Repository: Wan-Video/Wan2.2 Branch: main Commit: 42bf4cfaa384 Files: 69 Total size: 748.5 KB Directory structure: gitextract_8s745wnd/ ├── .gitignore ├── INSTALL.md ├── LICENSE.txt ├── Makefile ├── README.md ├── generate.py ├── pyproject.toml ├── requirements.txt ├── requirements_animate.txt ├── requirements_s2v.txt ├── tests/ │ ├── README.md │ └── test.sh └── wan/ ├── __init__.py ├── animate.py ├── configs/ │ ├── __init__.py │ ├── shared_config.py │ ├── wan_animate_14B.py │ ├── wan_i2v_A14B.py │ ├── wan_s2v_14B.py │ ├── wan_t2v_A14B.py │ └── wan_ti2v_5B.py ├── distributed/ │ ├── __init__.py │ ├── fsdp.py │ ├── sequence_parallel.py │ ├── ulysses.py │ └── util.py ├── image2video.py ├── modules/ │ ├── __init__.py │ ├── animate/ │ │ ├── __init__.py │ │ ├── animate_utils.py │ │ ├── clip.py │ │ ├── face_blocks.py │ │ ├── model_animate.py │ │ ├── motion_encoder.py │ │ ├── preprocess/ │ │ │ ├── UserGuider.md │ │ │ ├── __init__.py │ │ │ ├── human_visualization.py │ │ │ ├── pose2d.py │ │ │ ├── pose2d_utils.py │ │ │ ├── preprocess_data.py │ │ │ ├── process_pipepline.py │ │ │ ├── retarget_pose.py │ │ │ ├── sam_utils.py │ │ │ ├── utils.py │ │ │ └── video_predictor.py │ │ └── xlm_roberta.py │ ├── attention.py │ ├── model.py │ ├── s2v/ │ │ ├── __init__.py │ │ ├── audio_encoder.py │ │ ├── audio_utils.py │ │ ├── auxi_blocks.py │ │ ├── model_s2v.py │ │ ├── motioner.py │ │ └── s2v_utils.py │ ├── t5.py │ ├── tokenizers.py │ ├── vae2_1.py │ └── vae2_2.py ├── speech2video.py ├── text2video.py ├── textimage2video.py └── utils/ ├── __init__.py ├── fm_solvers.py ├── fm_solvers_unipc.py ├── prompt_extend.py ├── qwen_vl_utils.py ├── system_prompt.py └── utils.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ __pycache__/ .DS_Store .vscode* tmp_examples* new_checkpoint* batch_test* nohup* ================================================ FILE: INSTALL.md ================================================ # Installation Guide ## Install with pip ```bash pip install . pip install .[dev] # Installe aussi les outils de dev ``` ## Install with Poetry Ensure you have [Poetry](https://python-poetry.org/docs/#installation) installed on your system. To install all dependencies: ```bash poetry install ``` ### Handling `flash-attn` Installation Issues If `flash-attn` fails due to **PEP 517 build issues**, you can try one of the following fixes. #### No-Build-Isolation Installation (Recommended) ```bash poetry run pip install --upgrade pip setuptools wheel poetry run pip install flash-attn --no-build-isolation poetry install ``` #### Install from Git (Alternative) ```bash poetry run pip install git+https://github.com/Dao-AILab/flash-attention.git ``` --- ### Running the Model Once the installation is complete, you can run **Wan2.2** using: ```bash poetry run python generate.py --task t2v-A14B --size '1280*720' --ckpt_dir ./Wan2.2-T2V-A14B --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage." ``` #### Test ```bash bash tests/test.sh ``` #### Format ```bash black . isort . ``` ================================================ FILE: LICENSE.txt ================================================ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: Makefile ================================================ .PHONY: format format: isort generate.py wan yapf -i -r *.py generate.py wan ================================================ FILE: README.md ================================================ # Wan2.2

💜 Wan    ｜    🖥️ GitHub    |   🤗 Hugging Face   |   🤖 ModelScope   |    📑 Paper    |    📑 Blog    |    💬 Discord  
📕 使用指南(中文)   |    📘 User Guide(English)   |   💬 WeChat(微信)  
----- [**Wan: Open and Advanced Large-Scale Video Generative Models**](https://arxiv.org/abs/2503.20314) We are excited to introduce **Wan2.2**, a major upgrade to our foundational video models. With **Wan2.2**, we have focused on incorporating the following innovations: - 👍 **Effective MoE Architecture**: Wan2.2 introduces a Mixture-of-Experts (MoE) architecture into video diffusion models. By separating the denoising process cross timesteps with specialized powerful expert models, this enlarges the overall model capacity while maintaining the same computational cost. - 👍 **Cinematic-level Aesthetics**: Wan2.2 incorporates meticulously curated aesthetic data, complete with detailed labels for lighting, composition, contrast, color tone, and more. This allows for more precise and controllable cinematic style generation, facilitating the creation of videos with customizable aesthetic preferences. - 👍 **Complex Motion Generation**: Compared to Wan2.1, Wan2.2 is trained on a significantly larger data, with +65.6% more images and +83.2% more videos. This expansion notably enhances the model's generalization across multiple dimensions such as motions, semantics, and aesthetics, achieving TOP performance among all open-sourced and closed-sourced models. - 👍 **Efficient High-Definition Hybrid TI2V**: Wan2.2 open-sources a 5B model built with our advanced Wan2.2-VAE that achieves a compression ratio of **16×16×4**. This model supports both text-to-video and image-to-video generation at 720P resolution with 24fps and can also run on consumer-grade graphics cards like 4090. It is one of the fastest **720P@24fps** models currently available, capable of serving both the industrial and academic sectors simultaneously. ## Video Demos

## 🔥 Latest News!! * Nov 13, 2025: 👋 Wan2.2-Animate-14B has been integrated into Diffusers ([PR](https://github.com/huggingface/diffusers/pull/12526),[Weights](https://huggingface.co/Wan-AI/Wan2.2-Animate-14B-Diffusers)). Thanks to all community contributors. Enjoy! * Sep 19, 2025: 💃 We introduct **[Wan2.2-Animate-14B](https://humanaigc.github.io/wan-animate)**, an unified model for character animation and replacement with holistic movement and expression replication. We released the [model weights](#model-download) and [inference code](#run-wan-animate). And you can try it on [wan.video](https://wan.video/), [ModelScope Studio](https://www.modelscope.cn/studios/Wan-AI/Wan2.2-Animate) or [HuggingFace Space](https://huggingface.co/spaces/Wan-AI/Wan2.2-Animate)! * Aug 26, 2025: 🎵 We introduce **[Wan2.2-S2V-14B](https://humanaigc.github.io/wan-s2v-webpage)**, an audio-driven cinematic video generation model, including [inference code](#run-speech-to-video-generation), [model weights](#model-download), and [technical report](https://humanaigc.github.io/wan-s2v-webpage/content/wan-s2v.pdf)! Now you can try it on [wan.video](https://wan.video/), [ModelScope Gradio](https://www.modelscope.cn/studios/Wan-AI/Wan2.2-S2V) or [HuggingFace Gradio](https://huggingface.co/spaces/Wan-AI/Wan2.2-S2V)! * Jul 28, 2025: 👋 We have open a [HF space](https://huggingface.co/spaces/Wan-AI/Wan-2.2-5B) using the TI2V-5B model. Enjoy! * Jul 28, 2025: 👋 Wan2.2 has been integrated into ComfyUI ([CN](https://docs.comfy.org/zh-CN/tutorials/video/wan/wan2_2) | [EN](https://docs.comfy.org/tutorials/video/wan/wan2_2)). Enjoy! * Jul 28, 2025: 👋 Wan2.2's T2V, I2V and TI2V have been integrated into Diffusers ([T2V-A14B](https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B-Diffusers) | [I2V-A14B](https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B-Diffusers) | [TI2V-5B](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B-Diffusers)). Feel free to give it a try! * Jul 28, 2025: 👋 We've released the inference code and model weights of **Wan2.2**. * Sep 5, 2025: 👋 We add text-to-speech synthesis support with [CosyVoice](https://github.com/FunAudioLLM/CosyVoice) for Speech-to-Video generation task. ## Community Works If your research or project builds upon [**Wan2.1**](https://github.com/Wan-Video/Wan2.1) or [**Wan2.2**](https://github.com/Wan-Video/Wan2.2), and you would like more people to see it, please inform us. - [Prompt Relay](https://github.com/GordonChen19/Prompt-Relay), a plug-and-play, inference-time method for temporal control in video generation. Prompt Relay improves video quality and gives users precise control over what happens at each moment in the video. Visit their [webpage](https://gordonchen19.github.io/Prompt-Relay/) for more details. - [Helios](https://github.com/PKU-YuanGroup/Helios), a breakthrough video generation model base on **Wan2.1** that achieves minute-scale, high-quality video synthesis at 19.5 FPS on a single H100 GPU (about 10 FPS on a single Ascend NPU) —without relying on conventional long video anti-drifting strategies or standard video acceleration techniques. Visit their [webpage](https://pku-yuangroup.github.io/Helios-Page/) for more details. - [LightX2V](https://github.com/ModelTC/LightX2V), a lightweight and efficient video generation framework that integrates **Wan2.1** and **Wan2.2**, supporting multiple engineering acceleration techniques for fast inference. [LightX2V-HuggingFace](https://huggingface.co/lightx2v), offers a variety of Wan-based step-distillation models, quantized models, and lightweight VAE models. - [HuMo](https://github.com/Phantom-video/HuMo) proposed a unified, human-centric framework based on **Wan** to produce high-quality, fine-grained, and controllable human videos from multimodal inputs—including text, images, and audio. Visit their [webpage](https://phantom-video.github.io/HuMo/) for more details. - [FastVideo](https://github.com/hao-ai-lab/FastVideo) includes distilled **Wan** models with sparse attention that significanly speed up the inference time. - [Cache-dit](https://github.com/vipshop/cache-dit) offers Fully Cache Acceleration support for **Wan2.2** MoE with DBCache, TaylorSeer and Cache CFG. Visit their [example](https://github.com/vipshop/cache-dit/blob/main/examples/pipeline/run_wan_2.2.py) for more details. - [Kijai's ComfyUI WanVideoWrapper](https://github.com/kijai/ComfyUI-WanVideoWrapper) is an alternative implementation of **Wan** models for ComfyUI. Thanks to its Wan-only focus, it's on the frontline of getting cutting edge optimizations and hot research features, which are often hard to integrate into ComfyUI quickly due to its more rigid structure. - [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) provides comprehensive support for **Wan 2.2**, including low-GPU-memory layer-by-layer offload, FP8 quantization, sequence parallelism, LoRA training, full training. ## 📑 Todo List - Wan2.2 Text-to-Video - [x] Multi-GPU Inference code of the A14B and 14B models - [x] Checkpoints of the A14B and 14B models - [x] ComfyUI integration - [x] Diffusers integration - Wan2.2 Image-to-Video - [x] Multi-GPU Inference code of the A14B model - [x] Checkpoints of the A14B model - [x] ComfyUI integration - [x] Diffusers integration - Wan2.2 Text-Image-to-Video - [x] Multi-GPU Inference code of the 5B model - [x] Checkpoints of the 5B model - [x] ComfyUI integration - [x] Diffusers integration - Wan2.2-S2V Speech-to-Video - [x] Inference code of Wan2.2-S2V - [x] Checkpoints of Wan2.2-S2V-14B - [x] ComfyUI integration - [x] Diffusers integration - Wan2.2-Animate Character Animation and Replacement - [x] Inference code of Wan2.2-Animate - [x] Checkpoints of Wan2.2-Animate - [x] ComfyUI integration - [x] Diffusers integration ## Run Wan2.2 #### Installation Clone the repo: ```sh git clone https://github.com/Wan-Video/Wan2.2.git cd Wan2.2 ``` Install dependencies: ```sh # Ensure torch >= 2.4.0 # If the installation of `flash_attn` fails, try installing the other packages first and install `flash_attn` last pip install -r requirements.txt # If you want to use CosyVoice to synthesize speech for Speech-to-Video Generation, please install requirements_s2v.txt additionally pip install -r requirements_s2v.txt ``` #### Model Download | Models | Download Links | Description | |--------------------|---------------------------------------------------------------------------------------------------------------------------------------------|-------------| | T2V-A14B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B) 🤖 [ModelScope](https://modelscope.cn/models/Wan-AI/Wan2.2-T2V-A14B) | Text-to-Video MoE model, supports 480P & 720P | | I2V-A14B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B) 🤖 [ModelScope](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B) | Image-to-Video MoE model, supports 480P & 720P | | TI2V-5B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B) 🤖 [ModelScope](https://modelscope.cn/models/Wan-AI/Wan2.2-TI2V-5B) | High-compression VAE, T2V+I2V, supports 720P | | S2V-14B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.2-S2V-14B) 🤖 [ModelScope](https://modelscope.cn/models/Wan-AI/Wan2.2-S2V-14B) | Speech-to-Video model, supports 480P & 720P | | Animate-14B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.2-Animate-14B) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.2-Animate-14B) | Character animation and replacement | | > 💡Note: > The TI2V-5B model supports 720P video generation at **24 FPS**. Download models using huggingface-cli: ``` sh pip install "huggingface_hub[cli]" huggingface-cli download Wan-AI/Wan2.2-T2V-A14B --local-dir ./Wan2.2-T2V-A14B ``` Download models using modelscope-cli: ``` sh pip install modelscope modelscope download Wan-AI/Wan2.2-T2V-A14B --local_dir ./Wan2.2-T2V-A14B ``` #### Run Text-to-Video Generation This repository supports the `Wan2.2-T2V-A14B` Text-to-Video model and can simultaneously support video generation at 480P and 720P resolutions. ##### (1) Without Prompt Extension To facilitate implementation, we will start with a basic version of the inference process that skips the [prompt extension](#2-using-prompt-extention) step. - Single-GPU inference ``` sh python generate.py --task t2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-T2V-A14B --offload_model True --convert_model_dtype --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage." ``` > 💡 This command can run on a GPU with at least 80GB VRAM. > 💡If you encounter OOM (Out-of-Memory) issues, you can use the `--offload_model True`, `--convert_model_dtype` and `--t5_cpu` options to reduce GPU memory usage. - Multi-GPU inference using FSDP + DeepSpeed Ulysses We use [PyTorch FSDP](https://docs.pytorch.org/docs/stable/fsdp.html) and [DeepSpeed Ulysses](https://arxiv.org/abs/2309.14509) to accelerate inference. ``` sh torchrun --nproc_per_node=8 generate.py --task t2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-T2V-A14B --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage." ``` ##### (2) Using Prompt Extension Extending the prompts can effectively enrich the details in the generated videos, further enhancing the video quality. Therefore, we recommend enabling prompt extension. We provide the following two methods for prompt extension: - Use the Dashscope API for extension. - Apply for a `dashscope.api_key` in advance ([EN](https://www.alibabacloud.com/help/en/model-studio/getting-started/first-api-call-to-qwen) | [CN](https://help.aliyun.com/zh/model-studio/getting-started/first-api-call-to-qwen)). - Configure the environment variable `DASH_API_KEY` to specify the Dashscope API key. For users of Alibaba Cloud's international site, you also need to set the environment variable `DASH_API_URL` to 'https://dashscope-intl.aliyuncs.com/api/v1'. For more detailed instructions, please refer to the [dashscope document](https://www.alibabacloud.com/help/en/model-studio/developer-reference/use-qwen-by-calling-api?spm=a2c63.p38356.0.i1). - Use the `qwen-plus` model for text-to-video tasks and `qwen-vl-max` for image-to-video tasks. - You can modify the model used for extension with the parameter `--prompt_extend_model`. For example: ```sh DASH_API_KEY=your_key torchrun --nproc_per_node=8 generate.py --task t2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-T2V-A14B --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage" --use_prompt_extend --prompt_extend_method 'dashscope' --prompt_extend_target_lang 'zh' ``` - Using a local model for extension. - By default, the Qwen model on HuggingFace is used for this extension. Users can choose Qwen models or other models based on the available GPU memory size. - For text-to-video tasks, you can use models like `Qwen/Qwen2.5-14B-Instruct`, `Qwen/Qwen2.5-7B-Instruct` and `Qwen/Qwen2.5-3B-Instruct`. - For image-to-video tasks, you can use models like `Qwen/Qwen2.5-VL-7B-Instruct` and `Qwen/Qwen2.5-VL-3B-Instruct`. - Larger models generally provide better extension results but require more GPU memory. - You can modify the model used for extension with the parameter `--prompt_extend_model` , allowing you to specify either a local model path or a Hugging Face model. For example: ``` sh torchrun --nproc_per_node=8 generate.py --task t2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-T2V-A14B --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage" --use_prompt_extend --prompt_extend_method 'local_qwen' --prompt_extend_target_lang 'zh' ``` #### Run Image-to-Video Generation This repository supports the `Wan2.2-I2V-A14B` Image-to-Video model and can simultaneously support video generation at 480P and 720P resolutions. - Single-GPU inference ```sh python generate.py --task i2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-I2V-A14B --offload_model True --convert_model_dtype --image examples/i2v_input.JPG --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." ``` > This command can run on a GPU with at least 80GB VRAM. > 💡For the Image-to-Video task, the `size` parameter represents the area of the generated video, with the aspect ratio following that of the original input image. - Multi-GPU inference using FSDP + DeepSpeed Ulysses ```sh torchrun --nproc_per_node=8 generate.py --task i2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-I2V-A14B --image examples/i2v_input.JPG --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." ``` - Image-to-Video Generation without prompt ```sh DASH_API_KEY=your_key torchrun --nproc_per_node=8 generate.py --task i2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-I2V-A14B --prompt '' --image examples/i2v_input.JPG --dit_fsdp --t5_fsdp --ulysses_size 8 --use_prompt_extend --prompt_extend_method 'dashscope' ``` > 💡The model can generate videos solely from the input image. You can use prompt extension to generate prompt from the image. > The process of prompt extension can be referenced [here](#2-using-prompt-extention). #### Run Text-Image-to-Video Generation This repository supports the `Wan2.2-TI2V-5B` Text-Image-to-Video model and can support video generation at 720P resolutions. - Single-GPU Text-to-Video inference ```sh python generate.py --task ti2v-5B --size 1280*704 --ckpt_dir ./Wan2.2-TI2V-5B --offload_model True --convert_model_dtype --t5_cpu --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage" ``` > 💡Unlike other tasks, the 720P resolution of the Text-Image-to-Video task is `1280*704` or `704*1280`. > This command can run on a GPU with at least 24GB VRAM (e.g, RTX 4090 GPU). > 💡If you are running on a GPU with at least 80GB VRAM, you can remove the `--offload_model True`, `--convert_model_dtype` and `--t5_cpu` options to speed up execution. - Single-GPU Image-to-Video inference ```sh python generate.py --task ti2v-5B --size 1280*704 --ckpt_dir ./Wan2.2-TI2V-5B --offload_model True --convert_model_dtype --t5_cpu --image examples/i2v_input.JPG --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." ``` > 💡If the image parameter is configured, it is an Image-to-Video generation; otherwise, it defaults to a Text-to-Video generation. > 💡Similar to Image-to-Video, the `size` parameter represents the area of the generated video, with the aspect ratio following that of the original input image. - Multi-GPU inference using FSDP + DeepSpeed Ulysses ```sh torchrun --nproc_per_node=8 generate.py --task ti2v-5B --size 1280*704 --ckpt_dir ./Wan2.2-TI2V-5B --dit_fsdp --t5_fsdp --ulysses_size 8 --image examples/i2v_input.JPG --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." ``` > The process of prompt extension can be referenced [here](#2-using-prompt-extention). #### Run Speech-to-Video Generation This repository supports the `Wan2.2-S2V-14B` Speech-to-Video model and can simultaneously support video generation at 480P and 720P resolutions. - Single-GPU Speech-to-Video inference ```sh python generate.py --task s2v-14B --size 1024*704 --ckpt_dir ./Wan2.2-S2V-14B/ --offload_model True --convert_model_dtype --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard." --image "examples/i2v_input.JPG" --audio "examples/talk.wav" # Without setting --num_clip, the generated video length will automatically adjust based on the input audio length # You can use CosyVoice to generate audio with --enable_tts python generate.py --task s2v-14B --size 1024*704 --ckpt_dir ./Wan2.2-S2V-14B/ --offload_model True --convert_model_dtype --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard." --image "examples/i2v_input.JPG" --enable_tts --tts_prompt_audio "examples/zero_shot_prompt.wav" --tts_prompt_text "希望你以后能够做的比我还好呦。" --tts_text "收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。" ``` > 💡 This command can run on a GPU with at least 80GB VRAM. - Multi-GPU inference using FSDP + DeepSpeed Ulysses ```sh torchrun --nproc_per_node=8 generate.py --task s2v-14B --size 1024*704 --ckpt_dir ./Wan2.2-S2V-14B/ --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard." --image "examples/i2v_input.JPG" --audio "examples/talk.wav" ``` - Pose + Audio driven generation ```sh torchrun --nproc_per_node=8 generate.py --task s2v-14B --size 1024*704 --ckpt_dir ./Wan2.2-S2V-14B/ --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt "a person is singing" --image "examples/pose.png" --audio "examples/sing.MP3" --pose_video "./examples/pose.mp4" ``` > 💡For the Speech-to-Video task, the `size` parameter represents the area of the generated video, with the aspect ratio following that of the original input image. > 💡The model can generate videos from audio input combined with reference image and optional text prompt. > 💡The `--pose_video` parameter enables pose-driven generation, allowing the model to follow specific pose sequences while generating videos synchronized with audio input. > 💡The `--num_clip` parameter controls the number of video clips generated, useful for quick preview with shorter generation time. Please visit our project page to see more examples and learn about the scenarios suitable for this model. #### Run Wan-Animate Wan-Animate takes a video and a character image as input, and generates a video in either "animation" or "replacement" mode. 1. animation mode： The model generates a video of the character image that mimics the human motion in the input video. 2. replacement mode: The model replaces the character image with the input video. Please visit our [project page](https://humanaigc.github.io/wan-animate) to see more examples and learn about the scenarios suitable for this model. ##### (1) Preprocessing The input video should be preprocessed into several materials before be feed into the inference process. Please refer to the following processing flow, and more details about preprocessing can be found in [UserGuider](https://github.com/Wan-Video/Wan2.2/blob/main/wan/modules/animate/preprocess/UserGuider.md). * For animation ```bash python ./wan/modules/animate/preprocess/preprocess_data.py \ --ckpt_path ./Wan2.2-Animate-14B/process_checkpoint \ --video_path ./examples/wan_animate/animate/video.mp4 \ --refer_path ./examples/wan_animate/animate/image.jpeg \ --save_path ./examples/wan_animate/animate/process_results \ --resolution_area 1280 720 \ --retarget_flag \ --use_flux ``` * For replacement ```bash python ./wan/modules/animate/preprocess/preprocess_data.py \ --ckpt_path ./Wan2.2-Animate-14B/process_checkpoint \ --video_path ./examples/wan_animate/replace/video.mp4 \ --refer_path ./examples/wan_animate/replace/image.jpeg \ --save_path ./examples/wan_animate/replace/process_results \ --resolution_area 1280 720 \ --iterations 3 \ --k 7 \ --w_len 1 \ --h_len 1 \ --replace_flag ``` ##### (2) Run in animation mode * Single-GPU inference ```bash python generate.py --task animate-14B --ckpt_dir ./Wan2.2-Animate-14B/ --src_root_path ./examples/wan_animate/animate/process_results/ --refert_num 1 ``` * Multi-GPU inference using FSDP + DeepSpeed Ulysses ```bash python -m torch.distributed.run --nnodes 1 --nproc_per_node 8 generate.py --task animate-14B --ckpt_dir ./Wan2.2-Animate-14B/ --src_root_path ./examples/wan_animate/animate/process_results/ --refert_num 1 --dit_fsdp --t5_fsdp --ulysses_size 8 ``` * Diffusers Pipeline ```python from diffusers import WanAnimatePipeline from diffusers.utils import export_to_video, load_image, load_video device = "cuda:0" dtype = torch.bfloat16 model_id = "Wan-AI/Wan2.2-Animate-14B-Diffusers" pipe = WanAnimatePipeline.from_pretrained(model_id torch_dtype=dtype) pipe.to(device) seed = 42 prompt = "People in the video are doing actions." # Animation image = load_image("/path/to/animate/reference/image/src_ref.png") pose_video = load_video("/path/to/animate/pose/video/src_pose.mp4") face_video = load_video("/path/to/animate/face/video/src_face.mp4") animate_video = pipe( image=image, pose_video=pose_video, face_video=face_video, prompt=prompt, mode="animate", segment_frame_length=77, # clip_len in original code prev_segment_conditioning_frames=1, # refert_num in original code guidance_scale=1.0, num_inference_steps=20, generator=torch.Generator(device=device).manual_seed(seed), ).frames[0] export_to_video(animate_video, "diffusers_animate.mp4", fps=30) ``` ##### (3) Run in replacement mode * Single-GPU inference ```bash python generate.py --task animate-14B --ckpt_dir ./Wan2.2-Animate-14B/ --src_root_path ./examples/wan_animate/replace/process_results/ --refert_num 1 --replace_flag --use_relighting_lora ``` * Multi-GPU inference using FSDP + DeepSpeed Ulysses ```bash python -m torch.distributed.run --nnodes 1 --nproc_per_node 8 generate.py --task animate-14B --ckpt_dir ./Wan2.2-Animate-14B/ --src_root_path ./examples/wan_animate/replace/process_results/src_pose.mp4 --refert_num 1 --replace_flag --use_relighting_lora --dit_fsdp --t5_fsdp --ulysses_size 8 ``` * Diffusers Pipeline ```python # create pipeline as in the Animation code ☝️ # Replacement image = load_image("/path/to/replace/reference/image/src_ref.png") pose_video = load_video("/path/to/replace/pose/video/src_pose.mp4") face_video = load_video("/path/to/replace/face/video/src_face.mp4") background_video = load_video("/path/to/replace/background/video/src_bg.mp4") mask_video = load_video("/path/to/replace/mask/video/src_mask.mp4") replace_video = pipe( image=image, pose_video=pose_video, face_video=face_video, background_video=background_video, mask_video=mask_video, prompt=prompt, mode="replace", segment_frame_length=77, # clip_len in original code prev_segment_conditioning_frames=1, # refert_num in original code guidance_scale=1.0, num_inference_steps=20, generator=torch.Generator(device=device).manual_seed(seed), ).frames[0] export_to_video(replace_video, "diffusers_replace.mp4", fps=30) ``` > 💡 If you're using **Wan-Animate**, we do not recommend using LoRA models trained on `Wan2.2`, since weight changes during training may lead to unexpected behavior. ## Computational Efficiency on Different GPUs We test the computational efficiency of different **Wan2.2** models on different GPUs in the following table. The results are presented in the format: **Total time (s) / peak GPU memory (GB)**.
