kwargs: Dict[str, Any] = None, + class_labels: Optional[torch.LongTensor] = None, + added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None, + ) -> torch.Tensor: + + # Notice that normalization is always applied before the real computation in the following blocks. + # 0. Self-Attention + batch_size = hidden_states.shape[0] + + cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {} + num_in_batch = cross_attention_kwargs.pop('num_in_batch', 1) + mode = cross_attention_kwargs.pop('mode', None) + if not self.is_turbo: + mva_scale = cross_attention_kwargs.pop('mva_scale', 1.0) + ref_scale = cross_attention_kwargs.pop('ref_scale', 1.0) + else: + position_attn_mask = cross_attention_kwargs.pop("position_attn_mask", None) + position_voxel_indices = cross_attention_kwargs.pop("position_voxel_indices", None) + mva_scale = 1.0 + ref_scale = 1.0 + + condition_embed_dict = cross_attention_kwargs.pop("condition_embed_dict", None) + + if self.norm_type == "ada_norm": + norm_hidden_states = self.norm1(hidden_states, timestep) + elif self.norm_type == "ada_norm_zero": + norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1( + hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype + ) + elif self.norm_type in ["layer_norm", "layer_norm_i2vgen"]: + norm_hidden_states = self.norm1(hidden_states) + elif self.norm_type == "ada_norm_continuous": + norm_hidden_states = self.norm1(hidden_states, added_cond_kwargs["pooled_text_emb"]) + elif self.norm_type == "ada_norm_single": + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( + self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1) + ).chunk(6, dim=1) + norm_hidden_states = self.norm1(hidden_states) + norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa + else: + raise ValueError("Incorrect norm used") + + if self.pos_embed is not None: + norm_hidden_states = self.pos_embed(norm_hidden_states) + + # 1. Prepare GLIGEN inputs + cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {} + gligen_kwargs = cross_attention_kwargs.pop("gligen", None) + + attn_output = self.attn1( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None, + attention_mask=attention_mask, + **cross_attention_kwargs, + ) + + if self.norm_type == "ada_norm_zero": + attn_output = gate_msa.unsqueeze(1) * attn_output + elif self.norm_type == "ada_norm_single": + attn_output = gate_msa * attn_output + + hidden_states = attn_output + hidden_states + if hidden_states.ndim == 4: + hidden_states = hidden_states.squeeze(1) + + # 1.2 Reference Attention + if 'w' in mode: + condition_embed_dict[self.layer_name] = rearrange( + norm_hidden_states, '(b n) l c -> b (n l) c', + n=num_in_batch + ) # B, (N L), C + + if 'r' in mode and self.use_ra: + condition_embed = condition_embed_dict[self.layer_name].unsqueeze(1).repeat(1, num_in_batch, 1, + 1) # B N L C + condition_embed = rearrange(condition_embed, 'b n l c -> (b n) l c') + + attn_output = self.attn_refview( + norm_hidden_states, + encoder_hidden_states=condition_embed, + attention_mask=None, + **cross_attention_kwargs + ) + if not self.is_turbo: + ref_scale_timing = ref_scale + if isinstance(ref_scale, torch.Tensor): + ref_scale_timing = ref_scale.unsqueeze(1).repeat(1, num_in_batch).view(-1) + for _ in range(attn_output.ndim - 1): + ref_scale_timing = ref_scale_timing.unsqueeze(-1) + + hidden_states = ref_scale_timing * attn_output + hidden_states + + if hidden_states.ndim == 4: + hidden_states = hidden_states.squeeze(1) + + # 1.3 Multiview Attention + if num_in_batch > 1 and self.use_ma: + multivew_hidden_states = rearrange(norm_hidden_states, '(b n) l c -> b (n l) c', n=num_in_batch) + + if self.is_turbo: + position_mask = None + if position_attn_mask is not None: + if multivew_hidden_states.shape[1] in position_attn_mask: + position_mask = position_attn_mask[multivew_hidden_states.shape[1]] + position_indices = None + if position_voxel_indices is not None: + if multivew_hidden_states.shape[1] in position_voxel_indices: + position_indices = position_voxel_indices[multivew_hidden_states.shape[1]] + attn_output = self.attn_multiview( + multivew_hidden_states, + encoder_hidden_states=multivew_hidden_states, + attention_mask=position_mask, + position_indices=position_indices, + **cross_attention_kwargs + ) + else: + attn_output = self.attn_multiview( + multivew_hidden_states, + encoder_hidden_states=multivew_hidden_states, + **cross_attention_kwargs + ) + + attn_output = rearrange(attn_output, 'b (n l) c -> (b n) l c', n=num_in_batch) + + hidden_states = mva_scale * attn_output + hidden_states + if hidden_states.ndim == 4: + hidden_states = hidden_states.squeeze(1) + + # 1.2 GLIGEN Control + if gligen_kwargs is not None: + hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"]) + + # 3. Cross-Attention + if self.attn2 is not None: + if self.norm_type == "ada_norm": + norm_hidden_states = self.norm2(hidden_states, timestep) + elif self.norm_type in ["ada_norm_zero", "layer_norm", "layer_norm_i2vgen"]: + norm_hidden_states = self.norm2(hidden_states) + elif self.norm_type == "ada_norm_single": + # For PixArt norm2 isn't applied here: + # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L70C1-L76C103 + norm_hidden_states = hidden_states + elif self.norm_type == "ada_norm_continuous": + norm_hidden_states = self.norm2(hidden_states, added_cond_kwargs["pooled_text_emb"]) + else: + raise ValueError("Incorrect norm") + + if self.pos_embed is not None and self.norm_type != "ada_norm_single": + norm_hidden_states = self.pos_embed(norm_hidden_states) + + attn_output = self.attn2( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + **cross_attention_kwargs, + ) + + hidden_states = attn_output + hidden_states + + # 4. Feed-forward + # i2vgen doesn't have this norm 🤷‍♂️ + if self.norm_type == "ada_norm_continuous": + norm_hidden_states = self.norm3(hidden_states, added_cond_kwargs["pooled_text_emb"]) + elif not self.norm_type == "ada_norm_single": + norm_hidden_states = self.norm3(hidden_states) + + if self.norm_type == "ada_norm_zero": + norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None] + + if self.norm_type == "ada_norm_single": + norm_hidden_states = self.norm2(hidden_states) + norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp + + if self._chunk_size is not None: + # "feed_forward_chunk_size" can be used to save memory + ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size) + else: + ff_output = self.ff(norm_hidden_states) + + if self.norm_type == "ada_norm_zero": + ff_output = gate_mlp.unsqueeze(1) * ff_output + elif self.norm_type == "ada_norm_single": + ff_output = gate_mlp * ff_output + + hidden_states = ff_output + hidden_states + if hidden_states.ndim == 4: + hidden_states = hidden_states.squeeze(1) + + return hidden_states + +@torch.no_grad() +def compute_voxel_grid_mask(position, grid_resolution=8): + + position = position.half() + B,N,_,H,W = position.shape + assert H%grid_resolution==0 and W%grid_resolution==0 + + valid_mask = (position != 1).all(dim=2, keepdim=True) + valid_mask = valid_mask.expand_as(position) + position[valid_mask==False] = 0 + + + position = rearrange( + position, + 'b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w', + num_h=grid_resolution, num_w=grid_resolution + ) + valid_mask = rearrange( + valid_mask, + 'b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w', + num_h=grid_resolution, num_w=grid_resolution + ) + + grid_position = position.sum(dim=(-2, -1)) + count_masked = valid_mask.sum(dim=(-2, -1)) + + grid_position = grid_position / count_masked.clamp(min=1) + grid_position[count_masked<5] = 0 + + grid_position = grid_position.permute(0,1,4,2,3) + grid_position = rearrange(grid_position, 'b n c h w -> b n (h w) c') + + grid_position_expanded_1 = grid_position.unsqueeze(2).unsqueeze(4) # 形状变为 B, N, 1, L, 1, 3 + grid_position_expanded_2 = grid_position.unsqueeze(1).unsqueeze(3) # 形状变为 B, 1, N, 1, L, 3 + + # 计算欧氏距离 + distances = torch.norm(grid_position_expanded_1 - grid_position_expanded_2, dim=-1) # 形状为 B, N, N, L, L + + weights = distances + grid_distance = 1.73/grid_resolution + + #weights = weights*-32 + #weights = weights.clamp(min=-10000.0) + + weights = weights< grid_distance + + return weights + +def compute_multi_resolution_mask(position_maps, grid_resolutions=[32, 16, 8]): + position_attn_mask = {} + with torch.no_grad(): + for grid_resolution in grid_resolutions: + position_mask = compute_voxel_grid_mask(position_maps, grid_resolution) + position_mask = rearrange(position_mask, 'b ni nj li lj -> b (ni li) (nj lj)') + position_attn_mask[position_mask.shape[1]] = position_mask + return position_attn_mask + +@torch.no_grad() +def compute_discrete_voxel_indice(position, grid_resolution=8, voxel_resolution=128): + + position = position.half() + B,N,_,H,W = position.shape + assert H%grid_resolution==0 and W%grid_resolution==0 + + valid_mask = (position != 1).all(dim=2, keepdim=True) + valid_mask = valid_mask.expand_as(position) + position[valid_mask==False] = 0 + + position = rearrange( + position, + 'b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w', + num_h=grid_resolution, num_w=grid_resolution + ) + valid_mask = rearrange( + valid_mask, + 'b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w', + num_h=grid_resolution, num_w=grid_resolution + ) + + grid_position = position.sum(dim=(-2, -1)) + count_masked = valid_mask.sum(dim=(-2, -1)) + + grid_position = grid_position / count_masked.clamp(min=1) + grid_position[count_masked<5] = 0 + + grid_position = grid_position.permute(0,1,4,2,3).clamp(0, 1) # B N C H W + voxel_indices = grid_position * (voxel_resolution - 1) + voxel_indices = torch.round(voxel_indices).long() + return voxel_indices + +def compute_multi_resolution_discrete_voxel_indice( + position_maps, + grid_resolutions=[64, 32, 16, 8], + voxel_resolutions=[512, 256, 128, 64] +): + voxel_indices = {} + with torch.no_grad(): + for grid_resolution, voxel_resolution in zip(grid_resolutions, voxel_resolutions): + voxel_indice = compute_discrete_voxel_indice(position_maps, grid_resolution, voxel_resolution) + voxel_indice = rearrange(voxel_indice, 'b n c h w -> b (n h w) c') + voxel_indices[voxel_indice.shape[1]] = {'voxel_indices':voxel_indice, 'voxel_resolution':voxel_resolution} + return voxel_indices + +class UNet2p5DConditionModel(torch.nn.Module): + def __init__(self, unet: UNet2DConditionModel) -> None: + super().__init__() + self.unet = unet + + self.use_ma = True + self.use_ra = True + self.use_camera_embedding = True + self.use_dual_stream = True + self.is_turbo = False + + if self.use_dual_stream: + self.unet_dual = copy.deepcopy(unet) + self.init_attention(self.unet_dual) + self.init_attention(self.unet, use_ma=self.use_ma, use_ra=self.use_ra, is_turbo=self.is_turbo) + self.init_condition() + self.init_camera_embedding() + + @staticmethod + def from_pretrained(pretrained_model_name_or_path, **kwargs): + torch_dtype = kwargs.pop('torch_dtype', torch.float32) + config_path = os.path.join(pretrained_model_name_or_path, 'config.json') + unet_ckpt_path = os.path.join(pretrained_model_name_or_path, 'diffusion_pytorch_model.bin') + with open(config_path, 'r', encoding='utf-8') as file: + config = json.load(file) + unet = UNet2DConditionModel(**config) + unet = UNet2p5DConditionModel(unet) + unet_ckpt = torch.load(unet_ckpt_path, map_location='cpu', weights_only=True) + unet.load_state_dict(unet_ckpt, strict=True) + unet = unet.to(torch_dtype) + return unet + + def init_condition(self): + self.unet.conv_in = torch.nn.Conv2d( + 12, + self.unet.conv_in.out_channels, + kernel_size=self.unet.conv_in.kernel_size, + stride=self.unet.conv_in.stride, + padding=self.unet.conv_in.padding, + dilation=self.unet.conv_in.dilation, + groups=self.unet.conv_in.groups, + bias=self.unet.conv_in.bias is not None) + + self.unet.learned_text_clip_gen = nn.Parameter(torch.randn(1, 77, 1024)) + self.unet.learned_text_clip_ref = nn.Parameter(torch.randn(1, 77, 1024)) + + def init_camera_embedding(self): + + if self.use_camera_embedding: + time_embed_dim = 1280 + self.max_num_ref_image = 5 + self.max_num_gen_image = 12 * 3 + 4 * 2 + self.unet.class_embedding = nn.Embedding(self.max_num_ref_image + self.max_num_gen_image, time_embed_dim) + + def init_attention(self, unet, use_ma=False, use_ra=False, is_turbo=False): + + for down_block_i, down_block in enumerate(unet.down_blocks): + if hasattr(down_block, "has_cross_attention") and down_block.has_cross_attention: + for attn_i, attn in enumerate(down_block.attentions): + for transformer_i, transformer in enumerate(attn.transformer_blocks): + if isinstance(transformer, BasicTransformerBlock): + attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock( + transformer, + f'down_{down_block_i}_{attn_i}_{transformer_i}', + use_ma, use_ra, is_turbo + ) + + if hasattr(unet.mid_block, "has_cross_attention") and unet.mid_block.has_cross_attention: + for attn_i, attn in enumerate(unet.mid_block.attentions): + for transformer_i, transformer in enumerate(attn.transformer_blocks): + if isinstance(transformer, BasicTransformerBlock): + attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock( + transformer, + f'mid_{attn_i}_{transformer_i}', + use_ma, use_ra, is_turbo + ) + + for up_block_i, up_block in enumerate(unet.up_blocks): + if hasattr(up_block, "has_cross_attention") and up_block.has_cross_attention: + for attn_i, attn in enumerate(up_block.attentions): + for transformer_i, transformer in enumerate(attn.transformer_blocks): + if isinstance(transformer, BasicTransformerBlock): + attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock( + transformer, + f'up_{up_block_i}_{attn_i}_{transformer_i}', + use_ma, use_ra, is_turbo + ) + + def __getattr__(self, name: str): + try: + return super().__getattr__(name) + except AttributeError: + return getattr(self.unet, name) + + def forward( + self, sample, timestep, encoder_hidden_states, + *args, down_intrablock_additional_residuals=None, + down_block_res_samples=None, mid_block_res_sample=None, + **cached_condition, + ): + B, N_gen, _, H, W = sample.shape + assert H == W + + if self.use_camera_embedding: + camera_info_gen = cached_condition['camera_info_gen'] + self.max_num_ref_image + camera_info_gen = rearrange(camera_info_gen, 'b n -> (b n)') + else: + camera_info_gen = None + + sample = [sample] + if 'normal_imgs' in cached_condition: + sample.append(cached_condition["normal_imgs"]) + if 'position_imgs' in cached_condition: + sample.append(cached_condition["position_imgs"]) + sample = torch.cat(sample, dim=2) + + sample = rearrange(sample, 'b n c h w -> (b n) c h w') + + encoder_hidden_states_gen = encoder_hidden_states.unsqueeze(1).repeat(1, N_gen, 1, 1) + encoder_hidden_states_gen = rearrange(encoder_hidden_states_gen, 'b n l c -> (b n) l c') + + if self.use_ra: + if 'condition_embed_dict' in cached_condition: + condition_embed_dict = cached_condition['condition_embed_dict'] + else: + condition_embed_dict = {} + ref_latents = cached_condition['ref_latents'] + N_ref = ref_latents.shape[1] + if self.use_camera_embedding: + camera_info_ref = cached_condition['camera_info_ref'] + camera_info_ref = rearrange(camera_info_ref, 'b n -> (b n)') + else: + camera_info_ref = None + + ref_latents = rearrange(ref_latents, 'b n c h w -> (b n) c h w') + + encoder_hidden_states_ref = self.unet.learned_text_clip_ref.unsqueeze(1).repeat(B, N_ref, 1, 1) + encoder_hidden_states_ref = rearrange(encoder_hidden_states_ref, 'b n l c -> (b n) l c') + + noisy_ref_latents = ref_latents + timestep_ref = 0 + + if self.use_dual_stream: + unet_ref = self.unet_dual + else: + unet_ref = self.unet + unet_ref( + noisy_ref_latents, timestep_ref, + encoder_hidden_states=encoder_hidden_states_ref, + class_labels=camera_info_ref, + # **kwargs + return_dict=False, + cross_attention_kwargs={ + 'mode': 'w', 'num_in_batch': N_ref, + 'condition_embed_dict': condition_embed_dict}, + ) + cached_condition['condition_embed_dict'] = condition_embed_dict + else: + condition_embed_dict = None + + mva_scale = cached_condition.get('mva_scale', 1.0) + ref_scale = cached_condition.get('ref_scale', 1.0) + + if self.is_turbo: + cross_attention_kwargs_ = { + 'mode': 'r', 'num_in_batch': N_gen, + 'condition_embed_dict': condition_embed_dict, + 'position_attn_mask':position_attn_mask, + 'position_voxel_indices':position_voxel_indices, + 'mva_scale': mva_scale, + 'ref_scale': ref_scale, + } + else: + cross_attention_kwargs_ = { + 'mode': 'r', 'num_in_batch': N_gen, + 'condition_embed_dict': condition_embed_dict, + 'mva_scale': mva_scale, + 'ref_scale': ref_scale, + } + return self.unet( + sample, timestep, + encoder_hidden_states_gen, *args, + class_labels=camera_info_gen, + down_intrablock_additional_residuals=[ + sample.to(dtype=self.unet.dtype) for sample in down_intrablock_additional_residuals + ] if down_intrablock_additional_residuals is not None else None, + down_block_additional_residuals=[ + sample.to(dtype=self.unet.dtype) for sample in down_block_res_samples + ] if down_block_res_samples is not None else None, + mid_block_additional_residual=( + mid_block_res_sample.to(dtype=self.unet.dtype) + if mid_block_res_sample is not None else None + ), + return_dict=False, + cross_attention_kwargs=cross_attention_kwargs_, + ) + diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/vae/config.json b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/vae/config.json new file mode 100644 index 0000000..9421ac5 --- /dev/null +++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/vae/config.json @@ -0,0 +1,29 @@ +{ + "_class_name": "AutoencoderKL", + "_diffusers_version": "0.10.0.dev0", + "act_fn": "silu", + "block_out_channels": [ + 128, + 256, + 512, + 512 + ], + "down_block_types": [ + "DownEncoderBlock2D", + "DownEncoderBlock2D", + "DownEncoderBlock2D", + "DownEncoderBlock2D" + ], + "in_channels": 3, + "latent_channels": 4, + "layers_per_block": 2, + "norm_num_groups": 32, + "out_channels": 3, + "sample_size": 768, + "up_block_types": [ + "UpDecoderBlock2D", + "UpDecoderBlock2D", + "UpDecoderBlock2D", + "UpDecoderBlock2D" + ] +} diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-vae-v2-0-turbo/config.yaml b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-vae-v2-0-turbo/config.yaml new file mode 100644 index 0000000..4e84fc2 --- /dev/null +++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-vae-v2-0-turbo/config.yaml @@ -0,0 +1,15 @@ +target: Hunyuan3D_V2.hy3dgen.shapegen.models.ShapeVAE +params: + num_latents: 3072 + embed_dim: 64 + num_freqs: 8 + include_pi: false + heads: 16 + width: 1024 + num_decoder_layers: 16 + qkv_bias: false + qk_norm: true + scale_factor: 0.9990943042622529 + geo_decoder_mlp_expand_ratio: 1 + geo_decoder_downsample_ratio: 2 + geo_decoder_ln_post: false \ No newline at end of file diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-vae-v2-0/config.yaml b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-vae-v2-0/config.yaml new file mode 100644 index 0000000..f0ff063 --- /dev/null +++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-vae-v2-0/config.yaml @@ -0,0 +1,15 @@ +target: Hunyuan3D_V2.hy3dgen.shapegen.models.ShapeVAE +params: + num_latents: 3072 + embed_dim: 64 + num_freqs: 8 + include_pi: false + heads: 16 + width: 1024 + num_decoder_layers: 16 + qkv_bias: false + qk_norm: true + scale_factor: 0.9990943042622529 + geo_decoder_mlp_expand_ratio: 4 + geo_decoder_downsample_ratio: 1 + geo_decoder_ln_post: true \ No newline at end of file diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2mini/hunyuan3d-dit-v2-mini-fast/config.yaml b/Checkpoints/Diffusers/tencent/Hunyuan3D-2mini/hunyuan3d-dit-v2-mini-fast/config.yaml new file mode 100644 index 0000000..65890be --- /dev/null +++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2mini/hunyuan3d-dit-v2-mini-fast/config.yaml @@ -0,0 +1,69 @@ +model: + target: Hunyuan3D_V2.hy3dgen.shapegen.models.Hunyuan3DDiT + params: + in_channels: 64 + context_in_dim: 1536 + hidden_size: 1024 + mlp_ratio: 4.0 + num_heads: 16 + depth: 8 + depth_single_blocks: 16 + axes_dim: [ 64 ] + theta: 10000 + qkv_bias: true + guidance_embed: true + +vae: + target: Hunyuan3D_V2.hy3dgen.shapegen.models.ShapeVAE + params: + num_latents: 512 + embed_dim: 64 + num_freqs: 8 + include_pi: false + heads: 16 + width: 1024 + num_decoder_layers: 16 + qkv_bias: false + qk_norm: true + scale_factor: 1.0188137142395404 + +conditioner: + target: Hunyuan3D_V2.hy3dgen.shapegen.models.SingleImageEncoder + params: + main_image_encoder: + type: DinoImageEncoder # dino giant + kwargs: + config: + attention_probs_dropout_prob: 0.0 + drop_path_rate: 0.0 + hidden_act: gelu + hidden_dropout_prob: 0.0 + hidden_size: 1536 + image_size: 518 + initializer_range: 0.02 + layer_norm_eps: 1.e-6 + layerscale_value: 1.0 + mlp_ratio: 4 + model_type: dinov2 + num_attention_heads: 24 + num_channels: 3 + num_hidden_layers: 40 + patch_size: 14 + qkv_bias: true + torch_dtype: float32 + use_swiglu_ffn: true + image_size: 518 + +scheduler: + target: Hunyuan3D_V2.hy3dgen.shapegen.schedulers.FlowMatchEulerDiscreteScheduler + params: + num_train_timesteps: 1000 + +image_processor: + target: Hunyuan3D_V2.hy3dgen.shapegen.preprocessors.ImageProcessorV2 + params: + size: 512 + border_ratio: 0.15 + +pipeline: + target: Hunyuan3D_V2.hy3dgen.shapegen.pipelines.Hunyuan3DDiTFlowMatchingPipeline diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2mini/hunyuan3d-dit-v2-mini-turbo/config.yaml b/Checkpoints/Diffusers/tencent/Hunyuan3D-2mini/hunyuan3d-dit-v2-mini-turbo/config.yaml new file mode 100644 index 0000000..0ad6426 --- /dev/null +++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2mini/hunyuan3d-dit-v2-mini-turbo/config.yaml @@ -0,0 +1,70 @@ +model: + target: Hunyuan3D_V2.hy3dgen.shapegen.models.Hunyuan3DDiT + params: + in_channels: 64 + context_in_dim: 1536 + hidden_size: 1024 + mlp_ratio: 4.0 + num_heads: 16 + depth: 8 + depth_single_blocks: 16 + axes_dim: [ 64 ] + theta: 10000 + qkv_bias: true + guidance_embed: true + +vae: + target: Hunyuan3D_V2.hy3dgen.shapegen.models.ShapeVAE + params: + num_latents: 3072 + embed_dim: 64 + num_freqs: 8 + include_pi: false + heads: 16 + width: 1024 + num_decoder_layers: 16 + qkv_bias: false + qk_norm: true + scale_factor: 1.0188137142395404 + +conditioner: + target: Hunyuan3D_V2.hy3dgen.shapegen.models.SingleImageEncoder + params: + main_image_encoder: + type: DinoImageEncoder # dino giant + kwargs: + config: + attention_probs_dropout_prob: 0.0 + drop_path_rate: 0.0 + hidden_act: gelu + hidden_dropout_prob: 0.0 + hidden_size: 1536 + image_size: 518 + initializer_range: 0.02 + layer_norm_eps: 1.e-6 + layerscale_value: 1.0 + mlp_ratio: 4 + model_type: dinov2 + num_attention_heads: 24 + num_channels: 3 + num_hidden_layers: 40 + patch_size: 14 + qkv_bias: true + torch_dtype: float32 + use_swiglu_ffn: true + image_size: 1022 + +scheduler: + target: Hunyuan3D_V2.hy3dgen.shapegen.schedulers.ConsistencyFlowMatchEulerDiscreteScheduler + params: + num_train_timesteps: 1000 + pcm_timesteps: 100 + +image_processor: + target: Hunyuan3D_V2.hy3dgen.shapegen.preprocessors.ImageProcessorV2 + params: + size: 1022 + border_ratio: 0.15 + +pipeline: + target: Hunyuan3D_V2.hy3dgen.shapegen.pipelines.Hunyuan3DDiTFlowMatchingPipeline diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2mini/hunyuan3d-dit-v2-mini/config.yaml b/Checkpoints/Diffusers/tencent/Hunyuan3D-2mini/hunyuan3d-dit-v2-mini/config.yaml new file mode 100644 index 0000000..4c255d8 --- /dev/null +++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2mini/hunyuan3d-dit-v2-mini/config.yaml @@ -0,0 +1,69 @@ +model: + target: Hunyuan3D_V2.hy3dgen.shapegen.models.Hunyuan3DDiT + params: + in_channels: 64 + context_in_dim: 1536 + hidden_size: 1024 + mlp_ratio: 4.0 + num_heads: 16 + depth: 8 + depth_single_blocks: 16 + axes_dim: [ 64 ] + theta: 10000 + qkv_bias: true + guidance_embed: false + +vae: + target: Hunyuan3D_V2.hy3dgen.shapegen.models.ShapeVAE + params: + num_latents: 512 + embed_dim: 64 + num_freqs: 8 + include_pi: false + heads: 16 + width: 1024 + num_decoder_layers: 16 + qkv_bias: false + qk_norm: true + scale_factor: 1.0188137142395404 + +conditioner: + target: Hunyuan3D_V2.hy3dgen.shapegen.models.SingleImageEncoder + params: + main_image_encoder: + type: DinoImageEncoder # dino giant + kwargs: + config: + attention_probs_dropout_prob: 0.0 + drop_path_rate: 0.0 + hidden_act: gelu + hidden_dropout_prob: 0.0 + hidden_size: 1536 + image_size: 518 + initializer_range: 0.02 + layer_norm_eps: 1.e-6 + layerscale_value: 1.0 + mlp_ratio: 4 + model_type: dinov2 + num_attention_heads: 24 + num_channels: 3 + num_hidden_layers: 40 + patch_size: 14 + qkv_bias: true + torch_dtype: float32 + use_swiglu_ffn: true + image_size: 518 + +scheduler: + target: Hunyuan3D_V2.hy3dgen.shapegen.schedulers.FlowMatchEulerDiscreteScheduler + params: + num_train_timesteps: 1000 + +image_processor: + target: Hunyuan3D_V2.hy3dgen.shapegen.preprocessors.ImageProcessorV2 + params: + size: 512 + border_ratio: 0.15 + +pipeline: + target: Hunyuan3D_V2.hy3dgen.shapegen.pipelines.Hunyuan3DDiTFlowMatchingPipeline diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2mv/hunyuan3d-dit-v2-mv-turbo/config.yaml b/Checkpoints/Diffusers/tencent/Hunyuan3D-2mv/hunyuan3d-dit-v2-mv-turbo/config.yaml new file mode 100644 index 0000000..2df2d51 --- /dev/null +++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2mv/hunyuan3d-dit-v2-mv-turbo/config.yaml @@ -0,0 +1,70 @@ +model: + target: Hunyuan3D_V2.hy3dgen.shapegen.models.Hunyuan3DDiT + params: + in_channels: 64 + context_in_dim: 1536 + hidden_size: 1024 + mlp_ratio: 4.0 + num_heads: 16 + depth: 16 + depth_single_blocks: 32 + axes_dim: [ 64 ] + theta: 10000 + qkv_bias: true + guidance_embed: true + +vae: + target: Hunyuan3D_V2.hy3dgen.shapegen.models.ShapeVAE + params: + num_latents: 3072 + embed_dim: 64 + num_freqs: 8 + include_pi: false + heads: 16 + width: 1024 + num_decoder_layers: 16 + qkv_bias: false + qk_norm: true + scale_factor: 0.9990943042622529 + +conditioner: + target: Hunyuan3D_V2.hy3dgen.shapegen.models.SingleImageEncoder + params: + main_image_encoder: + type: DinoImageEncoderMV # dino giant + kwargs: + config: + attention_probs_dropout_prob: 0.0 + drop_path_rate: 0.0 + hidden_act: gelu + hidden_dropout_prob: 0.0 + hidden_size: 1536 + image_size: 518 + initializer_range: 0.02 + layer_norm_eps: 1.e-6 + layerscale_value: 1.0 + mlp_ratio: 4 + model_type: dinov2 + num_attention_heads: 24 + num_channels: 3 + num_hidden_layers: 40 + patch_size: 14 + qkv_bias: true + torch_dtype: float32 + use_swiglu_ffn: true + image_size: 518 + +scheduler: + target: Hunyuan3D_V2.hy3dgen.shapegen.schedulers.ConsistencyFlowMatchEulerDiscreteScheduler + params: + num_train_timesteps: 1000 + pcm_timesteps: 100 + +image_processor: + target: Hunyuan3D_V2.hy3dgen.shapegen.preprocessors.MVImageProcessorV2 + params: + size: 512 + border_ratio: 0.15 + +pipeline: + target: Hunyuan3D_V2.hy3dgen.shapegen.pipelines.Hunyuan3DDiTFlowMatchingPipeline diff --git a/Gen_3D_Modules/Hunyuan3D_V2/.gitignore b/Gen_3D_Modules/Hunyuan3D_V2/.gitignore new file mode 100644 index 0000000..35000cb --- /dev/null +++ b/Gen_3D_Modules/Hunyuan3D_V2/.gitignore @@ -0,0 +1,169 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +!hy3dgen/texgen/custom_rasterizer/lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py 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YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING, REPRODUCING, MODIFYING, PERFORMING, DISPLAYING OR DISTRIBUTING ANY OF THE TENCENT HUNYUAN 3D 2.0 WORKS OR OUTPUTS AND ASSUME ANY AND ALL RISKS ASSOCIATED WITH YOUR OR A THIRD PARTY’S USE OR DISTRIBUTION OF ANY OF THE TENCENT HUNYUAN 3D 2.0 WORKS OR OUTPUTS AND YOUR EXERCISE OF RIGHTS AND PERMISSIONS UNDER THIS AGREEMENT. +c. TO THE FULLEST EXTENT PERMITTED BY APPLICABLE LAW, IN NO EVENT SHALL TENCENT OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, FOR ANY DAMAGES, INCLUDING ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, EXEMPLARY, CONSEQUENTIAL OR PUNITIVE DAMAGES, OR LOST PROFITS OF ANY KIND ARISING FROM THIS AGREEMENT OR RELATED TO ANY OF THE TENCENT HUNYUAN 3D 2.0 WORKS OR OUTPUTS, EVEN IF TENCENT OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING. +8. SURVIVAL AND TERMINATION. +a. The term of this Agreement shall commence upon Your acceptance of this Agreement or access to the Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. +b. We may terminate this Agreement if You breach any of the terms or conditions of this Agreement. Upon termination of this Agreement, You must promptly delete and cease use of the Tencent Hunyuan 3D 2.0 Works. Sections 6(a), 6(c), 7 and 9 shall survive the termination of this Agreement. +9. GOVERNING LAW AND JURISDICTION. +a. This Agreement and any dispute arising out of or relating to it will be governed by the laws of the Hong Kong Special Administrative Region of the People’s Republic of China, without regard to conflict of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. +b. Exclusive jurisdiction and venue for any dispute arising out of or relating to this Agreement will be a court of competent jurisdiction in the Hong Kong Special Administrative Region of the People’s Republic of China, and Tencent and Licensee consent to the exclusive jurisdiction of such court with respect to any such dispute. +  +EXHIBIT A +ACCEPTABLE USE POLICY + +Tencent reserves the right to update this Acceptable Use Policy from time to time. +Last modified: November 5, 2024 + +Tencent endeavors to promote safe and fair use of its tools and features, including Tencent Hunyuan 3D 2.0. You agree not to use Tencent Hunyuan 3D 2.0 or Model Derivatives: +1. Outside the Territory; +2. In any way that violates any applicable national, federal, state, local, international or any other law or regulation; +3. To harm Yourself or others; +4. To repurpose or distribute output from Tencent Hunyuan 3D 2.0 or any Model Derivatives to harm Yourself or others; +5. To override or circumvent the safety guardrails and safeguards We have put in place; +6. For the purpose of exploiting, harming or attempting to exploit or harm minors in any way; +7. To generate or disseminate verifiably false information and/or content with the purpose of harming others or influencing elections; +8. To generate or facilitate false online engagement, including fake reviews and other means of fake online engagement; +9. To intentionally defame, disparage or otherwise harass others; +10. To generate and/or disseminate malware (including ransomware) or any other content to be used for the purpose of harming electronic systems; +11. To generate or disseminate personal identifiable information with the purpose of harming others; +12. To generate or disseminate information (including images, code, posts, articles), and place the information in any public context (including –through the use of bot generated tweets), without expressly and conspicuously identifying that the information and/or content is machine generated; +13. To impersonate another individual without consent, authorization, or legal right; +14. To make high-stakes automated decisions in domains that affect an individual’s safety, rights or wellbeing (e.g., law enforcement, migration, medicine/health, management of critical infrastructure, safety components of products, essential services, credit, employment, housing, education, social scoring, or insurance); +15. In a manner that violates or disrespects the social ethics and moral standards of other countries or regions; +16. To perform, facilitate, threaten, incite, plan, promote or encourage violent extremism or terrorism; +17. For any use intended to discriminate against or harm individuals or groups based on protected characteristics or categories, online or offline social behavior or known or predicted personal or personality characteristics; +18. To intentionally exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm; +19. For military purposes; +20. To engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or other professional practices. diff --git a/Gen_3D_Modules/Hunyuan3D_V2/NOTICE b/Gen_3D_Modules/Hunyuan3D_V2/NOTICE new file mode 100644 index 0000000..e02234d --- /dev/null +++ b/Gen_3D_Modules/Hunyuan3D_V2/NOTICE @@ -0,0 +1,214 @@ +Usage and Legal Notices: + +Tencent is pleased to support the open source community by making Hunyuan 3D 2.0 available. + +Copyright (C) 2025 THL A29 Limited, a Tencent company. All rights reserved. The below software and/or models in this distribution may have been modified by THL A29 Limited ("Tencent Modifications"). All Tencent Modifications are Copyright (C) THL A29 Limited. + +Hunyuan 3D 2.0 is licensed under the TENCENT HUNYUAN 3D 2.0 COMMUNITY LICENSE AGREEMENT except for the third-party components listed below, which is licensed under different terms. Hunyuan 3D 2.0 does not impose any additional limitations beyond what is outlined in the respective licenses of these third-party components. Users must comply with all terms and conditions of original licenses of these third-party components and must ensure that the usage of the third party components adheres to all relevant laws and regulations. + +For avoidance of doubts, Hunyuan 3D 2.0 means inference-enabling code, parameters, and weights of this Model only, which are made publicly available by Tencent in accordance with TENCENT HUNYUAN 3D 2.0 COMMUNITY LICENSE AGREEMENT. + + +Other dependencies and licenses: + + +Open Source Model Licensed under the MIT and CreativeML Open RAIL++-M License: +-------------------------------------------------------------------- +1. 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For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Model, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." +- "Contributor" means Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Model. + +Section II: INTELLECTUAL PROPERTY RIGHTS + +Both copyright and patent grants apply to the Model, Derivatives of the Model and Complementary Material. The Model and Derivatives of the Model are subject to additional terms as described in Section III. + +2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare, publicly display, publicly perform, sublicense, and distribute the Complementary Material, the Model, and Derivatives of the Model. +3. Grant of Patent License. Subject to the terms and conditions of this License and where and as applicable, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this paragraph) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Model and the Complementary Material, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Model to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Model and/or Complementary Material or a Contribution incorporated within the Model and/or Complementary Material constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for the Model and/or Work shall terminate as of the date such litigation is asserted or filed. + +Section III: CONDITIONS OF USAGE, DISTRIBUTION AND REDISTRIBUTION + +4. Distribution and Redistribution. You may host for Third Party remote access purposes (e.g. software-as-a-service), reproduce and distribute copies of the Model or Derivatives of the Model thereof in any medium, with or without modifications, provided that You meet the following conditions: +Use-based restrictions as referenced in paragraph 5 MUST be included as an enforceable provision by You in any type of legal agreement (e.g. a license) governing the use and/or distribution of the Model or Derivatives of the Model, and You shall give notice to subsequent users You Distribute to, that the Model or Derivatives of the Model are subject to paragraph 5. This provision does not apply to the use of Complementary Material. +You must give any Third Party recipients of the Model or Derivatives of the Model a copy of this License; +You must cause any modified files to carry prominent notices stating that You changed the files; +You must retain all copyright, patent, trademark, and attribution notices excluding those notices that do not pertain to any part of the Model, Derivatives of the Model. +You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions - respecting paragraph 4.a. - for use, reproduction, or Distribution of Your modifications, or for any such Derivatives of the Model as a whole, provided Your use, reproduction, and Distribution of the Model otherwise complies with the conditions stated in this License. +5. Use-based restrictions. The restrictions set forth in Attachment A are considered Use-based restrictions. Therefore You cannot use the Model and the Derivatives of the Model for the specified restricted uses. You may use the Model subject to this License, including only for lawful purposes and in accordance with the License. Use may include creating any content with, finetuning, updating, running, training, evaluating and/or reparametrizing the Model. You shall require all of Your users who use the Model or a Derivative of the Model to comply with the terms of this paragraph (paragraph 5). +6. The Output You Generate. Except as set forth herein, Licensor claims no rights in the Output You generate using the Model. You are accountable for the Output you generate and its subsequent uses. No use of the output can contravene any provision as stated in the License. + +Section IV: OTHER PROVISIONS + +7. Updates and Runtime Restrictions. To the maximum extent permitted by law, Licensor reserves the right to restrict (remotely or otherwise) usage of the Model in violation of this License. +8. Trademarks and related. Nothing in this License permits You to make use of Licensors’ trademarks, trade names, logos or to otherwise suggest endorsement or misrepresent the relationship between the parties; and any rights not expressly granted herein are reserved by the Licensors. +9. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Model and the Complementary Material (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Model, Derivatives of the Model, and the Complementary Material and assume any risks associated with Your exercise of permissions under this License. +10. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Model and the Complementary Material (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. +11. Accepting Warranty or Additional Liability. While redistributing the Model, Derivatives of the Model and the Complementary Material thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. +12. If any provision of this License is held to be invalid, illegal or unenforceable, the remaining provisions shall be unaffected thereby and remain valid as if such provision had not been set forth herein. + +END OF TERMS AND CONDITIONS + + + + +Attachment A + +Use Restrictions + +You agree not to use the Model or Derivatives of the Model: + +- In any way that violates any applicable national, federal, state, local or international law or regulation; +- For the purpose of exploiting, harming or attempting to exploit or harm minors in any way; +- To generate or disseminate verifiably false information and/or content with the purpose of harming others; +- To generate or disseminate personal identifiable information that can be used to harm an individual; +- To defame, disparage or otherwise harass others; +- For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation; +- For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics; +- To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm; +- For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories; +- To provide medical advice and medical results interpretation; +- To generate or disseminate information for the purpose to be used for administration of justice, law enforcement, immigration or asylum processes, such as predicting an individual will commit fraud/crime commitment (e.g. by text profiling, drawing causal relationships between assertions made in documents, indiscriminate and arbitrarily-targeted use). + + + +Open Source Model Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT and Other Licenses of the Third-Party Components therein: +-------------------------------------------------------------------- +1. HunyuanDiT +Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. + + +Terms of the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT: +-------------------------------------------------------------------- +TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT +Tencent Hunyuan Release Date: 2024/5/14 +By clicking to agree or by using, reproducing, modifying, distributing, performing or displaying any portion or element of the Tencent Hunyuan Works, including via any Hosted Service, You will be deemed to have recognized and accepted the content of this Agreement, which is effective immediately. +1. DEFINITIONS. +a. “Acceptable Use Policy” shall mean the policy made available by Tencent as set forth in the Exhibit A. +b. “Agreement” shall mean the terms and conditions for use, reproduction, distribution, modification, performance and displaying of the Hunyuan Works or any portion or element thereof set forth herein. +c. “Documentation” shall mean the specifications, manuals and documentation for Tencent Hunyuan made publicly available by Tencent. +d. “Hosted Service” shall mean a hosted service offered via an application programming interface (API), web access, or any other electronic or remote means. +e. “Licensee,” “You” or “Your” shall mean a natural person or legal entity exercising the rights granted by this Agreement and/or using the Tencent Hunyuan Works for any purpose and in any field of use. +f. “Materials” shall mean, collectively, Tencent’s proprietary Tencent Hunyuan and Documentation (and any portion thereof) as made available by Tencent under this Agreement. +g. “Model Derivatives” shall mean all: (i) modifications to Tencent Hunyuan or any Model Derivative of Tencent Hunyuan; (ii) works based on Tencent Hunyuan or any Model Derivative of Tencent Hunyuan; or (iii) any other machine learning model which is created by transfer of patterns of the weights, parameters, operations, or Output of Tencent Hunyuan or any Model Derivative of Tencent Hunyuan, to that model in order to cause that model to perform similarly to Tencent Hunyuan or a Model Derivative of Tencent Hunyuan, including distillation methods, methods that use intermediate data representations, or methods based on the generation of synthetic data Outputs by Tencent Hunyuan or a Model Derivative of Tencent Hunyuan for training that model. For clarity, Outputs by themselves are not deemed Model Derivatives. +h. “Output” shall mean the information and/or content output of Tencent Hunyuan or a Model Derivative that results from operating or otherwise using Tencent Hunyuan or a Model Derivative, including via a Hosted Service. +i. “Tencent,” “We” or “Us” shall mean THL A29 Limited. +j. “Tencent Hunyuan” shall mean the large language models, image/video/audio/3D generation models, and multimodal large language models and their software and algorithms, including trained model weights, parameters (including optimizer states), machine-learning model code, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing made publicly available by Us at https://huggingface.co/Tencent-Hunyuan/HunyuanDiT and https://github.com/Tencent/HunyuanDiT . +k. “Tencent Hunyuan Works” shall mean: (i) the Materials; (ii) Model Derivatives; and (iii) all derivative works thereof. +l. “Third Party” or “Third Parties” shall mean individuals or legal entities that are not under common control with Us or You. +m. “including” shall mean including but not limited to. +2. 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The trademark rights of “Tencent Hunyuan” are owned by Tencent or its affiliate.” +You may add Your own copyright statement to Your modifications and, except as set forth in this Section and in Section 5, may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Model Derivatives as a whole, provided Your use, reproduction, modification, distribution, performance and display of the work otherwise complies with the terms and conditions of this Agreement. If You receive Tencent Hunyuan Works from a Licensee as part of an integrated end user product, then this Section 3 of this Agreement will not apply to You. +4. 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You must include the use restrictions referenced in these Sections 5(a) and 5(b) as an enforceable provision in any agreement (e.g., license agreement, terms of use, etc.) governing the use and/or distribution of Tencent Hunyuan Works and You must provide notice to subsequent users to whom You distribute that Tencent Hunyuan Works are subject to the use restrictions in these Sections 5(a) and 5(b). +b. You must not use the Tencent Hunyuan Works or any Output or results of the Tencent Hunyuan Works to improve any other large language model (other than Tencent Hunyuan or Model Derivatives thereof). +6. INTELLECTUAL PROPERTY. +a. Subject to Tencent’s ownership of Tencent Hunyuan Works made by or for Tencent and intellectual property rights therein, conditioned upon Your compliance with the terms and conditions of this Agreement, as between You and Tencent, You will be the owner of any derivative works and modifications of the Materials and any Model Derivatives that are made by or for You. +b. No trademark licenses are granted under this Agreement, and in connection with the Tencent Hunyuan Works, Licensee may not use any name or mark owned by or associated with Tencent or any of its affiliates, except as required for reasonable and customary use in describing and distributing the Tencent Hunyuan Works. Tencent hereby grants You a license to use “Tencent Hunyuan” (the “Mark”) solely as required to comply with the provisions of Section 3(c), provided that You comply with any applicable laws related to trademark protection. All goodwill arising out of Your use of the Mark will inure to the benefit of Tencent. +c. If You commence a lawsuit or other proceedings (including a cross-claim or counterclaim in a lawsuit) against Us or any person or entity alleging that the Materials or any Output, or any portion of any of the foregoing, infringe any intellectual property or other right owned or licensable by You, then all licenses granted to You under this Agreement shall terminate as of the date such lawsuit or other proceeding is filed. You will defend, indemnify and hold harmless Us from and against any claim by any Third Party arising out of or related to Your or the Third Party’s use or distribution of the Tencent Hunyuan Works. +d. Tencent claims no rights in Outputs You generate. You and Your users are solely responsible for Outputs and their subsequent uses. +7. DISCLAIMERS OF WARRANTY AND LIMITATIONS OF LIABILITY. +a. We are not obligated to support, update, provide training for, or develop any further version of the Tencent Hunyuan Works or to grant any license thereto. +b. UNLESS AND ONLY TO THE EXTENT REQUIRED BY APPLICABLE LAW, THE TENCENT HUNYUAN WORKS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED “AS IS” WITHOUT ANY EXPRESS OR IMPLIED WARRANTIES OF ANY KIND INCLUDING ANY WARRANTIES OF TITLE, MERCHANTABILITY, NONINFRINGEMENT, COURSE OF DEALING, USAGE OF TRADE, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING, REPRODUCING, MODIFYING, PERFORMING, DISPLAYING OR DISTRIBUTING ANY OF THE TENCENT HUNYUAN WORKS OR OUTPUTS AND ASSUME ANY AND ALL RISKS ASSOCIATED WITH YOUR OR A THIRD PARTY’S USE OR DISTRIBUTION OF ANY OF THE TENCENT HUNYUAN WORKS OR OUTPUTS AND YOUR EXERCISE OF RIGHTS AND PERMISSIONS UNDER THIS AGREEMENT. +c. TO THE FULLEST EXTENT PERMITTED BY APPLICABLE LAW, IN NO EVENT SHALL TENCENT OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, FOR ANY DAMAGES, INCLUDING ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, EXEMPLARY, CONSEQUENTIAL OR PUNITIVE DAMAGES, OR LOST PROFITS OF ANY KIND ARISING FROM THIS AGREEMENT OR RELATED TO ANY OF THE TENCENT HUNYUAN WORKS OR OUTPUTS, EVEN IF TENCENT OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING. +8. SURVIVAL AND TERMINATION. +a. The term of this Agreement shall commence upon Your acceptance of this Agreement or access to the Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. +b. We may terminate this Agreement if You breach any of the terms or conditions of this Agreement. Upon termination of this Agreement, You must promptly delete and cease use of the Tencent Hunyuan Works. Sections 6(a), 6(c), 7 and 9 shall survive the termination of this Agreement. +9. GOVERNING LAW AND JURISDICTION. +a. This Agreement and any dispute arising out of or relating to it will be governed by the laws of the Hong Kong Special Administrative Region of the People’s Republic of China, without regard to conflict of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. +b. Exclusive jurisdiction and venue for any dispute arising out of or relating to this Agreement will be a court of competent jurisdiction in the Hong Kong Special Administrative Region of the People’s Republic of China, and Tencent and Licensee consent to the exclusive jurisdiction of such court with respect to any such dispute. +  + +EXHIBIT A +ACCEPTABLE USE POLICY + +Tencent reserves the right to update this Acceptable Use Policy from time to time. +Last modified: 2024/5/14 + +Tencent endeavors to promote safe and fair use of its tools and features, including Tencent Hunyuan. You agree not to use Tencent Hunyuan or Model Derivatives: +1. In any way that violates any applicable national, federal, state, local, international or any other law or regulation; +2. To harm Yourself or others; +3. To repurpose or distribute output from Tencent Hunyuan or any Model Derivatives to harm Yourself or others; +4. To override or circumvent the safety guardrails and safeguards We have put in place; +5. For the purpose of exploiting, harming or attempting to exploit or harm minors in any way; +6. To generate or disseminate verifiably false information and/or content with the purpose of harming others or influencing elections; +7. To generate or facilitate false online engagement, including fake reviews and other means of fake online engagement; +8. To intentionally defame, disparage or otherwise harass others; +9. To generate and/or disseminate malware (including ransomware) or any other content to be used for the purpose of harming electronic systems; +10. To generate or disseminate personal identifiable information with the purpose of harming others; +11. To generate or disseminate information (including images, code, posts, articles), and place the information in any public context (including –through the use of bot generated tweets), without expressly and conspicuously identifying that the information and/or content is machine generated; +12. To impersonate another individual without consent, authorization, or legal right; +13. To make high-stakes automated decisions in domains that affect an individual’s safety, rights or wellbeing (e.g., law enforcement, migration, medicine/health, management of critical infrastructure, safety components of products, essential services, credit, employment, housing, education, social scoring, or insurance); +14. In a manner that violates or disrespects the social ethics and moral standards of other countries or regions; +15. To perform, facilitate, threaten, incite, plan, promote or encourage violent extremism or terrorism; +16. For any use intended to discriminate against or harm individuals or groups based on protected characteristics or categories, online or offline social behavior or known or predicted personal or personality characteristics; +17. To intentionally exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm; +18. For military purposes; +19. To engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or other professional practices. + +For the license of other third party components, please refer to the following URL: +https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/blob/main/Notice + +-------------------------------------------------------------------- + +This Model also incorporates insights from Flux's neural network architechtures (https://github.com/black-forest-labs/flux?tab=readme-ov-file). Credits are given to the orginal authors. \ No newline at end of file diff --git a/Gen_3D_Modules/Hunyuan3D_V2/__init__.py b/Gen_3D_Modules/Hunyuan3D_V2/__init__.py new file mode 100644 index 0000000..e307c3f --- /dev/null +++ b/Gen_3D_Modules/Hunyuan3D_V2/__init__.py @@ -0,0 +1,23 @@ +# Open Source Model Licensed under the Apache License Version 2.0 +# and Other Licenses of the Third-Party Components therein: +# The below Model in this distribution may have been modified by THL A29 Limited +# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited. + +# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. +# The below software and/or models in this distribution may have been +# modified by THL A29 Limited ("Tencent Modifications"). +# All Tencent Modifications are Copyright (C) THL A29 Limited. + +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning 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zS6YrHL;G+>diwN09A}XQ1zU0SDKB4GU9ceYb^C+wot+6yH}dj~`S|(GT$fyWWu-ju zvVVN#dP&I@Wo2b)0-I%JS45UQ>)p9fQbtDDjW1s3$dQzsygVFGGMM!^xVy8I(Y}aK zJ321*_dC~>@87?_>Pd`Ua(1>s@1WeDrwo%QmD;DzeXV=p%LAJ8=g*JtA6ZMS()JBI zCCK3E>Dk=Zw>U4lv~;KNx^;eSzKeeK_X`cNW>r*H3We=hCYYcvARl=1@?dML%915Z zOuOUc{oEGRzJ04WR2x-UQE?+Pll{Ui0bO0)!l<=t)|f`E8Nt%aG`U$>+vC}~<22fb z23?$-R)~s)kBG0l;q2hByypb3RPTom zhCSPz-VWZXZ2I&mMp8=Z`Ol;+#pdF5xy^CGqcg7EeLY&#$=Ep!|EMZ!Y`dpq>iypU DN#b 0. else nn.Identity() @@ -196,6 +197,8 @@ class QKVMultiheadCrossAttention(nn.Module): self.q_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() self.k_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() + self.attn_processor = CrossAttentionProcessor() + def forward(self, q, kv): _, n_ctx, _ = q.shape bs, n_data, width = kv.shape @@ -206,10 +209,9 @@ class QKVMultiheadCrossAttention(nn.Module): q = self.q_norm(q) k = self.k_norm(k) - q, k, v = map(lambda t: rearrange(t, 'b n h d -> b h n d', h=self.heads), (q, k, v)) - out = F.scaled_dot_product_attention(q, k, v).transpose(1, 2).reshape(bs, n_ctx, -1) - + out = self.attn_processor(self, q, k, v) + out = out.transpose(1, 2).reshape(bs, n_ctx, -1) return out @@ -223,7 +225,8 @@ class MultiheadCrossAttention(nn.Module): n_data: Optional[int] = None, data_width: Optional[int] = None, norm_layer=nn.LayerNorm, - qk_norm: bool = False + qk_norm: bool = False, + kv_cache: bool = False, ): super().__init__() self.n_data = n_data @@ -240,10 +243,18 @@ class MultiheadCrossAttention(nn.Module): norm_layer=norm_layer, qk_norm=qk_norm ) + self.kv_cache = kv_cache + self.data = None def forward(self, x, data): x = self.c_q(x) - data = self.c_kv(data) + if self.kv_cache: + if self.data is None: + self.data = self.c_kv(data) + logger.info('Save kv cache,this should be called only once for one mesh') + data = self.data + else: + data = self.c_kv(data) x = self.attention(x, data) x = self.c_proj(x) return x @@ -256,6 +267,7 @@ class ResidualCrossAttentionBlock(nn.Module): n_data: Optional[int] = None, width: int, heads: int, + mlp_expand_ratio: int = 4, data_width: Optional[int] = None, qkv_bias: bool = True, norm_layer=nn.LayerNorm, @@ -278,7 +290,7 @@ class ResidualCrossAttentionBlock(nn.Module): self.ln_1 = norm_layer(width, elementwise_affine=True, eps=1e-6) self.ln_2 = norm_layer(data_width, elementwise_affine=True, eps=1e-6) self.ln_3 = norm_layer(width, elementwise_affine=True, eps=1e-6) - self.mlp = MLP(width=width) + self.mlp = MLP(width=width, expand_ratio=mlp_expand_ratio) def forward(self, x: torch.Tensor, data: torch.Tensor): x = x + self.attn(self.ln_1(x), self.ln_2(data)) @@ -312,7 +324,7 @@ class QKVMultiheadAttention(nn.Module): k = self.k_norm(k) q, k, v = map(lambda t: rearrange(t, 'b n h d -> b h n d', h=self.heads), (q, k, v)) - out = F.scaled_dot_product_attention(q, k, v).transpose(1, 2).reshape(bs, n_ctx, -1) + out = scaled_dot_product_attention(q, k, v).transpose(1, 2).reshape(bs, n_ctx, -1) return out @@ -430,207 +442,52 @@ class CrossAttentionDecoder(nn.Module): fourier_embedder: FourierEmbedder, width: int, heads: int, + mlp_expand_ratio: int = 4, + downsample_ratio: int = 1, + enable_ln_post: bool = True, qkv_bias: bool = True, qk_norm: bool = False, label_type: str = "binary" ): super().__init__() + self.enable_ln_post = enable_ln_post self.fourier_embedder = fourier_embedder - + self.downsample_ratio = downsample_ratio self.query_proj = nn.Linear(self.fourier_embedder.out_dim, width) - + if self.downsample_ratio != 1: + self.latents_proj = nn.Linear(width * downsample_ratio, width) + if self.enable_ln_post == False: + qk_norm = False self.cross_attn_decoder = ResidualCrossAttentionBlock( n_data=num_latents, width=width, + mlp_expand_ratio=mlp_expand_ratio, heads=heads, qkv_bias=qkv_bias, qk_norm=qk_norm ) - self.ln_post = nn.LayerNorm(width) + if self.enable_ln_post: + self.ln_post = nn.LayerNorm(width) self.output_proj = nn.Linear(width, out_channels) self.label_type = label_type + self.count = 0 - def forward(self, queries: torch.FloatTensor, latents: torch.FloatTensor): - queries = self.query_proj(self.fourier_embedder(queries).to(latents.dtype)) - x = self.cross_attn_decoder(queries, latents) - x = self.ln_post(x) + def set_cross_attention_processor(self, processor): + self.cross_attn_decoder.attn.attention.attn_processor = processor + + def set_default_cross_attention_processor(self): + self.cross_attn_decoder.attn.attention.attn_processor = CrossAttentionProcessor + + def forward(self, queries=None, query_embeddings=None, latents=None): + if query_embeddings is None: + query_embeddings = self.query_proj(self.fourier_embedder(queries).to(latents.dtype)) + self.count += query_embeddings.shape[1] + if self.downsample_ratio != 1: + latents = self.latents_proj(latents) + x = self.cross_attn_decoder(query_embeddings, latents) + if self.enable_ln_post: + x = self.ln_post(x) occ = self.output_proj(x) return occ - - -def generate_dense_grid_points(bbox_min: np.ndarray, - bbox_max: np.ndarray, - octree_depth: int, - indexing: str = "ij", - octree_resolution: int = None, - ): - length = bbox_max - bbox_min - num_cells = np.exp2(octree_depth) - if octree_resolution is not None: - num_cells = octree_resolution - - x = np.linspace(bbox_min[0], bbox_max[0], int(num_cells) + 1, dtype=np.float32) - y = np.linspace(bbox_min[1], bbox_max[1], int(num_cells) + 1, dtype=np.float32) - z = np.linspace(bbox_min[2], bbox_max[2], int(num_cells) + 1, dtype=np.float32) - [xs, ys, zs] = np.meshgrid(x, y, z, indexing=indexing) - xyz = np.stack((xs, ys, zs), axis=-1) - xyz = xyz.reshape(-1, 3) - grid_size = [int(num_cells) + 1, int(num_cells) + 1, int(num_cells) + 1] - - return xyz, grid_size, length - - -def center_vertices(vertices): - """Translate the vertices so that bounding box is centered at zero.""" - vert_min = vertices.min(dim=0)[0] - vert_max = vertices.max(dim=0)[0] - vert_center = 0.5 * (vert_min + vert_max) - return vertices - vert_center - - -class Latent2MeshOutput: - - def __init__(self, mesh_v=None, mesh_f=None): - self.mesh_v = mesh_v - self.mesh_f = mesh_f - - -class ShapeVAE(nn.Module): - def __init__( - self, - *, - num_latents: int, - embed_dim: int, - width: int, - heads: int, - num_decoder_layers: int, - num_freqs: int = 8, - include_pi: bool = True, - qkv_bias: bool = True, - qk_norm: bool = False, - label_type: str = "binary", - drop_path_rate: float = 0.0, - scale_factor: float = 1.0, - ): - super().__init__() - self.fourier_embedder = FourierEmbedder(num_freqs=num_freqs, include_pi=include_pi) - - self.post_kl = nn.Linear(embed_dim, width) - - self.transformer = Transformer( - n_ctx=num_latents, - width=width, - layers=num_decoder_layers, - heads=heads, - qkv_bias=qkv_bias, - qk_norm=qk_norm, - drop_path_rate=drop_path_rate - ) - - self.geo_decoder = CrossAttentionDecoder( - fourier_embedder=self.fourier_embedder, - out_channels=1, - num_latents=num_latents, - width=width, - heads=heads, - qkv_bias=qkv_bias, - qk_norm=qk_norm, - label_type=label_type, - ) - - self.scale_factor = scale_factor - self.latent_shape = (num_latents, embed_dim) - - def forward(self, latents): - latents = self.post_kl(latents) - latents = self.transformer(latents) - return latents - - @torch.no_grad() - def latents2mesh( - self, - latents: torch.FloatTensor, - bounds: Union[Tuple[float], List[float], float] = 1.1, - octree_depth: int = 7, - num_chunks: int = 10000, - mc_level: float = -1 / 512, - octree_resolution: int = None, - mc_algo: str = 'dmc', - ): - device = latents.device - - # 1. generate query points - if isinstance(bounds, float): - bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds] - bbox_min = np.array(bounds[0:3]) - bbox_max = np.array(bounds[3:6]) - bbox_size = bbox_max - bbox_min - xyz_samples, grid_size, length = generate_dense_grid_points( - bbox_min=bbox_min, - bbox_max=bbox_max, - octree_depth=octree_depth, - octree_resolution=octree_resolution, - indexing="ij" - ) - xyz_samples = torch.FloatTensor(xyz_samples) - - # 2. latents to 3d volume - batch_logits = [] - batch_size = latents.shape[0] - for start in tqdm(range(0, xyz_samples.shape[0], num_chunks), - desc=f"MC Level {mc_level} Implicit Function:"): - queries = xyz_samples[start: start + num_chunks, :].to(device) - queries = queries.half() - batch_queries = repeat(queries, "p c -> b p c", b=batch_size) - - logits = self.geo_decoder(batch_queries.to(latents.dtype), latents) - if mc_level == -1: - mc_level = 0 - logits = torch.sigmoid(logits) * 2 - 1 - print(f'Training with soft labels, inference with sigmoid and marching cubes level 0.') - batch_logits.append(logits) - grid_logits = torch.cat(batch_logits, dim=1) - grid_logits = grid_logits.view((batch_size, grid_size[0], grid_size[1], grid_size[2])).float() - - # 3. extract surface - outputs = [] - for i in range(batch_size): - try: - if mc_algo == 'mc': - vertices, faces, normals, _ = measure.marching_cubes( - grid_logits[i].cpu().numpy(), - mc_level, - method="lewiner" - ) - vertices = vertices / grid_size * bbox_size + bbox_min - elif mc_algo == 'dmc': - if not hasattr(self, 'dmc'): - try: - from diso import DiffDMC - except: - raise ImportError("Please install diso via `pip install diso`, or set mc_algo to 'mc'") - self.dmc = DiffDMC(dtype=torch.float32).to(device) - octree_resolution = 2 ** octree_depth if octree_resolution is None else octree_resolution - sdf = -grid_logits[i] / octree_resolution - verts, faces = self.dmc(sdf, deform=None, return_quads=False, normalize=True) - verts = center_vertices(verts) - vertices = verts.detach().cpu().numpy() - faces = faces.detach().cpu().numpy()[:, ::-1] - else: - raise ValueError(f"mc_algo {mc_algo} not supported.") - - outputs.append( - Latent2MeshOutput( - mesh_v=vertices.astype(np.float32), - mesh_f=np.ascontiguousarray(faces) - ) - ) - - except ValueError: - outputs.append(None) - except RuntimeError: - outputs.append(None) - - return outputs diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/autoencoders/attention_processors.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/autoencoders/attention_processors.py new file mode 100644 index 0000000..f7b232e --- /dev/null +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/autoencoders/attention_processors.py @@ -0,0 +1,96 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +import os + +import torch +import torch.nn.functional as F + +scaled_dot_product_attention = F.scaled_dot_product_attention +if os.environ.get('CA_USE_SAGEATTN', '0') == '1': + try: + from sageattention import sageattn + except ImportError: + raise ImportError('Please install the package "sageattention" to use this USE_SAGEATTN.') + scaled_dot_product_attention = sageattn + + +class CrossAttentionProcessor: + def __call__(self, attn, q, k, v): + out = scaled_dot_product_attention(q, k, v) + return out + + +class FlashVDMCrossAttentionProcessor: + def __init__(self, topk=None): + self.topk = topk + + def __call__(self, attn, q, k, v): + if k.shape[-2] == 3072: + topk = 1024 + elif k.shape[-2] == 512: + topk = 256 + else: + topk = k.shape[-2] // 3 + + if self.topk is True: + q1 = q[:, :, ::100, :] + sim = q1 @ k.transpose(-1, -2) + sim = torch.mean(sim, -2) + topk_ind = torch.topk(sim, dim=-1, k=topk).indices.squeeze(-2).unsqueeze(-1) + topk_ind = topk_ind.expand(-1, -1, -1, v.shape[-1]) + v0 = torch.gather(v, dim=-2, index=topk_ind) + k0 = torch.gather(k, dim=-2, index=topk_ind) + out = scaled_dot_product_attention(q, k0, v0) + elif self.topk is False: + out = scaled_dot_product_attention(q, k, v) + else: + idx, counts = self.topk + start = 0 + outs = [] + for grid_coord, count in zip(idx, counts): + end = start + count + q_chunk = q[:, :, start:end, :] + k0, v0 = self.select_topkv(q_chunk, k, v, topk) + out = scaled_dot_product_attention(q_chunk, k0, v0) + outs.append(out) + start += count + out = torch.cat(outs, dim=-2) + self.topk = False + return out + + def select_topkv(self, q_chunk, k, v, topk): + q1 = q_chunk[:, :, ::50, :] + sim = q1 @ k.transpose(-1, -2) + sim = torch.mean(sim, -2) + topk_ind = torch.topk(sim, dim=-1, k=topk).indices.squeeze(-2).unsqueeze(-1) + topk_ind = topk_ind.expand(-1, -1, -1, v.shape[-1]) + v0 = torch.gather(v, dim=-2, index=topk_ind) + k0 = torch.gather(k, dim=-2, index=topk_ind) + return k0, v0 + + +class FlashVDMTopMCrossAttentionProcessor(FlashVDMCrossAttentionProcessor): + def select_topkv(self, q_chunk, k, v, topk): + q1 = q_chunk[:, :, ::30, :] + sim = q1 @ k.transpose(-1, -2) + # sim = sim.to(torch.float32) + sim = sim.softmax(-1) + sim = torch.mean(sim, 1) + activated_token = torch.where(sim > 1e-6)[2] + index = torch.unique(activated_token, return_counts=True)[0].unsqueeze(0).unsqueeze(0).unsqueeze(-1) + index = index.expand(-1, v.shape[1], -1, v.shape[-1]) + v0 = torch.gather(v, dim=-2, index=index) + k0 = torch.gather(k, dim=-2, index=index) + return k0, v0 diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/autoencoders/model.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/autoencoders/model.py new file mode 100644 index 0000000..76f78da --- /dev/null +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/autoencoders/model.py @@ -0,0 +1,189 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +import os + +import torch +import torch.nn as nn +import yaml + +from .attention_blocks import FourierEmbedder, Transformer, CrossAttentionDecoder +from .surface_extractors import MCSurfaceExtractor, SurfaceExtractors +from .volume_decoders import VanillaVolumeDecoder, FlashVDMVolumeDecoding, HierarchicalVolumeDecoding +from ...utils import logger, synchronize_timer, smart_load_model + + +class VectsetVAE(nn.Module): + + @classmethod + @synchronize_timer('VectsetVAE Model Loading') + def from_single_file( + cls, + ckpt_path, + config_path, + device='cuda', + dtype=torch.float16, + use_safetensors=None, + **kwargs, + ): + # load config + with open(config_path, 'r') as f: + config = yaml.safe_load(f) + + # load ckpt + if use_safetensors: + ckpt_path = ckpt_path.replace('.ckpt', '.safetensors') + if not os.path.exists(ckpt_path): + raise FileNotFoundError(f"Model file {ckpt_path} not found") + + logger.info(f"Loading model from {ckpt_path}") + if use_safetensors: + import safetensors.torch + ckpt = safetensors.torch.load_file(ckpt_path, device='cpu') + else: + ckpt = torch.load(ckpt_path, map_location='cpu', weights_only=True) + + model_kwargs = config['params'] + model_kwargs.update(kwargs) + + model = cls(**model_kwargs) + model.load_state_dict(ckpt) + model.to(device=device, dtype=dtype) + return model + + @classmethod + def from_pretrained( + cls, + model_path, + device='cuda', + dtype=torch.float16, + use_safetensors=True, + variant='fp16', + subfolder='hunyuan3d-vae-v2-0', + **kwargs, + ): + config_path, ckpt_path = smart_load_model( + model_path, + subfolder=subfolder, + use_safetensors=use_safetensors, + variant=variant + ) + + return cls.from_single_file( + ckpt_path, + config_path, + device=device, + dtype=dtype, + use_safetensors=use_safetensors, + **kwargs + ) + + def __init__( + self, + volume_decoder=None, + surface_extractor=None + ): + super().