> The parameter settings for the tests presented in this table are as follows: > (1) Multi-GPU: 14B: `--ulysses_size 4/8 --dit_fsdp --t5_fsdp`, 5B: `--ulysses_size 4/8 --offload_model True --convert_model_dtype --t5_cpu`; Single-GPU: 14B: `--offload_model True --convert_model_dtype`, 5B: `--offload_model True --convert_model_dtype --t5_cpu` (--convert_model_dtype converts model parameter types to config.param_dtype); > (2) The distributed testing utilizes the built-in FSDP and Ulysses implementations, with FlashAttention3 deployed on Hopper architecture GPUs; > (3) Tests were run without the `--use_prompt_extend` flag; > (4) Reported results are the average of multiple samples taken after the warm-up phase. ------- ## Introduction of Wan2.2 **Wan2.2** builds on the foundation of Wan2.1 with notable improvements in generation quality and model capability. This upgrade is driven by a series of key technical innovations, mainly including the Mixture-of-Experts (MoE) architecture, upgraded training data, and high-compression video generation. ##### (1) Mixture-of-Experts (MoE) Architecture Wan2.2 introduces Mixture-of-Experts (MoE) architecture into the video generation diffusion model. MoE has been widely validated in large language models as an efficient approach to increase total model parameters while keeping inference cost nearly unchanged. In Wan2.2, the A14B model series adopts a two-expert design tailored to the denoising process of diffusion models: a high-noise expert for the early stages, focusing on overall layout; and a low-noise expert for the later stages, refining video details. Each expert model has about 14B parameters, resulting in a total of 27B parameters but only 14B active parameters per step, keeping inference computation and GPU memory nearly unchanged.
The transition point between the two experts is determined by the signal-to-noise ratio (SNR), a metric that decreases monotonically as the denoising step $t$ increases. At the beginning of the denoising process, $t$ is large and the noise level is high, so the SNR is at its minimum, denoted as ${SNR}_{min}$. In this stage, the high-noise expert is activated. We define a threshold step ${t}_{moe}$ corresponding to half of the ${SNR}_{min}$, and switch to the low-noise expert when $t<{t}_{moe}$.
To validate the effectiveness of the MoE architecture, four settings are compared based on their validation loss curves. The baseline **Wan2.1** model does not employ the MoE architecture. Among the MoE-based variants, the **Wan2.1 & High-Noise Expert** reuses the Wan2.1 model as the low-noise expert while uses the Wan2.2's high-noise expert, while the **Wan2.1 & Low-Noise Expert** uses Wan2.1 as the high-noise expert and employ the Wan2.2's low-noise expert. The **Wan2.2 (MoE)** (our final version) achieves the lowest validation loss, indicating that its generated video distribution is closest to ground-truth and exhibits superior convergence. ##### (2) Efficient High-Definition Hybrid TI2V To enable more efficient deployment, Wan2.2 also explores a high-compression design. In addition to the 27B MoE models, a 5B dense model, i.e., TI2V-5B, is released. It is supported by a high-compression Wan2.2-VAE, which achieves a $T\times H\times W$ compression ratio of $4\times16\times16$, increasing the overall compression rate to 64 while maintaining high-quality video reconstruction. With an additional patchification layer, the total compression ratio of TI2V-5B reaches $4\times32\times32$. Without specific optimization, TI2V-5B can generate a 5-second 720P video in under 9 minutes on a single consumer-grade GPU, ranking among the fastest 720P@24fps video generation models. This model also natively supports both text-to-video and image-to-video tasks within a single unified framework, covering both academic research and practical applications.
##### Comparisons to SOTAs We compared Wan2.2 with leading closed-source commercial models on our new Wan-Bench 2.0, evaluating performance across multiple crucial dimensions. The results demonstrate that Wan2.2 achieves superior performance compared to these leading models.
## Citation If you find our work helpful, please cite us. ``` @article{wan2025, title={Wan: Open and Advanced Large-Scale Video Generative Models}, author={Team Wan and Ang Wang and Baole Ai and Bin Wen and Chaojie Mao and Chen-Wei Xie and Di Chen and Feiwu Yu and Haiming Zhao and Jianxiao Yang and Jianyuan Zeng and Jiayu Wang and Jingfeng Zhang and Jingren Zhou and Jinkai Wang and Jixuan Chen and Kai Zhu and Kang Zhao and Keyu Yan and Lianghua Huang and Mengyang Feng and Ningyi Zhang and Pandeng Li and Pingyu Wu and Ruihang Chu and Ruili Feng and Shiwei Zhang and Siyang Sun and Tao Fang and Tianxing Wang and Tianyi Gui and Tingyu Weng and Tong Shen and Wei Lin and Wei Wang and Wei Wang and Wenmeng Zhou and Wente Wang and Wenting Shen and Wenyuan Yu and Xianzhong Shi and Xiaoming Huang and Xin Xu and Yan Kou and Yangyu Lv and Yifei Li and Yijing Liu and Yiming Wang and Yingya Zhang and Yitong Huang and Yong Li and You Wu and Yu Liu and Yulin Pan and Yun Zheng and Yuntao Hong and Yupeng Shi and Yutong Feng and Zeyinzi Jiang and Zhen Han and Zhi-Fan Wu and Ziyu Liu}, journal = {arXiv preprint arXiv:2503.20314}, year={2025} } ``` ## License Agreement The models in this repository are licensed under the Apache 2.0 License. We claim no rights over the your generated contents, granting you the freedom to use them while ensuring that your usage complies with the provisions of this license. You are fully accountable for your use of the models, which must not involve sharing any content that violates applicable laws, causes harm to individuals or groups, disseminates personal information intended for harm, spreads misinformation, or targets vulnerable populations. For a complete list of restrictions and details regarding your rights, please refer to the full text of the [license](LICENSE.txt). ## Acknowledgements We would like to thank the contributors to the [SD3](https://huggingface.co/stabilityai/stable-diffusion-3-medium), [Qwen](https://huggingface.co/Qwen), [umt5-xxl](https://huggingface.co/google/umt5-xxl), [diffusers](https://github.com/huggingface/diffusers) and [HuggingFace](https://huggingface.co) repositories, for their open research. ## Contact Us If you would like to leave a message to our research or product teams, feel free to join our [Discord](https://discord.gg/AKNgpMK4Yj) or [WeChat groups](https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg)! ================================================ FILE: generate.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import argparse import logging import os import sys import warnings from datetime import datetime warnings.filterwarnings('ignore') import random import torch import torch.distributed as dist from PIL import Image import wan from wan.configs import MAX_AREA_CONFIGS, SIZE_CONFIGS, SUPPORTED_SIZES, WAN_CONFIGS from wan.distributed.util import init_distributed_group from wan.utils.prompt_extend import DashScopePromptExpander, QwenPromptExpander from wan.utils.utils import merge_video_audio, save_video, str2bool EXAMPLE_PROMPT = { "t2v-A14B": { "prompt": "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage.", }, "i2v-A14B": { "prompt": "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside.", "image": "examples/i2v_input.JPG", }, "ti2v-5B": { "prompt": "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage.", }, "animate-14B": { "prompt": "视频中的人在做动作", "video": "", "pose": "", "mask": "", }, "s2v-14B": { "prompt": "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside.", "image": "examples/i2v_input.JPG", "audio": "examples/talk.wav", "tts_prompt_audio": "examples/zero_shot_prompt.wav", "tts_prompt_text": "希望你以后能够做的比我还好呦。", "tts_text": "收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。" }, } def _validate_args(args): # Basic check assert args.ckpt_dir is not None, "Please specify the checkpoint directory." assert args.task in WAN_CONFIGS, f"Unsupport task: {args.task}" assert args.task in EXAMPLE_PROMPT, f"Unsupport task: {args.task}" if args.prompt is None: args.prompt = EXAMPLE_PROMPT[args.task]["prompt"] if args.image is None and "image" in EXAMPLE_PROMPT[args.task]: args.image = EXAMPLE_PROMPT[args.task]["image"] if args.audio is None and args.enable_tts is False and "audio" in EXAMPLE_PROMPT[args.task]: args.audio = EXAMPLE_PROMPT[args.task]["audio"] if (args.tts_prompt_audio is None or args.tts_text is None) and args.enable_tts is True and "audio" in EXAMPLE_PROMPT[args.task]: args.tts_prompt_audio = EXAMPLE_PROMPT[args.task]["tts_prompt_audio"] args.tts_prompt_text = EXAMPLE_PROMPT[args.task]["tts_prompt_text"] args.tts_text = EXAMPLE_PROMPT[args.task]["tts_text"] if args.task == "i2v-A14B": assert args.image is not None, "Please specify the image path for i2v." cfg = WAN_CONFIGS[args.task] if args.sample_steps is None: args.sample_steps = cfg.sample_steps if args.sample_shift is None: args.sample_shift = cfg.sample_shift if args.sample_guide_scale is None: args.sample_guide_scale = cfg.sample_guide_scale if args.frame_num is None: args.frame_num = cfg.frame_num args.base_seed = args.base_seed if args.base_seed >= 0 else random.randint( 0, sys.maxsize) # Size check if not 's2v' in args.task: assert args.size in SUPPORTED_SIZES[ args. task], f"Unsupport size {args.size} for task {args.task}, supported sizes are: {', '.join(SUPPORTED_SIZES[args.task])}" def _parse_args(): parser = argparse.ArgumentParser( description="Generate a image or video from a text prompt or image using Wan" ) parser.add_argument( "--task", type=str, default="t2v-A14B", choices=list(WAN_CONFIGS.keys()), help="The task to run.") parser.add_argument( "--size", type=str, default="1280*720", choices=list(SIZE_CONFIGS.keys()), help="The area (width*height) of the generated video. For the I2V task, the aspect ratio of the output video will follow that of the input image." ) parser.add_argument( "--frame_num", type=int, default=None, help="How many frames of video are generated. The number should be 4n+1" ) parser.add_argument( "--ckpt_dir", type=str, default=None, help="The path to the checkpoint directory.") parser.add_argument( "--offload_model", type=str2bool, default=None, help="Whether to offload the model to CPU after each model forward, reducing GPU memory usage." ) parser.add_argument( "--ulysses_size", type=int, default=1, help="The size of the ulysses parallelism in DiT.") parser.add_argument( "--t5_fsdp", action="store_true", default=False, help="Whether to use FSDP for T5.") parser.add_argument( "--t5_cpu", action="store_true", default=False, help="Whether to place T5 model on CPU.") parser.add_argument( "--dit_fsdp", action="store_true", default=False, help="Whether to use FSDP for DiT.") parser.add_argument( "--save_file", type=str, default=None, help="The file to save the generated video to.") parser.add_argument( "--prompt", type=str, default=None, help="The prompt to generate the video from.") parser.add_argument( "--use_prompt_extend", action="store_true", default=False, help="Whether to use prompt extend.") parser.add_argument( "--prompt_extend_method", type=str, default="local_qwen", choices=["dashscope", "local_qwen"], help="The prompt extend method to use.") parser.add_argument( "--prompt_extend_model", type=str, default=None, help="The prompt extend model to use.") parser.add_argument( "--prompt_extend_target_lang", type=str, default="zh", choices=["zh", "en"], help="The target language of prompt extend.") parser.add_argument( "--base_seed", type=int, default=-1, help="The