__init__() + if volume_decoder is None: + volume_decoder = VanillaVolumeDecoder() + if surface_extractor is None: + surface_extractor = MCSurfaceExtractor() + self.volume_decoder = volume_decoder + self.surface_extractor = surface_extractor + + def latents2mesh(self, latents: torch.FloatTensor, **kwargs): + with synchronize_timer('Volume decoding'): + grid_logits = self.volume_decoder(latents, self.geo_decoder, **kwargs) + with synchronize_timer('Surface extraction'): + outputs = self.surface_extractor(grid_logits, **kwargs) + return outputs + + def enable_flashvdm_decoder( + self, + enabled: bool = True, + adaptive_kv_selection=True, + topk_mode='mean', + mc_algo='dmc', + ): + if enabled: + if adaptive_kv_selection: + self.volume_decoder = FlashVDMVolumeDecoding(topk_mode) + else: + self.volume_decoder = HierarchicalVolumeDecoding() + if mc_algo not in SurfaceExtractors.keys(): + raise ValueError(f'Unsupported mc_algo {mc_algo}, available: {list(SurfaceExtractors.keys())}') + self.surface_extractor = SurfaceExtractors[mc_algo]() + else: + self.volume_decoder = VanillaVolumeDecoder() + self.surface_extractor = MCSurfaceExtractor() + + +class ShapeVAE(VectsetVAE): + def __init__( + self, + *, + num_latents: int, + embed_dim: int, + width: int, + heads: int, + num_decoder_layers: int, + geo_decoder_downsample_ratio: int = 1, + geo_decoder_mlp_expand_ratio: int = 4, + geo_decoder_ln_post: bool = True, + num_freqs: int = 8, + include_pi: bool = True, + qkv_bias: bool = True, + qk_norm: bool = False, + label_type: str = "binary", + drop_path_rate: float = 0.0, + scale_factor: float = 1.0, + ): + super().__init__() + self.geo_decoder_ln_post = geo_decoder_ln_post + + self.fourier_embedder = FourierEmbedder(num_freqs=num_freqs, include_pi=include_pi) + + self.post_kl = nn.Linear(embed_dim, width) + + self.transformer = Transformer( + n_ctx=num_latents, + width=width, + layers=num_decoder_layers, + heads=heads, + qkv_bias=qkv_bias, + qk_norm=qk_norm, + drop_path_rate=drop_path_rate + ) + + self.geo_decoder = CrossAttentionDecoder( + fourier_embedder=self.fourier_embedder, + out_channels=1, + num_latents=num_latents, + mlp_expand_ratio=geo_decoder_mlp_expand_ratio, + downsample_ratio=geo_decoder_downsample_ratio, + enable_ln_post=self.geo_decoder_ln_post, + width=width // geo_decoder_downsample_ratio, + heads=heads // geo_decoder_downsample_ratio, + qkv_bias=qkv_bias, + qk_norm=qk_norm, + label_type=label_type, + ) + + self.scale_factor = scale_factor + self.latent_shape = (num_latents, embed_dim) + + def forward(self, latents): + latents = self.post_kl(latents) + latents = self.transformer(latents) + return latents diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/autoencoders/surface_extractors.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/autoencoders/surface_extractors.py new file mode 100644 index 0000000..f4d8f63 --- /dev/null +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/autoencoders/surface_extractors.py @@ -0,0 +1,100 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +from typing import Union, Tuple, List + +import numpy as np +import torch +from skimage import measure + + +class Latent2MeshOutput: + + def __init__(self, mesh_v=None, mesh_f=None): + self.mesh_v = mesh_v + self.mesh_f = mesh_f + + +def center_vertices(vertices): + """Translate the vertices so that bounding box is centered at zero.""" + vert_min = vertices.min(dim=0)[0] + vert_max = vertices.max(dim=0)[0] + vert_center = 0.5 * (vert_min + vert_max) + return vertices - vert_center + + +class SurfaceExtractor: + def _compute_box_stat(self, bounds: Union[Tuple[float], List[float], float], octree_resolution: int): + if isinstance(bounds, float): + bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds] + + bbox_min, bbox_max = np.array(bounds[0:3]), np.array(bounds[3:6]) + bbox_size = bbox_max - bbox_min + grid_size = [int(octree_resolution) + 1, int(octree_resolution) + 1, int(octree_resolution) + 1] + return grid_size, bbox_min, bbox_size + + def run(self, *args, **kwargs): + return NotImplementedError + + def __call__(self, grid_logits, **kwargs): + outputs = [] + for i in range(grid_logits.shape[0]): + try: + vertices, faces = self.run(grid_logits[i], **kwargs) + vertices = vertices.astype(np.float32) + faces = np.ascontiguousarray(faces) + outputs.append(Latent2MeshOutput(mesh_v=vertices, mesh_f=faces)) + + except Exception: + import traceback + traceback.print_exc() + outputs.append(None) + + return outputs + + +class MCSurfaceExtractor(SurfaceExtractor): + def run(self, grid_logit, *, mc_level, bounds, octree_resolution, **kwargs): + vertices, faces, normals, _ = measure.marching_cubes( + grid_logit.cpu().numpy(), + mc_level, + method="lewiner" + ) + grid_size, bbox_min, bbox_size = self._compute_box_stat(bounds, octree_resolution) + vertices = vertices / grid_size * bbox_size + bbox_min + return vertices, faces + + +class DMCSurfaceExtractor(SurfaceExtractor): + def run(self, grid_logit, *, octree_resolution, **kwargs): + device = grid_logit.device + if not hasattr(self, 'dmc'): + try: + from diso import DiffDMC + except: + raise ImportError("Please install diso via `pip install diso`, or set mc_algo to 'mc'") + self.dmc = DiffDMC(dtype=torch.float32).to(device) + sdf = -grid_logit / octree_resolution + sdf = sdf.to(torch.float32).contiguous() + verts, faces = self.dmc(sdf, deform=None, return_quads=False, normalize=True) + verts = center_vertices(verts) + vertices = verts.detach().cpu().numpy() + faces = faces.detach().cpu().numpy()[:, ::-1] + return vertices, faces + + +SurfaceExtractors = { + 'mc': MCSurfaceExtractor, + 'dmc': DMCSurfaceExtractor, +} diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/autoencoders/volume_decoders.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/autoencoders/volume_decoders.py new file mode 100644 index 0000000..d7bfd84 --- /dev/null +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/autoencoders/volume_decoders.py @@ -0,0 +1,435 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +from typing import Union, Tuple, List, Callable + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import repeat +from tqdm import tqdm + +from .attention_blocks import CrossAttentionDecoder +from .attention_processors import FlashVDMCrossAttentionProcessor, FlashVDMTopMCrossAttentionProcessor +from ...utils import logger + + +def extract_near_surface_volume_fn(input_tensor: torch.Tensor, alpha: float): + device = input_tensor.device + D = input_tensor.shape[0] + signed_val = 0.0 + + # 添加偏移并处理无效值 + val = input_tensor + alpha + valid_mask = val > -9000 # 假设-9000是无效值 + + # 改进的邻居获取函数（保持维度一致） + def get_neighbor(t, shift, axis): + """根据指定轴进行位移并保持维度一致""" + if shift == 0: + return t.clone() + + # 确定填充轴（输入为[D, D, D]对应z,y,x轴） + pad_dims = [0, 0, 0, 0, 0, 0] # 格式：[x前，x后，y前，y后，z前，z后] + + # 根据轴类型设置填充 + if axis == 0: # x轴（最后一个维度） + pad_idx = 0 if shift > 0 else 1 + pad_dims[pad_idx] = abs(shift) + elif axis == 1: # y轴（中间维度） + pad_idx = 2 if shift > 0 else 3 + pad_dims[pad_idx] = abs(shift) + elif axis == 2: # z轴（第一个维度） + pad_idx = 4 if shift > 0 else 5 + pad_dims[pad_idx] = abs(shift) + + # 执行填充（添加batch和channel维度适配F.pad） + padded = F.pad(t.unsqueeze(0).unsqueeze(0), pad_dims[::-1], mode='replicate') # 反转顺序适配F.pad + + # 构建动态切片索引 + slice_dims = [slice(None)] * 3 # 初始化为全切片 + if axis == 0: # x轴（dim=2） + if shift > 0: + slice_dims[0] = slice(shift, None) + else: + slice_dims[0] = slice(None, shift) + elif axis == 1: # y轴（dim=1） + if shift > 0: + slice_dims[1] = slice(shift, None) + else: + slice_dims[1] = slice(None, shift) + elif axis == 2: # z轴（dim=0） + if shift > 0: + slice_dims[2] = slice(shift, None) + else: + slice_dims[2] = slice(None, shift) + + # 应用切片并恢复维度 + padded = padded.squeeze(0).squeeze(0) + sliced = padded[slice_dims] + return sliced + + # 获取各方向邻居（确保维度一致） + left = get_neighbor(val, 1, axis=0) # x方向 + right = get_neighbor(val, -1, axis=0) + back = get_neighbor(val, 1, axis=1) # y方向 + front = get_neighbor(val, -1, axis=1) + down = get_neighbor(val, 1, axis=2) # z方向 + up = get_neighbor(val, -1, axis=2) + + # 处理边界无效值（使用where保持维度一致） + def safe_where(neighbor): + return torch.where(neighbor > -9000, neighbor, val) + + left = safe_where(left) + right = safe_where(right) + back = safe_where(back) + front = safe_where(front) + down = safe_where(down) + up = safe_where(up) + + # 计算符号一致性（转换为float32确保精度） + sign = torch.sign(val.to(torch.float32)) + neighbors_sign = torch.stack([ + torch.sign(left.to(torch.float32)), + torch.sign(right.to(torch.float32)), + torch.sign(back.to(torch.float32)), + torch.sign(front.to(torch.float32)), + torch.sign(down.to(torch.float32)), + torch.sign(up.to(torch.float32)) + ], dim=0) + + # 检查所有符号是否一致 + same_sign = torch.all(neighbors_sign == sign, dim=0) + + # 生成最终掩码 + mask = (~same_sign).to(torch.int32) + return mask * valid_mask.to(torch.int32) + + +def generate_dense_grid_points( + bbox_min: np.ndarray, + bbox_max: np.ndarray, + octree_resolution: int, + indexing: str = "ij", +): + length = bbox_max - bbox_min + num_cells = octree_resolution + + x = np.linspace(bbox_min[0], bbox_max[0], int(num_cells) + 1, dtype=np.float32) + y = np.linspace(bbox_min[1], bbox_max[1], int(num_cells) + 1, dtype=np.float32) + z = np.linspace(bbox_min[2], bbox_max[2], int(num_cells) + 1, dtype=np.float32) + [xs, ys, zs] = np.meshgrid(x, y, z, indexing=indexing) + xyz = np.stack((xs, ys, zs), axis=-1) + grid_size = [int(num_cells) + 1, int(num_cells) + 1, int(num_cells) + 1] + + return xyz, grid_size, length + + +class VanillaVolumeDecoder: + @torch.no_grad() + def __call__( + self, + latents: torch.FloatTensor, + geo_decoder: Callable, + bounds: Union[Tuple[float], List[float], float] = 1.01, + num_chunks: int = 10000, + octree_resolution: int = None, + enable_pbar: bool = True, + **kwargs, + ): + device = latents.device + dtype = latents.dtype + batch_size = latents.shape[0] + + # 1. generate query points + if isinstance(bounds, float): + bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds] + + bbox_min, bbox_max = np.array(bounds[0:3]), np.array(bounds[3:6]) + xyz_samples, grid_size, length = generate_dense_grid_points( + bbox_min=bbox_min, + bbox_max=bbox_max, + octree_resolution=octree_resolution, + indexing="ij" + ) + xyz_samples = torch.from_numpy(xyz_samples).to(device, dtype=dtype).contiguous().reshape(-1, 3) + + # 2. latents to 3d volume + batch_logits = [] + for start in tqdm(range(0, xyz_samples.shape[0], num_chunks), desc=f"Volume Decoding", + disable=not enable_pbar): + chunk_queries = xyz_samples[start: start + num_chunks, :] + chunk_queries = repeat(chunk_queries, "p c -> b p c", b=batch_size) + logits = geo_decoder(queries=chunk_queries, latents=latents) + batch_logits.append(logits) + + grid_logits = torch.cat(batch_logits, dim=1) + grid_logits = grid_logits.view((batch_size, *grid_size)).float() + + return grid_logits + + +class HierarchicalVolumeDecoding: + @torch.no_grad() + def __call__( + self, + latents: torch.FloatTensor, + geo_decoder: Callable, + bounds: Union[Tuple[float], List[float], float] = 1.01, + num_chunks: int = 10000, + mc_level: float = 0.0, + octree_resolution: int = None, + min_resolution: int = 63, + enable_pbar: bool = True, + **kwargs, + ): + device = latents.device + dtype = latents.dtype + + resolutions = [] + if octree_resolution < min_resolution: + resolutions.append(octree_resolution) + while octree_resolution >= min_resolution: + resolutions.append(octree_resolution) + octree_resolution = octree_resolution // 2 + resolutions.reverse() + + # 1. generate query points + if isinstance(bounds, float): + bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds] + bbox_min = np.array(bounds[0:3]) + bbox_max = np.array(bounds[3:6]) + bbox_size = bbox_max - bbox_min + + xyz_samples, grid_size, length = generate_dense_grid_points( + bbox_min=bbox_min, + bbox_max=bbox_max, + octree_resolution=resolutions[0], + indexing="ij" + ) + + dilate = nn.Conv3d(1, 1, 3, padding=1, bias=False, device=device, dtype=dtype) + dilate.weight = torch.nn.Parameter(torch.ones(dilate.weight.shape, dtype=dtype, device=device)) + + grid_size = np.array(grid_size) + xyz_samples = torch.from_numpy(xyz_samples).to(device, dtype=dtype).contiguous().reshape(-1, 3) + + # 2. latents to 3d volume + batch_logits = [] + batch_size = latents.shape[0] + for start in tqdm(range(0, xyz_samples.shape[0], num_chunks), + desc=f"Hierarchical Volume Decoding [r{resolutions[0] + 1}]"): + queries = xyz_samples[start: start + num_chunks, :] + batch_queries = repeat(queries, "p c -> b p c", b=batch_size) + logits = geo_decoder(queries=batch_queries, latents=latents) + batch_logits.append(logits) + + grid_logits = torch.cat(batch_logits, dim=1).view((batch_size, grid_size[0], grid_size[1], grid_size[2])) + + for octree_depth_now in resolutions[1:]: + grid_size = np.array([octree_depth_now + 1] * 3) + resolution = bbox_size / octree_depth_now + next_index = torch.zeros(tuple(grid_size), dtype=dtype, device=device) + next_logits = torch.full(next_index.shape, -10000., dtype=dtype, device=device) + curr_points = extract_near_surface_volume_fn(grid_logits.squeeze(0), mc_level) + curr_points += grid_logits.squeeze(0).abs() < 0.95 + + if octree_depth_now == resolutions[-1]: + expand_num = 0 + else: + expand_num = 1 + for i in range(expand_num): + curr_points = dilate(curr_points.unsqueeze(0).to(dtype)).squeeze(0) + (cidx_x, cidx_y, cidx_z) = torch.where(curr_points > 0) + next_index[cidx_x * 2, cidx_y * 2, cidx_z * 2] = 1 + for i in range(2 - expand_num): + next_index = dilate(next_index.unsqueeze(0)).squeeze(0) + nidx = torch.where(next_index > 0) + + next_points = torch.stack(nidx, dim=1) + next_points = (next_points * torch.tensor(resolution, dtype=next_points.dtype, device=device) + + torch.tensor(bbox_min, dtype=next_points.dtype, device=device)) + batch_logits = [] + for start in tqdm(range(0, next_points.shape[0], num_chunks), + desc=f"Hierarchical Volume Decoding [r{octree_depth_now + 1}]"): + queries = next_points[start: start + num_chunks, :] + batch_queries = repeat(queries, "p c -> b p c", b=batch_size) + logits = geo_decoder(queries=batch_queries.to(latents.dtype), latents=latents) + batch_logits.append(logits) + grid_logits = torch.cat(batch_logits, dim=1) + next_logits[nidx] = grid_logits[0, ..., 0] + grid_logits = next_logits.unsqueeze(0) + grid_logits[grid_logits == -10000.] = float('nan') + + return grid_logits + + +class FlashVDMVolumeDecoding: + def __init__(self, topk_mode='mean'): + if topk_mode not in ['mean', 'merge']: + raise ValueError(f'Unsupported topk_mode {topk_mode}, available: {["mean", "merge"]}') + + if topk_mode == 'mean': + self.processor = FlashVDMCrossAttentionProcessor() + else: + self.processor = FlashVDMTopMCrossAttentionProcessor() + + @torch.no_grad() + def __call__( + self, + latents: torch.FloatTensor, + geo_decoder: CrossAttentionDecoder, + bounds: Union[Tuple[float], List[float], float] = 1.01, + num_chunks: int = 10000, + mc_level: float = 0.0, + octree_resolution: int = None, + min_resolution: int = 63, + mini_grid_num: int = 4, + enable_pbar: bool = True, + **kwargs, + ): + processor = self.processor + geo_decoder.set_cross_attention_processor(processor) + + device = latents.device + dtype = latents.dtype + + resolutions = [] + if octree_resolution < min_resolution: + resolutions.append(octree_resolution) + while octree_resolution >= min_resolution: + resolutions.append(octree_resolution) + octree_resolution = octree_resolution // 2 + resolutions.reverse() + resolutions[0] = round(resolutions[0] / mini_grid_num) * mini_grid_num - 1 + for i, resolution in enumerate(resolutions[1:]): + resolutions[i + 1] = resolutions[0] * 2 ** (i + 1) + + logger.info(f"FlashVDMVolumeDecoding Resolution: {resolutions}") + + # 1. generate query points + if isinstance(bounds, float): + bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds] + bbox_min = np.array(bounds[0:3]) + bbox_max = np.array(bounds[3:6]) + bbox_size = bbox_max - bbox_min + + xyz_samples, grid_size, length = generate_dense_grid_points( + bbox_min=bbox_min, + bbox_max=bbox_max, + octree_resolution=resolutions[0], + indexing="ij" + ) + + dilate = nn.Conv3d(1, 1, 3, padding=1, bias=False, device=device, dtype=dtype) + dilate.weight = torch.nn.Parameter(torch.ones(dilate.weight.shape, dtype=dtype, device=device)) + + grid_size = np.array(grid_size) + + # 2. latents to 3d volume + xyz_samples = torch.from_numpy(xyz_samples).to(device, dtype=dtype) + batch_size = latents.shape[0] + mini_grid_size = xyz_samples.shape[0] // mini_grid_num + xyz_samples = xyz_samples.view( + mini_grid_num, mini_grid_size, + mini_grid_num, mini_grid_size, + mini_grid_num, mini_grid_size, 3 + ).permute( + 0, 2, 4, 1, 3, 5, 6 + ).reshape( + -1, mini_grid_size * mini_grid_size * mini_grid_size, 3 + ) + batch_logits = [] + num_batchs = max(num_chunks // xyz_samples.shape[1], 1) + for start in tqdm(range(0, xyz_samples.shape[0], num_batchs), + desc=f"FlashVDM Volume Decoding", disable=not enable_pbar): + queries = xyz_samples[start: start + num_batchs, :] + batch = queries.shape[0] + batch_latents = repeat(latents.squeeze(0), "p c -> b p c", b=batch) + processor.topk = True + logits = geo_decoder(queries=queries, latents=batch_latents) + batch_logits.append(logits) + grid_logits = torch.cat(batch_logits, dim=0).reshape( + mini_grid_num, mini_grid_num, mini_grid_num, + mini_grid_size, mini_grid_size, + mini_grid_size + ).permute(0, 3, 1, 4, 2, 5).contiguous().view( + (batch_size, grid_size[0], grid_size[1], grid_size[2]) + ) + + for octree_depth_now in resolutions[1:]: + grid_size = np.array([octree_depth_now + 1] * 3) + resolution = bbox_size / octree_depth_now + next_index = torch.zeros(tuple(grid_size), dtype=dtype, device=device) + next_logits = torch.full(next_index.shape, -10000., dtype=dtype, device=device) + curr_points = extract_near_surface_volume_fn(grid_logits.squeeze(0), mc_level) + curr_points += grid_logits.squeeze(0).abs() < 0.95 + + if octree_depth_now == resolutions[-1]: + expand_num = 0 + else: + expand_num = 1 + for i in range(expand_num): + curr_points = dilate(curr_points.unsqueeze(0).to(dtype)).squeeze(0) + (cidx_x, cidx_y, cidx_z) = torch.where(curr_points > 0) + + next_index[cidx_x * 2, cidx_y * 2, cidx_z * 2] = 1 + for i in range(2 - expand_num): + next_index = dilate(next_index.unsqueeze(0)).squeeze(0) + nidx = torch.where(next_index > 0) + + next_points = torch.stack(nidx, dim=1) + next_points = (next_points * torch.tensor(resolution, dtype=torch.float32, device=device) + + torch.tensor(bbox_min, dtype=torch.float32, device=device)) + + query_grid_num = 6 + min_val = next_points.min(axis=0).values + max_val = next_points.max(axis=0).values + vol_queries_index = (next_points - min_val) / (max_val - min_val) * (query_grid_num - 0.001) + index = torch.floor(vol_queries_index).long() + index = index[..., 0] * (query_grid_num ** 2) + index[..., 1] * query_grid_num + index[..., 2] + index = index.sort() + next_points = next_points[index.indices].unsqueeze(0).contiguous() + unique_values = torch.unique(index.values, return_counts=True) + grid_logits = torch.zeros((next_points.shape[1]), dtype=latents.dtype, device=latents.device) + input_grid = [[], []] + logits_grid_list = [] + start_num = 0 + sum_num = 0 + for grid_index, count in zip(unique_values[0].cpu().tolist(), unique_values[1].cpu().tolist()): + if sum_num + count < num_chunks or sum_num == 0: + sum_num += count + input_grid[0].append(grid_index) + input_grid[1].append(count) + else: + processor.topk = input_grid + logits_grid = geo_decoder(queries=next_points[:, start_num:start_num + sum_num], latents=latents) + start_num = start_num + sum_num + logits_grid_list.append(logits_grid) + input_grid = [[grid_index], [count]] + sum_num = count + if sum_num > 0: + processor.topk = input_grid + logits_grid = geo_decoder(queries=next_points[:, start_num:start_num + sum_num], latents=latents) + logits_grid_list.append(logits_grid) + logits_grid = torch.cat(logits_grid_list, dim=1) + grid_logits[index.indices] = logits_grid.squeeze(0).squeeze(-1) + next_logits[nidx] = grid_logits + grid_logits = next_logits.unsqueeze(0) + + grid_logits[grid_logits == -10000.] = float('nan') + + return grid_logits diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/conditioner.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/conditioner.py old mode 100644 new mode 100755 index 1af4c0c..d0d848c --- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/conditioner.py +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/conditioner.py @@ -22,6 +22,7 @@ # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. +import numpy as np import torch import torch.nn as nn from torchvision import transforms @@ -33,6 +34,26 @@ from transformers import ( ) +def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): + """ + embed_dim: output dimension for each position + pos: a list of positions to be encoded: size (M,) + out: (M, D) + """ + assert embed_dim % 2 == 0 + omega = np.arange(embed_dim // 2, dtype=np.float64) + omega /= embed_dim / 2. + omega = 1. / 10000 ** omega # (D/2,) + + pos = pos.reshape(-1) # (M,) + out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product + + emb_sin = np.sin(out) # (M, D/2) + emb_cos = np.cos(out) # (M, D/2) + + return np.concatenate([emb_sin, emb_cos], axis=1) + + class ImageEncoder(nn.Module): def __init__( self, @@ -67,7 +88,7 @@ class ImageEncoder(nn.Module): ] ) - def forward(self, image, mask=None, value_range=(-1, 1)): + def forward(self, image, mask=None, value_range=(-1, 1), **kwargs): if value_range is not None: low, high = value_range image = (image - low) / (high - low) @@ -82,7 +103,7 @@ class ImageEncoder(nn.Module): return last_hidden_state - def unconditional_embedding(self, batch_size): + def unconditional_embedding(self, batch_size, **kwargs): device = next(self.model.parameters()).device dtype = next(self.model.parameters()).dtype zero = torch.zeros( @@ -110,11 +131,82 @@ class DinoImageEncoder(ImageEncoder): std = [0.229, 0.224, 0.225] +class DinoImageEncoderMV(DinoImageEncoder): + def __init__( + self, + version=None, + config=None, + use_cls_token=True, + image_size=224, + view_num=4, + **kwargs, + ): + super().__init__(version, config, use_cls_token, image_size, **kwargs) + self.view_num = view_num + self.num_patches = self.num_patches + pos = np.arange(self.view_num, dtype=np.float32) + view_embedding = torch.from_numpy( + get_1d_sincos_pos_embed_from_grid(self.model.config.hidden_size, pos)).float() + + view_embedding = view_embedding.unsqueeze(1).repeat(1, self.num_patches, 1) + self.view_embed = view_embedding.unsqueeze(0) + + def forward(self, image, mask=None, value_range=(-1, 1), view_idxs=None): + if value_range is not None: + low, high = value_range + image = (image - low) / (high - low) + + image = image.to(self.model.device, dtype=self.model.dtype) + + bs, num_views, c, h, w = image.shape + image = image.view(bs * num_views, c, h, w) + + inputs = self.transform(image) + outputs = self.model(inputs) + + last_hidden_state = outputs.last_hidden_state + last_hidden_state = last_hidden_state.view( + bs, num_views, last_hidden_state.shape[-2], + last_hidden_state.shape[-1] + ) + + view_embedding = self.view_embed.to(last_hidden_state.dtype).to(last_hidden_state.device) + if view_idxs is not None: + assert len(view_idxs) == bs + view_embeddings = [] + for i in range(bs): + view_idx = view_idxs[i] + assert num_views == len(view_idx) + view_embeddings.append(self.view_embed[:, view_idx, ...]) + view_embedding = torch.cat(view_embeddings, 0).to(last_hidden_state.dtype).to(last_hidden_state.device) + + if num_views != self.view_num: + view_embedding = view_embedding[:, :num_views, ...] + last_hidden_state = last_hidden_state + view_embedding + last_hidden_state = last_hidden_state.view(bs, num_views * last_hidden_state.shape[-2], + last_hidden_state.shape[-1]) + return last_hidden_state + + def unconditional_embedding(self, batch_size, view_idxs=None, **kwargs): + device = next(self.model.parameters()).device + dtype = next(self.model.parameters()).dtype + zero = torch.zeros( + batch_size, + self.num_patches * len(view_idxs[0]), + self.model.config.hidden_size, + device=device, + dtype=dtype, + ) + return zero + + def build_image_encoder(config): if config['type'] == 'CLIPImageEncoder': return CLIPImageEncoder(**config['kwargs']) elif config['type'] == 'DinoImageEncoder': return DinoImageEncoder(**config['kwargs']) + elif config['type'] == 'DinoImageEncoderMV': + return DinoImageEncoderMV(**config['kwargs']) else: raise ValueError(f'Unknown image encoder type: {config["type"]}') @@ -129,17 +221,17 @@ class DualImageEncoder(nn.Module): self.main_image_encoder = build_image_encoder(main_image_encoder) self.additional_image_encoder = build_image_encoder(additional_image_encoder) - def forward(self, image, mask=None): + def forward(self, image, mask=None, **kwargs): outputs = { - 'main': self.main_image_encoder(image, mask=mask), - 'additional': self.additional_image_encoder(image, mask=mask), + 'main': self.main_image_encoder(image, mask=mask, **kwargs), + 'additional': self.additional_image_encoder(image, mask=mask, **kwargs), } return outputs - def unconditional_embedding(self, batch_size): + def unconditional_embedding(self, batch_size, **kwargs): outputs = { - 'main': self.main_image_encoder.unconditional_embedding(batch_size), - 'additional': self.additional_image_encoder.unconditional_embedding(batch_size), + 'main': self.main_image_encoder.unconditional_embedding(batch_size, **kwargs), + 'additional': self.additional_image_encoder.unconditional_embedding(batch_size, **kwargs), } return outputs @@ -152,14 +244,14 @@ class SingleImageEncoder(nn.Module): super().__init__() self.main_image_encoder = build_image_encoder(main_image_encoder) - def forward(self, image, mask=None): + def forward(self, image, mask=None, **kwargs): outputs = { - 'main': self.main_image_encoder(image, mask=mask), + 'main': self.main_image_encoder(image, mask=mask, **kwargs), } return outputs - def unconditional_embedding(self, batch_size): + def unconditional_embedding(self, batch_size, **kwargs): outputs = { - 'main': self.main_image_encoder.unconditional_embedding(batch_size), + 'main': self.main_image_encoder.unconditional_embedding(batch_size, **kwargs), } return outputs diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/denoisers/__init__.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/denoisers/__init__.py new file mode 100644 index 0000000..7260933 --- /dev/null +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/denoisers/__init__.py @@ -0,0 +1,15 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +from .hunyuan3ddit import Hunyuan3DDiT diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/hunyuan3ddit.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/denoisers/hunyuan3ddit.py old mode 100644 new mode 100755 similarity index 91% rename from Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/hunyuan3ddit.py rename to Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/denoisers/hunyuan3ddit.py index d1c7786..7873f16 --- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/hunyuan3ddit.py +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/denoisers/hunyuan3ddit.py @@ -1,13 +1,3 @@ -# Open Source Model Licensed under the Apache License Version 2.0 -# and Other Licenses of the Third-Party Components therein: -# The below Model in this distribution may have been modified by THL A29 Limited -# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited. - -# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. -# The below software and/or models in this distribution may have been -# modified by THL A29 Limited ("Tencent Modifications"). -# All Tencent Modifications are Copyright (C) THL A29 Limited. - # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined @@ -23,6 +13,7 @@ # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. import math +import os from dataclasses import dataclass from typing import List, Tuple, Optional @@ -30,9 +21,17 @@ import torch from einops import rearrange from torch import Tensor, nn +scaled_dot_product_attention = nn.functional.scaled_dot_product_attention +if os.environ.get('USE_SAGEATTN', '0') == '1': + try: + from sageattention import sageattn + except ImportError: + raise ImportError('Please install the package "sageattention" to use this USE_SAGEATTN.') + scaled_dot_product_attention = sageattn + def attention(q: Tensor, k: Tensor, v: Tensor, **kwargs) -> Tensor: - x = torch.nn.functional.scaled_dot_product_attention(q, k, v) + x = scaled_dot_product_attention(q, k, v) x = rearrange(x, "B H L D -> B L (H D)") return x @@ -61,6 +60,15 @@ def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 10 return embedding +class GELU(nn.Module): + def __init__(self, approximate='tanh'): + super().__init__() + self.approximate = approximate + + def forward(self, x: Tensor) -> Tensor: + return nn.functional.gelu(x.contiguous(), approximate=self.approximate) + + class MLPEmbedder(nn.Module): def __init__(self, in_dim: int, hidden_dim: int): super().__init__() @@ -163,7 +171,7 @@ class DoubleStreamBlock(nn.Module): self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) self.img_mlp = nn.Sequential( nn.Linear(hidden_size, mlp_hidden_dim, bias=True), - nn.GELU(approximate="tanh"), + GELU(approximate="tanh"), nn.Linear(mlp_hidden_dim, hidden_size, bias=True), ) @@ -174,7 +182,7 @@ class DoubleStreamBlock(nn.Module): self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) self.txt_mlp = nn.Sequential( nn.Linear(hidden_size, mlp_hidden_dim, bias=True), - nn.GELU(approximate="tanh"), + GELU(approximate="tanh"), nn.Linear(mlp_hidden_dim, hidden_size, bias=True), ) @@ -240,7 +248,7 @@ class SingleStreamBlock(nn.Module): self.hidden_size = hidden_size self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) - self.mlp_act = nn.GELU(approximate="tanh") + self.mlp_act = GELU(approximate="tanh") self.modulation = Modulation(hidden_size, double=False) def forward(self, x: Tensor, vec: Tensor, pe: Tensor) -> Tensor: @@ -287,6 +295,7 @@ class Hunyuan3DDiT(nn.Module): theta: int = 10_000, qkv_bias: bool = True, time_factor: float = 1000, + guidance_embed: bool = False, ckpt_path: Optional[str] = None, **kwargs, ): @@ -303,6 +312,7 @@ class Hunyuan3DDiT(nn.Module): self.qkv_bias = qkv_bias self.time_factor = time_factor self.out_channels = self.in_channels + self.guidance_embed = guidance_embed if hidden_size % num_heads != 0: raise ValueError( @@ -316,6 +326,9 @@ class Hunyuan3DDiT(nn.Module): self.latent_in = nn.Linear(self.in_channels, self.hidden_size, bias=True) self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) self.cond_in = nn.Linear(context_in_dim, self.hidden_size) + self.guidance_in = ( + MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if guidance_embed else nn.Identity() + ) self.double_blocks = nn.ModuleList( [ @@ -374,7 +387,14 @@ class Hunyuan3DDiT(nn.Module): ) -> Tensor: cond = contexts['main'] latent = self.latent_in(x) + vec = self.time_in(timestep_embedding(t, 256, self.time_factor).to(dtype=latent.dtype)) + if self.guidance_embed: + guidance = kwargs.get('guidance', None) + if guidance is None: + raise ValueError("Didn't get guidance strength for guidance distilled model.") + vec = vec + self.guidance_in(timestep_embedding(guidance, 256, self.time_factor)) + cond = self.cond_in(cond) pe = None diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/pipelines.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/pipelines.py old mode 100644 new mode 100755 index 8c13fda..82b4461 --- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/pipelines.py +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/pipelines.py @@ -1,13 +1,3 @@ -# Open Source Model Licensed under the Apache License Version 2.0 -# and Other Licenses of the Third-Party Components therein: -# The below Model in this distribution may have been modified by THL A29 Limited -# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited. - -# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. -# The below software and/or models in this distribution may have been -# modified by THL A29 Limited ("Tencent Modifications"). -# All Tencent Modifications are Copyright (C) THL A29 Limited. - # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined @@ -25,7 +15,6 @@ import copy import importlib import inspect -import logging import os from typing import List, Optional, Union @@ -35,11 +24,12 @@ import trimesh import yaml from PIL import Image from diffusers.utils.torch_utils import randn_tensor +from diffusers.utils.import_utils import is_accelerate_version, is_accelerate_available from tqdm import tqdm -import comfy.utils - -logger = logging.getLogger(__name__) +from .models.autoencoders import ShapeVAE +from .models.autoencoders import SurfaceExtractors +from .utils import logger, synchronize_timer, smart_load_model def retrieve_timesteps( @@ -101,6 +91,7 @@ def retrieve_timesteps( return timesteps, num_inference_steps +@synchronize_timer('Export to trimesh') def export_to_trimesh(mesh_output): if isinstance(mesh_output, list): outputs = [] @@ -137,7 +128,11 @@ def instantiate_from_config(config, **kwargs): class Hunyuan3DDiTPipeline: + model_cpu_offload_seq = "conditioner->model->vae" + _exclude_from_cpu_offload = [] + @classmethod + @synchronize_timer('Hunyuan3DDiTPipeline Model Loading') def from_single_file( cls, ckpt_path, @@ -156,7 +151,7 @@ class Hunyuan3DDiTPipeline: ckpt_path = ckpt_path.replace('.ckpt', '.safetensors') if not os.path.exists(ckpt_path): raise FileNotFoundError(f"Model file {ckpt_path} not found") - logger.info(f"Loading model from {ckpt_path}") + # logger.info(f"Loading model from {ckpt_path}") if use_safetensors: # parse safetensors @@ -170,7 +165,7 @@ class Hunyuan3DDiTPipeline: ckpt[model_name] = {} ckpt[model_name][new_key] = value else: - ckpt = torch.load(ckpt_path, map_location='cpu') + ckpt = torch.load(ckpt_path, map_location='cpu', weights_only=True) # load model model = instantiate_from_config(config['model']) model.load_state_dict(ckpt['model']) @@ -201,21 +196,27 @@ class Hunyuan3DDiTPipeline: def from_pretrained( cls, model_path, - ckpt_name='model.ckpt', - config_name='config.yaml', device='cuda', dtype=torch.float16, - use_safetensors=None, + use_safetensors=True, + variant='fp16', + subfolder='hunyuan3d-dit-v2-0', **kwargs, ): - model_path = os.path.join(model_path, 'hunyuan3d-dit-v2-0') - - config_path = os.path.join(model_path, config_name) - ckpt_path = os.path.join(model_path, ckpt_name) - - if not os.path.exists(model_path): - raise FileNotFoundError(f"Model path {model_path} not found") - + kwargs['from_pretrained_kwargs'] = dict( + model_path=model_path, + subfolder=subfolder, + use_safetensors=use_safetensors, + variant=variant, + dtype=dtype, + device=device, + ) + config_path, ckpt_path = smart_load_model( + model_path, + subfolder=subfolder, + use_safetensors=use_safetensors, + variant=variant + ) return cls.from_single_file( ckpt_path, config_path, @@ -241,29 +242,187 @@ class Hunyuan3DDiTPipeline: self.scheduler = scheduler self.conditioner = conditioner self.image_processor = image_processor - + self.kwargs = kwargs self.to(device, dtype) + def compile(self): + self.vae = torch.compile(self.vae) + self.model = torch.compile(self.model) + self.conditioner = torch.compile(self.conditioner) + + def enable_flashvdm( + self, + enabled: bool = True, + adaptive_kv_selection=True, + topk_mode='mean', + mc_algo='mc', + replace_vae=True, + ): + if enabled: + model_path = self.kwargs['from_pretrained_kwargs']['model_path'] + turbo_vae_mapping = { + 'Hunyuan3D-2': ('tencent/Hunyuan3D-2', 'hunyuan3d-vae-v2-0-turbo'), + 'Hunyuan3D-2mv': ('tencent/Hunyuan3D-2', 'hunyuan3d-vae-v2-0-turbo'), + 'Hunyuan3D-2mini': ('tencent/Hunyuan3D-2mini', 'hunyuan3d-vae-v2-mini-turbo'), + } + model_name = model_path.split('/')[-1] + if replace_vae and model_name in turbo_vae_mapping: + model_path, subfolder = turbo_vae_mapping[model_name] + self.vae = ShapeVAE.from_pretrained( + model_path, subfolder=subfolder, + use_safetensors=self.kwargs['from_pretrained_kwargs']['use_safetensors'], + device=self.device, + ) + self.vae.enable_flashvdm_decoder( + enabled=enabled, + adaptive_kv_selection=adaptive_kv_selection, + topk_mode=topk_mode, + mc_algo=mc_algo + ) + else: + model_path = self.kwargs['from_pretrained_kwargs']['model_path'] + vae_mapping = { + 'Hunyuan3D-2': ('tencent/Hunyuan3D-2', 'hunyuan3d-vae-v2-0'), + 'Hunyuan3D-2mv': ('tencent/Hunyuan3D-2', 'hunyuan3d-vae-v2-0'), + 'Hunyuan3D-2mini': ('tencent/Hunyuan3D-2mini', 'hunyuan3d-vae-v2-mini'), + } + model_name = model_path.split('/')[-1] + if model_name in vae_mapping: + model_path, subfolder = vae_mapping[model_name] + self.vae = ShapeVAE.from_pretrained(model_path, subfolder=subfolder) + self.vae.enable_flashvdm_decoder(enabled=False) + def to(self, device=None, dtype=None): - if device is not None: - self.device = torch.device(device) - self.vae.to(device) - self.model.to(device) - self.conditioner.to(device) if dtype is not None: self.dtype = dtype self.vae.to(dtype=dtype) self.model.to(dtype=dtype) self.conditioner.to(dtype=dtype) - + if device is not None: + self.device = torch.device(device) + self.vae.to(device) + self.model.to(device) + self.conditioner.to(device) return self - def encode_cond(self, image, mask, do_classifier_free_guidance, dual_guidance): + @property + def _execution_device(self): + r""" + Returns the device on which the pipeline's models will be executed. After calling + [`~DiffusionPipeline.enable_sequential_cpu_offload`] the execution device can only be inferred from + Accelerate's module hooks. + """ + for name, model in self.components.items(): + if not isinstance(model, torch.nn.Module) or name in self._exclude_from_cpu_offload: + continue + + if not hasattr(model, "_hf_hook"): + return self.device + for module in model.modules(): + if ( + hasattr(module, "_hf_hook") + and hasattr(module._hf_hook, "execution_device") + and module._hf_hook.execution_device is not None + ): + return torch.device(module._hf_hook.execution_device) + return self.device + + def enable_model_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = "cuda"): + r""" + Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared + to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` + method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with + `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. + + Arguments: + gpu_id (`int`, *optional*): + The ID of the accelerator that shall be used in inference. If not specified, it will default to 0. + device (`torch.Device` or `str`, *optional*, defaults to "cuda"): + The PyTorch device type of the accelerator that shall be used in inference. If not specified, it will + default to "cuda". + """ + if self.model_cpu_offload_seq is None: + raise ValueError( + "Model CPU offload cannot be enabled because no `model_cpu_offload_seq` class attribute is set." + ) + + if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): + from accelerate import cpu_offload_with_hook + else: + raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") + + torch_device = torch.device(device) + device_index = torch_device.index + + if gpu_id is not None and device_index is not None: + raise ValueError( + f"You have passed both `gpu_id`={gpu_id} and an index as part of the passed device `device`={device}" + f"Cannot pass both. Please make sure to either not define `gpu_id` or not pass the index as part of the device: `device`={torch_device.type}" + ) + + # _offload_gpu_id should be set to passed gpu_id (or id in passed `device`) or default to previously set id or default to 0 + self._offload_gpu_id = gpu_id or torch_device.index or getattr(self, "_offload_gpu_id", 0) + + device_type = torch_device.type + device = torch.device(f"{device_type}:{self._offload_gpu_id}") + + if self.device.type != "cpu": + self.to("cpu") + device_mod = getattr(torch, self.device.type, None) + if hasattr(device_mod, "empty_cache") and device_mod.is_available(): + device_mod.empty_cache() # otherwise we don't see the memory savings (but they probably exist) + + all_model_components = {k: v for k, v in self.components.items() if isinstance(v, torch.nn.Module)} + + self._all_hooks = [] + hook = None + for model_str in self.model_cpu_offload_seq.split("->"): + model = all_model_components.pop(model_str, None) + if not isinstance(model, torch.nn.Module): + continue + + _, hook = cpu_offload_with_hook(model, device, prev_module_hook=hook) + self._all_hooks.append(hook) + + # CPU offload models that are not in the seq chain unless they are explicitly excluded + # these models will stay on CPU until maybe_free_model_hooks is called + # some models cannot be in the seq chain because they are iteratively called, such as controlnet + for name, model in all_model_components.items(): + if not isinstance(model, torch.nn.Module): + continue + + if name in self._exclude_from_cpu_offload: + model.to(device) + else: + _, hook = cpu_offload_with_hook(model, device) + self._all_hooks.append(hook) + + def maybe_free_model_hooks(self): + r""" + Function that offloads all components, removes all model hooks that were added when using + `enable_model_cpu_offload` and then applies them again. In case the model has not been offloaded this function + is a no-op. Make sure to add this function to the end of the `__call__` function of your pipeline so that it + functions correctly when applying enable_model_cpu_offload. + """ + if not hasattr(self, "_all_hooks") or len(self._all_hooks) == 0: + # `enable_model_cpu_offload` has not be called, so silently do nothing + return + + for hook in self._all_hooks: + # offload model and remove hook from model + hook.offload() + hook.remove() + + # make sure the model is in the same state as before calling it + self.enable_model_cpu_offload() + + @synchronize_timer('Encode cond') + def encode_cond(self, image, additional_cond_inputs, do_classifier_free_guidance, dual_guidance): bsz = image.shape[0] - cond = self.conditioner(image=image, mask=mask) + cond = self.conditioner(image=image, **additional_cond_inputs) if do_classifier_free_guidance: - un_cond = self.conditioner.unconditional_embedding(bsz) + un_cond = self.conditioner.unconditional_embedding(bsz, **additional_cond_inputs) if dual_guidance: un_cond_drop_main = copy.deepcopy(un_cond) @@ -279,8 +438,6 @@ class Hunyuan3DDiTPipeline: cond = cat_recursive(cond, un_cond_drop_main, un_cond) else: - un_cond = self.conditioner.unconditional_embedding(bsz) - def cat_recursive(a, b): if isinstance(a, torch.Tensor): return torch.cat([a, b], dim=0).to(self.dtype) @@ -326,25 +483,27 @@ class Hunyuan3DDiTPipeline: latents = latents * getattr(self.scheduler, 'init_noise_sigma', 1.0) return latents - def prepare_image(self, image): + def prepare_image(self, image) -> dict: if isinstance(image, str) and not os.path.exists(image): raise FileNotFoundError(f"Couldn't find image at path {image}") if not isinstance(image, list): image = [image] - image_pts = [] - mask_pts = [] - for img in image: - image_pt, mask_pt = self.image_processor(img, return_mask=True) - image_pts.append(image_pt) - mask_pts.append(mask_pt) - image_pts = torch.cat(image_pts, dim=0).to(self.device, dtype=self.dtype) - if mask_pts[0] is not None: - mask_pts = torch.cat(mask_pts, dim=0).to(self.device, dtype=self.dtype) - else: - mask_pts = None - return image_pts, mask_pts + outputs = [] + for img in image: + output = self.image_processor(img) + outputs.append(output) + + cond_input = {k: [] for k in outputs[0].keys()} + for output in outputs: + for key, value in output.items(): + cond_input[key].append(value) + for key, value in cond_input.items(): + if isinstance(value[0], torch.Tensor): + cond_input[key] = torch.cat(value, dim=0) + + return cond_input def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32): """ @@ -374,6 +533,17 @@ class Hunyuan3DDiTPipeline: assert emb.shape == (w.shape[0], embedding_dim) return emb + def set_surface_extractor(self, mc_algo): + if mc_algo is None: + return + logger.info('The parameters `mc_algo` is deprecated, and will be removed in future versions.\n' + 'Please use: \n' + 'from hy3dgen.shapegen.models.autoencoders import SurfaceExtractors\n' + 'pipeline.vae.surface_extractor = SurfaceExtractors[mc_algo]() instead\n') + if mc_algo not in SurfaceExtractors.keys(): + raise ValueError(f"Unknown mc_algo {mc_algo}") + self.vae.surface_extractor = SurfaceExtractors[mc_algo]() + @torch.no_grad() def __call__( self, @@ -390,7 +560,7 @@ class Hunyuan3DDiTPipeline: octree_resolution=384, mc_level=-1 / 512, num_chunks=8000, - mc_algo='mc', + mc_algo=None, output_type: Optional[str] = "trimesh", enable_pbar=True, **kwargs, @@ -398,17 +568,22 @@ class Hunyuan3DDiTPipeline: callback = kwargs.pop("callback", None) callback_steps = kwargs.pop("callback_steps", None) + self.set_surface_extractor(mc_algo) + device = self.device dtype = self.dtype do_classifier_free_guidance = guidance_scale >= 0 and \ getattr(self.model, 'guidance_cond_proj_dim', None) is None dual_guidance = dual_guidance_scale >= 0 and dual_guidance - image, mask = self.prepare_image(image) - cond = self.encode_cond(image=image, - mask=mask, - do_classifier_free_guidance=do_classifier_free_guidance, - dual_guidance=dual_guidance) + cond_inputs = self.prepare_image(image) + image = cond_inputs.pop('image') + cond = self.encode_cond( + image=image, + additional_cond_inputs=cond_inputs, + do_classifier_free_guidance=do_classifier_free_guidance, + dual_guidance=False, + ) batch_size = image.shape[0] t_dtype = torch.long @@ -420,46 +595,45 @@ class Hunyuan3DDiTPipeline: guidance_cond = None if getattr(self.model, 'guidance_cond_proj_dim', None) is not None: - print('Using lcm guidance scale') + logger.info('Using lcm guidance scale') guidance_scale_tensor = torch.tensor(guidance_scale - 1).repeat(batch_size) guidance_cond = self.get_guidance_scale_embedding( guidance_scale_tensor, embedding_dim=self.model.guidance_cond_proj_dim ).to(device=device, dtype=latents.dtype) - - - for i, t in enumerate(tqdm(timesteps, disable=not enable_pbar, desc="Diffusion Sampling:", leave=False)): - # expand the latents if we are doing classifier free guidance - if do_classifier_free_guidance: - latent_model_input = torch.cat([latents] * (3 if dual_guidance else 2)) - else: - latent_model_input = latents - latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) - - # predict the noise residual - timestep_tensor = torch.tensor([t], dtype=t_dtype, device=device) - timestep_tensor = timestep_tensor.expand(latent_model_input.shape[0]) - noise_pred = self.model(latent_model_input, timestep_tensor, cond, guidance_cond=guidance_cond) - - # no drop, drop clip, all drop - if do_classifier_free_guidance: - if dual_guidance: - noise_pred_clip, noise_pred_dino, noise_pred_uncond = noise_pred.chunk(3) - noise_pred = ( - noise_pred_uncond - + guidance_scale * (noise_pred_clip - noise_pred_dino) - + dual_guidance_scale * (noise_pred_dino - noise_pred_uncond) - ) + with synchronize_timer('Diffusion Sampling'): + for i, t in enumerate(tqdm(timesteps, disable=not enable_pbar, desc="Diffusion Sampling:", leave=False)): + # expand the latents if we are doing classifier free guidance + if do_classifier_free_guidance: + latent_model_input = torch.cat([latents] * (3 if dual_guidance else 2)) else: - noise_pred_cond, noise_pred_uncond = noise_pred.chunk(2) - noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond) + latent_model_input = latents + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) - # compute the previous noisy sample x_t -> x_t-1 - outputs = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs) - latents = outputs.prev_sample + # predict the noise residual + timestep_tensor = torch.tensor([t], dtype=t_dtype, device=device) + timestep_tensor = timestep_tensor.expand(latent_model_input.shape[0]) + noise_pred = self.model(latent_model_input, timestep_tensor, cond, guidance_cond=guidance_cond) - if callback is not None and i % callback_steps == 0: - step_idx = i // getattr(self.scheduler, "order", 1) - callback(step_idx, t, outputs) + # no drop, drop clip, all drop + if do_classifier_free_guidance: + if dual_guidance: + noise_pred_clip, noise_pred_dino, noise_pred_uncond = noise_pred.chunk(3) + noise_pred = ( + noise_pred_uncond + + guidance_scale * (noise_pred_clip - noise_pred_dino) + + dual_guidance_scale * (noise_pred_dino - noise_pred_uncond) + ) + else: + noise_pred_cond, noise_pred_uncond = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond) + + # compute the previous noisy sample x_t -> x_t-1 + outputs = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs) + latents = outputs.prev_sample + + if callback is not None and i % callback_steps == 0: + step_idx = i // getattr(self.scheduler, "order", 1) + callback(step_idx, t, outputs) return self._export( latents, @@ -467,7 +641,17 @@ class Hunyuan3DDiTPipeline: box_v, mc_level, num_chunks, octree_resolution, mc_algo, ) - def _export(self, latents, output_type, box_v, mc_level, num_chunks, octree_resolution, mc_algo): + def _export( + self, + latents, + output_type='trimesh', + box_v=1.01, + mc_level=0.0, + num_chunks=20000, + octree_resolution=256, + mc_algo='mc', + enable_pbar=True + ): if not output_type == "latent": latents = 1. / self.vae.scale_factor * latents latents = self.vae(latents) @@ -478,6 +662,7 @@ class Hunyuan3DDiTPipeline: num_chunks=num_chunks, octree_resolution=octree_resolution, mc_algo=mc_algo, + enable_pbar=enable_pbar, ) else: outputs = latents @@ -490,20 +675,20 @@ class Hunyuan3DDiTPipeline: class Hunyuan3DDiTFlowMatchingPipeline(Hunyuan3DDiTPipeline): - @torch.no_grad() + @torch.inference_mode() def __call__( self, - image: Union[str, List[str], Image.Image] = None, + image: Union[str, List[str], Image.Image, dict, List[dict]] = None, num_inference_steps: int = 50, timesteps: List[int] = None, sigmas: List[float] = None, eta: float = 0.0, - guidance_scale: float = 7.5, + guidance_scale: float = 5.0, generator=None, box_v=1.01, octree_resolution=384, mc_level=0.0, - mc_algo='mc', + mc_algo=None, num_chunks=8000, output_type: Optional[str] = "trimesh", enable_pbar=True, @@ -512,6 +697,8 @@ class Hunyuan3DDiTFlowMatchingPipeline(Hunyuan3DDiTPipeline): callback = kwargs.pop("callback", None) callback_steps = kwargs.pop("callback_steps", None) + self.set_surface_extractor(mc_algo) + device = self.device dtype = self.dtype do_classifier_free_guidance = guidance_scale >= 0 and not ( @@ -519,10 +706,11 @@ class Hunyuan3DDiTFlowMatchingPipeline(Hunyuan3DDiTPipeline): self.model.guidance_embed is True ) - image, mask = self.prepare_image(image) + cond_inputs = self.prepare_image(image) + image = cond_inputs.pop('image') cond = self.encode_cond( image=image, - mask=mask, + additional_cond_inputs=cond_inputs, do_classifier_free_guidance=do_classifier_free_guidance, dual_guidance=False, ) @@ -543,36 +731,36 @@ class Hunyuan3DDiTFlowMatchingPipeline(Hunyuan3DDiTPipeline): if hasattr(self.model, 'guidance_embed') and \ self.model.guidance_embed is True: guidance = torch.tensor([guidance_scale] * batch_size, device=device, dtype=dtype) + # logger.info(f'Using guidance embed with scale {guidance_scale}') - comfy_pbar = comfy.utils.ProgressBar(num_inference_steps) - for i, t in enumerate(tqdm(timesteps, disable=not enable_pbar, desc="Diffusion Sampling:")): - # expand the latents if we are doing classifier free guidance - if do_classifier_free_guidance: - latent_model_input = torch.cat([latents] * 2) - else: - latent_model_input = latents + with synchronize_timer('Diffusion Sampling'): + for i, t in enumerate(tqdm(timesteps, disable=not enable_pbar, desc="Diffusion Sampling:")): + # expand the latents if we are doing classifier free guidance + if do_classifier_free_guidance: + latent_model_input = torch.cat([latents] * 2) + else: + latent_model_input = latents - # NOTE: we assume model get timesteps ranged from 0 to 1 - timestep = t.expand(latent_model_input.shape[0]).to( - latents.dtype) / self.scheduler.config.num_train_timesteps - noise_pred = self.model(latent_model_input, timestep, cond, guidance=guidance) + # NOTE: we assume model get timesteps ranged from 0 to 1 + timestep = t.expand(latent_model_input.shape[0]).to( + latents.dtype) / self.scheduler.config.num_train_timesteps + noise_pred = self.model(latent_model_input, timestep, cond, guidance=guidance) - if do_classifier_free_guidance: - noise_pred_cond, noise_pred_uncond = noise_pred.chunk(2) - noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond) + if do_classifier_free_guidance: + noise_pred_cond, noise_pred_uncond = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond) - # compute the previous noisy sample x_t -> x_t-1 - outputs = self.scheduler.step(noise_pred, t, latents) - latents = outputs.prev_sample + # compute the previous noisy sample x_t -> x_t-1 + outputs = self.scheduler.step(noise_pred, t, latents) + latents = outputs.prev_sample - if callback is not None and i % callback_steps == 0: - step_idx = i // getattr(self.scheduler, "order", 1) - callback(step_idx, t, outputs) - - comfy_pbar.update_absolute(i + 1) + if callback is not None and i % callback_steps == 0: + step_idx = i // getattr(self.scheduler, "order", 1) + callback(step_idx, t, outputs) return self._export( latents, output_type, box_v, mc_level, num_chunks, octree_resolution, mc_algo, + enable_pbar=enable_pbar, ) diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/postprocessors.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/postprocessors.py old mode 100644 new mode 100755 index 0500fa2..d258369 --- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/postprocessors.py +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/postprocessors.py @@ -1,13 +1,3 @@ -# Open Source Model Licensed under the Apache License Version 2.0 -# and Other Licenses of the Third-Party Components therein: -# The below Model in this distribution may have been modified by THL A29 Limited -# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited. - -# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. -# The below software and/or models in this distribution may have been -# modified by THL A29 Limited ("Tencent Modifications"). -# All Tencent Modifications are Copyright (C) THL A29 Limited. - # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined @@ -26,10 +16,13 @@ import os import tempfile from typing import Union +import numpy as np import pymeshlab +import torch import trimesh -from .models.vae import Latent2MeshOutput +from .models.autoencoders import Latent2MeshOutput +from .utils import synchronize_timer def load_mesh(path): @@ -42,6 +35,9 @@ def load_mesh(path): def reduce_face(mesh: pymeshlab.MeshSet, max_facenum: int = 200000): + if max_facenum > mesh.current_mesh().face_number(): + return mesh + mesh.apply_filter( "meshing_decimation_quadric_edge_collapse", targetfacenum=max_facenum, @@ -64,15 +60,9 @@ def remove_floater(mesh: pymeshlab.MeshSet): def pymeshlab2trimesh(mesh: pymeshlab.MeshSet): - temp_file = tempfile.NamedTemporaryFile(suffix='.ply', delete=True) - temp_file.close() - temp_file_name = temp_file.name - - mesh.save_current_mesh(temp_file_name) - mesh = trimesh.load(temp_file_name) - if os.path.exists(temp_file_name): - os.remove(temp_file_name) - + with tempfile.NamedTemporaryFile(suffix='.ply', delete=False) as temp_file: + mesh.save_current_mesh(temp_file.name) + mesh = trimesh.load(temp_file.name) # 检查加载的对象类型 if isinstance(mesh, trimesh.Scene): combined_mesh = trimesh.Trimesh() @@ -84,23 +74,17 @@ def pymeshlab2trimesh(mesh: pymeshlab.MeshSet): def trimesh2pymeshlab(mesh: trimesh.Trimesh): - temp_file = tempfile.NamedTemporaryFile(suffix='.ply', delete=True) - temp_file.close() - temp_file_name = temp_file.name - - if isinstance(mesh, trimesh.scene.Scene): - for idx, obj in enumerate(mesh.geometry.values()): - if idx == 0: - temp_mesh = obj - else: - temp_mesh = temp_mesh + obj - mesh = temp_mesh - mesh.export(temp_file_name) - mesh = pymeshlab.MeshSet() - mesh.load_new_mesh(temp_file_name) - if os.path.exists(temp_file_name): - os.remove(temp_file_name) - + with tempfile.NamedTemporaryFile(suffix='.ply', delete=False) as temp_file: + if isinstance(mesh, trimesh.scene.Scene): + for idx, obj in enumerate(mesh.geometry.values()): + if idx == 0: + temp_mesh = obj + else: + temp_mesh = temp_mesh + obj + mesh = temp_mesh + mesh.export(temp_file.name) + mesh = pymeshlab.MeshSet() + mesh.load_new_mesh(temp_file.name) return mesh @@ -132,6 +116,7 @@ def import_mesh(mesh: Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutpu class FaceReducer: + @synchronize_timer('FaceReducer') def __call__( self, mesh: Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutput, str], @@ -144,6 +129,7 @@ class FaceReducer: class FloaterRemover: + @synchronize_timer('FloaterRemover') def __call__( self, mesh: Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutput, str], @@ -155,21 +141,62 @@ class FloaterRemover: class DegenerateFaceRemover: + @synchronize_timer('DegenerateFaceRemover') def __call__( self, mesh: Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutput, str], ) -> Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutput]: ms = import_mesh(mesh) - temp_file = tempfile.NamedTemporaryFile(suffix='.ply', delete=True) - temp_file.close() - temp_file_name = temp_file.name + with tempfile.NamedTemporaryFile(suffix='.ply', delete=False) as temp_file: + ms.save_current_mesh(temp_file.name) + ms = pymeshlab.MeshSet() + ms.load_new_mesh(temp_file.name) - ms.save_current_mesh(temp_file_name) - ms = pymeshlab.MeshSet() - ms.load_new_mesh(temp_file_name) - if os.path.exists(temp_file_name): - os.remove(temp_file_name) - mesh = export_mesh(mesh, ms) return mesh + + +def mesh_normalize(mesh): + """ + Normalize mesh vertices to sphere + """ + scale_factor = 1.2 + vtx_pos = np.asarray(mesh.vertices) + max_bb = (vtx_pos - 0).max(0)[0] + min_bb = (vtx_pos - 0).min(0)[0] + + center = (max_bb + min_bb) / 2 + + scale = torch.norm(torch.tensor(vtx_pos - center, dtype=torch.float32), dim=1).max() * 2.0 + + vtx_pos = (vtx_pos - center) * (scale_factor / float(scale)) + mesh.vertices = vtx_pos + + return mesh + + +class MeshSimplifier: + def __init__(self, executable: str = None): + if executable is None: + CURRENT_DIR = os.path.dirname(os.path.abspath(__file__)) + executable = os.path.join(CURRENT_DIR, "mesh_simplifier.bin") + self.executable = executable + + @synchronize_timer('MeshSimplifier') + def __call__( + self, + mesh: Union[trimesh.Trimesh], + ) -> Union[trimesh.Trimesh]: + with tempfile.NamedTemporaryFile(suffix='.obj', delete=False) as temp_input: + with tempfile.NamedTemporaryFile(suffix='.obj', delete=False) as temp_output: + mesh.export(temp_input.name) + os.system(f'{self.executable} {temp_input.name} {temp_output.name}') + ms = trimesh.load(temp_output.name, process=False) + if isinstance(ms, trimesh.Scene): + combined_mesh = trimesh.Trimesh() + for geom in ms.geometry.values(): + combined_mesh = trimesh.util.concatenate([combined_mesh, geom]) + ms = combined_mesh + ms = mesh_normalize(ms) + return ms diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/schedulers.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/schedulers.py old mode 100644 new mode 100755 index 0069f5c..13f0da8 --- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/schedulers.py +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/schedulers.py @@ -12,6 +12,20 @@ # See the License for the specific language governing permissions and # limitations under the License. +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + import math from dataclasses import dataclass from typing import List, Optional, Tuple, Union @@ -305,3 +319,162 @@ class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin): def __len__(self): return self.config.num_train_timesteps + + +@dataclass +class ConsistencyFlowMatchEulerDiscreteSchedulerOutput(BaseOutput): + prev_sample: torch.FloatTensor + pred_original_sample: torch.FloatTensor + + +class ConsistencyFlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin): + _compatibles = [] + order = 1 + + @register_to_config + def __init__( + self, + num_train_timesteps: int = 1000, + pcm_timesteps: int = 50, + ): + sigmas = np.linspace(0, 1, num_train_timesteps) + step_ratio = num_train_timesteps // pcm_timesteps + + euler_timesteps = (np.arange(1, pcm_timesteps) * step_ratio).round().astype(np.int64) - 1 + euler_timesteps = np.asarray([0] + euler_timesteps.tolist()) + + self.euler_timesteps = euler_timesteps + self.sigmas = sigmas[self.euler_timesteps] + self.sigmas = torch.from_numpy((self.sigmas.copy())).to(dtype=torch.float32) + self.timesteps = self.sigmas * num_train_timesteps + self._step_index = None + self._begin_index = None + self.sigmas = self.sigmas.to("cpu") # to avoid too much CPU/GPU communication + + @property + def step_index(self): + """ + The index counter for current timestep. It will increase 1 after each scheduler step. + """ + return self._step_index + + @property + def begin_index(self): + """ + The index for the first timestep. It should be set from pipeline with `set_begin_index` method. + """ + return self._begin_index + + # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index + def set_begin_index(self, begin_index: int = 0): + """ + Sets the begin index for the scheduler. This function should be run from pipeline before the inference. + + Args: + begin_index (`int`): + The begin index for the scheduler. + """ + self._begin_index = begin_index + + def _sigma_to_t(self, sigma): + return sigma * self.config.num_train_timesteps + + def set_timesteps( + self, + num_inference_steps: int = None, + device: Union[str, torch.device] = None, + sigmas: Optional[List[float]] = None, + ): + """ + Sets the discrete timesteps used for the diffusion chain (to be run before inference). + + Args: + num_inference_steps (`int`): + The number of diffusion steps used when generating samples with a pre-trained model. + device (`str` or `torch.device`, *optional*): + The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. + """ + self.num_inference_steps = num_inference_steps if num_inference_steps is not None else len(sigmas) + inference_indices = np.linspace( + 0, self.config.pcm_timesteps, num=self.num_inference_steps, endpoint=False + ) + inference_indices = np.floor(inference_indices).astype(np.int64) + inference_indices = torch.from_numpy(inference_indices).long() + + self.sigmas_ = self.sigmas[inference_indices] + timesteps = self.sigmas_ * self.config.num_train_timesteps + self.timesteps = timesteps.to(device=device) + self.sigmas_ = torch.cat( + [self.sigmas_, torch.ones(1, device=self.sigmas_.device)] + ) + + self._step_index = None + self._begin_index = None + + def index_for_timestep(self, timestep, schedule_timesteps=None): + if schedule_timesteps is None: + schedule_timesteps = self.timesteps + + indices = (schedule_timesteps == timestep).nonzero() + + # The sigma index that is taken for the **very** first `step` + # is always the second index (or the last index if there is only 1) + # This way we can ensure we don't accidentally skip a sigma in + # case we start in the middle of the denoising schedule (e.g. for image-to-image) + pos = 1 if len(indices) > 1 else 0 + + return indices[pos].item() + + def _init_step_index(self, timestep): + if self.begin_index is None: + if isinstance(timestep, torch.Tensor): + timestep = timestep.to(self.timesteps.device) + self._step_index = self.index_for_timestep(timestep) + else: + self._step_index = self._begin_index + + def step( + self, + model_output: torch.FloatTensor, + timestep: Union[float, torch.FloatTensor], + sample: torch.FloatTensor, + generator: Optional[torch.Generator] = None, + return_dict: bool = True, + ) -> Union[ConsistencyFlowMatchEulerDiscreteSchedulerOutput, Tuple]: + if ( + isinstance(timestep, int) + or isinstance(timestep, torch.IntTensor) + or isinstance(timestep, torch.LongTensor) + ): + raise ValueError( + ( + "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to" + " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass" + " one of the `scheduler.timesteps` as a timestep." + ), + ) + + if self.step_index is None: + self._init_step_index(timestep) + + sample = sample.to(torch.float32) + + sigma = self.sigmas_[self.step_index] + sigma_next = self.sigmas_[self.step_index + 1] + + prev_sample = sample + (sigma_next - sigma) * model_output + prev_sample = prev_sample.to(model_output.dtype) + + pred_original_sample = sample + (1.0 - sigma) * model_output + pred_original_sample = pred_original_sample.to(model_output.dtype) + + self._step_index += 1 + + if not return_dict: + return (prev_sample,) + + return ConsistencyFlowMatchEulerDiscreteSchedulerOutput(prev_sample=prev_sample, + pred_original_sample=pred_original_sample) + + def __len__(self): + return self.config.num_train_timesteps diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/utils.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/utils.py new file mode 100644 index 0000000..6ac8f5d --- /dev/null +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/utils.py @@ -0,0 +1,126 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +import logging +import os +from functools import wraps + +import torch + + +def get_logger(name): + logger = logging.getLogger(name) + logger.setLevel(logging.INFO) + + console_handler = logging.StreamHandler() + console_handler.setLevel(logging.INFO) + + formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') + console_handler.setFormatter(formatter) + logger.addHandler(console_handler) + return logger + + +logger = get_logger('hy3dgen.shapgen') + + +class synchronize_timer: + """ Synchronized timer to count the inference time of `nn.Module.forward`. + + Supports both context manager and decorator usage. + + Example as context manager: + ```python + with synchronize_timer('name') as t: + run() + ``` + + Example as decorator: + ```python + @synchronize_timer('Export to trimesh') + def export_to_trimesh(mesh_output): + pass + ``` + """ + + def __init__(self, name=None): + self.name = name + + def __enter__(self): + """Context manager entry: start timing.""" + if os.environ.get('HY3DGEN_DEBUG', '0') == '1': + self.start = torch.cuda.Event(enable_timing=True) + self.end = torch.cuda.Event(enable_timing=True) + self.start.record() + return lambda: self.time + + def __exit__(self, exc_type, exc_value, exc_tb): + """Context manager exit: stop timing and log results.""" + if os.environ.get('HY3DGEN_DEBUG', '0') == '1': + self.end.record() + torch.cuda.synchronize() + self.time = self.start.elapsed_time(self.end) + if self.name is not None: + logger.info(f'{self.name} takes {self.time} ms') + + def __call__(self, func): + """Decorator: wrap the function to time its execution.""" + + @wraps(func) + def wrapper(*args, **kwargs): + with self: + result = func(*args, **kwargs) + return result + + return wrapper + + +def smart_load_model( + model_path, + subfolder, + use_safetensors, + variant, +): + original_model_path = model_path + # try local path + base_dir = os.environ.get('HY3DGEN_MODELS', '~/.cache/hy3dgen') + model_path = os.path.expanduser(os.path.join(base_dir, model_path, subfolder)) + logger.info(f'Try to load model from local path: {model_path}') + if not os.path.exists(model_path): + logger.info('Model path not exists, try to download from huggingface') + try: + from huggingface_hub import snapshot_download + # 只下载指定子目录 + path = snapshot_download( + repo_id=original_model_path, + allow_patterns=[f"{subfolder}/*"], # 关键修改：模式匹配子文件夹 + ) + model_path = os.path.join(path, subfolder) # 保持路径拼接逻辑不变 + except ImportError: + logger.warning( + "You need to install HuggingFace Hub to load models from the hub." + ) + raise RuntimeError(f"Model path {model_path} not found") + except Exception as e: + raise e + + if not os.path.exists(model_path): + raise FileNotFoundError(f"Model path {original_model_path} not found") + + extension = 'ckpt' if not use_safetensors else 'safetensors' + variant = '' if variant is None else f'.{variant}' + ckpt_name = f'model{variant}.{extension}' + config_path = os.path.join(model_path, 'config.yaml') + ckpt_path = os.path.join(model_path, ckpt_name) + return config_path, ckpt_path diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/__init__.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/__init__.py index 1f890f0..7054c57 100644 --- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/__init__.py +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/__init__.py @@ -1,13 +1,3 @@ -# Open Source Model Licensed under the Apache License Version 2.0 -# and Other Licenses of the Third-Party Components therein: -# The below Model in this distribution may have been modified by THL A29 Limited -# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited. - -# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. -# The below software and/or models in this distribution may have been -# modified by THL A29 Limited ("Tencent Modifications"). -# All Tencent Modifications are Copyright (C) THL A29 Limited. - # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/__init__.