seed to use for generating the video.") parser.add_argument( "--image", type=str, default=None, help="The image to generate the video from.") parser.add_argument( "--sample_solver", type=str, default='unipc', choices=['unipc', 'dpm++'], help="The solver used to sample.") parser.add_argument( "--sample_steps", type=int, default=None, help="The sampling steps.") parser.add_argument( "--sample_shift", type=float, default=None, help="Sampling shift factor for flow matching schedulers.") parser.add_argument( "--sample_guide_scale", type=float, default=None, help="Classifier free guidance scale.") parser.add_argument( "--convert_model_dtype", action="store_true", default=False, help="Whether to convert model paramerters dtype.") # animate parser.add_argument( "--src_root_path", type=str, default=None, help="The file of the process output path. Default None.") parser.add_argument( "--refert_num", type=int, default=77, help="How many frames used for temporal guidance. Recommended to be 1 or 5." ) parser.add_argument( "--replace_flag", action="store_true", default=False, help="Whether to use replace.") parser.add_argument( "--use_relighting_lora", action="store_true", default=False, help="Whether to use relighting lora.") # following args only works for s2v parser.add_argument( "--num_clip", type=int, default=None, help="Number of video clips to generate, the whole video will not exceed the length of audio." ) parser.add_argument( "--audio", type=str, default=None, help="Path to the audio file, e.g. wav, mp3") parser.add_argument( "--enable_tts", action="store_true", default=False, help="Use CosyVoice to synthesis audio") parser.add_argument( "--tts_prompt_audio", type=str, default=None, help="Path to the tts prompt audio file, e.g. wav, mp3. Must be greater than 16khz, and between 5s to 15s.") parser.add_argument( "--tts_prompt_text", type=str, default=None, help="Content to the tts prompt audio. If provided, must exactly match tts_prompt_audio") parser.add_argument( "--tts_text", type=str, default=None, help="Text wish to synthesize") parser.add_argument( "--pose_video", type=str, default=None, help="Provide Dw-pose sequence to do Pose Driven") parser.add_argument( "--start_from_ref", action="store_true", default=False, help="whether set the reference image as the starting point for generation" ) parser.add_argument( "--infer_frames", type=int, default=80, help="Number of frames per clip, 48 or 80 or others (must be multiple of 4) for 14B s2v" ) args = parser.parse_args() _validate_args(args) return args def _init_logging(rank): # logging if rank == 0: # set format logging.basicConfig( level=logging.INFO, format="[%(asctime)s] %(levelname)s: %(message)s", handlers=[logging.StreamHandler(stream=sys.stdout)]) else: logging.basicConfig(level=logging.ERROR) def generate(args): rank = int(os.getenv("RANK", 0)) world_size = int(os.getenv("WORLD_SIZE", 1)) local_rank = int(os.getenv("LOCAL_RANK", 0)) device = local_rank _init_logging(rank) if args.offload_model is None: args.offload_model = False if world_size > 1 else True logging.info( f"offload_model is not specified, set to {args.offload_model}.") if world_size > 1: torch.cuda.set_device(local_rank) dist.init_process_group( backend="nccl", init_method="env://", rank=rank, world_size=world_size) else: assert not ( args.t5_fsdp or args.dit_fsdp ), f"t5_fsdp and dit_fsdp are not supported in non-distributed environments." assert not ( args.ulysses_size > 1 ), f"sequence parallel are not supported in non-distributed environments." if args.ulysses_size > 1: assert args.ulysses_size == world_size, f"The number of ulysses_size should be equal to the world size." init_distributed_group() if args.use_prompt_extend: if args.prompt_extend_method == "dashscope": prompt_expander = DashScopePromptExpander( model_name=args.prompt_extend_model, task=args.task, is_vl=args.image is not None) elif args.prompt_extend_method == "local_qwen": prompt_expander = QwenPromptExpander( model_name=args.prompt_extend_model, task=args.task, is_vl=args.image is not None, device=rank) else: raise NotImplementedError( f"Unsupport prompt_extend_method: {args.prompt_extend_method}") cfg = WAN_CONFIGS[args.task] if args.ulysses_size > 1: assert cfg.num_heads % args.ulysses_size == 0, f"`{cfg.num_heads=}` cannot be divided evenly by `{args.ulysses_size=}`." logging.info(f"Generation job args: {args}") logging.info(f"Generation model config: {cfg}") if dist.is_initialized(): base_seed = [args.base_seed] if rank == 0 else [None] dist.broadcast_object_list(base_seed, src=0) args.base_seed = base_seed[0] logging.info(f"Input prompt: {args.prompt}") img = None if args.image is not None: img = Image.open(args.image).convert("RGB") logging.info(f"Input image: {args.image}") # prompt extend if args.use_prompt_extend: logging.info("Extending prompt ...") if rank == 0: prompt_output = prompt_expander( args.prompt, image=img, tar_lang=args.prompt_extend_target_lang, seed=args.base_seed) if prompt_output.status == False: logging.info( f"Extending prompt failed: {prompt_output.message}") logging.info("Falling back to original prompt.") input_prompt = args.prompt else: input_prompt = prompt_output.prompt input_prompt = [input_prompt] else: input_prompt = [None] if dist.is_initialized(): dist.broadcast_object_list(input_prompt, src=0) args.prompt = input_prompt[0] logging.info(f"Extended prompt: {args.prompt}") if "t2v" in args.task: logging.info("Creating WanT2V pipeline.") wan_t2v = wan.WanT2V( config=cfg, checkpoint_dir=args.ckpt_dir, device_id=device, rank=rank, t5_fsdp=args.t5_fsdp, dit_fsdp=args.dit_fsdp, use_sp=(args.ulysses_size > 1), t5_cpu=args.t5_cpu, convert_model_dtype=args.convert_model_dtype, ) logging.info(f"Generating video ...") video = wan_t2v.generate( args.prompt, size=SIZE_CONFIGS[args.size], frame_num=args.frame_num, shift=args.sample_shift, sample_solver=args.sample_solver, sampling_steps=args.sample_steps, guide_scale=args.sample_guide_scale, seed=args.base_seed, offload_model=args.offload_model) elif "ti2v" in args.task: logging.info("Creating WanTI2V pipeline.") wan_ti2v = wan.WanTI2V( config=cfg, checkpoint_dir=args.ckpt_dir, device_id=device, rank=rank, t5_fsdp=args.t5_fsdp, dit_fsdp=args.dit_fsdp, use_sp=(args.ulysses_size > 1), t5_cpu=args.t5_cpu, convert_model_dtype=args.convert_model_dtype, ) logging.info(f"Generating video ...") video = wan_ti2v.generate( args.prompt, img=img, size=SIZE_CONFIGS[args.size], max_area=MAX_AREA_CONFIGS[args.size], frame_num=args.frame_num, shift=args.sample_shift, sample_solver=args.sample_solver, sampling_steps=args.sample_steps, guide_scale=args.sample_guide_scale, seed=args.base_seed, offload_model=args.offload_model) elif "animate" in args.task: logging.info("Creating Wan-Animate pipeline.") wan_animate = wan.WanAnimate( config=cfg, checkpoint_dir=args.ckpt_dir, device_id=device, rank=rank, t5_fsdp=args.t5_fsdp, dit_fsdp=args.dit_fsdp, use_sp=(args.ulysses_size > 1), t5_cpu=args.t5_cpu, convert_model_dtype=args.convert_model_dtype, use_relighting_lora=args.use_relighting_lora ) logging.info(f"Generating video ...") video = wan_animate.generate( src_root_path=args.src_root_path, replace_flag=args.replace_flag, refert_num = args.refert_num, clip_len=args.frame_num, shift=args.sample_shift, sample_solver=args.sample_solver, sampling_steps=args.sample_steps, guide_scale=args.sample_guide_scale, seed=args.base_seed, offload_model=args.offload_model) elif "s2v" in args.task: logging.info("Creating WanS2V pipeline.") wan_s2v = wan.WanS2V( config=cfg, checkpoint_dir=args.ckpt_dir, device_id=device, rank=rank, t5_fsdp=args.t5_fsdp, dit_fsdp=args.dit_fsdp, use_sp=(args.ulysses_size > 1), t5_cpu=args.t5_cpu, convert_model_dtype=args.convert_model_dtype, ) logging.info(f"Generating video ...") video = wan_s2v.generate( input_prompt=args.prompt, ref_image_path=args.image, audio_path=args.audio, enable_tts=args.enable_tts, tts_prompt_audio=args.tts_prompt_audio, tts_prompt_text=args.tts_prompt_text, tts_text=args.tts_text, num_repeat=args.num_clip, pose_video=args.pose_video, max_area=MAX_AREA_CONFIGS[args.size], infer_frames=args.infer_frames, shift=args.sample_shift, sample_solver=args.sample_solver, sampling_steps=args.sample_steps, guide_scale=args.sample_guide_scale, seed=args.base_seed, offload_model=args.offload_model, init_first_frame=args.start_from_ref, ) else: logging.info("Creating WanI2V pipeline.") wan_i2v = wan.WanI2V( config=cfg, checkpoint_dir=args.ckpt_dir, device_id=device, rank=rank, t5_fsdp=args.t5_fsdp, dit_fsdp=args.dit_fsdp, use_sp=(args.ulysses_size > 1), t5_cpu=args.t5_cpu, convert_model_dtype=args.convert_model_dtype, ) logging.info("Generating video ...") video = wan_i2v.generate( args.prompt, img, max_area=MAX_AREA_CONFIGS[args.size], frame_num=args.frame_num, shift=args.sample_shift, sample_solver=args.sample_solver, sampling_steps=args.sample_steps, guide_scale=args.sample_guide_scale, seed=args.base_seed, offload_model=args.offload_model) if rank == 0: if args.save_file is None: formatted_time = datetime.now().strftime("%Y%m%d_%H%M%S") formatted_prompt = args.prompt.replace(" ", "_").replace("/", "_")[:50] suffix = '.mp4' args.save_file = f"{args.task}_{args.size.replace('*','x') if sys.platform=='win32' else args.size}_{args.ulysses_size}_{formatted_prompt}_{formatted_time}" + suffix logging.info(f"Saving generated video to {args.save_file}") save_video( tensor=video[None], save_file=args.save_file, fps=cfg.sample_fps, nrow=1, normalize=True, value_range=(-1, 1)) if "s2v" in args.task: if args.enable_tts is False: merge_video_audio(video_path=args.save_file, audio_path=args.audio) else: merge_video_audio(video_path=args.save_file, audio_path="tts.wav") del video torch.cuda.synchronize() if dist.is_initialized(): dist.barrier() dist.destroy_process_group() logging.info("Finished.") if __name__ == "__main__": args = _parse_args() generate(args) ================================================ FILE: pyproject.toml ================================================ [build-system] requires = ["setuptools>=61.0"] build-backend = "setuptools.build_meta" [project] name = "wan" version = "2.2.0" description = "Wan: Open and Advanced Large-Scale Video Generative Models" authors = [ { name = "Wan Team", email = "wan.ai@alibabacloud.com" } ] license = { file = "LICENSE.txt" } readme = "README.md" requires-python = ">=3.10,<4.0" dependencies = [ "torch>=2.4.0", "torchvision>=0.19.0", "opencv-python>=4.9.0.80", "diffusers>=0.31.0", "transformers>=4.49.0", "tokenizers>=0.20.3", "accelerate>=1.1.1", "tqdm", "imageio", "easydict", "ftfy", "dashscope", "imageio-ffmpeg", "flash_attn", "numpy>=1.23.5,<2" ] [project.optional-dependencies] dev = [ "pytest", "black", "flake8", "isort", "mypy", "huggingface-hub[cli]" ] [project.urls] homepage = "https://wanxai.com" documentation = "https://github.com/Wan-Video/Wan2.2" repository = "https://github.com/Wan-Video/Wan2.2" huggingface = "https://huggingface.co/Wan-AI/" modelscope = "https://modelscope.cn/organization/Wan-AI" discord = "https://discord.gg/p5XbdQV7" [tool.setuptools] packages = ["wan"] [tool.setuptools.package-data] "wan" = ["**/*.py"] [tool.black] line-length = 88 [tool.isort] profile = "black" [tool.mypy] strict = true ================================================ FILE: requirements.txt ================================================ torch>=2.4.0 torchvision>=0.19.0 torchaudio opencv-python>=4.9.0.80 diffusers>=0.31.0 transformers>=4.49.0,<=4.51.3 tokenizers>=0.20.3 accelerate>=1.1.1 tqdm imageio[ffmpeg] easydict ftfy dashscope imageio-ffmpeg flash_attn numpy>=1.23.5,<2 ================================================ FILE: requirements_animate.txt ================================================ decord peft onnxruntime pandas matplotlib -e git+https://github.com/facebookresearch/sam2.git@0e78a118995e66bb27d78518c4bd9a3e95b4e266#egg=SAM-2 loguru sentencepiece ================================================ FILE: requirements_s2v.txt ================================================ openai-whisper HyperPyYAML onnxruntime inflect wetext omegaconf conformer hydra-core lightning rich gdown matplotlib wget pyarrow pyworld librosa decord modelscope GitPython ================================================ FILE: tests/README.md ================================================ Put all your models (Wan2.2-T2V-A14B, Wan2.2-I2V-A14B, Wan2.2-TI2V-5B) in a folder and specify the max GPU number you want to use. ```bash bash ./tests/test.sh ``` ================================================ FILE: tests/test.sh ================================================ #!