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/__init__.py new file mode 100644 index 0000000..f471e1a --- /dev/null +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/__init__.py @@ -0,0 +1,22 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +''' +from .hierarchy import BuildHierarchy, BuildHierarchyWithColor +from .io_obj import LoadObj, LoadObjWithTexture +from .render import rasterize, interpolate +''' +from .io_glb import * +from .io_obj import * +from .render import * diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/io_glb.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/io_glb.py new file mode 100644 index 0000000..f1daf7f --- /dev/null +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/io_glb.py @@ -0,0 +1,241 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +import base64 +import io +import os + +import numpy as np +from PIL import Image as PILImage +from pygltflib import GLTF2 +from scipy.spatial.transform import Rotation as R + + +# Function to extract buffer data +def get_buffer_data(gltf, buffer_view): + buffer = gltf.buffers[buffer_view.buffer] + buffer_data = gltf.get_data_from_buffer_uri(buffer.uri) + byte_offset = buffer_view.byteOffset if buffer_view.byteOffset else 0 + byte_length = buffer_view.byteLength + return buffer_data[byte_offset:byte_offset + byte_length] + + +# Function to extract attribute data +def get_attribute_data(gltf, accessor_index): + accessor = gltf.accessors[accessor_index] + buffer_view = gltf.bufferViews[accessor.bufferView] + buffer_data = get_buffer_data(gltf, buffer_view) + + comptype = {5120: np.int8, 5121: np.uint8, 5122: np.int16, 5123: np.uint16, 5125: np.uint32, 5126: np.float32} + dtype = comptype[accessor.componentType] + + t2n = {'SCALAR': 1, 'VEC2': 2, 'VEC3': 3, 'VEC4': 4, 'MAT2': 4, 'MAT3': 9, 'MAT4': 16} + num_components = t2n[accessor.type] + + # Calculate the correct slice of data + byte_offset = accessor.byteOffset if accessor.byteOffset else 0 + byte_stride = buffer_view.byteStride if buffer_view.byteStride else num_components * np.dtype(dtype).itemsize + count = accessor.count + + # Extract the attribute data + attribute_data = np.zeros((count, num_components), dtype=dtype) + for i in range(count): + start = byte_offset + i * byte_stride + end = start + num_components * np.dtype(dtype).itemsize + attribute_data[i] = np.frombuffer(buffer_data[start:end], dtype=dtype) + + return attribute_data + + +# Function to extract image data +def get_image_data(gltf, image, folder): + if image.uri: + if image.uri.startswith('data:'): + # Data URI + header, encoded = image.uri.split(',', 1) + data = base64.b64decode(encoded) + else: + # External file + fn = image.uri + if not os.path.isabs(fn): + fn = folder + '/' + fn + with open(fn, 'rb') as f: + data = f.read() + else: + buffer_view = gltf.bufferViews[image.bufferView] + data = get_buffer_data(gltf, buffer_view) + return data + + +# Function to convert triangle strip to triangles +def convert_triangle_strip_to_triangles(indices): + triangles = [] + for i in range(len(indices) - 2): + if i % 2 == 0: + triangles.append([indices[i], indices[i + 1], indices[i + 2]]) + else: + triangles.append([indices[i], indices[i + 2], indices[i + 1]]) + return np.array(triangles).reshape(-1, 3) + + +# Function to convert triangle fan to triangles +def convert_triangle_fan_to_triangles(indices): + triangles = [] + for i in range(1, len(indices) - 1): + triangles.append([indices[0], indices[i], indices[i + 1]]) + return np.array(triangles).reshape(-1, 3) + + +# Function to get the transformation matrix from a node +def get_node_transform(node): + if node.matrix: + return np.array(node.matrix).reshape(4, 4).T + else: + T = np.eye(4) + if node.translation: + T[:3, 3] = node.translation + if node.rotation: + R_mat = R.from_quat(node.rotation).as_matrix() + T[:3, :3] = R_mat + if node.scale: + S = np.diag(node.scale + [1]) + T = T @ S + return T + + +def get_world_transform(gltf, node_index, parents, world_transforms): + if parents[node_index] == -2: + return world_transforms[node_index] + + node = gltf.nodes[node_index] + if parents[node_index] == -1: + world_transforms[node_index] = get_node_transform(node) + parents[node_index] = -2 + return world_transforms[node_index] + + parent_index = parents[node_index] + parent_transform = get_world_transform(gltf, parent_index, parents, world_transforms) + world_transforms[node_index] = parent_transform @ get_node_transform(node) + parents[node_index] = -2 + return world_transforms[node_index] + + +def LoadGlb(path): + # Load the GLB file using pygltflib + gltf = GLTF2().load(path) + + primitives = [] + images = {} + # Iterate through the meshes in the GLB file + + world_transforms = [np.identity(4) for i in range(len(gltf.nodes))] + parents = [-1 for i in range(len(gltf.nodes))] + for node_index, node in enumerate(gltf.nodes): + for idx in node.children: + parents[idx] = node_index + # for i in range(len(gltf.nodes)): + # get_world_transform(gltf, i, parents, world_transform) + + for node_index, node in enumerate(gltf.nodes): + if node.mesh is not None: + world_transform = get_world_transform(gltf, node_index, parents, world_transforms) + # Iterate through the primitives in the mesh + mesh = gltf.meshes[node.mesh] + for primitive in mesh.primitives: + # Access the attributes of the primitive + attributes = primitive.attributes.__dict__ + mode = primitive.mode if primitive.mode is not None else 4 # Default to TRIANGLES + result = {} + if primitive.indices is not None: + indices = get_attribute_data(gltf, primitive.indices) + if mode == 4: # TRIANGLES + face_indices = indices.reshape(-1, 3) + elif mode == 5: # TRIANGLE_STRIP + face_indices = convert_triangle_strip_to_triangles(indices) + elif mode == 6: # TRIANGLE_FAN + face_indices = convert_triangle_fan_to_triangles(indices) + else: + continue + result['F'] = face_indices + + # Extract vertex positions + if 'POSITION' in attributes and attributes['POSITION'] is not None: + positions = get_attribute_data(gltf, attributes['POSITION']) + # Apply the world transformation to the positions + positions_homogeneous = np.hstack([positions, np.ones((positions.shape[0], 1))]) + transformed_positions = (world_transform @ positions_homogeneous.T).T[:, :3] + result['V'] = transformed_positions + + # Extract vertex colors + if 'COLOR_0' in attributes and attributes['COLOR_0'] is not None: + colors = get_attribute_data(gltf, attributes['COLOR_0']) + if colors.shape[-1] > 3: + colors = colors[..., :3] + result['VC'] = colors + + # Extract UVs + if 'TEXCOORD_0' in attributes and not attributes['TEXCOORD_0'] is None: + uvs = get_attribute_data(gltf, attributes['TEXCOORD_0']) + result['UV'] = uvs + + if primitive.material is not None: + material = gltf.materials[primitive.material] + if ( + material.pbrMetallicRoughness is not None + and material.pbrMetallicRoughness.baseColorTexture is not None + ): + texture_index = material.pbrMetallicRoughness.baseColorTexture.index + texture = gltf.textures[texture_index] + image_index = texture.source + if not image_index in images: + image = gltf.images[image_index] + image_data = get_image_data(gltf, image, os.path.dirname(path)) + pil_image = PILImage.open(io.BytesIO(image_data)) + if pil_image.mode != 'RGB': + pil_image = pil_image.convert('RGB') + images[image_index] = pil_image + result['TEX'] = image_index + elif material.emissiveTexture is not None: + texture_index = material.emissiveTexture.index + texture = gltf.textures[texture_index] + image_index = texture.source + if not image_index in images: + image = gltf.images[image_index] + image_data = get_image_data(gltf, image, os.path.dirname(path)) + pil_image = PILImage.open(io.BytesIO(image_data)) + if pil_image.mode != 'RGB': + pil_image = pil_image.convert('RGB') + images[image_index] = pil_image + result['TEX'] = image_index + else: + if material.pbrMetallicRoughness is not None: + base_color = material.pbrMetallicRoughness.baseColorFactor + else: + base_color = np.array([0.8, 0.8, 0.8], dtype=np.float32) + result['MC'] = base_color + + primitives.append(result) + + return primitives, images + + +def RotatePrimitives(primitives, transform): + for i in range(len(primitives)): + if 'V' in primitives[i]: + primitives[i]['V'] = primitives[i]['V'] @ transform.T + + +if __name__ == '__main__': + path = 'data/test.glb' + LoadGlb(path) diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/io_obj.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/io_obj.py new file mode 100644 index 0000000..e40d500 --- /dev/null +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/io_obj.py @@ -0,0 +1,66 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +import cv2 +import numpy as np + + +def LoadObj(fn): + lines = [l.strip() for l in open(fn)] + vertices = [] + faces = [] + for l in lines: + words = [w for w in l.split(' ') if w != ''] + if len(words) == 0: + continue + if words[0] == 'v': + v = [float(words[i]) for i in range(1, 4)] + vertices.append(v) + elif words[0] == 'f': + f = [int(words[i]) - 1 for i in range(1, 4)] + faces.append(f) + + return np.array(vertices).astype('float32'), np.array(faces).astype('int32') + + +def LoadObjWithTexture(fn, tex_fn): + lines = [l.strip() for l in open(fn)] + vertices = [] + vertex_textures = [] + faces = [] + face_textures = [] + for l in lines: + words = [w for w in l.split(' ') if w != ''] + if len(words) == 0: + continue + if words[0] == 'v': + v = [float(words[i]) for i in range(1, len(words))] + vertices.append(v) + elif words[0] == 'vt': + v = [float(words[i]) for i in range(1, len(words))] + vertex_textures.append(v) + elif words[0] == 'f': + f = [] + ft = [] + for i in range(1, len(words)): + t = words[i].split('/') + f.append(int(t[0]) - 1) + ft.append(int(t[1]) - 1) + for i in range(2, len(f)): + faces.append([f[0], f[i - 1], f[i]]) + face_textures.append([ft[0], ft[i - 1], ft[i]]) + + tex_image = cv2.cvtColor(cv2.imread(tex_fn), cv2.COLOR_BGR2RGB) + return np.array(vertices).astype('float32'), np.array(vertex_textures).astype('float32'), np.array(faces).astype( + 'int32'), np.array(face_textures).astype('int32'), tex_image diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/render.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/render.py new file mode 100644 index 0000000..2d4d3f7 --- /dev/null +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/render.py @@ -0,0 +1,31 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +import custom_rasterizer_kernel +import torch + + +def rasterize(pos, tri, resolution, clamp_depth=torch.zeros(0), use_depth_prior=0): + assert (pos.device == tri.device) + findices, barycentric = custom_rasterizer_kernel.rasterize_image(pos[0], tri, clamp_depth, resolution[1], + resolution[0], 1e-6, use_depth_prior) + return findices, barycentric + + +def interpolate(col, findices, barycentric, tri): + f = findices - 1 + (findices == 0) + vcol = col[0, tri.long()[f.long()]] + result = barycentric.view(*barycentric.shape, 1) * vcol + result = torch.sum(result, axis=-2) + return result.view(1, *result.shape) diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/__init__.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/__init__.py new file mode 100644 index 0000000..1614ff8 --- /dev/null +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/__init__.py @@ -0,0 +1,13 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/grid_neighbor.cpp b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/grid_neighbor.cpp new file mode 100644 index 0000000..65ab321 --- /dev/null +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/grid_neighbor.cpp @@ -0,0 +1,574 @@ +#include "rasterizer.h" +#include + +inline int pos2key(float* p, int resolution) { + int x = (p[0] * 0.5 + 0.5) * resolution; + int y = (p[1] * 0.5 + 0.5) * resolution; + int z = (p[2] * 0.5 + 0.5) * resolution; + return (x * resolution + y) * resolution + z; +} + +inline void key2pos(int key, int resolution, float* p) { + int x = key / resolution / resolution; + int y = key / resolution % resolution; + int z = key % resolution; + p[0] = ((x + 0.5) / resolution - 0.5) * 2; + p[1] = ((y + 0.5) / resolution - 0.5) * 2; + p[2] = ((z + 0.5) / resolution - 0.5) * 2; +} + +inline void key2cornerpos(int key, int resolution, float* p) { + int x = key / resolution / resolution; + int y = key / resolution % resolution; + int z = key % resolution; + p[0] = ((x + 0.75) / resolution - 0.5) * 2; + p[1] = ((y + 0.25) / resolution - 0.5) * 2; + p[2] = ((z + 0.75) / resolution - 0.5) * 2; +} + +inline float* pos_ptr(int l, int i, int j, torch::Tensor t) { + float* pdata = t.data_ptr(); + int height = t.size(1); + int width = t.size(2); + return &pdata[((l * height + i) * width + j) * 4]; +} + +struct Grid +{ + std::vector seq2oddcorner; + std::vector seq2evencorner; + std::vector seq2grid; + std::vector seq2normal; + std::vector seq2neighbor; + std::unordered_map grid2seq; + std::vector downsample_seq; + int num_origin_seq; + int resolution; + int stride; +}; + +inline void pos_from_seq(Grid& grid, int seq, float* p) { + auto k = grid.seq2grid[seq]; + key2pos(k, grid.resolution, p); +} + +inline int fetch_seq(Grid& grid, int l, int i, int j, torch::Tensor pdata) { + float* p = pos_ptr(l, i, j, pdata); + if (p[3] == 0) + return -1; + auto key = pos2key(p, grid.resolution); + int seq = grid.grid2seq[key]; + return seq; +} + +inline int fetch_last_seq(Grid& grid, int i, int j, torch::Tensor pdata) { + int num_layers = pdata.size(0); + int l = 0; + int idx = fetch_seq(grid, l, i, j, pdata); + while (l < num_layers - 1) { + l += 1; + int new_idx = fetch_seq(grid, l, i, j, pdata); + if (new_idx == -1) + break; + idx = new_idx; + } + return idx; +} + +inline int fetch_nearest_seq(Grid& grid, int i, int j, int dim, float d, torch::Tensor pdata) { + float p[3]; + float max_dist = 1e10; + int best_idx = -1; + int num_layers = pdata.size(0); + for (int l = 0; l < num_layers; ++l) { + int idx = fetch_seq(grid, l, i, j, pdata); + if (idx == -1) + break; + pos_from_seq(grid, idx, p); + float dist = std::abs(d - p[(dim + 2) % 3]); + if (dist < max_dist) { + max_dist = dist; + best_idx = idx; + } + } + return best_idx; +} + +inline int fetch_nearest_seq_layer(Grid& grid, int i, int j, int dim, float d, torch::Tensor pdata) { + float p[3]; + float max_dist = 1e10; + int best_layer = -1; + int num_layers = pdata.size(0); + for (int l = 0; l < num_layers; ++l) { + int idx = fetch_seq(grid, l, i, j, pdata); + if (idx == -1) + break; + pos_from_seq(grid, idx, p); + float dist = std::abs(d - p[(dim + 2) % 3]); + if (dist < max_dist) { + max_dist = dist; + best_layer = l; + } + } + return best_layer; +} + +void FetchNeighbor(Grid& grid, int seq, float* pos, int dim, int boundary_info, std::vector& view_layer_positions, + int* output_indices) +{ + auto t = view_layer_positions[dim]; + int height = t.size(1); + int width = t.size(2); + int top = 0; + int ci = 0; + int cj = 0; + if (dim == 0) { + ci = (pos[1]/2+0.5)*height; + cj = (pos[0]/2+0.5)*width; + } + else if (dim == 1) { + ci = (pos[1]/2+0.5)*height; + cj = (pos[2]/2+0.5)*width; + } + else { + ci = (-pos[2]/2+0.5)*height; + cj = (pos[0]/2+0.5)*width; + } + int stride = grid.stride; + for (int ni = ci + stride; ni >= ci - stride; ni -= stride) { + for (int nj = cj - stride; nj <= cj + stride; nj += stride) { + int idx = -1; + if (ni == ci && nj == cj) + idx = seq; + else if (!(ni < 0 || ni >= height || nj < 0 || nj >= width)) { + if (boundary_info == -1) + idx = fetch_seq(grid, 0, ni, nj, t); + else if (boundary_info == 1) + idx = fetch_last_seq(grid, ni, nj, t); + else + idx = fetch_nearest_seq(grid, ni, nj, dim, pos[(dim + 2) % 3], t); + } + output_indices[top] = idx; + top += 1; + } + } +} + +void DownsampleGrid(Grid& src, Grid& tar) +{ + src.downsample_seq.resize(src.seq2grid.size(), -1); + tar.resolution = src.resolution / 2; + tar.stride = src.stride * 2; + float pos[3]; + std::vector seq2normal_count; + for (int i = 0; i < src.seq2grid.size(); ++i) { + key2pos(src.seq2grid[i], src.resolution, pos); + int k = pos2key(pos, tar.resolution); + int s = seq2normal_count.size(); + if (!tar.grid2seq.count(k)) { + tar.grid2seq[k] = tar.seq2grid.size(); + tar.seq2grid.emplace_back(k); + seq2normal_count.emplace_back(0); + seq2normal_count.emplace_back(0); + seq2normal_count.emplace_back(0); + //tar.seq2normal.emplace_back(src.seq2normal[i]); + } else { + s = tar.grid2seq[k] * 3; + } + seq2normal_count[s + src.seq2normal[i]] += 1; + src.downsample_seq[i] = tar.grid2seq[k]; + } + tar.seq2normal.resize(seq2normal_count.size() / 3); + for (int i = 0; i < seq2normal_count.size(); i += 3) { + int t = 0; + for (int j = 1; j < 3; ++j) { + if (seq2normal_count[i + j] > seq2normal_count[i + t]) + t = j; + } + tar.seq2normal[i / 3] = t; + } +} + +void NeighborGrid(Grid& grid, std::vector view_layer_positions, int v) +{ + grid.seq2evencorner.resize(grid.seq2grid.size(), 0); + grid.seq2oddcorner.resize(grid.seq2grid.size(), 0); + std::unordered_set visited_seq; + for (int vd = 0; vd < 3; ++vd) { + auto t = view_layer_positions[vd]; + auto t0 = view_layer_positions[v]; + int height = t.size(1); + int width = t.size(2); + int num_layers = t.size(0); + int num_view_layers = t0.size(0); + for (int i = 0; i < height; ++i) { + for (int j = 0; j < width; ++j) { + for (int l = 0; l < num_layers; ++l) { + int seq = fetch_seq(grid, l, i, j, t); + if (seq == -1) + break; + int dim = grid.seq2normal[seq]; + if (dim != v) + continue; + + float pos[3]; + pos_from_seq(grid, seq, pos); + + int ci = 0; + int cj = 0; + if (dim == 0) { + ci = (pos[1]/2+0.5)*height; + cj = (pos[0]/2+0.5)*width; + } + else if (dim == 1) { + ci = (pos[1]/2+0.5)*height; + cj = (pos[2]/2+0.5)*width; + } + else { + ci = (-pos[2]/2+0.5)*height; + cj = (pos[0]/2+0.5)*width; + } + + if ((ci % (grid.stride * 2) < grid.stride) && (cj % (grid.stride * 2) >= grid.stride)) + grid.seq2evencorner[seq] = 1; + + if ((ci % (grid.stride * 2) >= grid.stride) && (cj % (grid.stride * 2) < grid.stride)) + grid.seq2oddcorner[seq] = 1; + + bool is_boundary = false; + if (vd == v) { + if (l == 0 || l == num_layers - 1) + is_boundary = true; + else { + int seq_new = fetch_seq(grid, l + 1, i, j, t); + if (seq_new == -1) + is_boundary = true; + } + } + int boundary_info = 0; + if (is_boundary && (l == 0)) + boundary_info = -1; + else if (is_boundary) + boundary_info = 1; + if (visited_seq.count(seq)) + continue; + visited_seq.insert(seq); + + FetchNeighbor(grid, seq, pos, dim, boundary_info, view_layer_positions, &grid.seq2neighbor[seq * 9]); + } + } + } + } +} + +void PadGrid(Grid& src, Grid& tar, std::vector& view_layer_positions) { + auto& downsample_seq = src.downsample_seq; + auto& seq2evencorner = src.seq2evencorner; + auto& seq2oddcorner = src.seq2oddcorner; + int indices[9]; + std::vector mapped_even_corners(tar.seq2grid.size(), 0); + std::vector mapped_odd_corners(tar.seq2grid.size(), 0); + for (int i = 0; i < downsample_seq.size(); ++i) { + if (seq2evencorner[i] > 0) { + mapped_even_corners[downsample_seq[i]] = 1; + } + if (seq2oddcorner[i] > 0) { + mapped_odd_corners[downsample_seq[i]] = 1; + } + } + auto& tar_seq2normal = tar.seq2normal; + auto& tar_seq2grid = tar.seq2grid; + for (int i = 0; i < tar_seq2grid.size(); ++i) { + if (mapped_even_corners[i] == 1 && mapped_odd_corners[i] == 1) + continue; + auto k = tar_seq2grid[i]; + float p[3]; + key2cornerpos(k, tar.resolution, p); + + int src_key = pos2key(p, src.resolution); + if (!src.grid2seq.count(src_key)) { + int seq = src.seq2grid.size(); + src.grid2seq[src_key] = seq; + src.seq2evencorner.emplace_back((mapped_even_corners[i] == 0)); + src.seq2oddcorner.emplace_back((mapped_odd_corners[i] == 0)); + src.seq2grid.emplace_back(src_key); + src.seq2normal.emplace_back(tar_seq2normal[i]); + FetchNeighbor(src, seq, p, tar_seq2normal[i], 0, view_layer_positions, indices); + for (int j = 0; j < 9; ++j) { + src.seq2neighbor.emplace_back(indices[j]); + } + src.downsample_seq.emplace_back(i); + } else { + int seq = src.grid2seq[src_key]; + if (mapped_even_corners[i] == 0) + src.seq2evencorner[seq] = 1; + if (mapped_odd_corners[i] == 0) + src.seq2oddcorner[seq] = 1; + } + } +} + +std::vector> build_hierarchy(std::vector view_layer_positions, + std::vector view_layer_normals, int num_level, int resolution) +{ + if (view_layer_positions.size() != 3 || num_level < 1) { + printf("Alert! We require 3 layers and at least 1 level! (%d %d)\n", view_layer_positions.size(), num_level); + return {{},{},{},{}}; + } + + std::vector grids; + grids.resize(num_level); + + std::vector seq2pos; + auto& seq2grid = grids[0].seq2grid; + auto& seq2normal = grids[0].seq2normal; + auto& grid2seq = grids[0].grid2seq; + grids[0].resolution = resolution; + grids[0].stride = 1; + + auto int64_options = torch::TensorOptions().dtype(torch::kInt64).requires_grad(false); + auto float_options = torch::TensorOptions().dtype(torch::kFloat32).requires_grad(false); + + for (int v = 0; v < 3; ++v) { + int num_layers = view_layer_positions[v].size(0); + int height = view_layer_positions[v].size(1); + int width = view_layer_positions[v].size(2); + float* data = view_layer_positions[v].data_ptr(); + float* data_normal = view_layer_normals[v].data_ptr(); + for (int l = 0; l < num_layers; ++l) { + for (int i = 0; i < height; ++i) { + for (int j = 0; j < width; ++j) { + float* p = &data[(i * width + j) * 4]; + float* n = &data_normal[(i * width + j) * 3]; + if (p[3] == 0) + continue; + auto k = pos2key(p, resolution); + if (!grid2seq.count(k)) { + int dim = 0; + for (int d = 0; d < 3; ++d) { + if (std::abs(n[d]) > std::abs(n[dim])) + dim = d; + } + dim = (dim + 1) % 3; + grid2seq[k] = seq2grid.size(); + seq2grid.emplace_back(k); + seq2pos.push_back(p[0]); + seq2pos.push_back(p[1]); + seq2pos.push_back(p[2]); + seq2normal.emplace_back(dim); + } + } + } + data += (height * width * 4); + data_normal += (height * width * 3); + } + } + + for (int i = 0; i < num_level - 1; ++i) { + DownsampleGrid(grids[i], grids[i + 1]); + } + + for (int l = 0; l < num_level; ++l) { + grids[l].seq2neighbor.resize(grids[l].seq2grid.size() * 9, -1); + grids[l].num_origin_seq = grids[l].seq2grid.size(); + for (int d = 0; d < 3; ++d) { + NeighborGrid(grids[l], view_layer_positions, d); + } + } + + for (int i = num_level - 2; i >= 0; --i) { + PadGrid(grids[i], grids[i + 1], view_layer_positions); + } + for (int i = grids[0].num_origin_seq; i < grids[0].seq2grid.size(); ++i) { + int k = grids[0].seq2grid[i]; + float p[3]; + key2pos(k, grids[0].resolution, p); + seq2pos.push_back(p[0]); + seq2pos.push_back(p[1]); + seq2pos.push_back(p[2]); + } + + std::vector texture_positions(2); + std::vector grid_neighbors(grids.size()); + std::vector grid_downsamples(grids.size() - 1); + std::vector grid_evencorners(grids.size()); + std::vector grid_oddcorners(grids.size()); + + texture_positions[0] = torch::zeros({static_cast(seq2pos.size() / 3), static_cast(3)}, float_options); + texture_positions[1] = torch::zeros({static_cast(seq2pos.size() / 3)}, float_options); + float* positions_out_ptr = texture_positions[0].data_ptr(); + memcpy(positions_out_ptr, seq2pos.data(), sizeof(float) * seq2pos.size()); + positions_out_ptr = texture_positions[1].data_ptr(); + for (int i = 0; i < grids[0].seq2grid.size(); ++i) { + positions_out_ptr[i] = (i < grids[0].num_origin_seq); + } + + for (int i = 0; i < grids.size(); ++i) { + grid_neighbors[i] = torch::zeros({static_cast(grids[i].seq2grid.size()), static_cast(9)}, int64_options); + int64_t* nptr = grid_neighbors[i].data_ptr(); + for (int j = 0; j < grids[i].seq2neighbor.size(); ++j) { + nptr[j] = grids[i].seq2neighbor[j]; + } + + grid_evencorners[i] = torch::zeros({static_cast(grids[i].seq2evencorner.size())}, int64_options); + grid_oddcorners[i] = torch::zeros({static_cast(grids[i].seq2oddcorner.size())}, int64_options); + int64_t* dptr = grid_evencorners[i].data_ptr(); + for (int j = 0; j < grids[i].seq2evencorner.size(); ++j) { + dptr[j] = grids[i].seq2evencorner[j]; + } + dptr = grid_oddcorners[i].data_ptr(); + for (int j = 0; j < grids[i].seq2oddcorner.size(); ++j) { + dptr[j] = grids[i].seq2oddcorner[j]; + } + if (i + 1 < grids.size()) { + grid_downsamples[i] = torch::zeros({static_cast(grids[i].downsample_seq.size())}, int64_options); + int64_t* dptr = grid_downsamples[i].data_ptr(); + for (int j = 0; j < grids[i].downsample_seq.size(); ++j) { + dptr[j] = grids[i].downsample_seq[j]; + } + } + + } + return {texture_positions, grid_neighbors, grid_downsamples, grid_evencorners, grid_oddcorners}; +} + +std::vector> build_hierarchy_with_feat( + std::vector view_layer_positions, + std::vector view_layer_normals, + std::vector view_layer_feats, + int num_level, int resolution) +{ + if (view_layer_positions.size() != 3 || num_level < 1) { + printf("Alert! We require 3 layers and at least 1 level! (%d %d)\n", view_layer_positions.size(), num_level); + return {{},{},{},{}}; + } + + std::vector grids; + grids.resize(num_level); + + std::vector seq2pos; + std::vector seq2feat; + auto& seq2grid = grids[0].seq2grid; + auto& seq2normal = grids[0].seq2normal; + auto& grid2seq = grids[0].grid2seq; + grids[0].resolution = resolution; + grids[0].stride = 1; + + auto int64_options = torch::TensorOptions().dtype(torch::kInt64).requires_grad(false); + auto float_options = torch::TensorOptions().dtype(torch::kFloat32).requires_grad(false); + + int feat_channel = 3; + for (int v = 0; v < 3; ++v) { + int num_layers = view_layer_positions[v].size(0); + int height = view_layer_positions[v].size(1); + int width = view_layer_positions[v].size(2); + float* data = view_layer_positions[v].data_ptr(); + float* data_normal = view_layer_normals[v].data_ptr(); + float* data_feat = view_layer_feats[v].data_ptr(); + feat_channel = view_layer_feats[v].size(3); + for (int l = 0; l < num_layers; ++l) { + for (int i = 0; i < height; ++i) { + for (int j = 0; j < width; ++j) { + float* p = &data[(i * width + j) * 4]; + float* n = &data_normal[(i * width + j) * 3]; + float* f = &data_feat[(i * width + j) * feat_channel]; + if (p[3] == 0) + continue; + auto k = pos2key(p, resolution); + if (!grid2seq.count(k)) { + int dim = 0; + for (int d = 0; d < 3; ++d) { + if (std::abs(n[d]) > std::abs(n[dim])) + dim = d; + } + dim = (dim + 1) % 3; + grid2seq[k] = seq2grid.size(); + seq2grid.emplace_back(k); + seq2pos.push_back(p[0]); + seq2pos.push_back(p[1]); + seq2pos.push_back(p[2]); + for (int c = 0; c < feat_channel; ++c) { + seq2feat.emplace_back(f[c]); + } + seq2normal.emplace_back(dim); + } + } + } + data += (height * width * 4); + data_normal += (height * width * 3); + data_feat += (height * width * feat_channel); + } + } + + for (int i = 0; i < num_level - 1; ++i) { + DownsampleGrid(grids[i], grids[i + 1]); + } + + for (int l = 0; l < num_level; ++l) { + grids[l].seq2neighbor.resize(grids[l].seq2grid.size() * 9, -1); + grids[l].num_origin_seq = grids[l].seq2grid.size(); + for (int d = 0; d < 3; ++d) { + NeighborGrid(grids[l], view_layer_positions, d); + } + } + + for (int i = num_level - 2; i >= 0; --i) { + PadGrid(grids[i], grids[i + 1], view_layer_positions); + } + for (int i = grids[0].num_origin_seq; i < grids[0].seq2grid.size(); ++i) { + int k = grids[0].seq2grid[i]; + float p[3]; + key2pos(k, grids[0].resolution, p); + seq2pos.push_back(p[0]); + seq2pos.push_back(p[1]); + seq2pos.push_back(p[2]); + for (int c = 0; c < feat_channel; ++c) { + seq2feat.emplace_back(0.5); + } + } + + std::vector texture_positions(2); + std::vector texture_feats(1); + std::vector grid_neighbors(grids.size()); + std::vector grid_downsamples(grids.size() - 1); + std::vector grid_evencorners(grids.size()); + std::vector grid_oddcorners(grids.size()); + + texture_positions[0] = torch::zeros({static_cast(seq2pos.size() / 3), static_cast(3)}, float_options); + texture_positions[1] = torch::zeros({static_cast(seq2pos.size() / 3)}, float_options); + texture_feats[0] = torch::zeros({static_cast(seq2feat.size() / feat_channel), static_cast(feat_channel)}, float_options); + float* positions_out_ptr = texture_positions[0].data_ptr(); + memcpy(positions_out_ptr, seq2pos.data(), sizeof(float) * seq2pos.size()); + positions_out_ptr = texture_positions[1].data_ptr(); + for (int i = 0; i < grids[0].seq2grid.size(); ++i) { + positions_out_ptr[i] = (i < grids[0].num_origin_seq); + } + float* feats_out_ptr = texture_feats[0].data_ptr(); + memcpy(feats_out_ptr, seq2feat.data(), sizeof(float) * seq2feat.size()); + + for (int i = 0; i < grids.size(); ++i) { + grid_neighbors[i] = torch::zeros({static_cast(grids[i].seq2grid.size()), static_cast(9)}, int64_options); + int64_t* nptr = grid_neighbors[i].data_ptr(); + for (int j = 0; j < grids[i].seq2neighbor.size(); ++j) { + nptr[j] = grids[i].seq2neighbor[j]; + } + grid_evencorners[i] = torch::zeros({static_cast(grids[i].seq2evencorner.size())}, int64_options); + grid_oddcorners[i] = torch::zeros({static_cast(grids[i].seq2oddcorner.size())}, int64_options); + int64_t* dptr = grid_evencorners[i].data_ptr(); + for (int j = 0; j < grids[i].seq2evencorner.size(); ++j) { + dptr[j] = grids[i].seq2evencorner[j]; + } + dptr = grid_oddcorners[i].data_ptr(); + for (int j = 0; j < grids[i].seq2oddcorner.size(); ++j) { + dptr[j] = grids[i].seq2oddcorner[j]; + } + if (i + 1 < grids.size()) { + grid_downsamples[i] = torch::zeros({static_cast(grids[i].downsample_seq.size())}, int64_options); + int64_t* dptr = grid_downsamples[i].data_ptr(); + for (int j = 0; j < grids[i].downsample_seq.size(); ++j) { + dptr[j] = grids[i].downsample_seq[j]; + } + } + } + return {texture_positions, texture_feats, grid_neighbors, grid_downsamples, grid_evencorners, grid_oddcorners}; +} diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer.cpp b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer.cpp new file mode 100644 index 0000000..4af6eeb --- /dev/null +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer.cpp @@ -0,0 +1,139 @@ +#include "rasterizer.h" + +void rasterizeTriangleCPU(int idx, float* vt0, float* vt1, float* vt2, int width, int height, INT64* zbuffer, float* d, float occlusion_truncation) { + float x_min = std::min(vt0[0], std::min(vt1[0],vt2[0])); + float x_max = std::max(vt0[0], std::max(vt1[0],vt2[0])); + float y_min = std::min(vt0[1], std::min(vt1[1],vt2[1])); + float y_max = std::max(vt0[1], std::max(vt1[1],vt2[1])); + + for (int px = x_min; px < x_max + 1; ++px) { + if (px < 0 || px >= width) + continue; + for (int py = y_min; py < y_max + 1; ++py) { + if (py < 0 || py >= height) + continue; + float vt[2] = {px + 0.5, py + 0.5}; + float baryCentricCoordinate[3]; + calculateBarycentricCoordinate(vt0, vt1, vt2, vt, baryCentricCoordinate); + if (isBarycentricCoordInBounds(baryCentricCoordinate)) { + int pixel = py * width + px; + if (zbuffer == 0) { + zbuffer[pixel] = (INT64)(idx + 1); + continue; + } + + float depth = baryCentricCoordinate[0] * vt0[2] + baryCentricCoordinate[1] * vt1[2] + baryCentricCoordinate[2] * vt2[2]; + float depth_thres = 0; + if (d) { + depth_thres = d[pixel] * 0.49999f + 0.5f + occlusion_truncation; + } + + int z_quantize = depth * (2<<17); + INT64 token = (INT64)z_quantize * MAXINT + (INT64)(idx + 1); + if (depth < depth_thres) + continue; + zbuffer[pixel] = std::min(zbuffer[pixel], token); + } + } + } +} + +void barycentricFromImgcoordCPU(float* V, int* F, int* findices, INT64* zbuffer, int width, int height, int num_vertices, int num_faces, + float* barycentric_map, int pix) +{ + INT64 f = zbuffer[pix] % MAXINT; + if (f == (MAXINT-1)) { + findices[pix] = 0; + barycentric_map[pix * 3] = 0; + barycentric_map[pix * 3 + 1] = 0; + barycentric_map[pix * 3 + 2] = 0; + return; + } + findices[pix] = f; + f -= 1; + float barycentric[3] = {0, 0, 0}; + if (f >= 0) { + float vt[2] = {float(pix % width) + 0.5f, float(pix / width) + 0.5f}; + float* vt0_ptr = V + (F[f * 3] * 4); + float* vt1_ptr = V + (F[f * 3 + 1] * 4); + float* vt2_ptr = V + (F[f * 3 + 2] * 4); + + float vt0[2] = {(vt0_ptr[0] / vt0_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt0_ptr[1] / vt0_ptr[3]) * (height - 1) + 0.5f}; + float vt1[2] = {(vt1_ptr[0] / vt1_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt1_ptr[1] / vt1_ptr[3]) * (height - 1) + 0.5f}; + float vt2[2] = {(vt2_ptr[0] / vt2_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt2_ptr[1] / vt2_ptr[3]) * (height - 1) + 0.5f}; + + calculateBarycentricCoordinate(vt0, vt1, vt2, vt, barycentric); + + barycentric[0] = barycentric[0] / vt0_ptr[3]; + barycentric[1] = barycentric[1] / vt1_ptr[3]; + barycentric[2] = barycentric[2] / vt2_ptr[3]; + float w = 1.0f / (barycentric[0] + barycentric[1] + barycentric[2]); + barycentric[0] *= w; + barycentric[1] *= w; + barycentric[2] *= w; + + } + barycentric_map[pix * 3] = barycentric[0]; + barycentric_map[pix * 3 + 1] = barycentric[1]; + barycentric_map[pix * 3 + 2] = barycentric[2]; +} + +void rasterizeImagecoordsKernelCPU(float* V, int* F, float* d, INT64* zbuffer, float occlusion_trunc, int width, int height, int num_vertices, int num_faces, int f) +{ + float* vt0_ptr = V + (F[f * 3] * 4); + float* vt1_ptr = V + (F[f * 3 + 1] * 4); + float* vt2_ptr = V + (F[f * 3 + 2] * 4); + + float vt0[3] = {(vt0_ptr[0] / vt0_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt0_ptr[1] / vt0_ptr[3]) * (height - 1) + 0.5f, vt0_ptr[2] / vt0_ptr[3] * 0.49999f + 0.5f}; + float vt1[3] = {(vt1_ptr[0] / vt1_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt1_ptr[1] / vt1_ptr[3]) * (height - 1) + 0.5f, vt1_ptr[2] / vt1_ptr[3] * 0.49999f + 0.5f}; + float vt2[3] = {(vt2_ptr[0] / vt2_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt2_ptr[1] / vt2_ptr[3]) * (height - 1) + 0.5f, vt2_ptr[2] / vt2_ptr[3] * 0.49999f + 0.5f}; + + rasterizeTriangleCPU(f, vt0, vt1, vt2, width, height, zbuffer, d, occlusion_trunc); +} + +std::vector rasterize_image_cpu(torch::Tensor V, torch::Tensor F, torch::Tensor D, + int width, int height, float occlusion_truncation, int use_depth_prior) +{ + int num_faces = F.size(0); + int num_vertices = V.size(0); + auto options = torch::TensorOptions().dtype(torch::kInt32).requires_grad(false); + auto INT64_options = torch::TensorOptions().dtype(torch::kInt64).requires_grad(false); + auto findices = torch::zeros({height, width}, options); + INT64 maxint = (INT64)MAXINT * (INT64)MAXINT + (MAXINT - 1); + auto z_min = torch::ones({height, width}, INT64_options) * (int64_t)maxint; + + if (!use_depth_prior) { + for (int i = 0; i < num_faces; ++i) { + rasterizeImagecoordsKernelCPU(V.data_ptr(), F.data_ptr(), 0, + (INT64*)z_min.data_ptr(), occlusion_truncation, width, height, num_vertices, num_faces, i); + } + } else { + for (int i = 0; i < num_faces; ++i) + rasterizeImagecoordsKernelCPU(V.data_ptr(), F.data_ptr(), D.data_ptr(), + (INT64*)z_min.data_ptr(), occlusion_truncation, width, height, num_vertices, num_faces, i); + } + + auto float_options = torch::TensorOptions().dtype(torch::kFloat32).requires_grad(false); + auto barycentric = torch::zeros({height, width, 3}, float_options); + for (int i = 0; i < width * height; ++i) + barycentricFromImgcoordCPU(V.data_ptr(), F.data_ptr(), + findices.data_ptr(), (INT64*)z_min.data_ptr(), width, height, num_vertices, num_faces, barycentric.data_ptr(), i); + + return {findices, barycentric}; +} + +std::vector rasterize_image(torch::Tensor V, torch::Tensor F, torch::Tensor D, + int width, int height, float occlusion_truncation, int use_depth_prior) +{ + int device_id = V.get_device(); + if (device_id == -1) + return rasterize_image_cpu(V, F, D, width, height, occlusion_truncation, use_depth_prior); + else + return rasterize_image_gpu(V, F, D, width, height, occlusion_truncation, use_depth_prior); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("rasterize_image", &rasterize_image, "Custom image rasterization"); + m.def("build_hierarchy", &build_hierarchy, "Custom image rasterization"); + m.def("build_hierarchy_with_feat", &build_hierarchy_with_feat, "Custom image rasterization"); +} diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer.h b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer.h new file mode 100644 index 0000000..cf4f987 --- /dev/null +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer.h @@ -0,0 +1,54 @@ +#ifndef RASTERIZER_H_ +#define RASTERIZER_H_ + +#include +#include +#include +#include // For CUDA context + +#define INT64 unsigned long long +#define MAXINT 2147483647 + +__host__ __device__ inline float calculateSignedArea2(float* a, float* b, float* c) { + return ((c[0] - a[0]) * (b[1] - a[1]) - (b[0] - a[0]) * (c[1] - a[1])); +} + +__host__ __device__ inline void calculateBarycentricCoordinate(float* a, float* b, float* c, float* p, + float* barycentric) +{ + float beta_tri = calculateSignedArea2(a, p, c); + float gamma_tri = calculateSignedArea2(a, b, p); + float area = calculateSignedArea2(a, b, c); + if (area == 0) { + barycentric[0] = -1.0; + barycentric[1] = -1.0; + barycentric[2] = -1.0; + return; + } + float tri_inv = 1.0 / area; + float beta = beta_tri * tri_inv; + float gamma = gamma_tri * tri_inv; + float alpha = 1.0 - beta - gamma; + barycentric[0] = alpha; + barycentric[1] = beta; + barycentric[2] = gamma; +} + +__host__ __device__ inline bool isBarycentricCoordInBounds(float* barycentricCoord) { + return barycentricCoord[0] >= 0.0 && barycentricCoord[0] <= 1.0 && + barycentricCoord[1] >= 0.0 && barycentricCoord[1] <= 1.0 && + barycentricCoord[2] >= 0.0 && barycentricCoord[2] <= 1.0; +} + +std::vector rasterize_image_gpu(torch::Tensor V, torch::Tensor F, torch::Tensor D, + int width, int height, float occlusion_truncation, int use_depth_prior); + +std::vector> build_hierarchy(std::vector view_layer_positions, std::vector view_layer_normals, int num_level, int resolution); + +std::vector> build_hierarchy_with_feat( + std::vector view_layer_positions, + std::vector view_layer_normals, + std::vector view_layer_feats, + int num_level, int resolution); + +#endif \ No newline at end of file diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer_gpu.cu b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer_gpu.cu new file mode 100644 index 0000000..cc6f354 --- /dev/null +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer_gpu.cu @@ -0,0 +1,127 @@ +#include "rasterizer.h" + +__device__ void rasterizeTriangleGPU(int idx, float* vt0, float* vt1, float* vt2, int width, int height, INT64* zbuffer, float* d, float occlusion_truncation) { + float x_min = std::min(vt0[0], std::min(vt1[0],vt2[0])); + float x_max = std::max(vt0[0], std::max(vt1[0],vt2[0])); + float y_min = std::min(vt0[1], std::min(vt1[1],vt2[1])); + float y_max = std::max(vt0[1], std::max(vt1[1],vt2[1])); + + for (int px = x_min; px < x_max + 1; ++px) { + if (px < 0 || px >= width) + continue; + for (int py = y_min; py < y_max + 1; ++py) { + if (py < 0 || py >= height) + continue; + float vt[2] = {px + 0.5f, py + 0.5f}; + float baryCentricCoordinate[3]; + calculateBarycentricCoordinate(vt0, vt1, vt2, vt, baryCentricCoordinate); + if (isBarycentricCoordInBounds(baryCentricCoordinate)) { + int pixel = py * width + px; + if (zbuffer == 0) { + atomicExch(&zbuffer[pixel], (INT64)(idx + 1)); + continue; + } + float depth = baryCentricCoordinate[0] * vt0[2] + baryCentricCoordinate[1] * vt1[2] + baryCentricCoordinate[2] * vt2[2]; + float depth_thres = 0; + if (d) { + depth_thres = d[pixel] * 0.49999f + 0.5f + occlusion_truncation; + } + + int z_quantize = depth * (2<<17); + INT64 token = (INT64)z_quantize * MAXINT + (INT64)(idx + 1); + if (depth < depth_thres) + continue; + atomicMin(&zbuffer[pixel], token); + } + } + } +} + +__global__ void barycentricFromImgcoordGPU(float* V, int* F, int* findices, INT64* zbuffer, int width, int height, int num_vertices, int num_faces, + float* barycentric_map) +{ + int pix = blockIdx.x * blockDim.x + threadIdx.x; + if (pix >= width * height) + return; + INT64 f = zbuffer[pix] % MAXINT; + if (f == (MAXINT-1)) { + findices[pix] = 0; + barycentric_map[pix * 3] = 0; + barycentric_map[pix * 3 + 1] = 0; + barycentric_map[pix * 3 + 2] = 0; + return; + } + findices[pix] = f; + f -= 1; + float barycentric[3] = {0, 0, 0}; + if (f >= 0) { + float vt[2] = {float(pix % width) + 0.5f, float(pix / width) + 0.5f}; + float* vt0_ptr = V + (F[f * 3] * 4); + float* vt1_ptr = V + (F[f * 3 + 1] * 4); + float* vt2_ptr = V + (F[f * 3 + 2] * 4); + + float vt0[2] = {(vt0_ptr[0] / vt0_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt0_ptr[1] / vt0_ptr[3]) * (height - 1) + 0.5f}; + float vt1[2] = {(vt1_ptr[0] / vt1_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt1_ptr[1] / vt1_ptr[3]) * (height - 1) + 0.5f}; + float vt2[2] = {(vt2_ptr[0] / vt2_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt2_ptr[1] / vt2_ptr[3]) * (height - 1) + 0.5f}; + + calculateBarycentricCoordinate(vt0, vt1, vt2, vt, barycentric); + + barycentric[0] = barycentric[0] / vt0_ptr[3]; + barycentric[1] = barycentric[1] / vt1_ptr[3]; + barycentric[2] = barycentric[2] / vt2_ptr[3]; + float w = 1.0f / (barycentric[0] + barycentric[1] + barycentric[2]); + barycentric[0] *= w; + barycentric[1] *= w; + barycentric[2] *= w; + + } + barycentric_map[pix * 3] = barycentric[0]; + barycentric_map[pix * 3 + 1] = barycentric[1]; + barycentric_map[pix * 3 + 2] = barycentric[2]; +} + +__global__ void rasterizeImagecoordsKernelGPU(float* V, int* F, float* d, INT64* zbuffer, float occlusion_trunc, int width, int height, int num_vertices, int num_faces) +{ + int f = blockIdx.x * blockDim.x + threadIdx.x; + if (f >= num_faces) + return; + + float* vt0_ptr = V + (F[f * 3] * 4); + float* vt1_ptr = V + (F[f * 3 + 1] * 4); + float* vt2_ptr = V + (F[f * 3 + 2] * 4); + + float vt0[3] = {(vt0_ptr[0] / vt0_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt0_ptr[1] / vt0_ptr[3]) * (height - 1) + 0.5f, vt0_ptr[2] / vt0_ptr[3] * 0.49999f + 0.5f}; + float vt1[3] = {(vt1_ptr[0] / vt1_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt1_ptr[1] / vt1_ptr[3]) * (height - 1) + 0.5f, vt1_ptr[2] / vt1_ptr[3] * 0.49999f + 0.5f}; + float vt2[3] = {(vt2_ptr[0] / vt2_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt2_ptr[1] / vt2_ptr[3]) * (height - 1) + 0.5f, vt2_ptr[2] / vt2_ptr[3] * 0.49999f + 0.5f}; + + rasterizeTriangleGPU(f, vt0, vt1, vt2, width, height, zbuffer, d, occlusion_trunc); +} + +std::vector rasterize_image_gpu(torch::Tensor V, torch::Tensor F, torch::Tensor D, + int width, int height, float occlusion_truncation, int use_depth_prior) +{ + int device_id = V.get_device(); + cudaSetDevice(device_id); + int num_faces = F.size(0); + int num_vertices = V.size(0); + auto options = torch::TensorOptions().dtype(torch::kInt32).device(torch::kCUDA, device_id).requires_grad(false); + auto INT64_options = torch::TensorOptions().dtype(torch::kInt64).device(torch::kCUDA, device_id).requires_grad(false); + auto findices = torch::zeros({height, width}, options); + INT64 maxint = (INT64)MAXINT * (INT64)MAXINT + (MAXINT - 1); + auto z_min = torch::ones({height, width}, INT64_options) * (int64_t)maxint; + + if (!use_depth_prior) { + rasterizeImagecoordsKernelGPU<<<(num_faces+255)/256,256,0,at::cuda::getCurrentCUDAStream()>>>(V.data_ptr(), F.data_ptr(), 0, + (INT64*)z_min.data_ptr(), occlusion_truncation, width, height, num_vertices, num_faces); + } else { + rasterizeImagecoordsKernelGPU<<<(num_faces+255)/256,256,0,at::cuda::getCurrentCUDAStream()>>>(V.data_ptr(), F.data_ptr(), D.data_ptr(), + (INT64*)z_min.data_ptr(), occlusion_truncation, width, height, num_vertices, num_faces); + } + + auto float_options = torch::TensorOptions().dtype(torch::kFloat32).device(torch::kCUDA, device_id).requires_grad(false); + auto barycentric = torch::zeros({height, width, 3}, float_options); + barycentricFromImgcoordGPU<<<(width * height + 255)/256, 256>>>(V.data_ptr(), F.data_ptr(), + findices.data_ptr(), (INT64*)z_min.data_ptr(), width, height, num_vertices, num_faces, barycentric.data_ptr()); + + return {findices, barycentric}; +} diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/setup.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/setup.py new file mode 100644 index 0000000..3e312a7 --- /dev/null +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/setup.py @@ -0,0 +1,26 @@ +from setuptools import setup, find_packages +from torch.utils.cpp_extension import BuildExtension, CUDAExtension + +# build custom rasterizer +# build with `python setup.py install` +# nvcc is needed + +custom_rasterizer_module = CUDAExtension('custom_rasterizer_kernel', [ + 'lib/custom_rasterizer_kernel/rasterizer.cpp', + 'lib/custom_rasterizer_kernel/grid_neighbor.cpp', + 'lib/custom_rasterizer_kernel/rasterizer_gpu.cu', +]) + +setup( + packages=find_packages(), + version='0.1', + name='custom_rasterizer', + include_package_data=True, + package_dir={'': '.'}, + ext_modules=[ + custom_rasterizer_module, + ], + cmdclass={ + 'build_ext': BuildExtension + } +) diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/__init__.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/__init__.py index e307c3f..8bb2bf8 100644 --- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/__init__.py +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/__init__.py @@ -1,13 +1,3 @@ -# Open Source Model Licensed under the Apache License Version 2.0 -# and Other Licenses of the Third-Party Components therein: -# The below Model in this distribution may have been modified by THL A29 Limited -# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited. - -# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. -# The below software and/or models in this distribution may have been -# modified by THL A29 Limited ("Tencent Modifications"). -# All Tencent Modifications are Copyright (C) THL A29 Limited. - # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined @@ -20,4 +10,4 @@ # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available -# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. \ No newline at end of file diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/camera_utils.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/camera_utils.py index 289710a..b67727c 100644 --- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/camera_utils.py +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/camera_utils.py @@ -1,13 +1,3 @@ -# Open Source Model Licensed under the Apache License Version 2.0 -# and Other Licenses of the Third-Party Components therein: -# The below Model in this distribution may have been modified by THL A29 Limited -# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited. - -# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. -# The below software and/or models in this distribution may have been -# modified by THL A29 Limited ("Tencent Modifications"). -# All Tencent Modifications are Copyright (C) THL A29 Limited. - # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/compile_mesh_painter.sh b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/compile_mesh_painter.sh deleted file mode 100644 index 056be9d..0000000 --- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/compile_mesh_painter.sh +++ /dev/null @@ -1 +0,0 @@ -c++ -O3 -Wall -shared -std=c++11 -fPIC `python3 -m pybind11 --includes` mesh_processor.cpp -o mesh_processor`python3-config --extension-suffix` \ No newline at end of file diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/mesh_processor.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/mesh_processor.py index a96955c..5a731cc 100644 --- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/mesh_processor.py +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/mesh_processor.py @@ -1,3 +1,17 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + import numpy as np def meshVerticeInpaint_smooth(texture, mask, vtx_pos, vtx_uv, pos_idx, uv_idx): diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/mesh_render.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/mesh_render.py index c85b80e..6f83a36 100644 --- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/mesh_render.py +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/mesh_render.py @@ -1,13 +1,3 @@ -# Open Source Model Licensed under the Apache License Version 2.0 -# and Other Licenses of the Third-Party Components therein: -# The below Model in this distribution may have been modified by THL A29 Limited -# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited. - -# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. -# The below software and/or models in this distribution may have been -# modified by THL A29 Limited ("Tencent Modifications"). -# All Tencent Modifications are Copyright (C) THL A29 Limited. - # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined @@ -484,7 +474,7 @@ class MeshRender(): visible_mask = torch.clamp(rast_out[..., -1:], 0, 1) normal = normal * visible_mask + \ torch.tensor(bg_color, dtype=torch.float32, device=self.device) * (1 - - visible_mask) # Mask out background. + visible_mask) if normalize_rgb: normal = (normal + 1) * 0.5 @@ -594,7 +584,7 @@ class MeshRender(): position = position * visible_mask + \ torch.tensor(bg_color, dtype=torch.float32, device=self.device) * (1 - - visible_mask) # Mask out background. + visible_mask) if self.use_antialias: position = self.raster_antialias(position, rast_out, pos_clip, self.pos_idx) diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/mesh_utils.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/mesh_utils.py index ca0ba1a..fa5694a 100644 --- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/mesh_utils.py +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/mesh_utils.py @@ -1,13 +1,3 @@ -# Open Source Model Licensed under the Apache License Version 2.0 -# and Other Licenses of the Third-Party Components therein: -# The below Model in this distribution may have been modified by THL A29 Limited -# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited. - -# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. -# The below software and/or models in this distribution may have been -# modified by THL A29 Limited ("Tencent Modifications"). -# All Tencent Modifications are Copyright (C) THL A29 Limited. - # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/setup.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/setup.py index 902d842..1bfdb10 100644 --- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/setup.py +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/setup.py @@ -1,40 +1,55 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + from setuptools import setup, Extension import pybind11 import sys import platform -# Common compile arguments -common_compile_args = [] +def get_platform_specific_args(): + system = platform.system().lower() + cpp_std = 'c++14' # Make configurable if needed + + if sys.platform == 'win32': + compile_args = ['/O2', f'/std:{cpp_std}', '/EHsc', '/MP', '/DWIN32_LEAN_AND_MEAN', '/bigobj'] + link_args = [] + extra_includes = [] + elif system == 'linux': + compile_args = ['-O3', f'-std={cpp_std}', '-fPIC', '-Wall', '-Wextra', '-pthread'] + link_args = ['-fPIC', '-pthread'] + extra_includes = [] + elif sys.platform == 'darwin': + compile_args = ['-O3', f'-std={cpp_std}', '-fPIC', '-Wall', '-Wextra', + '-stdlib=libc++', '-mmacosx-version-min=10.14'] + link_args = ['-fPIC', '-stdlib=libc++', '-mmacosx-version-min=10.14', '-dynamiclib'] + extra_includes = [] + else: + raise RuntimeError(f"Unsupported platform: {system}") + + return compile_args, link_args, extra_includes -if sys.platform == 'win32': - # Windows-specific compile arguments - extra_compile_args = [ - '/O2', # Optimization level - '/std:c++14', # C++ standard (using C++14 for better compatibility) - '/EHsc', # Exception handling model - '/MP', # Multi-process compilation - '/DWIN32_LEAN_AND_MEAN', # Exclude rarely-used Windows headers - ] - extra_link_args = [] -else: - # Linux/Unix compile arguments - extra_compile_args = [ - '-O3', # Optimization level - '-std=c++14', # C++ standard - '-fPIC', # Position independent code - ] - extra_link_args = ['-fPIC'] +extra_compile_args, extra_link_args, platform_includes = get_platform_specific_args() +include_dirs = [pybind11.get_include(), pybind11.get_include(user=True)] +include_dirs.extend(platform_includes) ext_modules = [ Extension( "mesh_processor", ["mesh_processor.cpp"], - include_dirs=[ - pybind11.get_include(), - pybind11.get_include(user=True) - ], + include_dirs=include_dirs, language='c++', - extra_compile_args=common_compile_args + extra_compile_args, + extra_compile_args=extra_compile_args, extra_link_args=extra_link_args, ), ] diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/hunyuanpaint/__init__.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/hunyuanpaint/__init__.py index e307c3f..8bb2bf8 100644 --- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/hunyuanpaint/__init__.py +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/hunyuanpaint/__init__.py @@ -1,13 +1,3 @@ -# Open Source Model Licensed under the Apache License Version 2.0 -# and Other Licenses of the Third-Party Components therein: -# The below Model in this distribution may have been modified by THL A29 Limited -# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited. - -# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. -# The below software and/or models in this distribution may have been -# modified by THL A29 Limited ("Tencent Modifications"). -# All Tencent Modifications are Copyright (C) THL A29 Limited. - # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined @@ -20,4 +10,4 @@ # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available -# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. \ No newline at end of file diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/hunyuanpaint/pipeline.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/hunyuanpaint/pipeline.py index 436ce34..38f3777 100644 --- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/hunyuanpaint/pipeline.py +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/hunyuanpaint/pipeline.py @@ -1,13 +1,3 @@ -# Open Source Model Licensed under the Apache License Version 2.0 -# and Other Licenses of the Third-Party Components therein: -# The below Model in this distribution may have been modified by THL A29 Limited -# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited. - -# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. -# The below software and/or models in this distribution may have been -# modified by THL A29 Limited ("Tencent Modifications"). -# All Tencent Modifications are Copyright (C) THL A29 Limited. - # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined @@ -29,26 +19,164 @@ import numpy as np import torch import torch.distributed import torch.utils.checkpoint +import transformers from PIL import Image +import diffusers from diffusers import ( AutoencoderKL, + DDPMScheduler, DiffusionPipeline, + EulerAncestralDiscreteScheduler, + UNet2DConditionModel, ImagePipelineOutput ) from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback from diffusers.image_processor import PipelineImageInput from diffusers.image_processor import VaeImageProcessor from diffusers.pipelines.stable_diffusion.pipeline_output import StableDiffusionPipelineOutput -from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipeline, retrieve_timesteps, \ - rescale_noise_cfg -from diffusers.schedulers import KarrasDiffusionSchedulers +from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipeline, \ + retrieve_timesteps, rescale_noise_cfg +from diffusers.schedulers import KarrasDiffusionSchedulers, LCMScheduler from diffusers.utils import deprecate from einops import rearrange -from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer +from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection -from .unet.modules import UNet2p5DConditionModel +from .unet.modules import UNet2p5DConditionModel, \ + compute_multi_resolution_mask, compute_multi_resolution_discrete_voxel_indice + +def guidance_scale_embedding(w, embedding_dim=512, dtype=torch.float32): + """ + See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298 + + Args: + timesteps (`torch.Tensor`): + generate embedding vectors at these timesteps + embedding_dim (`int`, *optional*, defaults to 512): + dimension of the embeddings to generate + dtype: + data type of the generated embeddings + + Returns: + `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)` + """ + assert len(w.shape) == 1 + w = w * 1000.0 + + half_dim = embedding_dim // 2 + emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb) + emb = w.to(dtype)[:, None] * emb[None, :] + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) + if embedding_dim % 2 == 1: # zero pad + emb = torch.nn.functional.pad(emb, (0, 1)) + assert emb.shape == (w.shape[0], embedding_dim) + return emb +def append_dims(x, target_dims): + """Appends dimensions to the end of a tensor until it has target_dims dimensions.""" + dims_to_append = target_dims - x.ndim + if dims_to_append < 0: + raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less") + return x[(...,) + (None,) * dims_to_append] + + +# From LCMScheduler.get_scalings_for_boundary_condition_discrete +def scalings_for_boundary_conditions(timestep, sigma_data=0.5, timestep_scaling=10.0): + scaled_timestep = timestep_scaling * timestep + c_skip = sigma_data ** 2 / (scaled_timestep ** 2 + sigma_data ** 2) + c_out = scaled_timestep / (scaled_timestep ** 2 + sigma_data ** 2) ** 0.5 + return c_skip, c_out + + +# Compare LCMScheduler.step, Step 4 +def get_predicted_original_sample(model_output, timesteps, sample, prediction_type, alphas, sigmas, N_gen): + alphas = extract_into_tensor(alphas, timesteps, sample.shape, N_gen) + sigmas = extract_into_tensor(sigmas, timesteps, sample.shape, N_gen) + model_output = rearrange(model_output, '(b n) c h w -> b n c h w', n=N_gen) + if prediction_type == "epsilon": + pred_x_0 = (sample - sigmas * model_output) / alphas + elif prediction_type == "sample": + pred_x_0 = model_output + elif prediction_type == "v_prediction": + pred_x_0 = alphas * sample - sigmas * model_output + else: + raise ValueError( + f"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`" + f" are supported." + ) + + return pred_x_0 + + +# Based on step 4 in DDIMScheduler.step +def get_predicted_noise(model_output, timesteps, sample, prediction_type, alphas, sigmas, N_gen): + alphas = extract_into_tensor(alphas, timesteps, sample.shape, N_gen) + sigmas = extract_into_tensor(sigmas, timesteps, sample.shape, N_gen) + model_output = rearrange(model_output, '(b n) c h w -> b n c h w', n=N_gen) + if prediction_type == "epsilon": + pred_epsilon = model_output + elif prediction_type == "sample": + pred_epsilon = (sample - alphas * model_output) / sigmas + elif prediction_type == "v_prediction": + pred_epsilon = alphas * model_output + sigmas * sample + else: + raise ValueError( + f"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`" + f" are supported." + ) + + return pred_epsilon + +def extract_into_tensor(a, t, x_shape, N_gen): + # b, *_ = t.shape + out = a.gather(-1, t) + out = out.repeat(N_gen) + out = rearrange(out, '(b n) -> b n', n=N_gen) + b, c, *_ = out.shape + return out.reshape(b, c, *((1,) * (len(x_shape) - 2))) + +class DDIMSolver: + def __init__(self, alpha_cumprods, timesteps=1000, ddim_timesteps=50): + # DDIM sampling parameters + step_ratio = timesteps // ddim_timesteps + self.ddim_timesteps = (np.arange(1, ddim_timesteps + 1) * step_ratio).round().astype(np.int64) - 1 + self.ddim_alpha_cumprods = alpha_cumprods[self.ddim_timesteps] + self.ddim_alpha_cumprods_prev = np.asarray( + [alpha_cumprods[0]] + alpha_cumprods[self.ddim_timesteps[:-1]].tolist() + ) + # convert to torch tensors + self.ddim_timesteps = torch.from_numpy(self.ddim_timesteps).long() + self.ddim_alpha_cumprods = torch.from_numpy(self.ddim_alpha_cumprods) + self.ddim_alpha_cumprods_prev = torch.from_numpy(self.ddim_alpha_cumprods_prev) + + def to(self, device): + self.ddim_timesteps = self.ddim_timesteps.to(device) + self.ddim_alpha_cumprods = self.ddim_alpha_cumprods.to(device) + self.ddim_alpha_cumprods_prev = self.ddim_alpha_cumprods_prev.to(device) + return self + + def ddim_step(self, pred_x0, pred_noise, timestep_index, N_gen): + alpha_cumprod_prev = extract_into_tensor(self.ddim_alpha_cumprods_prev, timestep_index, pred_x0.shape, N_gen) + dir_xt = (1.0 - alpha_cumprod_prev).sqrt() * pred_noise + x_prev = alpha_cumprod_prev.sqrt() * pred_x0 + dir_xt + return x_prev + + +@torch.no_grad() +def update_ema(target_params, source_params, rate=0.99): + """ + Update target parameters to be closer to those of source parameters using + an exponential moving average. + + :param target_params: the target parameter sequence. + :param source_params: the source parameter sequence. + :param rate: the EMA rate (closer to 1 means slower). + """ + + for targ, src in zip(target_params, source_params): + targ.detach().mul_(rate).add_(src, alpha=1 - rate) + def to_rgb_image(maybe_rgba: Image.Image): if maybe_rgba.mode == 'RGB': return maybe_rgba @@ -87,9 +215,18 @@ class HunyuanPaintPipeline(StableDiffusionPipeline): safety_checker=safety_checker, feature_extractor=torch.compile(feature_extractor) if use_torch_compile else feature_extractor, ) + self.solver = DDIMSolver( + scheduler.alphas_cumprod.numpy(), + timesteps=scheduler.config.num_train_timesteps, + ddim_timesteps=30, + ).to('cuda') self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) + self.is_turbo = False + def set_turbo(self, is_turbo: bool): + self.is_turbo = is_turbo + @torch.no_grad() def encode_images(self, images): B = images.shape[0] @@ -119,17 +256,23 @@ class HunyuanPaintPipeline(StableDiffusionPipeline): return_dict=True, **cached_condition, ): + device = self._execution_device + if image is None: raise ValueError("Inputting embeddings not supported for this pipeline. Please pass an image.") assert not isinstance(image, torch.Tensor) - image = to_rgb_image(image) + if not isinstance(image, List): + image = [image] + + image = [to_rgb_image(img) for img in image] - image_vae = torch.tensor(np.array(image) / 255.0) - image_vae = image_vae.unsqueeze(0).permute(0, 3, 1, 2).unsqueeze(0) - image_vae = image_vae.to(device=self.vae.device, dtype=self.vae.dtype) + image_vae = [torch.tensor(np.array(img) / 255.0) for img in image] + image_vae = [img_vae.unsqueeze(0).permute(0, 3, 1, 2).unsqueeze(0) for img_vae in image_vae] + image_vae = torch.cat(image_vae, dim=1) + image_vae = image_vae.to(device=device, dtype=self.vae.dtype) - batch_size = image_vae.shape[0] + batch_size, N_ref = image_vae.shape[0], image_vae.shape[1] assert batch_size == 1 assert num_images_per_prompt == 1 @@ -165,24 +308,34 @@ class HunyuanPaintPipeline(StableDiffusionPipeline): if isinstance(cached_condition["position_imgs"], List): cached_condition["position_imgs"] = convert_pil_list_to_tensor(cached_condition["position_imgs"]) + cached_condition['position_maps'] = cached_condition['position_imgs'] cached_condition["position_imgs"] = self.encode_images(cached_condition["position_imgs"]) if 'camera_info_gen' in cached_condition: camera_info = cached_condition['camera_info_gen'] # B,N if isinstance(camera_info, List): camera_info = torch.tensor(camera_info) - camera_info = camera_info.to(image_vae.device).to(torch.int64) + camera_info = camera_info.to(device).to(torch.int64) cached_condition['camera_info_gen'] = camera_info if 'camera_info_ref' in cached_condition: camera_info = cached_condition['camera_info_ref'] # B,N if isinstance(camera_info, List): camera_info = torch.tensor(camera_info) - camera_info = camera_info.to(image_vae.device).to(torch.int64) + camera_info = camera_info.to(device).to(torch.int64) cached_condition['camera_info_ref'] = camera_info cached_condition['ref_latents'] = ref_latents - if guidance_scale > 1: + if self.is_turbo: + if 'position_maps' in cached_condition: + cached_condition['position_attn_mask'] = ( + compute_multi_resolution_mask(cached_condition['position_maps']) + ) + cached_condition['position_voxel_indices'] = ( + compute_multi_resolution_discrete_voxel_indice(cached_condition['position_maps']) + ) + + if (guidance_scale > 1) and (not self.is_turbo): negative_ref_latents = torch.zeros_like(cached_condition['ref_latents']) cached_condition['ref_latents'] = torch.cat([negative_ref_latents, cached_condition['ref_latents']]) cached_condition['ref_scale'] = torch.as_tensor([0.0, 1.0]).to(cached_condition['ref_latents']) @@ -321,7 +474,8 @@ class HunyuanPaintPipeline(StableDiffusionPipeline): plain tuple. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in - [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). + [`self.processor`] + (https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). guidance_rescale (`float`, *optional*, defaults to 0.0): Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when @@ -356,13 +510,15 @@ class HunyuanPaintPipeline(StableDiffusionPipeline): deprecate( "callback", "1.0.0", - "Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`", + "Passing `callback` as an input argument to `__call__` is deprecated,", + "consider using `callback_on_step_end`", ) if callback_steps is not None: deprecate( "callback_steps", "1.0.0", - "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`", + "Passing `callback_steps` as an input argument to `__call__` is deprecated,", + "consider using `callback_on_step_end`", ) if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)): @@ -412,7 +568,7 @@ class HunyuanPaintPipeline(StableDiffusionPipeline): prompt, device, num_images_per_prompt, - self.do_classifier_free_guidance, + self.do_classifier_free_guidance if self.is_turbo else False, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, @@ -423,7 +579,7 @@ class HunyuanPaintPipeline(StableDiffusionPipeline): # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes - if self.do_classifier_free_guidance: + if (self.do_classifier_free_guidance) and (not self.is_turbo): prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) if ip_adapter_image is not None or ip_adapter_image_embeds is not None: @@ -432,13 +588,21 @@ class HunyuanPaintPipeline(StableDiffusionPipeline): ip_adapter_image_embeds, device, batch_size * num_images_per_prompt, - self.do_classifier_free_guidance, + self.do_classifier_free_guidance if self.is_turbo else False, ) - # 4. Prepare timesteps - timesteps, num_inference_steps = retrieve_timesteps( - self.scheduler, num_inference_steps, device, timesteps, sigmas - ) + # 4. Prepare + if self.is_turbo: + bsz = 3 + N_gen = 15 + index = torch.range(29, 0, -bsz, device='cuda').long() + timesteps = self.solver.ddim_timesteps[index] + self.scheduler.set_timesteps(timesteps=timesteps.cpu(), device='cuda') + else: + timesteps, num_inference_steps = retrieve_timesteps( + self.scheduler, num_inference_steps, device, timesteps, sigmas + ) + assert num_images_per_prompt == 1 # 5. Prepare latent variables num_channels_latents = self.unet.config.in_channels @@ -481,7 +645,11 @@ class HunyuanPaintPipeline(StableDiffusionPipeline): # expand the latents if we are doing classifier free guidance latents = rearrange(latents, '(b n) c h w -> b n c h w', n=kwargs['num_in_batch']) - latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents + latent_model_input = ( + torch.cat([latents] * 2) + if ((self.do_classifier_free_guidance) and (not self.is_turbo)) + else latents + ) latent_model_input = rearrange(latent_model_input, 'b n c h w -> (b n) c h w') latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) latent_model_input = rearrange(latent_model_input, '(b n) c h w ->b n c h w', n=kwargs['num_in_batch']) @@ -499,11 +667,11 @@ class HunyuanPaintPipeline(StableDiffusionPipeline): )[0] latents = rearrange(latents, 'b n c h w -> (b n) c h w') # perform guidance - if self.do_classifier_free_guidance: + if (self.do_classifier_free_guidance) and (not self.is_turbo): noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond) - if self.do_classifier_free_guidance and self.guidance_rescale > 0.0: + if (self.do_classifier_free_guidance) and (self.guidance_rescale > 0.0) and (not self.is_turbo): # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale) diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/hunyuanpaint/unet/__init__.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/hunyuanpaint/unet/__init__.py index e307c3f..8bb2bf8 100644 --- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/hunyuanpaint/unet/__init__.py +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/hunyuanpaint/unet/__init__.py @@ -1,13 +1,3 @@ -# Open Source Model Licensed under the Apache License Version 2.0 -# and Other Licenses of the Third-Party Components therein: -# The below Model in this distribution may have been modified by THL A29 Limited -# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited. - -# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. -# The below software and/or models in this distribution may have been -# modified by THL A29 Limited ("Tencent Modifications"). -# All Tencent Modifications are Copyright (C) THL A29 Limited. - # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined @@ -20,4 +10,4 @@ # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available -# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. \ No newline at end of file diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/hunyuanpaint/unet/modules.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/hunyuanpaint/unet/modules.py index 5d16bc6..f558cd7 100644 --- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/hunyuanpaint/unet/modules.py +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/hunyuanpaint/unet/modules.py @@ -1,13 +1,3 @@ -# Open Source Model Licensed under the Apache License Version 2.0 -# and Other Licenses of the Third-Party Components therein: -# The below Model in this distribution may have been modified by THL A29 Limited -# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited. - -# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. -# The below software and/or models in this distribution may have been -# modified by THL A29 Limited ("Tencent Modifications"). -# All Tencent Modifications are Copyright (C) THL A29 Limited. - # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined @@ -22,14 +12,14 @@ # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. - import copy import json import os -from typing import Any, Dict, Optional +from typing import Any, Dict, List, Optional, Tuple, Union import torch import torch.nn as nn +import torch.nn.functional as F from diffusers.models import UNet2DConditionModel from diffusers.models.attention_processor import Attention from diffusers.models.transformers.transformer_2d import BasicTransformerBlock @@ -40,7 +30,9 @@ def _chunked_feed_forward(ff: nn.Module, hidden_states: torch.Tensor, chunk_dim: # "feed_forward_chunk_size" can be used to save memory if hidden_states.shape[chunk_dim] % chunk_size != 0: raise ValueError( - f"`hidden_states` dimension to be chunked: {hidden_states.shape[chunk_dim]} has to be divisible by chunk size: {chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`." + f"`hidden_states` dimension to be chunked: {hidden_states.shape[chunk_dim]}" + f"has to be divisible by chunk size: {chunk_size}." + f" Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`." ) num_chunks = hidden_states.shape[chunk_dim] // chunk_size @@ -52,12 +44,13 @@ def _chunked_feed_forward(ff: nn.Module, hidden_states: torch.Tensor, chunk_dim: class Basic2p5DTransformerBlock(torch.nn.Module): - def __init__(self, transformer: BasicTransformerBlock, layer_name, use_ma=True, use_ra=True) -> None: + def __init__(self, transformer: BasicTransformerBlock,layer_name,use_ma=True,use_ra=True,is_turbo=False) -> None: super().__init__() self.transformer = transformer self.layer_name = layer_name self.use_ma = use_ma self.use_ra = use_ra + self.is_turbo = is_turbo # multiview attn if self.use_ma: @@ -84,6 +77,23 @@ class Basic2p5DTransformerBlock(torch.nn.Module): upcast_attention=self.attn1.upcast_attention, out_bias=True, ) + if self.is_turbo: + self._initialize_attn_weights() + + def _initialize_attn_weights(self): + + if self.use_ma: + self.attn_multiview.load_state_dict(self.attn1.state_dict()) + with torch.no_grad(): + for layer in self.attn_multiview.to_out: + for param in layer.parameters(): + param.zero_() + if self.use_ra: + self.attn_refview.load_state_dict(self.attn1.state_dict()) + with torch.no_grad(): + for layer in self.attn_refview.to_out: + for param in layer.parameters(): + param.zero_() def __getattr__(self, name: str): try: @@ -110,8 +120,15 @@ class Basic2p5DTransformerBlock(torch.nn.Module): cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {} num_in_batch = cross_attention_kwargs.pop('num_in_batch', 1) mode = cross_attention_kwargs.pop('mode', None) - mva_scale = cross_attention_kwargs.pop('mva_scale', 1.0) - ref_scale = cross_attention_kwargs.pop('ref_scale', 1.0) + if not self.is_turbo: + mva_scale = cross_attention_kwargs.pop('mva_scale', 1.0) + ref_scale = cross_attention_kwargs.pop('ref_scale', 1.0) + else: + position_attn_mask = cross_attention_kwargs.pop("position_attn_mask", None) + position_voxel_indices = cross_attention_kwargs.pop("position_voxel_indices", None) + mva_scale = 1.0 + ref_scale = 1.0 + condition_embed_dict = cross_attention_kwargs.pop("condition_embed_dict", None) if self.norm_type == "ada_norm": @@ -158,8 +175,10 @@ class Basic2p5DTransformerBlock(torch.nn.Module): # 1.2 Reference Attention if 'w' in mode: - condition_embed_dict[self.layer_name] = rearrange(norm_hidden_states, '(b n) l c -> b (n l) c', - n=num_in_batch) # B, (N L), C + condition_embed_dict[self.layer_name] = rearrange( + norm_hidden_states, '(b n) l c -> b (n l) c', + n=num_in_batch + ) # B, (N L), C if 'r' in mode and self.use_ra: condition_embed = condition_embed_dict[self.layer_name].unsqueeze(1).repeat(1, num_in_batch, 1, @@ -172,12 +191,15 @@ class Basic2p5DTransformerBlock(torch.nn.Module): attention_mask=None, **cross_attention_kwargs ) - ref_scale_timing = ref_scale - if isinstance(ref_scale, torch.Tensor): - ref_scale_timing = ref_scale.unsqueeze(1).repeat(1, num_in_batch).view(-1) - for _ in range(attn_output.ndim - 1): - ref_scale_timing = ref_scale_timing.unsqueeze(-1) + if not self.is_turbo: + ref_scale_timing = ref_scale + if isinstance(ref_scale, torch.Tensor): + ref_scale_timing = ref_scale.unsqueeze(1).repeat(1, num_in_batch).view(-1) + for _ in range(attn_output.ndim - 1): + ref_scale_timing = ref_scale_timing.unsqueeze(-1) + hidden_states = ref_scale_timing * attn_output + hidden_states + if hidden_states.ndim == 4: hidden_states = hidden_states.squeeze(1) @@ -185,14 +207,31 @@ class Basic2p5DTransformerBlock(torch.nn.Module): if num_in_batch > 1 and self.use_ma: multivew_hidden_states = rearrange(norm_hidden_states, '(b n) l c -> b (n l) c', n=num_in_batch) - attn_output = self.attn_multiview( - multivew_hidden_states, - encoder_hidden_states=multivew_hidden_states, - **cross_attention_kwargs - ) + if self.is_turbo: + position_mask = None + if position_attn_mask is not None: + if multivew_hidden_states.shape[1] in position_attn_mask: + position_mask = position_attn_mask[multivew_hidden_states.shape[1]] + position_indices = None + if position_voxel_indices is not None: + if multivew_hidden_states.shape[1] in position_voxel_indices: + position_indices = position_voxel_indices[multivew_hidden_states.shape[1]] + attn_output = self.attn_multiview( + multivew_hidden_states, + encoder_hidden_states=multivew_hidden_states, + attention_mask=position_mask, + position_indices=position_indices, + **cross_attention_kwargs + ) + else: + attn_output = self.attn_multiview( + multivew_hidden_states, + encoder_hidden_states=multivew_hidden_states, + **cross_attention_kwargs + ) attn_output = rearrange(attn_output, 'b (n l) c -> (b n) l c', n=num_in_batch) - + hidden_states = mva_scale * attn_output + hidden_states if hidden_states.ndim == 4: hidden_states = hidden_states.squeeze(1) @@ -259,6 +298,108 @@ class Basic2p5DTransformerBlock(torch.nn.Module): return hidden_states +@torch.no_grad() +def compute_voxel_grid_mask(position, grid_resolution=8): + + position = position.half() + B,N,_,H,W = position.shape + assert H%grid_resolution==0 and W%grid_resolution==0 + + valid_mask = (position != 1).all(dim=2, keepdim=True) + valid_mask = valid_mask.expand_as(position) + position[valid_mask==False] = 0 + + + position = rearrange( + position, + 'b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w', + num_h=grid_resolution, num_w=grid_resolution + ) + valid_mask = rearrange( + valid_mask, + 'b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w', + num_h=grid_resolution, num_w=grid_resolution + ) + + grid_position = position.sum(dim=(-2, -1)) + count_masked = valid_mask.sum(dim=(-2, -1)) + + grid_position = grid_position / count_masked.clamp(min=1) + grid_position[count_masked<5] = 0 + + grid_position = grid_position.permute(0,1,4,2,3) + grid_position = rearrange(grid_position, 'b n c h w -> b n (h w) c') + + grid_position_expanded_1 = grid_position.unsqueeze(2).unsqueeze(4) # 形状变为 B, N, 1, L, 1, 3 + grid_position_expanded_2 = grid_position.unsqueeze(1).unsqueeze(3) # 形状变为 B, 1, N, 1, L, 3 + + # 计算欧氏距离 + distances = torch.norm(grid_position_expanded_1 - grid_position_expanded_2, dim=-1) # 形状为 B, N, N, L, L + + weights = distances + grid_distance = 1.73/grid_resolution + + #weights = weights*-32 + #weights = weights.clamp(min=-10000.0) + + weights = weights< grid_distance + + return weights + +def compute_multi_resolution_mask(position_maps, grid_resolutions=[32, 16, 8]): + position_attn_mask = {} + with torch.no_grad(): + for grid_resolution in grid_resolutions: + position_mask = compute_voxel_grid_mask(position_maps, grid_resolution) + position_mask = rearrange(position_mask, 'b ni nj li lj -> b (ni li) (nj lj)') + position_attn_mask[position_mask.shape[1]] = position_mask + return position_attn_mask + +@torch.no_grad() +def compute_discrete_voxel_indice(position, grid_resolution=8, voxel_resolution=128): + + position = position.half() + B,N,_,H,W = position.shape + assert H%grid_resolution==0 and W%grid_resolution==0 + + valid_mask = (position != 1).all(dim=2, keepdim=True) + valid_mask = valid_mask.expand_as(position) + position[valid_mask==False] = 0 + + position = rearrange( + position, + 'b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w', + num_h=grid_resolution, num_w=grid_resolution + ) + valid_mask = rearrange( + valid_mask, + 'b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w', + num_h=grid_resolution, num_w=grid_resolution + ) + + grid_position = position.sum(dim=(-2, -1)) + count_masked = valid_mask.sum(dim=(-2, -1)) + + grid_position = grid_position / count_masked.clamp(min=1) + grid_position[count_masked<5] = 0 + + grid_position = grid_position.permute(0,1,4,2,3).clamp(0, 1) # B N C H W + voxel_indices = grid_position * (voxel_resolution - 1) + voxel_indices = torch.round(voxel_indices).long() + return voxel_indices + +def compute_multi_resolution_discrete_voxel_indice( + position_maps, + grid_resolutions=[64, 32, 16, 8], + voxel_resolutions=[512, 256, 128, 64] +): + voxel_indices = {} + with torch.no_grad(): + for grid_resolution, voxel_resolution in zip(grid_resolutions, voxel_resolutions): + voxel_indice = compute_discrete_voxel_indice(position_maps, grid_resolution, voxel_resolution) + voxel_indice = rearrange(voxel_indice, 'b n c h w -> b (n h w) c') + voxel_indices[voxel_indice.shape[1]] = {'voxel_indices':voxel_indice, 'voxel_resolution':voxel_resolution} + return voxel_indices class UNet2p5DConditionModel(torch.nn.Module): def __init__(self, unet: UNet2DConditionModel) -> None: @@ -269,11 +410,12 @@ class UNet2p5DConditionModel(torch.nn.Module): self.use_ra = True self.use_camera_embedding = True self.use_dual_stream = True + self.is_turbo = False if self.use_dual_stream: self.unet_dual = copy.deepcopy(unet) self.init_attention(self.unet_dual) - self.init_attention(self.unet, use_ma=self.use_ma, use_ra=self.use_ra) + self.init_attention(self.unet, use_ma=self.use_ma, use_ra=self.use_ra, is_turbo=self.is_turbo) self.init_condition() self.init_camera_embedding() @@ -313,33 +455,39 @@ class UNet2p5DConditionModel(torch.nn.Module): self.max_num_gen_image = 12 * 3 + 4 * 2 self.unet.class_embedding = nn.Embedding(self.max_num_ref_image + self.max_num_gen_image, time_embed_dim) - def init_attention(self, unet, use_ma=False, use_ra=False): + def init_attention(self, unet, use_ma=False, use_ra=False, is_turbo=False): for down_block_i, down_block in enumerate(unet.down_blocks): if hasattr(down_block, "has_cross_attention") and down_block.has_cross_attention: for attn_i, attn in enumerate(down_block.attentions): for transformer_i, transformer in enumerate(attn.transformer_blocks): if isinstance(transformer, BasicTransformerBlock): - attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock(transformer, - f'down_{down_block_i}_{attn_i}_{transformer_i}', - use_ma, use_ra) + attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock( + transformer, + f'down_{down_block_i}_{attn_i}_{transformer_i}', + use_ma, use_ra, is_turbo + ) if hasattr(unet.mid_block, "has_cross_attention") and unet.mid_block.has_cross_attention: for attn_i, attn in enumerate(unet.mid_block.attentions): for transformer_i, transformer in enumerate(attn.transformer_blocks): if isinstance(transformer, BasicTransformerBlock): - attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock(transformer, - f'mid_{attn_i}_{transformer_i}', - use_ma, use_ra) + attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock( + transformer, + f'mid_{attn_i}_{transformer_i}', + use_ma, use_ra, is_turbo + ) for up_block_i, up_block in enumerate(unet.up_blocks): if hasattr(up_block, "has_cross_attention") and up_block.has_cross_attention: for attn_i, attn in enumerate(up_block.attentions): for transformer_i, transformer in enumerate(attn.transformer_blocks): if isinstance(transformer, BasicTransformerBlock): - attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock(transformer, - f'up_{up_block_i}_{attn_i}_{transformer_i}', - use_ma, use_ra) + attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock( + transformer, + f'up_{up_block_i}_{attn_i}_{transformer_i}', + use_ma, use_ra, is_turbo + ) def __getattr__(self, name: str): try: @@ -416,6 +564,22 @@ class UNet2p5DConditionModel(torch.nn.Module): mva_scale = cached_condition.get('mva_scale', 1.0) ref_scale = cached_condition.get('ref_scale', 1.0) + if self.is_turbo: + cross_attention_kwargs_ = { + 'mode': 'r', 'num_in_batch': N_gen, + 'condition_embed_dict': condition_embed_dict, + 'position_attn_mask':position_attn_mask, + 'position_voxel_indices':position_voxel_indices, + 'mva_scale': mva_scale, + 'ref_scale': ref_scale, + } + else: + cross_attention_kwargs_ = { + 'mode': 'r', 'num_in_batch': N_gen, + 'condition_embed_dict': condition_embed_dict, + 'mva_scale': mva_scale, + 'ref_scale': ref_scale, + } return self.unet( sample, timestep, encoder_hidden_states_gen, *args, @@ -431,10 +595,5 @@ class UNet2p5DConditionModel(torch.nn.Module): if mid_block_res_sample is not None else None ), return_dict=False, - cross_attention_kwargs={ - 'mode': 'r', 'num_in_batch': N_gen, - 'condition_embed_dict': condition_embed_dict, - 'mva_scale': mva_scale, - 'ref_scale': ref_scale, - }, + cross_attention_kwargs=cross_attention_kwargs_, ) diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/pipelines.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/pipelines.py index 00bd8da..9f82240 100644 --- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/pipelines.py +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/pipelines.py @@ -1,13 +1,3 @@ -# Open Source Model Licensed under the Apache License Version 2.0 -# and Other Licenses of the Third-Party Components therein: -# The below Model in this distribution may have been modified by THL A29 Limited -# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited. - -# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. -# The below software and/or models in this distribution may have been -# modified by THL A29 Limited ("Tencent Modifications"). -# All Tencent Modifications are Copyright (C) THL A29 Limited. - # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined @@ -24,17 +14,17 @@ import logging -import os - import numpy as np +import os import torch from PIL import Image +from typing import List, Union, Optional -import comfy.utils from .differentiable_renderer.mesh_render import MeshRender from .utils.dehighlight_utils import Light_Shadow_Remover from .utils.multiview_utils import Multiview_Diffusion_Net +from .utils.imagesuper_utils import Image_Super_Net from .utils.uv_warp_utils import mesh_uv_wrap logger = logging.getLogger(__name__) @@ -42,7 +32,7 @@ logger = logging.getLogger(__name__) class Hunyuan3DTexGenConfig: - def __init__(self, light_remover_ckpt_path, multiview_ckpt_path): + def __init__(self, light_remover_ckpt_path, multiview_ckpt_path, subfolder_name): self.device = 'cuda' self.light_remover_ckpt_path = light_remover_ckpt_path self.multiview_ckpt_path = multiview_ckpt_path @@ -52,23 +42,48 @@ class Hunyuan3DTexGenConfig: self.candidate_view_weights = [1, 0.1, 0.5, 0.1, 0.05, 0.05] self.render_size = 2048 - self.texture_size = 1024 + self.texture_size = 2048 self.bake_exp = 4 self.merge_method = 'fast' + self.pipe_dict = {'hunyuan3d-paint-v2-0': 'hunyuanpaint', 'hunyuan3d-paint-v2-0-turbo': 'hunyuanpaint-turbo'} + self.pipe_name = self.pipe_dict[subfolder_name] + class Hunyuan3DPaintPipeline: @classmethod - def from_pretrained(cls, model_path, device='cuda', dtype=torch.float16, **kwargs): - # try local path - delight_model_path = os.path.join(model_path, 'hunyuan3d-delight-v2-0') - multiview_model_path = os.path.join(model_path, 'hunyuan3d-paint-v2-0') - - if not (os.path.exists(delight_model_path) and os.path.exists(multiview_model_path)): - raise FileNotFoundError(f"Model path {delight_model_path} or {multiview_model_path} not found") + def from_pretrained(cls, model_path, subfolder='hunyuan3d-paint-v2-0-turbo'): + original_model_path = model_path + if not os.path.exists(model_path): + # try local path + base_dir = os.environ.get('HY3DGEN_MODELS', '~/.cache/hy3dgen') + model_path = os.path.expanduser(os.path.join(base_dir, model_path)) - return cls(Hunyuan3DTexGenConfig(delight_model_path, multiview_model_path)) + delight_model_path = os.path.join(model_path, 'hunyuan3d-delight-v2-0') + multiview_model_path = os.path.join(model_path, subfolder) + if not os.path.exists(delight_model_path) or not os.path.exists(multiview_model_path): + try: + import huggingface_hub + # download from huggingface + model_path = huggingface_hub.snapshot_download( + repo_id=original_model_path, allow_patterns=["hunyuan3d-delight-v2-0/*"] + ) + model_path = huggingface_hub.snapshot_download( + repo_id=original_model_path, allow_patterns=[f'{subfolder}/*'] + ) + delight_model_path = os.path.join(model_path, 'hunyuan3d-delight-v2-0') + multiview_model_path = os.path.join(model_path, subfolder) + return cls(Hunyuan3DTexGenConfig(delight_model_path, multiview_model_path, subfolder)) + except ImportError: + logger.warning( + "You need to install HuggingFace Hub to load models from the hub." + ) + raise RuntimeError(f"Model path {model_path} not found") + else: + return cls(Hunyuan3DTexGenConfig(delight_model_path, multiview_model_path, subfolder)) + + raise FileNotFoundError(f"Model path {original_model_path} not found and we could not find it at huggingface") def __init__(self, config): self.config = config @@ -78,6 +93,16 @@ class Hunyuan3DPaintPipeline: texture_size=self.config.texture_size) self.load_models() + + def to(self, device=None, dtype=None): # ⬅️  тот же уровень, что и __init__ + if device is not None: + for m in self.models.values(): + m.pipeline.to(device) + self.render.to(device) + if dtype is not None: + for m in self.models.values(): + m.pipeline.to(dtype=dtype) + return self def load_models(self): # empty cude cache @@ -85,16 +110,11 @@ class Hunyuan3DPaintPipeline: # Load model self.models['delight_model'] = Light_Shadow_Remover(self.config) self.models['multiview_model'] = Multiview_Diffusion_Net(self.config) - - def to(self, device=None, dtype=None): - if device is not None: - for model in self.models.values(): - model.pipeline.to(device) - if dtype is not None: - for model in self.models.values(): - model.pipeline.to(dtype=dtype) + # self.models['super_model'] = Image_Super_Net(self.config) - return self + def enable_model_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = "cuda"): + self.models['delight_model'].pipeline.enable_model_cpu_offload(gpu_id=gpu_id, device=device) + self.models['multiview_model'].pipeline.enable_model_cpu_offload(gpu_id=gpu_id, device=device) def render_normal_multiview(self, camera_elevs, camera_azims, use_abs_coor=True): normal_maps = [] @@ -140,7 +160,7 @@ class Hunyuan3DPaintPipeline: texture = torch.tensor(texture_np / 255).float().to(texture.device) return texture - + def recenter_image(self, image, border_ratio=0.2): if image.mode == 'RGB': return image @@ -178,26 +198,25 @@ class Hunyuan3DPaintPipeline: @torch.no_grad() def __call__(self, mesh, image): - comfy_pbar = comfy.utils.ProgressBar(4) - step_num_i = 0 + if not isinstance(image, List): + image = [image] - print("Load image and predict delight") - if isinstance(image, str): - image_prompt = Image.open(image) - else: - image_prompt = image + images_prompt = [] + for i in range(len(image)): + if isinstance(image[i], str): + image_prompt = Image.open(image[i]) + else: + image_prompt = image[i] + images_prompt.append(image_prompt) + + images_prompt = [self.recenter_image(image_prompt) for image_prompt in images_prompt] - image_prompt = self.recenter_image(image_prompt) - image_prompt = self.models['delight_model'](image_prompt) - - step_num_i += 1 - comfy_pbar.update_absolute(step_num_i) + images_prompt = [self.models['delight_model'](image_prompt) for image_prompt in images_prompt] mesh = mesh_uv_wrap(mesh) - print("Rendering normal and position maps of the mesh") self.render.load_mesh(mesh) - + selected_camera_elevs, selected_camera_azims, selected_view_weights = \ self.config.candidate_camera_elevs, self.config.candidate_camera_azims, self.config.candidate_view_weights @@ -205,32 +224,21 @@ class Hunyuan3DPaintPipeline: selected_camera_elevs, selected_camera_azims, use_abs_coor=True) position_maps = self.render_position_multiview( selected_camera_elevs, selected_camera_azims) - - step_num_i += 1 - comfy_pbar.update_absolute(step_num_i) - print("Predicting multiview texture") camera_info = [(((azim // 30) + 9) % 12) // {-20: 1, 0: 1, 20: 1, -90: 3, 90: 3}[ elev] + {-20: 0, 0: 12, 20: 24, -90: 36, 90: 40}[elev] for azim, elev in zip(selected_camera_azims, selected_camera_elevs)] - multiviews = self.models['multiview_model'](image_prompt, normal_maps + position_maps, camera_info) + multiviews = self.models['multiview_model'](images_prompt, normal_maps + position_maps, camera_info) for i in range(len(multiviews)): + # multiviews[i] = self.models['super_model'](multiviews[i]) multiviews[i] = multiviews[i].resize( (self.config.render_size, self.config.render_size)) - - step_num_i += 1 - comfy_pbar.update_absolute(step_num_i) - print("Baking texture on to the mesh") texture, mask = self.bake_from_multiview(multiviews, selected_camera_elevs, selected_camera_azims, selected_view_weights, method=self.config.merge_method) - step_num_i += 1 - comfy_pbar.update_absolute(step_num_i) - - print("Inpainting texture") mask_np = (mask.squeeze(-1).cpu().numpy() * 255).astype(np.uint8) texture = self.texture_inpaint(texture, mask_np) @@ -238,7 +246,4 @@ class Hunyuan3DPaintPipeline: self.render.set_texture(texture) textured_mesh = self.render.save_mesh() - step_num_i += 1 - comfy_pbar.update_absolute(step_num_i) - return textured_mesh diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/__init__.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/__init__.py index e307c3f..8bb2bf8 100644 --- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/__init__.py +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/__init__.py @@ -1,13 +1,3 @@ -# Open Source Model Licensed under the Apache License Version 2.0 -# and Other Licenses of the Third-Party Components therein: -# The below Model in this distribution may have been modified by THL A29 Limited -# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited. - -# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. -# The below software and/or models in this distribution may have been -# modified by THL A29 Limited ("Tencent Modifications"). -# All Tencent Modifications are Copyright (C) THL A29 Limited. - # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined @@ -20,4 +10,4 @@ # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available -# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. \ No newline at end of file diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/alignImg4Tex_utils.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/alignImg4Tex_utils.py index 0a09c17..34df204 100644 --- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/alignImg4Tex_utils.py +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/alignImg4Tex_utils.py @@ -1,13 +1,3 @@ -# Open Source Model Licensed under the Apache License Version 2.0 -# and Other Licenses of the Third-Party Components therein: -# The below Model in this distribution may have been modified by THL A29 Limited -# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited. - -# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. -# The below software and/or models in this distribution may have been -# modified by THL A29 Limited ("Tencent Modifications"). -# All Tencent Modifications are Copyright (C) THL A29 Limited. - # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined @@ -22,7 +12,6 @@ # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. - import torch from diffusers import EulerAncestralDiscreteScheduler from diffusers import StableDiffusionControlNetPipeline, StableDiffusionXLControlNetImg2ImgPipeline, ControlNetModel, \ diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/counter_utils.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/counter_utils.py index e0374fc..383a515 100644 --- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/counter_utils.py +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/counter_utils.py @@ -1,13 +1,3 @@ -# Open Source Model Licensed under the Apache License Version 2.0 -# and Other Licenses of the Third-Party Components therein: -# The below Model in this distribution may have been modified by THL A29 Limited -# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited. - -# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. -# The below software and/or models in this distribution may have been -# modified by THL A29 Limited ("Tencent Modifications"). -# All Tencent Modifications are Copyright (C) THL A29 Limited. - # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/dehighlight_utils.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/dehighlight_utils.py index d9724ae..9b52368 100644 --- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/dehighlight_utils.py +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/dehighlight_utils.py @@ -1,13 +1,3 @@ -# Open Source Model Licensed under the Apache License Version 2.0 -# and Other Licenses of the Third-Party Components therein: -# The below Model in this distribution may have been modified by THL A29 Limited -# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited. - -# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. -# The below software and/or models in this distribution may have been -# modified by THL A29 Limited ("Tencent Modifications"). -# All Tencent Modifications are Copyright (C) THL A29 Limited. - # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined @@ -44,6 +34,36 @@ class Light_Shadow_Remover(): pipeline.set_progress_bar_config(disable=True) self.pipeline = pipeline.to(self.device, torch.float16) + + def recorrect_rgb(self, src_image, target_image, alpha_channel, scale=0.95): + + def flat_and_mask(bgr, a): + mask = torch.where(a > 0.5, True, False) + bgr_flat = bgr.reshape(-1, bgr.shape[-1]) + mask_flat = mask.reshape(-1) + bgr_flat_masked = bgr_flat[mask_flat, :] + return bgr_flat_masked + + src_flat = flat_and_mask(src_image, alpha_channel) + target_flat = flat_and_mask(target_image, alpha_channel) + corrected_bgr = torch.zeros_like(src_image) + + for i in range(3): + src_mean, src_stddev = torch.mean(src_flat[:, i]), torch.std(src_flat[:, i]) + target_mean, target_stddev = torch.mean(target_flat[:, i]), torch.std(target_flat[:, i]) + corrected_bgr[:, :, i] = torch.clamp( + (src_image[:, :, i] - scale * src_mean) * + (target_stddev / src_stddev) + scale * target_mean, + 0, 1) + + src_mse = torch.mean((src_image - target_image) ** 2) + modify_mse = torch.mean((corrected_bgr - target_image) ** 2) + if src_mse < modify_mse: + corrected_bgr = torch.cat([src_image, alpha_channel], dim=-1) + else: + corrected_bgr = torch.cat([corrected_bgr, alpha_channel], dim=-1) + + return corrected_bgr @torch.no_grad() def __call__(self, image): @@ -81,4 +101,10 @@ class Light_Shadow_Remover(): guidance_scale=self.cfg_text, ).images[0] + image_tensor = torch.tensor(np.array(image)/255.0).to(self.device) + rgb_src = image_tensor[:,:,:3] + image = self.recorrect_rgb(rgb_src, rgb_target, alpha) + image = image[:,:,:3]*image[:,:,3:] + torch.ones_like(image[:,:,:3])*(1.0-image[:,:,3:]) + image = Image.fromarray((image.cpu().numpy()*255).astype(np.uint8)) + return image diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/imagesuper_utils.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/imagesuper_utils.py new file mode 100644 index 0000000..0b893c5 --- /dev/null +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/imagesuper_utils.py @@ -0,0 +1,34 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +import torch +from diffusers import StableDiffusionUpscalePipeline + +class Image_Super_Net(): + def __init__(self, config): + self.up_pipeline_x4 = StableDiffusionUpscalePipeline.from_pretrained( + 'stabilityai/stable-diffusion-x4-upscaler', + torch_dtype=torch.float16, + ).to(config.device) + self.up_pipeline_x4.set_progress_bar_config(disable=True) + + def __call__(self, image, prompt=''): + with torch.no_grad(): + upscaled_image = self.up_pipeline_x4( + prompt=[prompt], + image=image, + num_inference_steps=5, + ).images[0] + + return upscaled_image diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/multiview_utils.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/multiview_utils.py index de10b6b..4d6a6ba 100644 --- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/multiview_utils.py +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/multiview_utils.py @@ -1,13 +1,3 @@ -# Open Source Model Licensed under the Apache License Version 2.0 -# and Other Licenses of the Third-Party Components therein: -# The below Model in this distribution may have been modified by THL A29 Limited -# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited. - -# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. -# The below software and/or models in this distribution may have been -# modified by THL A29 Limited ("Tencent Modifications"). -# All Tencent Modifications are Copyright (C) THL A29 Limited. - # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined @@ -27,8 +17,9 @@ import random import numpy as np import torch +from typing import List from diffusers import DiffusionPipeline -from diffusers import EulerAncestralDiscreteScheduler +from diffusers import EulerAncestralDiscreteScheduler, LCMScheduler class Multiview_Diffusion_Net(): @@ -44,8 +35,14 @@ class Multiview_Diffusion_Net(): multiview_ckpt_path, custom_pipeline=custom_pipeline_path, torch_dtype=torch.float16) - pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config, - timestep_spacing='trailing') + if config.pipe_name in ['hunyuanpaint']: + pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config, + timestep_spacing='trailing') + elif config.pipe_name in ['hunyuanpaint-turbo']: + pipeline.scheduler = LCMScheduler.from_config(pipeline.scheduler.config, + timestep_spacing='trailing') + pipeline.set_turbo(True) + # pipeline.prepare() pipeline.set_progress_bar_config(disable=True) self.pipeline = pipeline.to(self.device) @@ -56,11 +53,14 @@ class Multiview_Diffusion_Net(): torch.manual_seed(seed) os.environ["PL_GLOBAL_SEED"] = str(seed) - def __call__(self, input_image, control_images, camera_info): + def __call__(self, input_images, control_images, camera_info): self.seed_everything(0) - input_image = input_image.resize((self.view_size, self.view_size)) + if not isinstance(input_images, List): + input_images = [input_images] + + input_images = [input_image.resize((self.view_size, self.view_size)) for input_image in input_images] for i in range(len(control_images)): control_images[i] = control_images[i].resize((self.view_size, self.view_size)) if control_images[i].mode == 'L': @@ -82,5 +82,6 @@ class Multiview_Diffusion_Net(): kwargs["normal_imgs"] = normal_image kwargs["position_imgs"] = position_image - mvd_image = self.pipeline(input_image, num_inference_steps=30, **kwargs).images + mvd_image = self.pipeline(input_images, num_inference_steps=30, **kwargs).images + return mvd_image diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/simplify_mesh_utils.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/simplify_mesh_utils.py index 915284d..5c23999 100644 --- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/simplify_mesh_utils.py +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/simplify_mesh_utils.py @@ -1,13 +1,3 @@ -# Open Source Model Licensed under the Apache License Version 2.0 -# and Other Licenses of the Third-Party Components therein: -# The below Model in this distribution may have been modified by THL A29 Limited -# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited. - -# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. -# The below software and/or models in this distribution may have been -# modified by THL A29 Limited ("Tencent Modifications"). -# All Tencent Modifications are Copyright (C) THL A29 Limited. - # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/uv_warp_utils.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/uv_warp_utils.py index 00d2a5d..f55a924 100644 --- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/uv_warp_utils.py +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/uv_warp_utils.py @@ -1,13 +1,3 @@ -# Open Source Model Licensed under the Apache License Version 2.0 -# and Other Licenses of the Third-Party Components therein: -# The below Model in this distribution may have been modified by THL A29 Limited -# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited. - -# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. -# The below software and/or models in this distribution may have been -# modified by THL A29 Limited ("Tencent Modifications"). -# All Tencent Modifications are Copyright (C) THL A29 Limited. - # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined @@ -30,6 +20,9 @@ def mesh_uv_wrap(mesh): if isinstance(mesh, trimesh.Scene): mesh = mesh.dump(concatenate=True) + if len(mesh.faces) > 500000000: + raise ValueError("The mesh has more than 500,000,000 faces, which is not supported.") + vmapping, indices, uvs = xatlas.parametrize(mesh.vertices, mesh.faces) mesh.vertices = mesh.vertices[vmapping] diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/text2image.