/bin/bash set -x unset NCCL_DEBUG if [ "$#" -eq 2 ]; then MODEL_DIR=$(realpath "$1") GPUS=$2 else echo "Usage: $0 " exit 1 fi SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" REPO_ROOT="$(dirname "$SCRIPT_DIR")" cd "$REPO_ROOT" || exit 1 PY_FILE=./generate.py function t2v_A14B() { CKPT_DIR="$MODEL_DIR/Wan2.2-T2V-A14B" # # 1-GPU Test # echo -e "\n\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> t2v_A14B 1-GPU Test: " # python $PY_FILE --task t2v-A14B --size 480*832 --ckpt_dir $CKPT_DIR # Multiple GPU Test echo -e "\n\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> t2v_A14B Multiple GPU Test: " torchrun --nproc_per_node=$GPUS $PY_FILE --task t2v-A14B --ckpt_dir $CKPT_DIR --size 832*480 --dit_fsdp --t5_fsdp --ulysses_size $GPUS echo -e "\n\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> t2v_A14B Multiple GPU Test: " torchrun --nproc_per_node=$GPUS $PY_FILE --task t2v-A14B --ckpt_dir $CKPT_DIR --size 720*1280 --dit_fsdp --t5_fsdp --ulysses_size $GPUS echo -e "\n\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> t2v_A14B Multiple GPU Test: " torchrun --nproc_per_node=$GPUS $PY_FILE --task t2v-A14B --ckpt_dir $CKPT_DIR --size 1280*720 --dit_fsdp --t5_fsdp --ulysses_size $GPUS echo -e "\n\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> t2v_A14B Multiple GPU, prompt extend local_qwen: " torchrun --nproc_per_node=$GPUS $PY_FILE --task t2v-A14B --ckpt_dir $CKPT_DIR --size 480*832 --dit_fsdp --t5_fsdp --ulysses_size $GPUS --use_prompt_extend --prompt_extend_model "Qwen/Qwen2.5-3B-Instruct" --prompt_extend_target_lang "en" } function i2v_A14B() { CKPT_DIR="$MODEL_DIR/Wan2.2-I2V-A14B" # echo -e "\n\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> i2v_14B 1-GPU Test: " # python $PY_FILE --task i2v-A14B --size 832*480 --ckpt_dir $CKPT_DIR # Multiple GPU Test echo -e "\n\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> i2v_14B Multiple GPU Test: " torchrun --nproc_per_node=$GPUS $PY_FILE --task i2v-A14B --ckpt_dir $CKPT_DIR --size 832*480 --dit_fsdp --t5_fsdp --ulysses_size $GPUS echo -e "\n\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> i2v_14B Multiple GPU, prompt extend local_qwen: " torchrun --nproc_per_node=$GPUS $PY_FILE --task i2v-A14B --ckpt_dir $CKPT_DIR --size 720*1280 --dit_fsdp --t5_fsdp --ulysses_size $GPUS --use_prompt_extend --prompt_extend_model "Qwen/Qwen2.5-VL-3B-Instruct" --prompt_extend_target_lang "en" echo -e "\n\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> i2v_14B Multiple GPU, prompt extend local_qwen: " torchrun --nproc_per_node=$GPUS $PY_FILE --task i2v-A14B --ckpt_dir $CKPT_DIR --size 1280*720 --dit_fsdp --t5_fsdp --ulysses_size $GPUS --use_prompt_extend --prompt_extend_model "Qwen/Qwen2.5-VL-3B-Instruct" --prompt_extend_target_lang "en" if [ -n "${DASH_API_KEY+x}" ]; then echo -e "\n\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> i2v_14B Multiple GPU, prompt extend dashscope: " torchrun --nproc_per_node=$GPUS $PY_FILE --task i2v-A14B --ckpt_dir $CKPT_DIR --size 480*832 --dit_fsdp --t5_fsdp --ulysses_size $GPUS --use_prompt_extend --prompt_extend_method "dashscope" else echo -e "\n\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> No DASH_API_KEY found, skip the dashscope extend test." fi } function ti2v_5B() { CKPT_DIR="$MODEL_DIR/Wan2.2-TI2V-5B" # # 1-GPU Test # echo -e "\n\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> ti2v_5B t2v 1-GPU Test: " # python $PY_FILE --task ti2v-5B --size 1280*704 --ckpt_dir $CKPT_DIR # Multiple GPU Test echo -e "\n\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> ti2v_5B t2v Multiple GPU Test: " torchrun --nproc_per_node=$GPUS $PY_FILE --task ti2v-5B --ckpt_dir $CKPT_DIR --size 1280*704 --dit_fsdp --t5_fsdp --ulysses_size $GPUS echo -e "\n\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> ti2v_5B t2v Multiple GPU, prompt extend local_qwen: " torchrun --nproc_per_node=$GPUS $PY_FILE --task ti2v-5B --ckpt_dir $CKPT_DIR --size 704*1280 --dit_fsdp --t5_fsdp --ulysses_size $GPUS --use_prompt_extend --prompt_extend_model "Qwen/Qwen2.5-3B-Instruct" --prompt_extend_target_lang "en" echo -e "\n\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> ti2v_5B i2v Multiple GPU Test: " torchrun --nproc_per_node=$GPUS $PY_FILE --task ti2v-5B --ckpt_dir $CKPT_DIR --size 704*1280 --dit_fsdp --t5_fsdp --ulysses_size $GPUS --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." --image "examples/i2v_input.JPG" echo -e "\n\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> ti2v_5B i2v Multiple GPU, prompt extend local_qwen: " torchrun --nproc_per_node=$GPUS $PY_FILE --task ti2v-5B --ckpt_dir $CKPT_DIR --size 1280*704 --dit_fsdp --t5_fsdp --ulysses_size $GPUS --use_prompt_extend --prompt_extend_model "Qwen/Qwen2.5-3B-Instruct" --prompt_extend_target_lang 'en' --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." --image "examples/i2v_input.JPG" } t2v_A14B i2v_A14B ti2v_5B ================================================ FILE: wan/__init__.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. from . import configs, distributed, modules from .image2video import WanI2V from .speech2video import WanS2V from .text2video import WanT2V from .textimage2video import WanTI2V from .animate import WanAnimate ================================================ FILE: wan/animate.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import logging import math import os import cv2 import types from copy import deepcopy from functools import partial from einops import rearrange import numpy as np import torch import torch.distributed as dist from peft import set_peft_model_state_dict from decord import VideoReader from tqdm import tqdm import torch.nn.functional as F from .distributed.fsdp import shard_model from .distributed.sequence_parallel import sp_attn_forward, sp_dit_forward from .distributed.util import get_world_size from .modules.animate import WanAnimateModel from .modules.animate import CLIPModel from .modules.t5 import T5EncoderModel from .modules.vae2_1 import Wan2_1_VAE from .modules.animate.animate_utils import TensorList, get_loraconfig from .utils.fm_solvers import ( FlowDPMSolverMultistepScheduler, get_sampling_sigmas, retrieve_timesteps, ) from .utils.fm_solvers_unipc import FlowUniPCMultistepScheduler class WanAnimate: def __init__( self, config, checkpoint_dir, device_id=0, rank=0, t5_fsdp=False, dit_fsdp=False, use_sp=False, t5_cpu=False, init_on_cpu=True, convert_model_dtype=False, use_relighting_lora=False ): r""" Initializes the generation model components. Args: config (EasyDict): Object containing model parameters initialized from config.py checkpoint_dir (`str`): Path to directory containing model checkpoints device_id (`int`, *optional*, defaults to 0): Id of target GPU device rank (`int`, *optional*, defaults to 0): Process rank for distributed training t5_fsdp (`bool`, *optional*, defaults to False): Enable FSDP sharding for T5 model dit_fsdp (`bool`, *optional*, defaults to False): Enable FSDP sharding for DiT model use_sp (`bool`, *optional*, defaults to False): Enable distribution strategy of sequence parallel. t5_cpu (`bool`, *optional*, defaults to False): Whether to place T5 model on CPU. Only works without t5_fsdp. init_on_cpu (`bool`, *optional*, defaults to True): Enable initializing Transformer Model on CPU. Only works without FSDP or USP. convert_model_dtype (`bool`, *optional*, defaults to False): Convert DiT model parameters dtype to 'config.param_dtype'. Only works without FSDP. use_relighting_lora (`bool`, *optional*, defaults to False): Whether to use relighting lora for character replacement. """ self.device = torch.device(f"cuda:{device_id}") self.config = config self.rank = rank self.t5_cpu = t5_cpu self.init_on_cpu = init_on_cpu self.num_train_timesteps = config.num_train_timesteps self.param_dtype = config.param_dtype if t5_fsdp or dit_fsdp or use_sp: self.init_on_cpu = False shard_fn = partial(shard_model, device_id=device_id) self.text_encoder = T5EncoderModel( text_len=config.text_len, dtype=config.t5_dtype, device=torch.device('cpu'), checkpoint_path=os.path.join(checkpoint_dir, config.t5_checkpoint), tokenizer_path=os.path.join(checkpoint_dir, config.t5_tokenizer), shard_fn=shard_fn if t5_fsdp else None, ) self.clip = CLIPModel( dtype=torch.float16, device=self.device, checkpoint_path=os.path.join(checkpoint_dir, config.clip_checkpoint), tokenizer_path=os.path.join(checkpoint_dir, config.clip_tokenizer)) self.vae = Wan2_1_VAE( vae_pth=os.path.join(checkpoint_dir, config.vae_checkpoint), device=self.device) logging.info(f"Creating WanAnimate from {checkpoint_dir}") if not dit_fsdp: self.noise_model = WanAnimateModel.from_pretrained( checkpoint_dir, torch_dtype=self.param_dtype, device_map=self.device) else: self.noise_model = WanAnimateModel.from_pretrained( checkpoint_dir, torch_dtype=self.param_dtype) self.noise_model = self._configure_model( model=self.noise_model, use_sp=use_sp, dit_fsdp=dit_fsdp, shard_fn=shard_fn, convert_model_dtype=convert_model_dtype, use_lora=use_relighting_lora, checkpoint_dir=checkpoint_dir, config=config ) if use_sp: self.sp_size = get_world_size() else: self.sp_size = 1 self.sample_neg_prompt = config.sample_neg_prompt self.sample_prompt = config.prompt def _configure_model(self, model, use_sp, dit_fsdp, shard_fn, convert_model_dtype, use_lora, checkpoint_dir, config): """ Configures a model object. This includes setting evaluation modes, applying distributed parallel strategy, and handling device placement. Args: model (torch.nn.Module): The model instance to configure. use_sp (`bool`): Enable distribution strategy of sequence parallel. dit_fsdp (`bool`): Enable FSDP sharding for DiT model. shard_fn (callable): The function to apply FSDP sharding. convert_model_dtype (`bool`): Convert DiT model parameters dtype to 'config.param_dtype'. Only works without FSDP. Returns: torch.nn.Module: The configured model. """ model.eval().requires_grad_(False) if use_sp: for block in model.blocks: block.self_attn.forward = types.MethodType( sp_attn_forward, block.self_attn) model.use_context_parallel = True if dist.is_initialized(): dist.barrier() if use_lora: logging.info("Loading Relighting Lora. ") lora_config = get_loraconfig( transformer=model, rank=128, alpha=128 ) model.add_adapter(lora_config) lora_path = os.path.join(checkpoint_dir, config.lora_checkpoint) peft_state_dict = torch.load(lora_path)["state_dict"] set_peft_model_state_dict(model, peft_state_dict) if dit_fsdp: model = shard_fn(model, use_lora=use_lora) else: if convert_model_dtype: model.to(self.param_dtype) if not self.init_on_cpu: model.to(self.device) return model def inputs_padding(self, array, target_len): idx = 0 flip = False target_array = [] while len(target_array) < target_len: target_array.append(deepcopy(array[idx])) if flip: idx -= 1 else: idx += 1 if idx == 0 or idx == len(array) - 1: flip = not flip return target_array[:target_len] def get_valid_len(self, real_len, clip_len=81, overlap=1): real_clip_len = clip_len - overlap last_clip_num = (real_len - overlap) % real_clip_len if last_clip_num == 0: extra = 0 else: extra = real_clip_len - last_clip_num target_len = real_len + extra return target_len def get_i2v_mask(self, lat_t, lat_h, lat_w, mask_len=1, mask_pixel_values=None, device="cuda"): if mask_pixel_values is None: msk = torch.zeros(1, (lat_t-1) * 4 + 1, lat_h, lat_w, device=device) else: msk = mask_pixel_values.clone() msk[:, :mask_len] = 1 msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1) msk = msk.view(1, msk.shape[1] // 4, 4, lat_h, lat_w) msk = msk.transpose(1, 2)[0] return msk def padding_resize(self, img_ori, height=512, width=512, padding_color=(0, 0, 0), interpolation=cv2.INTER_LINEAR): ori_height = img_ori.shape[0] ori_width = img_ori.shape[1] channel = img_ori.shape[2] img_pad = np.zeros((height, width, channel)) if channel == 1: img_pad[:, :, 0] = padding_color[0] else: img_pad[:, :, 0] = padding_color[0] img_pad[:, :, 1] = padding_color[1] img_pad[:, :, 2] = padding_color[2] if (ori_height / ori_width) > (height / width): new_width = int(height / ori_height * ori_width) img = cv2.resize(img_ori, (new_width, height), interpolation=interpolation) padding = int((width - new_width) / 2) if len(img.shape) == 2: img = img[:, :, np.newaxis] img_pad[:, padding: padding + new_width, :] = img else: new_height = int(width / ori_width * ori_height) img = cv2.resize(img_ori, (width, new_height), interpolation=interpolation) padding = int((height - new_height) / 2) if len(img.shape) == 2: img = img[:, :, np.newaxis] img_pad[padding: padding + new_height, :, :] = img img_pad = np.uint8(img_pad) return img_pad def prepare_source(self, src_pose_path, src_face_path, src_ref_path): pose_video_reader = VideoReader(src_pose_path) pose_len = len(pose_video_reader) pose_idxs = list(range(pose_len)) cond_images = pose_video_reader.get_batch(pose_idxs).asnumpy() face_video_reader = VideoReader(src_face_path) face_len = len(face_video_reader) face_idxs = list(range(face_len)) face_images = face_video_reader.get_batch(face_idxs).asnumpy() height, width = cond_images[0].shape[:2] refer_images = cv2.imread(src_ref_path)[..., ::-1] refer_images = self.padding_resize(refer_images, height=height, width=width) return cond_images, face_images, refer_images def prepare_source_for_replace(self, src_bg_path, src_mask_path): bg_video_reader = VideoReader(src_bg_path) bg_len = len(bg_video_reader) bg_idxs = list(range(bg_len)) bg_images = bg_video_reader.get_batch(bg_idxs).asnumpy() mask_video_reader = VideoReader(src_mask_path) mask_len = len(mask_video_reader) mask_idxs = list(range(mask_len)) mask_images = mask_video_reader.get_batch(mask_idxs).asnumpy() mask_images = mask_images[:, :, :, 0] / 255 return bg_images, mask_images def generate( self, src_root_path, replace_flag=False, clip_len=77, refert_num=1, shift=5.0, sample_solver='dpm++', sampling_steps=20, guide_scale=1, input_prompt="", n_prompt="", seed=-1, offload_model=True, ): r""" Generates video frames from input image using diffusion process. Args: src_root_path ('str'): Process output path replace_flag (`bool`, *optional*, defaults to False): Whether to use character replace. clip_len (`int`, *optional*, defaults to 77): How many frames to generate per clips. The number should be 4n+1 refert_num (`int`, *optional*, defaults to 1): How many frames used for temporal guidance. Recommended to be 1 or 5. shift (`float`, *optional*, defaults to 5.0): Noise schedule shift parameter. sample_solver (`str`, *optional*, defaults to 'dpm++'): Solver used to sample the video. sampling_steps (`int`, *optional*, defaults to 20): Number of diffusion sampling steps. Higher values improve quality but slow generation guide_scale (`float` or tuple[`float`], *optional*, defaults 1.0): Classifier-free guidance scale. We only use it for expression control. In most cases, it's not necessary and faster generation can be achieved without it. When expression adjustments are needed, you may consider using this feature. input_prompt (`str`): Text prompt for content generation. We don't recommend custom prompts (although they work) n_prompt (`str`, *optional*, defaults to ""): Negative prompt for content exclusion. If not given, use `config.sample_neg_prompt` seed (`int`, *optional*, defaults to -1): Random seed for noise generation. If -1, use random seed offload_model (`bool`, *optional*, defaults to True): If True, offloads models to CPU during generation to save VRAM Returns: torch.Tensor: Generated video frames tensor. Dimensions: (C, N, H, W) where: - C: Color channels (3 for RGB) - N: Number of frames - H: Frame height - W: Frame width """ assert refert_num == 1 or refert_num == 5, "refert_num should be 1 or 5." seed_g = torch.Generator(device=self.device) seed_g.manual_seed(seed) if n_prompt == "": n_prompt = self.sample_neg_prompt if input_prompt == "": input_prompt = self.sample_prompt src_pose_path = os.path.join(src_root_path, "src_pose.mp4") src_face_path = os.path.join(src_root_path, "src_face.mp4") src_ref_path = os.path.join(src_root_path, "src_ref.png") cond_images, face_images, refer_images = self.prepare_source(src_pose_path=src_pose_path, src_face_path=src_face_path, src_ref_path=src_ref_path) if not self.t5_cpu: self.text_encoder.model.to(self.device) context = self.text_encoder([input_prompt], self.device) context_null = self.text_encoder([n_prompt], self.device) if offload_model: self.text_encoder.model.cpu() else: context = self.text_encoder([input_prompt], torch.device('cpu')) context_null = self.text_encoder([n_prompt], torch.device('cpu')) context = [t.to(self.device) for t in context] context_null = [t.to(self.device) for t in context_null] real_frame_len = len(cond_images) target_len = self.get_valid_len(real_frame_len, clip_len, overlap=refert_num) logging.info('real frames: {} target frames: {}'.format(real_frame_len, target_len)) cond_images = self.inputs_padding(cond_images, target_len) face_images = self.inputs_padding(face_images, target_len) if replace_flag: src_bg_path = os.path.join(src_root_path, "src_bg.mp4") src_mask_path = os.path.join(src_root_path, "src_mask.mp4") bg_images, mask_images = self.prepare_source_for_replace(src_bg_path, src_mask_path) bg_images = self.inputs_padding(bg_images, target_len) mask_images = self.inputs_padding(mask_images, target_len) height, width = refer_images.shape[:2] start = 0 end = clip_len all_out_frames = [] while True: if start + refert_num >= len(cond_images): break if start == 0: mask_reft_len = 0 else: mask_reft_len = refert_num batch = { "conditioning_pixel_values": torch.zeros(1, 3, clip_len, height, width), "bg_pixel_values": torch.zeros(1, 3, clip_len, height, width), "mask_pixel_values": torch.zeros(1, 1, clip_len, height, width), "face_pixel_values": torch.zeros(1, 3, clip_len, 512, 512), "refer_pixel_values": torch.zeros(1, 3, height, width), "refer_t_pixel_values": torch.zeros(refert_num, 3, height, width) } batch["conditioning_pixel_values"] = rearrange( torch.tensor(np.stack(cond_images[start:end]) / 127.5 - 1), "t h w c -> 1 c t h w", ) batch["face_pixel_values"] = rearrange( torch.tensor(np.stack(face_images[start:end]) / 127.5 - 1), "t h w c -> 1 c t h w", ) batch["refer_pixel_values"] = rearrange( torch.tensor(refer_images / 127.5 - 1), "h w c -> 1 c h w" ) if start > 0: batch["refer_t_pixel_values"] = rearrange( out_frames[0, :, -refert_num:].clone().detach(), "c t h w -> t c h w", ) batch["refer_t_pixel_values"] = rearrange(batch["refer_t_pixel_values"], "t c h w -> 1 c t h w", ) if replace_flag: batch["bg_pixel_values"] = rearrange( torch.tensor(np.stack(bg_images[start:end]) / 127.5 - 1), "t h w c -> 1 c t h w", ) batch["mask_pixel_values"] = rearrange( torch.tensor(np.stack(mask_images[start:end])[:, :, :, None]), "t h w c -> 1 t c h w", ) for key, value in batch.items(): if isinstance(value, torch.Tensor): batch[key] = value.to(device=self.device, dtype=torch.bfloat16) ref_pixel_values = batch["refer_pixel_values"] refer_t_pixel_values = batch["refer_t_pixel_values"] conditioning_pixel_values = batch["conditioning_pixel_values"] face_pixel_values = batch["face_pixel_values"] B, _, H, W = ref_pixel_values.shape T = clip_len lat_h = H // 8 lat_w = W // 8 lat_t = T // 4 + 1 target_shape = [lat_t + 1, lat_h, lat_w] noise = [ torch.randn( 16, target_shape[0], target_shape[1], target_shape[2], dtype=torch.float32, device=self.device, generator=seed_g, ) ] max_seq_len = int(math.ceil(np.prod(target_shape) // 4 / self.sp_size)) * self.sp_size if max_seq_len % self.sp_size != 0: raise ValueError(f"max_seq_len {max_seq_len} is not divisible by sp_size {self.sp_size}") with ( torch.autocast(device_type=str(self.device), dtype=torch.bfloat16, enabled=True), torch.no_grad() ): if sample_solver == 'unipc': sample_scheduler = FlowUniPCMultistepScheduler( num_train_timesteps=self.num_train_timesteps, shift=1, use_dynamic_shifting=False) sample_scheduler.set_timesteps( sampling_steps, device=self.device, shift=shift) timesteps = sample_scheduler.timesteps elif sample_solver == 'dpm++': sample_scheduler = FlowDPMSolverMultistepScheduler( num_train_timesteps=self.num_train_timesteps, shift=1, use_dynamic_shifting=False) sampling_sigmas = get_sampling_sigmas(sampling_steps, shift) timesteps, _ = retrieve_timesteps( sample_scheduler, device=self.device, sigmas=sampling_sigmas) else: raise NotImplementedError("Unsupported solver.") latents = noise pose_latents_no_ref = self.vae.encode(conditioning_pixel_values.to(torch.bfloat16)) pose_latents_no_ref = torch.stack(pose_latents_no_ref) pose_latents = torch.cat([pose_latents_no_ref], dim=2) ref_pixel_values = rearrange(ref_pixel_values, "t c h w -> 1 c t h w") ref_latents = self.vae.encode(ref_pixel_values.to(torch.bfloat16)) ref_latents = torch.stack(ref_latents) mask_ref = self.get_i2v_mask(1, lat_h, lat_w, 1, device=self.device) y_ref = torch.concat([mask_ref, ref_latents[0]]).to(dtype=torch.bfloat16, device=self.device) img = ref_pixel_values[0, :, 0] clip_context = self.clip.visual([img[:, None, :, :]]).to(dtype=torch.bfloat16, device=self.device) if mask_reft_len > 0: if replace_flag: bg_pixel_values = batch["bg_pixel_values"] y_reft = self.vae.encode( [ torch.concat([refer_t_pixel_values[0, :, :mask_reft_len], bg_pixel_values[0, :, mask_reft_len:]], dim=1).to(self.device) ] )[0] mask_pixel_values = 1 - batch["mask_pixel_values"] mask_pixel_values = rearrange(mask_pixel_values, "b t c h w -> (b t) c h w") mask_pixel_values = F.interpolate(mask_pixel_values, size=(H//8, W//8), mode='nearest') mask_pixel_values = rearrange(mask_pixel_values, "(b t) c h w -> b t c h w", b=1)[:,:,0] msk_reft = self.get_i2v_mask(lat_t, lat_h, lat_w, mask_reft_len, mask_pixel_values=mask_pixel_values, device=self.device) else: y_reft = self.vae.encode( [ torch.concat( [ torch.nn.functional.interpolate(refer_t_pixel_values[0, :, :mask_reft_len].cpu(), size=(H, W), mode="bicubic"), torch.zeros(3, T - mask_reft_len, H, W), ], dim=1, ).to(self.device) ] )[0] msk_reft = self.get_i2v_mask(lat_t, lat_h, lat_w, mask_reft_len, device=self.device) else: if replace_flag: bg_pixel_values = batch["bg_pixel_values"] mask_pixel_values = 1 - batch["mask_pixel_values"] mask_pixel_values = rearrange(mask_pixel_values, "b t c h w -> (b t) c h w") mask_pixel_values = F.interpolate(mask_pixel_values, size=(H//8, W//8), mode='nearest') mask_pixel_values = rearrange(mask_pixel_values, "(b t) c h w -> b t c h w", b=1)[:,:,0] y_reft = self.vae.encode( [ torch.concat( [ bg_pixel_values[0], ], dim=1, ).to(self.device) ] )[0] msk_reft = self.get_i2v_mask(lat_t, lat_h, lat_w, mask_reft_len, mask_pixel_values=mask_pixel_values, device=self.device) else: y_reft = self.vae.encode( [ torch.concat( [ torch.zeros(3, T - mask_reft_len, H, W), ], dim=1, ).to(self.device) ] )[0] msk_reft = self.get_i2v_mask(lat_t, lat_h, lat_w, mask_reft_len, device=self.device) y_reft = torch.concat([msk_reft, y_reft]).to(dtype=torch.bfloat16, device=self.device) y = torch.concat([y_ref, y_reft], dim=1) arg_c = { "context": context, "seq_len": max_seq_len, "clip_fea": clip_context.to(dtype=torch.bfloat16, device=self.device), "y": [y], "pose_latents": pose_latents, "face_pixel_values": face_pixel_values, } if guide_scale > 1: face_pixel_values_uncond = face_pixel_values * 0 - 1 arg_null = { "context": context_null, "seq_len": max_seq_len, "clip_fea": clip_context.to(dtype=torch.bfloat16, device=self.device), "y": [y], "pose_latents": pose_latents, "face_pixel_values": face_pixel_values_uncond, } for i, t in enumerate(tqdm(timesteps)): latent_model_input = latents timestep = [t] timestep = torch.stack(timestep) noise_pred_cond = TensorList( self.noise_model(TensorList(latent_model_input), t=timestep, **arg_c) ) if guide_scale > 1: noise_pred_uncond = TensorList( self.noise_model( TensorList(latent_model_input), t=timestep, **arg_null ) ) noise_pred = noise_pred_uncond + guide_scale * ( noise_pred_cond - noise_pred_uncond ) else: noise_pred = noise_pred_cond temp_x0 = sample_scheduler.step( noise_pred[0].unsqueeze(0), t, latents[0].unsqueeze(0), return_dict=False, generator=seed_g, )[0] latents[0] = temp_x0.squeeze(0) x0 = latents x0 = [x.to(dtype=torch.float32) for x in x0] out_frames = torch.stack(self.vae.decode([x0[0][:, 1:]])) if start != 0: out_frames = out_frames[:, :, refert_num:] all_out_frames.append(out_frames.cpu()) start += clip_len - refert_num end += clip_len - refert_num videos = torch.cat(all_out_frames, dim=2)[:, :, :real_frame_len] return videos[0] if self.rank == 0 else None ================================================ FILE: wan/configs/__init__.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import copy import os os.environ['TOKENIZERS_PARALLELISM'] = 'false' from .wan_i2v_A14B import i2v_A14B from .wan_s2v_14B import s2v_14B from .wan_t2v_A14B import t2v_A14B from .wan_ti2v_5B import ti2v_5B from .wan_animate_14B import animate_14B WAN_CONFIGS = { 't2v-A14B': t2v_A14B, 'i2v-A14B': i2v_A14B, 'ti2v-5B': ti2v_5B, 'animate-14B': animate_14B, 's2v-14B': s2v_14B, } SIZE_CONFIGS = { '720*1280': (720, 1280), '1280*720': (1280, 720), '480*832': (480, 832), '832*480': (832, 480), '704*1280': (704, 1280), '1280*704': (1280, 704), '1024*704': (1024, 704), '704*1024': (704, 1024), } MAX_AREA_CONFIGS = { '720*1280': 720 * 1280, '1280*720': 1280 * 720, '480*832': 480 * 832, '832*480': 832 * 480, '704*1280': 704 * 1280, '1280*704': 1280 * 704, '1024*704': 1024 * 704, '704*1024': 704 * 1024, } SUPPORTED_SIZES = { 't2v-A14B': ('720*1280', '1280*720', '480*832', '832*480'), 'i2v-A14B': ('720*1280', '1280*720', '480*832', '832*480'), 'ti2v-5B': ('704*1280', '1280*704'), 's2v-14B': ('720*1280', '1280*720', '480*832', '832*480', '1024*704', '704*1024', '704*1280', '1280*704'), 'animate-14B': ('720*1280', '1280*720') } ================================================ FILE: wan/configs/shared_config.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import torch from easydict import EasyDict #------------------------ Wan shared config ------------------------# wan_shared_cfg = EasyDict() # t5 wan_shared_cfg.t5_model = 'umt5_xxl' wan_shared_cfg.t5_dtype = torch.bfloat16 wan_shared_cfg.text_len = 512 # transformer wan_shared_cfg.param_dtype = torch.bfloat16 # inference wan_shared_cfg.num_train_timesteps = 1000 wan_shared_cfg.sample_fps = 16 wan_shared_cfg.sample_neg_prompt = '色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走' wan_shared_cfg.frame_num = 81 ================================================ FILE: wan/configs/wan_animate_14B.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. from easydict import EasyDict from .shared_config import wan_shared_cfg #------------------------ Wan animate 14B ------------------------# animate_14B = EasyDict(__name__='Config: Wan animate 14B') animate_14B.update(wan_shared_cfg) animate_14B.t5_checkpoint = 'models_t5_umt5-xxl-enc-bf16.pth' animate_14B.t5_tokenizer = 'google/umt5-xxl' animate_14B.clip_checkpoint = 'models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth' animate_14B.clip_tokenizer = 'xlm-roberta-large' animate_14B.lora_checkpoint = 'relighting_lora.ckpt' # vae animate_14B.vae_checkpoint = 'Wan2.1_VAE.pth' animate_14B.vae_stride = (4, 8, 8) # transformer animate_14B.patch_size = (1, 2, 2) animate_14B.dim = 5120 animate_14B.ffn_dim = 13824 animate_14B.freq_dim = 256 animate_14B.num_heads = 40 animate_14B.num_layers = 40 animate_14B.window_size = (-1, -1) animate_14B.qk_norm = True animate_14B.cross_attn_norm = True animate_14B.eps = 1e-6 animate_14B.use_face_encoder = True animate_14B.motion_encoder_dim = 512 # inference animate_14B.sample_shift = 5.0 animate_14B.sample_steps = 20 animate_14B.sample_guide_scale = 1.0 animate_14B.frame_num = 77 animate_14B.sample_fps = 30 animate_14B.prompt = '视频中的人在做动作' ================================================ FILE: wan/configs/wan_i2v_A14B.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import torch from easydict import EasyDict from .shared_config import wan_shared_cfg #------------------------ Wan I2V A14B ------------------------# i2v_A14B = EasyDict(__name__='Config: Wan I2V A14B') i2v_A14B.update(wan_shared_cfg) i2v_A14B.t5_checkpoint = 'models_t5_umt5-xxl-enc-bf16.pth' i2v_A14B.t5_tokenizer = 'google/umt5-xxl' # vae i2v_A14B.vae_checkpoint = 'Wan2.1_VAE.pth' i2v_A14B.vae_stride = (4, 8, 8) # transformer i2v_A14B.patch_size = (1, 2, 2) i2v_A14B.dim = 5120 i2v_A14B.ffn_dim = 13824 i2v_A14B.freq_dim = 256 i2v_A14B.num_heads = 40 i2v_A14B.num_layers = 40 i2v_A14B.window_size = (-1, -1) i2v_A14B.qk_norm = True i2v_A14B.cross_attn_norm = True i2v_A14B.eps = 1e-6 i2v_A14B.low_noise_checkpoint = 'low_noise_model' i2v_A14B.high_noise_checkpoint = 'high_noise_model' # inference i2v_A14B.sample_shift = 5.0 i2v_A14B.sample_steps = 40 i2v_A14B.boundary = 0.900 i2v_A14B.sample_guide_scale = (3.5, 3.5) # low noise, high noise ================================================ FILE: wan/configs/wan_s2v_14B.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. from easydict import EasyDict from .shared_config import wan_shared_cfg #------------------------ Wan S2V 14B ------------------------# s2v_14B = EasyDict(__name__='Config: Wan S2V 14B') s2v_14B.update(wan_shared_cfg) # t5 s2v_14B.t5_checkpoint = 'models_t5_umt5-xxl-enc-bf16.pth' s2v_14B.t5_tokenizer = 'google/umt5-xxl' # vae s2v_14B.vae_checkpoint = 'Wan2.1_VAE.pth' s2v_14B.vae_stride = (4, 8, 8) # wav2vec s2v_14B.wav2vec = "wav2vec2-large-xlsr-53-english" s2v_14B.num_heads = 40 # transformer s2v_14B.transformer = EasyDict( __name__="Config: Transformer config for WanModel_S2V") s2v_14B.transformer.patch_size = (1, 2, 2) s2v_14B.transformer.dim = 5120 s2v_14B.transformer.ffn_dim = 13824 s2v_14B.transformer.freq_dim = 256 s2v_14B.transformer.num_heads = 40 s2v_14B.transformer.num_layers = 40 s2v_14B.transformer.window_size = (-1, -1) s2v_14B.transformer.qk_norm = True s2v_14B.transformer.cross_attn_norm = True s2v_14B.transformer.eps = 1e-6 s2v_14B.transformer.enable_adain = True s2v_14B.transformer.adain_mode = "attn_norm" s2v_14B.transformer.audio_inject_layers = [ 0, 4, 8, 12, 16, 20, 24, 27, 30, 33, 36, 39 ] s2v_14B.transformer.zero_init = True s2v_14B.transformer.zero_timestep = True s2v_14B.transformer.enable_motioner = False s2v_14B.transformer.add_last_motion = True s2v_14B.transformer.trainable_token = False s2v_14B.transformer.enable_tsm = False s2v_14B.transformer.enable_framepack = True s2v_14B.transformer.framepack_drop_mode = 'padd' s2v_14B.transformer.audio_dim = 1024 s2v_14B.transformer.motion_frames = 73 s2v_14B.transformer.cond_dim = 16 # inference s2v_14B.sample_neg_prompt = "画面模糊，最差质量，画面模糊，细节模糊不清，情绪激动剧烈，手快速抖动，字幕，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" s2v_14B.drop_first_motion = True s2v_14B.sample_shift = 3 s2v_14B.sample_steps = 40 s2v_14B.sample_guide_scale = 4.5 ================================================ FILE: wan/configs/wan_t2v_A14B.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. from easydict import EasyDict from .shared_config import wan_shared_cfg #------------------------ Wan T2V A14B ------------------------# t2v_A14B = EasyDict(__name__='Config: Wan T2V A14B') t2v_A14B.update(wan_shared_cfg) # t5 t2v_A14B.t5_checkpoint = 'models_t5_umt5-xxl-enc-bf16.pth' t2v_A14B.t5_tokenizer = 'google/umt5-xxl' # vae t2v_A14B.vae_checkpoint = 'Wan2.1_VAE.pth' t2v_A14B.vae_stride = (4, 8, 8) # transformer t2v_A14B.patch_size = (1, 2, 2) t2v_A14B.dim = 5120 t2v_A14B.ffn_dim = 13824 t2v_A14B.freq_dim = 256 t2v_A14B.num_heads = 40 t2v_A14B.num_layers = 40 t2v_A14B.window_size = (-1, -1) t2v_A14B.qk_norm = True t2v_A14B.cross_attn_norm = True t2v_A14B.eps = 1e-6 t2v_A14B.low_noise_checkpoint = 'low_noise_model' t2v_A14B.high_noise_checkpoint = 'high_noise_model' # inference t2v_A14B.sample_shift = 12.0 t2v_A14B.sample_steps = 40 t2v_A14B.boundary = 0.875 t2v_A14B.sample_guide_scale = (3.0, 4.0) # low noise, high noise ================================================ FILE: wan/configs/wan_ti2v_5B.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. from easydict import EasyDict from .shared_config import wan_shared_cfg #------------------------ Wan TI2V 5B ------------------------# ti2v_5B = EasyDict(__name__='Config: Wan TI2V 5B') ti2v_5B.update(wan_shared_cfg) # t5 ti2v_5B.t5_checkpoint = 'models_t5_umt5-xxl-enc-bf16.pth' ti2v_5B.t5_tokenizer = 'google/umt5-xxl' # vae ti2v_5B.vae_checkpoint = 'Wan2.2_VAE.pth' ti2v_5B.vae_stride = (4, 16, 16) # transformer ti2v_5B.patch_size = (1, 2, 2) ti2v_5B.dim = 3072 ti2v_5B.ffn_dim = 14336 ti2v_5B.freq_dim = 256 ti2v_5B.num_heads = 24 ti2v_5B.num_layers = 30 ti2v_5B.window_size = (-1, -1) ti2v_5B.qk_norm = True ti2v_5B.cross_attn_norm = True ti2v_5B.eps = 1e-6 # inference ti2v_5B.sample_fps = 24 ti2v_5B.sample_shift = 5.0 ti2v_5B.sample_steps = 50 ti2v_5B.sample_guide_scale = 5.0 ti2v_5B.frame_num = 121 ================================================ FILE: wan/distributed/__init__.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. ================================================ FILE: wan/distributed/fsdp.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import gc from functools import partial import torch from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from torch.distributed.fsdp import MixedPrecision, ShardingStrategy from torch.distributed.fsdp.wrap import lambda_auto_wrap_policy from torch.distributed.utils import _free_storage def shard_model( model, device_id, param_dtype=torch.bfloat16, reduce_dtype=torch.float32, buffer_dtype=torch.float32, process_group=None, sharding_strategy=ShardingStrategy.FULL_SHARD, sync_module_states=True, use_lora=False ): model = FSDP( module=model, process_group=process_group, sharding_strategy=sharding_strategy, auto_wrap_policy=partial( lambda_auto_wrap_policy, lambda_fn=lambda m: m in model.blocks), mixed_precision=MixedPrecision( param_dtype=param_dtype, reduce_dtype=reduce_dtype, buffer_dtype=buffer_dtype), device_id=device_id, sync_module_states=sync_module_states, use_orig_params=True if use_lora else False) return model def free_model(model): for m in model.modules(): if isinstance(m, FSDP): _free_storage(m._handle.flat_param.data) del model gc.collect() torch.cuda.empty_cache() ================================================ FILE: wan/distributed/sequence_parallel.