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/text2image.py index 405833e..2c8a3ab 100644 --- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/text2image.py +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/text2image.py @@ -1,13 +1,3 @@ -# Open Source Model Licensed under the Apache License Version 2.0 -# and Other Licenses of the Third-Party Components therein: -# The below Model in this distribution may have been modified by THL A29 Limited -# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited. - -# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. -# The below software and/or models in this distribution may have been -# modified by THL A29 Limited ("Tencent Modifications"). -# All Tencent Modifications are Copyright (C) THL A29 Limited. - # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined @@ -22,7 +12,6 @@ # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. - import os import random diff --git a/Gen_3D_Modules/Hunyuan3D_V2/requirements.txt b/Gen_3D_Modules/Hunyuan3D_V2/requirements.txt new file mode 100644 index 0000000..abdab84 --- /dev/null +++ b/Gen_3D_Modules/Hunyuan3D_V2/requirements.txt @@ -0,0 +1,40 @@ +ninja +pybind11 + +diffusers +einops +opencv-python +numpy +torch +transformers +torchvision +#taming-transformers-rom1504 +#ConfigArgParse +#ipdb +omegaconf + +#sentencepiece +tqdm + +# Mesh Processing +trimesh +pymeshlab +pygltflib +xatlas +#kornia +#facexlib + +# Training +accelerate +#pytorch_lightning +#scikit-learn +#scikit-image + +# Demo only +gradio +fastapi +uvicorn +rembg +onnxruntime +#gevent +#geventhttpclient \ No newline at end of file diff --git a/Gen_3D_Modules/Hunyuan3D_V2/setup.py b/Gen_3D_Modules/Hunyuan3D_V2/setup.py new file mode 100644 index 0000000..5339da1 --- /dev/null +++ b/Gen_3D_Modules/Hunyuan3D_V2/setup.py @@ -0,0 +1,46 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +from setuptools import setup, find_packages + +setup( + name="hy3dgen", + version="2.0.2", + url="https://github.com/Tencent/Hunyuan3D-2", + packages=find_packages(), + include_package_data=True, + package_data={"hy3dgen": ["assets/*", "assets/**/*"]}, + install_requires=[ + 'gradio', + "tqdm>=4.66.3", + 'numpy', + 'ninja', + 'diffusers', + 'pybind11', + 'opencv-python', + 'einops', + "transformers>=4.48.0", + 'omegaconf', + 'trimesh', + 'pymeshlab', + 'pygltflib', + 'xatlas', + 'accelerate', + 'gradio', + 'fastapi', + 'uvicorn', + 'rembg', + 'onnxruntime' + ] +) diff --git a/README.md b/README.md index f6fe816..1280a0e 100644 --- a/README.md +++ b/README.md @@ -46,6 +46,10 @@ Your ComfyUI Root Directory\python_embeded\python.exe install.py - For use cases please check out [Example Workflows](./_Example_Workflows/). [**Last update: 29/April/2025**] - **Note:** you need to put [Example Inputs Files & Folders](_Example_Workflows/_Example_Inputs_Files/) under ComfyUI Root Directory\ComfyUI\input folder before you can run the example workflow +- **Hunyuan3D_V2**: turbo, mini, fast + - Single image to 3D Mesh + - Model weights: https://huggingface.co/tencent/Hunyuan3D-2, https://huggingface.co/tencent/Hunyuan3D-2mini + - **TripoSG**: [VAST-AI-Research/TripoSG](https://github.com/VAST-AI-Research/TripoSG) - Single image (Reference or Scribble) to 3D Mesh - Model weights: https://huggingface.co/VAST-AI/TripoSG, https://huggingface.co/VAST-AI/TripoSG-scribble diff --git a/_Example_Workflows/Hunyuan3D_V2_Turbo.json b/_Example_Workflows/Hunyuan3D_V2_Turbo.json new file mode 100644 index 0000000..93fbab1 --- /dev/null +++ b/_Example_Workflows/Hunyuan3D_V2_Turbo.json @@ -0,0 +1,517 @@ +{ + "id": "c466dde6-0bf8-4e82-b5b4-536d8f5ed7d4", + "revision": 0, + "last_node_id": 56, + "last_link_id": 99, + "nodes": [ + { + "id": 13, + "type": "LoadImage", + "pos": [ + 207.519287109375, + 475.5122375488281 + ], + "size": [ + 362.0579528808594, + 476.63336181640625 + ], + "flags": {}, + "order": 0, + "mode": 0, + "inputs": [], + "outputs": [ + { + "name": "IMAGE", + "type": "IMAGE", + "slot_index": 0, + "links": [ + 91, + 97 + ] + }, + { + "name": "MASK", + "type": "MASK", + "slot_index": 1, + "links": [ + 12 + ] + } + ], + "properties": { + "Node name for S&R": "LoadImage" + }, + "widgets_values": [ + "squirrel_girl_2.png", + "image" + ] + }, + { + "id": 52, + "type": "[Comfy3D] Load Hunyuan3D V2 ShapeGen Pipeline", + "pos": [ + 214.01866149902344, + 275.7831115722656 + ], + "size": [ + 332.6565856933594, + 106 + ], + "flags": {}, + "order": 1, + "mode": 0, + "inputs": [], + "outputs": [ + { + "name": "shapegen_pipe", + "type": "DIFFUSERS_PIPE", + "links": [ + 96 + ] + } + ], + "properties": { + "Node name for S&R": "[Comfy3D] Load Hunyuan3D V2 ShapeGen Pipeline" + }, + "widgets_values": [ + "Hunyuan3D-2mini-Turbo", + "safetensors", + true + ] + }, + { + "id": 55, + "type": "[Comfy3D] Hunyuan3D V2 ShapeGen", + "pos": [ + 631.484375, + 388.9334716796875 + ], + "size": [ + 315, + 194 + ], + "flags": {}, + "order": 4, + "mode": 0, + "inputs": [ + { + "name": "shapegen_pipe", + "type": "DIFFUSERS_PIPE", + "link": 96 + }, + { + "name": "reference_image", + "type": "IMAGE", + "link": 97 + }, + { + "name": "reference_mask", + "type": "MASK", + "link": 95 + } + ], + "outputs": [ + { + "name": "mesh", + "type": "MESH", + "links": [ + 98, + 99 + ] + } + ], + "properties": { + "Node name for S&R": "[Comfy3D] Hunyuan3D V2 ShapeGen" + }, + "widgets_values": [ + 578810140342528, + "randomize", + 5, + 5, + 256 + ] + }, + { + "id": 14, + "type": "InvertMask", + "pos": [ + 661.98876953125, + 647.8867797851562 + ], + "size": [ + 191.73150634765625, + 26 + ], + "flags": { + "collapsed": false + }, + "order": 3, + "mode": 0, + "inputs": [ + { + "name": "mask", + "type": "MASK", + "link": 12 + } + ], + "outputs": [ + { + "name": "MASK", + "type": "MASK", + "slot_index": 0, + "links": [ + 90, + 95 + ] + } + ], + "properties": { + "Node name for S&R": "InvertMask" + }, + "widgets_values": [] + }, + { + "id": 28, + "type": "[Comfy3D] Save 3D Mesh", + "pos": [ + 991.1575927734375, + 641.75634765625 + ], + "size": [ + 348.07330322265625, + 58 + ], + "flags": {}, + "order": 7, + "mode": 0, + "inputs": [ + { + "name": "mesh", + "type": "MESH", + "link": 94 + } + ], + "outputs": [ + { + "name": "save_path", + "type": "STRING", + "slot_index": 0, + "links": [ + 43 + ] + } + ], + "properties": { + "Node name for S&R": "[Comfy3D] Save 3D Mesh" + }, + "widgets_values": [ + "Hunyuan3D_V2_Test/mesh_tex.glb" + ] + }, + { + "id": 54, + "type": "[Comfy3D] Hunyuan3D V2 Paint Model Turbo", + "pos": [ + 991.2599487304688, + 479.9261169433594 + ], + "size": [ + 355.41339111328125, + 87.61331176757812 + ], + "flags": {}, + "order": 5, + "mode": 0, + "inputs": [ + { + "name": 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+ 1383.8515625, + 175.73828125 + ], + "size": [ + 600, + 500 + ], + "flags": {}, + "order": 8, + "mode": 0, + "inputs": [ + { + "name": "mesh_file_path", + "type": "STRING", + "widget": { + "name": "mesh_file_path" + }, + "link": 15 + } + ], + "outputs": [], + "properties": { + "Node name for S&R": "[Comfy3D] Preview 3DMesh" + }, + "widgets_values": [ + "", + null + ] + } + ], + "links": [ + [ + 12, + 13, + 1, + 14, + 0, + "MASK" + ], + [ + 15, + 16, + 0, + 15, + 0, + "STRING" + ], + [ + 43, + 28, + 0, + 27, + 0, + "STRING" + ], + [ + 90, + 14, + 0, + 54, + 2, + "MASK" + ], + [ + 91, + 13, + 0, + 54, + 1, + "IMAGE" + ], + [ + 93, + 40, + 0, + 54, + 0, + "DIFFUSERS_PIPE" + ], + [ + 94, + 54, + 0, + 28, + 0, + "MESH" + ], + [ + 95, + 14, + 0, + 55, + 2, + "MASK" + ], + [ + 96, + 52, + 0, + 55, + 0, + "DIFFUSERS_PIPE" + ], + [ + 97, + 13, + 0, + 55, + 1, + "IMAGE" + ], + [ + 98, + 55, + 0, + 54, + 3, + "MESH" + ], + [ + 99, + 55, + 0, + 16, + 0, + "MESH" + ] + ], + "groups": [], + "config": {}, + "extra": { + "ds": { + "scale": 0.7513148009015778, + "offset": [ + 22.151181360471924, + -126.36629411730921 + ] + }, + "frontendVersion": "1.17.11", + "node_versions": { + "comfy-core": "0.3.30", + "ComfyUI-3D-Pack": "2c629522408180cff31f87b492c848d94948a811" + }, + "VHS_latentpreview": false, + "VHS_latentpreviewrate": 0, + "VHS_MetadataImage": true, + "VHS_KeepIntermediate": true + }, + "version": 0.4 +} \ No newline at end of file diff --git a/__init__.py b/__init__.py index 4040c2c..cc8c101 100644 --- a/__init__.py +++ b/__init__.py @@ -4,7 +4,8 @@ import folder_paths as comfy_paths from pyhocon import ConfigFactory import logging -ROOT_PATH = os.path.join(comfy_paths.get_folder_paths("custom_nodes")[0], "ComfyUI-3D-Pack") +# ROOT_PATH = os.path.join(comfy_paths.get_folder_paths("custom_nodes")[0], "ComfyUI-3D-Pack") +ROOT_PATH = os.path.dirname(os.path.realpath(__file__)) MODULE_PATH = os.path.join(ROOT_PATH, "Gen_3D_Modules") MV_ALGO_PATH = os.path.join(ROOT_PATH, "MVs_Algorithms") diff --git a/nodes.py b/nodes.py index f75a9ce..bbdfa5a 100644 --- a/nodes.py +++ b/nodes.py @@ -143,7 +143,7 @@ DIFFUSERS_SCHEDULER_DICT = OrderedDict([ ("KDPM2DiscreteScheduler,", KDPM2DiscreteScheduler), ]) -ROOT_PATH = os.path.join(comfy_paths.get_folder_paths("custom_nodes")[0], "ComfyUI-3D-Pack") +ROOT_PATH = os.path.dirname(os.path.realpath(__file__)) CKPT_ROOT_PATH = os.path.join(ROOT_PATH, "Checkpoints") CKPT_DIFFUSERS_PATH = os.path.join(CKPT_ROOT_PATH, "Diffusers") CONFIG_ROOT_PATH = os.path.join(ROOT_PATH, "Configs") @@ -179,6 +179,7 @@ DEVICE_STR = "cuda" if torch.cuda.is_available() else "cpu" DEVICE = torch.device(DEVICE_STR) HF_DOWNLOAD_IGNORE = ["*.yaml", "*.json", "*.py", ".png", ".jpg", ".gif"] +# HF_DOWNLOAD_IGNORE = ["*.json", "*.py", ".png", ".jpg", ".gif"] class Preview_3DGS: @@ -3923,7 +3924,8 @@ class Hunyuan3D_V1_Reconstruction_Model: mesh.auto_normal() return (mesh,) - + +# deprecated class Hunyuan3D_V2_DiT_Flow_Matching_Model: @classmethod @@ -3979,6 +3981,7 @@ class Hunyuan3D_V2_DiT_Flow_Matching_Model: return (mesh,) +# deprecated class Hunyuan3D_V2_Paint_Model: @classmethod @@ -4250,7 +4253,199 @@ class TripoSG_Scribble_Model: return (mesh,) - - +class Load_Hunyuan3D_V2_ShapeGen_Pipeline: + CATEGORY = "Comfy3D/Algorithm" + RETURN_TYPES = ("DIFFUSERS_PIPE",) + RETURN_NAMES = ("shapegen_pipe",) + FUNCTION = "load" + _REPO_ID_BASE = "tencent" + + _MODES = { + "Hunyuan3D-2": ("Hunyuan3D-2", "hunyuan3d-dit-v2-0", 30), + "Hunyuan3D-2-Fast": ("Hunyuan3D-2", "hunyuan3d-dit-v2-0-fast", 5), + "Hunyuan3D-2-Turbo": ("Hunyuan3D-2", "hunyuan3d-dit-v2-0-turbo", 5), + "Hunyuan3D-2mini": ("Hunyuan3D-2mini", "hunyuan3d-dit-v2-mini", 30), + "Hunyuan3D-2mini-Fast": ("Hunyuan3D-2mini", "hunyuan3d-dit-v2-mini-fast", 5), + "Hunyuan3D-2mini-Turbo": ("Hunyuan3D-2mini", "hunyuan3d-dit-v2-mini-turbo", 5), + } + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "generation_mode": (list(cls._MODES.keys()),), + "weights_format" : (["safetensors", "ckpt"],), + "flash_vdm" : ("BOOLEAN", {"default": True}), + } + } + + @staticmethod + def _ensure_weights(repo: str, subfolder: str, use_safetensors: bool): + base_dir = os.path.join(CKPT_DIFFUSERS_PATH, f"{Load_Hunyuan3D_V2_ShapeGen_Pipeline._REPO_ID_BASE}/{repo}") + ckpt_file = "model.fp16.safetensors" if use_safetensors else "model.fp16.ckpt" + ckpt_path = os.path.join(base_dir, subfolder, ckpt_file) + + if not os.path.exists(ckpt_path): + snapshot_download( + repo_id=f"{Load_Hunyuan3D_V2_ShapeGen_Pipeline._REPO_ID_BASE}/{repo}", + repo_type="model", + local_dir=base_dir, + local_dir_use_symlinks=False, + resume_download=True, + ignore_patterns = HF_DOWNLOAD_IGNORE + ) + + @staticmethod + def _build_pipe(repo: str, subfolder: str, use_safetensors: bool, flash_vdm: bool): + Load_Hunyuan3D_V2_ShapeGen_Pipeline._ensure_weights(repo, subfolder, use_safetensors) + + model_dir = os.path.join(CKPT_DIFFUSERS_PATH, + f"{Load_Hunyuan3D_V2_ShapeGen_Pipeline._REPO_ID_BASE}/{repo}", + subfolder) + ckpt = os.path.join(model_dir, "model.fp16.safetensors" if use_safetensors else "model.fp16.ckpt") + cfg = os.path.join(model_dir, "config.yaml") + + pipe = Hunyuan3DDiTFlowMatchingPipeline.from_single_file( + ckpt_path=ckpt, + config_path=cfg, + device="cuda", + dtype=torch.float16, + use_safetensors=use_safetensors, + from_pretrained_kwargs={ + "model_path": f"{Load_Hunyuan3D_V2_ShapeGen_Pipeline._REPO_ID_BASE}/{repo}", + "subfolder": subfolder, + "use_safetensors": use_safetensors, + }, + ) + + if flash_vdm and any(tag in subfolder for tag in ("turbo", "fast")): + pipe.enable_flashvdm(replace_vae=False) + + return pipe.to("cuda", torch.float16) + + def load(self, generation_mode, weights_format, flash_vdm): + repo, subfolder, def_steps = self._MODES[generation_mode] + use_safe = (weights_format == "safetensors") + pipe = self._build_pipe(repo, subfolder, use_safe, flash_vdm) + pipe.default_steps = def_steps + return (pipe,) + +class Hunyuan3D_V2_ShapeGen: + + CATEGORY = "Comfy3D/Algorithm" + RETURN_TYPES = ("MESH",) + RETURN_NAMES = ("mesh",) + FUNCTION = "run" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "shapegen_pipe" : ("DIFFUSERS_PIPE",), + "reference_image" : ("IMAGE",), + "reference_mask" : ("MASK",), + "seed" : ("INT", {"default": 1234, "min": 0, "max": 0xffffffffffffffff}), + "guidance_scale" : ("FLOAT", {"default": 5.0, "min": 0.0, "step": 0.1}), + "num_inference_steps": ("INT", {"default": 5, "min": 0}), + "octree_resolution" : ("INT", {"default": 256, "min": 64}), + } + } + + @torch.no_grad() + def run(self, shapegen_pipe, reference_image, reference_mask, seed, + guidance_scale, num_inference_steps, octree_resolution): + + img = torch_imgs_to_pils(reference_image, reference_mask)[0] + + steps = (shapegen_pipe.default_steps if num_inference_steps == 0 + else num_inference_steps) + gen = torch.Generator(device=shapegen_pipe.device).manual_seed(seed) + + mesh = shapegen_pipe( + image = img, + num_inference_steps = steps, + guidance_scale = guidance_scale, + generator = gen, + octree_resolution = octree_resolution, + output_type = "trimesh", + )[0] + + for fn in (FloaterRemover(), DegenerateFaceRemover(), FaceReducer()): + mesh = fn(mesh) + + return (Mesh.load_trimesh(given_mesh=mesh),) +class Load_Hunyuan3D_V2_TexGen_Pipeline: + CATEGORY = "Comfy3D/Algorithm" + RETURN_TYPES = ("DIFFUSERS_PIPE",) + RETURN_NAMES = ("texgen_pipe",) + FUNCTION = "load" + + MODEL2REPO = { + "Standard": ("tencent/Hunyuan3D-2", "hunyuan3d-paint-v2-0"), + "Turbo": ("tencent/Hunyuan3D-2", "hunyuan3d-paint-v2-0-turbo"), + } + + @classmethod + def INPUT_TYPES(cls): + return {"required": { + "generation_mode": (list(cls.MODEL2REPO.keys()),), + "weights_format": (["safetensors","ckpt"],), + }} + + @staticmethod + def _ensure_weights(repo_id: str, subfolder: str, use_safetensors: bool): + base_dir = os.path.join(CKPT_DIFFUSERS_PATH, repo_id) + os.makedirs(base_dir, exist_ok=True) + target_dir = os.path.join(base_dir, subfolder) + if not os.path.exists(target_dir): + snapshot_download( + repo_id=repo_id, + repo_type="model", + local_dir=base_dir, + local_dir_use_symlinks=False, + resume_download=True, + ignore_patterns = HF_DOWNLOAD_IGNORE + ) + + def load(self, generation_mode, weights_format): + repo_id,subfolder=self.MODEL2REPO[generation_mode] + Load_Hunyuan3D_V2_TexGen_Pipeline._ensure_weights(repo_id,subfolder, + weights_format=="safetensors") + pipe=Hunyuan3DPaintPipeline.from_pretrained( + model_path=repo_id, + subfolder=subfolder, + ) + return (pipe.to("cuda",torch.float16),) + +class Hunyuan3D_V2_Paint_Model_Turbo: + CATEGORY = "Comfy3D/Algorithm" + RETURN_TYPES = ("MESH",) + RETURN_NAMES = ("mesh",) + FUNCTION = "run" + + @classmethod + def INPUT_TYPES(cls): + return {"required": { + "hunyuan3d_v2_texgen_pipe": ("DIFFUSERS_PIPE",), + "reference_image": ("IMAGE",), + "reference_mask": ("MASK",), + "mesh": ("MESH",), + }} + + @torch.no_grad() + def run(self, hunyuan3d_v2_texgen_pipe, + reference_image, reference_mask, mesh): + + img_pil = torch_imgs_to_pils(reference_image, reference_mask)[0] + + v_np = mesh.v.detach().cpu().numpy() + f_np = mesh.f.detach().cpu().numpy() + tri = trimesh.Trimesh(vertices=v_np, faces=f_np) + + textured = hunyuan3d_v2_texgen_pipe(tri, img_pil) + m_out = Mesh.load_trimesh(given_mesh=textured) + m_out.auto_normal() + return (m_out,) + \ No newline at end of file From 1b523d0831a54acf610bc1ceb7085bb5307d3059 Mon Sep 17 00:00:00 2001 From: DenisKochetov <57500456+DenisKochetov@users.noreply.github.com> Date: Wed, 7 May 2025 20:59:34 +0400 Subject: [PATCH 05/36] add demo video --- README.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 1280a0e..6e2bbde 100644 --- a/README.md +++ b/README.md @@ -47,8 +47,10 @@ Your ComfyUI Root Directory\python_embeded\python.exe install.py - **Note:** you need to put [Example Inputs Files & Folders](_Example_Workflows/_Example_Inputs_Files/) under ComfyUI Root Directory\ComfyUI\input folder before you can run the example workflow - **Hunyuan3D_V2**: turbo, mini, fast - - Single image to 3D Mesh - - Model weights: https://huggingface.co/tencent/Hunyuan3D-2, https://huggingface.co/tencent/Hunyuan3D-2mini + - Single image to 3D Mesh + - Model weights: https://huggingface.co/tencent/Hunyuan3D-2, https://huggingface.co/tencent/Hunyuan3D-2mini + + - **TripoSG**: [VAST-AI-Research/TripoSG](https://github.com/VAST-AI-Research/TripoSG) - Single image (Reference or Scribble) to 3D Mesh From b0951721dccceace33434137bc965848632ea78a Mon Sep 17 00:00:00 2001 From: DenisKochetov <57500456+DenisKochetov@users.noreply.github.com> Date: Wed, 7 May 2025 21:00:00 +0400 Subject: [PATCH 06/36] remove commented code --- nodes.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/nodes.py b/nodes.py index bbdfa5a..0df710d 100644 --- a/nodes.py +++ b/nodes.py @@ -179,7 +179,6 @@ DEVICE_STR = "cuda" if torch.cuda.is_available() else "cpu" DEVICE = torch.device(DEVICE_STR) HF_DOWNLOAD_IGNORE = ["*.yaml", "*.json", "*.py", ".png", ".jpg", ".gif"] -# HF_DOWNLOAD_IGNORE = ["*.json", "*.py", ".png", ".jpg", ".gif"] class Preview_3DGS: @@ -4448,4 +4447,4 @@ class Hunyuan3D_V2_Paint_Model_Turbo: m_out = Mesh.load_trimesh(given_mesh=textured) m_out.auto_normal() return (m_out,) - \ No newline at end of file + From feaa3e03b1c0f4d25995a850b0bc186b87996663 Mon Sep 17 00:00:00 2001 From: Denis Kochetov Date: Wed, 7 May 2025 21:51:08 +0000 Subject: [PATCH 07/36] add multiview --- .../hunyuan3d-dit-v2-mv-fast/config.yaml | 69 ++++++ .../hunyuan3d-dit-v2-mv/config.yaml | 68 ++++++ .../Hunyuan3D_V2/hy3dgen/texgen/pipelines.py | 4 +- nodes.py | 212 ++++++++++++------ 4 files changed, 284 insertions(+), 69 deletions(-) create mode 100644 Checkpoints/Diffusers/tencent/Hunyuan3D-2mv/hunyuan3d-dit-v2-mv-fast/config.yaml create mode 100644 Checkpoints/Diffusers/tencent/Hunyuan3D-2mv/hunyuan3d-dit-v2-mv/config.yaml diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2mv/hunyuan3d-dit-v2-mv-fast/config.yaml b/Checkpoints/Diffusers/tencent/Hunyuan3D-2mv/hunyuan3d-dit-v2-mv-fast/config.yaml new file mode 100644 index 0000000..0cb7655 --- /dev/null +++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2mv/hunyuan3d-dit-v2-mv-fast/config.yaml @@ -0,0 +1,69 @@ +model: + target: Hunyuan3D_V2.hy3dgen.shapegen.models.Hunyuan3DDiT + params: + in_channels: 64 + context_in_dim: 1536 + hidden_size: 1024 + mlp_ratio: 4.0 + num_heads: 16 + depth: 16 + depth_single_blocks: 32 + axes_dim: [ 64 ] + theta: 10000 + qkv_bias: true + guidance_embed: true + +vae: + target: Hunyuan3D_V2.hy3dgen.shapegen.models.ShapeVAE + params: + num_latents: 3072 + embed_dim: 64 + num_freqs: 8 + include_pi: false + heads: 16 + width: 1024 + num_decoder_layers: 16 + qkv_bias: false + qk_norm: true + scale_factor: 0.9990943042622529 + +conditioner: + target: Hunyuan3D_V2.hy3dgen.shapegen.models.SingleImageEncoder + params: + main_image_encoder: + type: DinoImageEncoderMV # dino giant + kwargs: + config: + attention_probs_dropout_prob: 0.0 + drop_path_rate: 0.0 + hidden_act: gelu + hidden_dropout_prob: 0.0 + hidden_size: 1536 + image_size: 518 + initializer_range: 0.02 + layer_norm_eps: 1.e-6 + layerscale_value: 1.0 + mlp_ratio: 4 + model_type: dinov2 + num_attention_heads: 24 + num_channels: 3 + num_hidden_layers: 40 + patch_size: 14 + qkv_bias: true + torch_dtype: float32 + use_swiglu_ffn: true + image_size: 518 + +scheduler: + target: Hunyuan3D_V2.hy3dgen.shapegen.schedulers.FlowMatchEulerDiscreteScheduler + params: + num_train_timesteps: 1000 + +image_processor: + target: Hunyuan3D_V2.hy3dgen.shapegen.preprocessors.MVImageProcessorV2 + params: + size: 512 + border_ratio: 0.15 + +pipeline: + target: Hunyuan3D_V2.hy3dgen.shapegen.pipelines.Hunyuan3DDiTFlowMatchingPipeline \ No newline at end of file diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2mv/hunyuan3d-dit-v2-mv/config.yaml b/Checkpoints/Diffusers/tencent/Hunyuan3D-2mv/hunyuan3d-dit-v2-mv/config.yaml new file mode 100644 index 0000000..29fac42 --- /dev/null +++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2mv/hunyuan3d-dit-v2-mv/config.yaml @@ -0,0 +1,68 @@ +model: + target: Hunyuan3D_V2.hy3dgen.shapegen.models.Hunyuan3DDiT + params: + in_channels: 64 + context_in_dim: 1536 + hidden_size: 1024 + mlp_ratio: 4.0 + num_heads: 16 + depth: 16 + depth_single_blocks: 32 + axes_dim: [ 64 ] + theta: 10000 + qkv_bias: true + +vae: + target: Hunyuan3D_V2.hy3dgen.shapegen.models.ShapeVAE + params: + num_latents: 3072 + embed_dim: 64 + num_freqs: 8 + include_pi: false + heads: 16 + width: 1024 + num_decoder_layers: 16 + qkv_bias: false + qk_norm: true + scale_factor: 0.9990943042622529 + +conditioner: + target: Hunyuan3D_V2.hy3dgen.shapegen.models.SingleImageEncoder + params: + main_image_encoder: + type: DinoImageEncoderMV # dino giant + kwargs: + config: + attention_probs_dropout_prob: 0.0 + drop_path_rate: 0.0 + hidden_act: gelu + hidden_dropout_prob: 0.0 + hidden_size: 1536 + image_size: 518 + initializer_range: 0.02 + layer_norm_eps: 1.e-6 + layerscale_value: 1.0 + mlp_ratio: 4 + model_type: dinov2 + num_attention_heads: 24 + num_channels: 3 + num_hidden_layers: 40 + patch_size: 14 + qkv_bias: true + torch_dtype: float32 + use_swiglu_ffn: true + image_size: 518 + +scheduler: + target: Hunyuan3D_V2.hy3dgen.shapegen.schedulers.FlowMatchEulerDiscreteScheduler + params: + num_train_timesteps: 1000 + +image_processor: + target: Hunyuan3D_V2.hy3dgen.shapegen.preprocessors.MVImageProcessorV2 + params: + size: 512 + border_ratio: 0.15 + +pipeline: + target: Hunyuan3D_V2.hy3dgen.shapegen.pipelines.Hunyuan3DDiTFlowMatchingPipeline \ No newline at end of file diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/pipelines.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/pipelines.py index 9f82240..2110588 100644 --- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/pipelines.py +++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/pipelines.py @@ -93,8 +93,8 @@ class Hunyuan3DPaintPipeline: texture_size=self.config.texture_size) self.load_models() - - def to(self, device=None, dtype=None): # ⬅️  тот же уровень, что и __init__ + + def to(self, device=None, dtype=None): if device is not None: for m in self.models.values(): m.pipeline.to(device) diff --git a/nodes.py b/nodes.py index 0df710d..7036f71 100644 --- a/nodes.py +++ b/nodes.py @@ -97,6 +97,7 @@ from Hunyuan3D_V1.mvd.hunyuan3d_mvd_lite_pipeline import Hunyuan3D_MVD_Lite_Pipe from Hunyuan3D_V1.infer import Views2Mesh from Hunyuan3D_V2.hy3dgen.shapegen import FaceReducer, FloaterRemover, DegenerateFaceRemover, Hunyuan3DDiTFlowMatchingPipeline from Hunyuan3D_V2.hy3dgen.texgen import Hunyuan3DPaintPipeline +from Hunyuan3D_V2.hy3dgen.rembg import BackgroundRemover from TRELLIS.trellis.pipelines import TrellisImageTo3DPipeline from TRELLIS.trellis.utils import postprocessing_utils from TripoSG.pipelines.pipeline_triposg import TripoSGPipeline @@ -4262,11 +4263,14 @@ class Load_Hunyuan3D_V2_ShapeGen_Pipeline: _MODES = { "Hunyuan3D-2": ("Hunyuan3D-2", "hunyuan3d-dit-v2-0", 30), - "Hunyuan3D-2-Fast": ("Hunyuan3D-2", "hunyuan3d-dit-v2-0-fast", 5), + "Hunyuan3D-2-Fast": ("Hunyuan3D-2", "hunyuan3d-dit-v2-0-fast", 20), "Hunyuan3D-2-Turbo": ("Hunyuan3D-2", "hunyuan3d-dit-v2-0-turbo", 5), "Hunyuan3D-2mini": ("Hunyuan3D-2mini", "hunyuan3d-dit-v2-mini", 30), - "Hunyuan3D-2mini-Fast": ("Hunyuan3D-2mini", "hunyuan3d-dit-v2-mini-fast", 5), + "Hunyuan3D-2mini-Fast": ("Hunyuan3D-2mini", "hunyuan3d-dit-v2-mini-fast", 20), "Hunyuan3D-2mini-Turbo": ("Hunyuan3D-2mini", "hunyuan3d-dit-v2-mini-turbo", 5), + "Hunyuan3D-2mv": ("Hunyuan3D-2mv", "hunyuan3d-dit-v2-mv", 30), + "Hunyuan3D-2mv-Fast": ("Hunyuan3D-2mv", "hunyuan3d-dit-v2-mv-fast", 20), + "Hunyuan3D-2mv-Turbo": ("Hunyuan3D-2mv", "hunyuan3d-dit-v2-mv-turbo", 5), } @classmethod @@ -4290,7 +4294,6 @@ class Load_Hunyuan3D_V2_ShapeGen_Pipeline: repo_id=f"{Load_Hunyuan3D_V2_ShapeGen_Pipeline._REPO_ID_BASE}/{repo}", repo_type="model", local_dir=base_dir, - local_dir_use_symlinks=False, resume_download=True, ignore_patterns = HF_DOWNLOAD_IGNORE ) @@ -4329,51 +4332,6 @@ class Load_Hunyuan3D_V2_ShapeGen_Pipeline: pipe = self._build_pipe(repo, subfolder, use_safe, flash_vdm) pipe.default_steps = def_steps return (pipe,) - -class Hunyuan3D_V2_ShapeGen: - - CATEGORY = "Comfy3D/Algorithm" - RETURN_TYPES = ("MESH",) - RETURN_NAMES = ("mesh",) - FUNCTION = "run" - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "shapegen_pipe" : ("DIFFUSERS_PIPE",), - "reference_image" : ("IMAGE",), - "reference_mask" : ("MASK",), - "seed" : ("INT", {"default": 1234, "min": 0, "max": 0xffffffffffffffff}), - "guidance_scale" : ("FLOAT", {"default": 5.0, "min": 0.0, "step": 0.1}), - "num_inference_steps": ("INT", {"default": 5, "min": 0}), - "octree_resolution" : ("INT", {"default": 256, "min": 64}), - } - } - - @torch.no_grad() - def run(self, shapegen_pipe, reference_image, reference_mask, seed, - guidance_scale, num_inference_steps, octree_resolution): - - img = torch_imgs_to_pils(reference_image, reference_mask)[0] - - steps = (shapegen_pipe.default_steps if num_inference_steps == 0 - else num_inference_steps) - gen = torch.Generator(device=shapegen_pipe.device).manual_seed(seed) - - mesh = shapegen_pipe( - image = img, - num_inference_steps = steps, - guidance_scale = guidance_scale, - generator = gen, - octree_resolution = octree_resolution, - output_type = "trimesh", - )[0] - - for fn in (FloaterRemover(), DegenerateFaceRemover(), FaceReducer()): - mesh = fn(mesh) - - return (Mesh.load_trimesh(given_mesh=mesh),) class Load_Hunyuan3D_V2_TexGen_Pipeline: CATEGORY = "Comfy3D/Algorithm" @@ -4403,7 +4361,6 @@ class Load_Hunyuan3D_V2_TexGen_Pipeline: repo_id=repo_id, repo_type="model", local_dir=base_dir, - local_dir_use_symlinks=False, resume_download=True, ignore_patterns = HF_DOWNLOAD_IGNORE ) @@ -4418,33 +4375,154 @@ class Load_Hunyuan3D_V2_TexGen_Pipeline: ) return (pipe.to("cuda",torch.float16),) -class Hunyuan3D_V2_Paint_Model_Turbo: - CATEGORY = "Comfy3D/Algorithm" - RETURN_TYPES = ("MESH",) - RETURN_NAMES = ("mesh",) - FUNCTION = "run" +class Hunyuan3D_V2_Paint_Model_Turbo_MV: + """ + Texture-painting pipeline using a list of PIL images. + If list contains 1 image → single-view; if >1 → multi-view mode. + """ + + CATEGORY = "Comfy3D/Algorithm" + RETURN_TYPES = ("MESH",) + RETURN_NAMES = ("mesh",) + FUNCTION = "run" @classmethod def INPUT_TYPES(cls): - return {"required": { - "hunyuan3d_v2_texgen_pipe": ("DIFFUSERS_PIPE",), - "reference_image": ("IMAGE",), - "reference_mask": ("MASK",), - "mesh": ("MESH",), - }} + return { + "required": { + "hunyuan3d_v2_texgen_pipe": ("DIFFUSERS_PIPE",), + "mesh": ("MESH",), + "images": ("LIST",), + } + } @torch.no_grad() - def run(self, hunyuan3d_v2_texgen_pipe, - reference_image, reference_mask, mesh): - - img_pil = torch_imgs_to_pils(reference_image, reference_mask)[0] + def run(self, hunyuan3d_v2_texgen_pipe, mesh, images): + if not isinstance(images, list) or len(images) == 0: + raise Exception("[Hunyuan3D_V2_Paint_Model_Turbo_MV] 'images' must be a non-empty list of PIL images") v_np = mesh.v.detach().cpu().numpy() f_np = mesh.f.detach().cpu().numpy() - tri = trimesh.Trimesh(vertices=v_np, faces=f_np) + tri = trimesh.Trimesh(vertices=v_np, faces=f_np) + + try: + textured = hunyuan3d_v2_texgen_pipe(tri, images) + except Exception as e: + raise Exception(f"[Hunyuan3D_V2_Paint_Model_Turbo_MV] Texture generation failed: {str(e)}") - textured = hunyuan3d_v2_texgen_pipe(tri, img_pil) m_out = Mesh.load_trimesh(given_mesh=textured) m_out.auto_normal() return (m_out,) - + +class Multi_Background_Remover: + """ + Converts 1 to 4 image inputs (front/back/left/right) to a list of processed PIL images. + Applies RGBA conversion and background removal. + Suitable for feeding directly into ShapeGen or Paint models. + """ + + CATEGORY = "Comfy3D/Preprocessors" + RETURN_TYPES = ("LIST",) # List of PIL images + RETURN_NAMES = ("images",) + FUNCTION = "run" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image_front": ("IMAGE",), + }, + "optional": { + "image_back": ("IMAGE",), + "image_left": ("IMAGE",), + } + } + + @torch.no_grad() + def run( + self, + image_front, + image_back=None, + image_left=None + ): + rmbg = BackgroundRemover() + + mv_inputs = { + k: v for k, v in { + "front": image_front, + "back": image_back, + "left": image_left + }.items() if v is not None + } + + images = [] + for key, tensor_img in mv_inputs.items(): + pil_img = torch_imgs_to_pils(tensor_img)[0].convert("RGBA") + if pil_img.mode == "RGB": + pil_img = rmbg(pil_img.convert("RGB")) + images.append(pil_img) + + return (images,) + +class Hunyuan3D_V2_ShapeGen_MV: + """ + Shape generation pipeline using a list of processed PIL images. + If len(images) == 1 → single-view; if >1 → multi-view dict. + """ + + CATEGORY = "Comfy3D/Algorithm" + RETURN_TYPES = ("MESH",) + RETURN_NAMES = ("mesh",) + FUNCTION = "run" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "shapegen_pipe": ("DIFFUSERS_PIPE",), + "images": ("LIST",), + "seed": ("INT", {"default": 1234, "min": 0, "max": 0xffffffffffffffff}), + "guidance_scale": ("FLOAT", {"default": 5.0, "min": 0.0, "step": 0.1}), + "num_inference_steps": ("INT", {"default": 5, "min": 0, "tooltip": "Turbo: 5; Fast/Standard: 30–40"}), + "octree_resolution": ("INT", {"default": 256, "min": 64}), + } + } + + @torch.no_grad() + def run( + self, + shapegen_pipe, + images, + seed=1234, + guidance_scale=5.0, + num_inference_steps=5, + octree_resolution=256 + ): + if not isinstance(images, list) or len(images) == 0: + raise Exception("[Hunyuan3D_V2_ShapeGen_MV] 'images' must be a non-empty list of PIL images") + + if len(images) == 1: + image = images[0] + else: + directions = ["front", "back", "left"] + image = {k: v for k, v in zip(directions, images)} + + steps = shapegen_pipe.default_steps if num_inference_steps == 0 else num_inference_steps + gen = torch.Generator(device=shapegen_pipe.device).manual_seed(seed) + + try: + mesh = shapegen_pipe( + image=image, + num_inference_steps=steps, + guidance_scale=guidance_scale, + generator=gen, + octree_resolution=octree_resolution, + output_type="trimesh", + )[0] + except Exception as e: + raise Exception(f"[Hunyuan3D_V2_ShapeGen_MV] Shape generation failed: {str(e)}") + + for fn in (FloaterRemover(), DegenerateFaceRemover(), FaceReducer()): + mesh = fn(mesh) + + return (Mesh.load_trimesh(given_mesh=mesh),) \ No newline at end of file From ffc873a1d7db97c9405385b1fca58b089e12704f Mon Sep 17 00:00:00 2001 From: Denis Kochetov Date: Thu, 8 May 2025 02:13:03 +0000 Subject: [PATCH 08/36] fix texturing multi view --- Gen_3D_Modules/Hunyuan3D_V2/assets/1.glb | Bin 720764 -> 0 bytes Gen_3D_Modules/Hunyuan3D_V2/assets/demo.png | Bin 151014 -> 0 bytes .../Hunyuan3D_V2/assets/env_maps/gradient.jpg | Bin 68235 -> 0 bytes .../Hunyuan3D_V2/assets/env_maps/white.jpg | Bin 8819 -> 0 bytes .../Hunyuan3D_V2/assets/example_prompts.txt | 5 - .../assets/modelviewer-template.html | 81 ----------- .../assets/modelviewer-textured-template.html | 136 ------------------ .../Hunyuan3D_V2/assets/qrcode/discord.png | Bin 56689 -> 0 bytes .../Hunyuan3D_V2/assets/qrcode/wechat.png | Bin 4745 -> 0 bytes .../Hunyuan3D_V2/assets/qrcode/x.png | Bin 64166 -> 0 bytes .../assets/qrcode/xiaohongshu.png | Bin 84703 -> 0 bytes .../hy3dgen/shapegen/preprocessors.py | 74 +++++++--- .../Hunyuan3D_V2/hy3dgen/texgen/pipelines.py | 41 ++---- nodes.py | 51 ++++--- 14 files changed, 96 insertions(+), 292 deletions(-) delete mode 100644 Gen_3D_Modules/Hunyuan3D_V2/assets/1.glb delete 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[**Last update: 29/April/2025**] - **Note:** you need to put [Example Inputs Files & Folders](_Example_Workflows/_Example_Inputs_Files/) under ComfyUI Root Directory\ComfyUI\input folder before you can run the example workflow -- **Hunyuan3D_V2**: turbo, mini, fast +- **Hunyuan3D_V2**: turbo, mini, fast, multiview - Single image to 3D Mesh + - Multi-views to 3D Mesh with RGB texture - Model weights: https://huggingface.co/tencent/Hunyuan3D-2, https://huggingface.co/tencent/Hunyuan3D-2mini - - + + - **TripoSG**: [VAST-AI-Research/TripoSG](https://github.com/VAST-AI-Research/TripoSG) - Single image (Reference or Scribble) to 3D Mesh From 2ee2bbcedce5bdf98f69a6a5a81ee6380db301be Mon Sep 17 00:00:00 2001 From: Denis Kochetov Date: Thu, 8 May 2025 02:38:23 +0000 Subject: [PATCH 10/36] updated workflow --- ..._Turbo.json => Hunyuan3D_V2_Turbo_MV.json} | 735 ++++++++++-------- 1 file changed, 401 insertions(+), 334 deletions(-) rename _Example_Workflows/{Hunyuan3D_V2_Turbo.json => Hunyuan3D_V2_Turbo_MV.json} (60%) diff --git a/_Example_Workflows/Hunyuan3D_V2_Turbo.json b/_Example_Workflows/Hunyuan3D_V2_Turbo_MV.json similarity index 60% rename from _Example_Workflows/Hunyuan3D_V2_Turbo.json rename to _Example_Workflows/Hunyuan3D_V2_Turbo_MV.json index 93fbab1..0c8a299 100644 --- a/_Example_Workflows/Hunyuan3D_V2_Turbo.json +++ b/_Example_Workflows/Hunyuan3D_V2_Turbo_MV.json @@ -1,194 +1,28 @@ { - "id": "c466dde6-0bf8-4e82-b5b4-536d8f5ed7d4", + "id": "ecf3b5f9-96c2-411d-92cb-d8bd8cebbe0f", "revision": 0, - "last_node_id": 56, - "last_link_id": 99, + "last_node_id": 89, + "last_link_id": 172, "nodes": [ { - "id": 13, - "type": "LoadImage", - "pos": [ - 207.519287109375, - 475.5122375488281 - ], - "size": [ - 362.0579528808594, - 476.63336181640625 - ], - "flags": {}, - "order": 0, - "mode": 0, - "inputs": [], - "outputs": [ - { - "name": "IMAGE", - "type": "IMAGE", - "slot_index": 0, - "links": [ - 91, - 97 - ] - }, - { - "name": "MASK", - "type": "MASK", - 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