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import torch import torch.cuda.amp as amp from ..modules.model import sinusoidal_embedding_1d from .ulysses import distributed_attention from .util import gather_forward, get_rank, get_world_size def pad_freqs(original_tensor, target_len): seq_len, s1, s2 = original_tensor.shape pad_size = target_len - seq_len padding_tensor = torch.ones( pad_size, s1, s2, dtype=original_tensor.dtype, device=original_tensor.device) padded_tensor = torch.cat([original_tensor, padding_tensor], dim=0) return padded_tensor @torch.amp.autocast('cuda', enabled=False) def rope_apply(x, grid_sizes, freqs): """ x: [B, L, N, C]. grid_sizes: [B, 3]. freqs: [M, C // 2]. """ s, n, c = x.size(1), x.size(2), x.size(3) // 2 # split freqs freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1) # loop over samples output = [] for i, (f, h, w) in enumerate(grid_sizes.tolist()): seq_len = f * h * w # precompute multipliers x_i = torch.view_as_complex(x[i, :s].to(torch.float64).reshape( s, n, -1, 2)) freqs_i = torch.cat([ freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1), freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1), freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1) ], dim=-1).reshape(seq_len, 1, -1) # apply rotary embedding sp_size = get_world_size() sp_rank = get_rank() freqs_i = pad_freqs(freqs_i, s * sp_size) s_per_rank = s freqs_i_rank = freqs_i[(sp_rank * s_per_rank):((sp_rank + 1) * s_per_rank), :, :] x_i = torch.view_as_real(x_i * freqs_i_rank).flatten(2) x_i = torch.cat([x_i, x[i, s:]]) # append to collection output.append(x_i) return torch.stack(output).float() def sp_dit_forward( self, x, t, context, seq_len, y=None, ): """ x: A list of videos each with shape [C, T, H, W]. t: [B]. context: A list of text embeddings each with shape [L, C]. """ if self.model_type == 'i2v': assert y is not None # params device = self.patch_embedding.weight.device if self.freqs.device != device: self.freqs = self.freqs.to(device) if y is not None: x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)] # embeddings x = [self.patch_embedding(u.unsqueeze(0)) for u in x] grid_sizes = torch.stack( [torch.tensor(u.shape[2:], dtype=torch.long) for u in x]) x = [u.flatten(2).transpose(1, 2) for u in x] seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.long) assert seq_lens.max() <= seq_len x = torch.cat([ torch.cat([u, u.new_zeros(1, seq_len - u.size(1), u.size(2))], dim=1) for u in x ]) # time embeddings if t.dim() == 1: t = t.expand(t.size(0), seq_len) with torch.amp.autocast('cuda', dtype=torch.float32): bt = t.size(0) t = t.flatten() e = self.time_embedding( sinusoidal_embedding_1d(self.freq_dim, t).unflatten(0, (bt, seq_len)).float()) e0 = self.time_projection(e).unflatten(2, (6, self.dim)) assert e.dtype == torch.float32 and e0.dtype == torch.float32 # context context_lens = None context = self.text_embedding( torch.stack([ torch.cat([u, u.new_zeros(self.text_len - u.size(0), u.size(1))]) for u in context ])) # Context Parallel x = torch.chunk(x, get_world_size(), dim=1)[get_rank()] e = torch.chunk(e, get_world_size(), dim=1)[get_rank()] e0 = torch.chunk(e0, get_world_size(), dim=1)[get_rank()] # arguments kwargs = dict( e=e0, seq_lens=seq_lens, grid_sizes=grid_sizes, freqs=self.freqs, context=context, context_lens=context_lens) for block in self.blocks: x = block(x, **kwargs) # head x = self.head(x, e) # Context Parallel x = gather_forward(x, dim=1) # unpatchify x = self.unpatchify(x, grid_sizes) return [u.float() for u in x] def sp_attn_forward(self, x, seq_lens, grid_sizes, freqs, dtype=torch.bfloat16): b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim half_dtypes = (torch.float16, torch.bfloat16) def half(x): return x if x.dtype in half_dtypes else x.to(dtype) # query, key, value function def qkv_fn(x): q = self.norm_q(self.q(x)).view(b, s, n, d) k = self.norm_k(self.k(x)).view(b, s, n, d) v = self.v(x).view(b, s, n, d) return q, k, v q, k, v = qkv_fn(x) q = rope_apply(q, grid_sizes, freqs) k = rope_apply(k, grid_sizes, freqs) x = distributed_attention( half(q), half(k), half(v), seq_lens, window_size=self.window_size, ) # output x = x.flatten(2) x = self.o(x) return x ================================================ FILE: wan/distributed/ulysses.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import torch import torch.distributed as dist from ..modules.attention import flash_attention from .util import all_to_all def distributed_attention( q, k, v, seq_lens, window_size=(-1, -1), ): """ Performs distributed attention based on DeepSpeed Ulysses attention mechanism. please refer to https://arxiv.org/pdf/2309.14509 Args: q: [B, Lq // p, Nq, C1]. k: [B, Lk // p, Nk, C1]. v: [B, Lk // p, Nk, C2]. Nq must be divisible by Nk. seq_lens: [B], length of each sequence in batch window_size: (left right). If not (-1, -1), apply sliding window local attention. """ if not dist.is_initialized(): raise ValueError("distributed group should be initialized.") b = q.shape[0] # gather q/k/v sequence q = all_to_all(q, scatter_dim=2, gather_dim=1) k = all_to_all(k, scatter_dim=2, gather_dim=1) v = all_to_all(v, scatter_dim=2, gather_dim=1) # apply attention x = flash_attention( q, k, v, k_lens=seq_lens, window_size=window_size, ) # scatter q/k/v sequence x = all_to_all(x, scatter_dim=1, gather_dim=2) return x ================================================ FILE: wan/distributed/util.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import torch import torch.distributed as dist def init_distributed_group(): """r initialize sequence parallel group. """ if not dist.is_initialized(): dist.init_process_group(backend='nccl') def get_rank(): return dist.get_rank() def get_world_size(): return dist.get_world_size() def all_to_all(x, scatter_dim, gather_dim, group=None, **kwargs): """ `scatter` along one dimension and `gather` along another. """ world_size = get_world_size() if world_size > 1: inputs = [u.contiguous() for u in x.chunk(world_size, dim=scatter_dim)] outputs = [torch.empty_like(u) for u in inputs] dist.all_to_all(outputs, inputs, group=group, **kwargs) x = torch.cat(outputs, dim=gather_dim).contiguous() return x def all_gather(tensor): world_size = dist.get_world_size() if world_size == 1: return [tensor] tensor_list = [torch.empty_like(tensor) for _ in range(world_size)] torch.distributed.all_gather(tensor_list, tensor) return tensor_list def gather_forward(input, dim): # skip if world_size == 1 world_size = dist.get_world_size() if world_size == 1: return input # gather sequence output = all_gather(input) return torch.cat(output, dim=dim).contiguous() ================================================ FILE: wan/image2video.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import gc import logging import math import os import random import sys import types from contextlib import contextmanager from functools import partial import numpy as np import torch import torch.cuda.amp as amp import torch.distributed as dist import torchvision.transforms.functional as TF from tqdm import tqdm from .distributed.fsdp import shard_model from .distributed.sequence_parallel import sp_attn_forward, sp_dit_forward from .distributed.util import get_world_size from .modules.model import WanModel from .modules.t5 import T5EncoderModel from .modules.vae2_1 import Wan2_1_VAE from .utils.fm_solvers import ( FlowDPMSolverMultistepScheduler, get_sampling_sigmas, retrieve_timesteps, ) from .utils.fm_solvers_unipc import FlowUniPCMultistepScheduler class WanI2V: def __init__( self, config, checkpoint_dir, device_id=0, rank=0, t5_fsdp=False, dit_fsdp=False, use_sp=False, t5_cpu=False, init_on_cpu=True, convert_model_dtype=False, ): r""" Initializes the image-to-video generation model components. Args: config (EasyDict): Object containing model parameters initialized from config.py checkpoint_dir (`str`): Path to directory containing model checkpoints device_id (`int`, *optional*, defaults to 0): Id of target GPU device rank (`int`, *optional*, defaults to 0): Process rank for distributed training t5_fsdp (`bool`, *optional*, defaults to False): Enable FSDP sharding for T5 model dit_fsdp (`bool`, *optional*, defaults to False): Enable FSDP sharding for DiT model use_sp (`bool`, *optional*, defaults to False): Enable distribution strategy of sequence parallel. t5_cpu (`bool`, *optional*, defaults to False): Whether to place T5 model on CPU. Only works without t5_fsdp. init_on_cpu (`bool`, *optional*, defaults to True): Enable initializing Transformer Model on CPU. Only works without FSDP or USP. convert_model_dtype (`bool`, *optional*, defaults to False): Convert DiT model parameters dtype to 'config.param_dtype'. Only works without FSDP. """ self.device = torch.device(f"cuda:{device_id}") self.config = config self.rank = rank self.t5_cpu = t5_cpu self.init_on_cpu = init_on_cpu self.num_train_timesteps = config.num_train_timesteps self.boundary = config.boundary self.param_dtype = config.param_dtype if t5_fsdp or dit_fsdp or use_sp: self.init_on_cpu = False shard_fn = partial(shard_model, device_id=device_id) self.text_encoder = T5EncoderModel( text_len=config.text_len, dtype=config.t5_dtype, device=torch.device('cpu'), checkpoint_path=os.path.join(checkpoint_dir, config.t5_checkpoint), tokenizer_path=os.path.join(checkpoint_dir, config.t5_tokenizer), shard_fn=shard_fn if t5_fsdp else None, ) self.vae_stride = config.vae_stride self.patch_size = config.patch_size self.vae = Wan2_1_VAE( vae_pth=os.path.join(checkpoint_dir, config.vae_checkpoint), device=self.device) logging.info(f"Creating WanModel from {checkpoint_dir}") self.low_noise_model = WanModel.from_pretrained( checkpoint_dir, subfolder=config.low_noise_checkpoint) self.low_noise_model = self._configure_model( model=self.low_noise_model, use_sp=use_sp, dit_fsdp=dit_fsdp, shard_fn=shard_fn, convert_model_dtype=convert_model_dtype) self.high_noise_model = WanModel.from_pretrained( checkpoint_dir, subfolder=config.high_noise_checkpoint) self.high_noise_model = self._configure_model( model=self.high_noise_model, use_sp=use_sp, dit_fsdp=dit_fsdp, shard_fn=shard_fn, convert_model_dtype=convert_model_dtype) if use_sp: self.sp_size = get_world_size() else: self.sp_size = 1 self.sample_neg_prompt = config.sample_neg_prompt def _configure_model(self, model, use_sp, dit_fsdp, shard_fn, convert_model_dtype): """ Configures a model object. This includes setting evaluation modes, applying distributed parallel strategy, and handling device placement. Args: model (torch.nn.Module): The model instance to configure. use_sp (`bool`): Enable distribution strategy of sequence parallel. dit_fsdp (`bool`): Enable FSDP sharding for DiT model. shard_fn (callable): The function to apply FSDP sharding. convert_model_dtype (`bool`): Convert DiT model parameters dtype to 'config.param_dtype'. Only works without FSDP. Returns: torch.nn.Module: The configured model. """ model.eval().requires_grad_(False) if use_sp: for block in model.blocks: block.self_attn.forward = types.MethodType( sp_attn_forward, block.self_attn) model.forward = types.MethodType(sp_dit_forward, model) if dist.is_initialized(): dist.barrier() if dit_fsdp: model = shard_fn(model) else: if convert_model_dtype: model.to(self.param_dtype) if not self.init_on_cpu: model.to(self.device) return model def _prepare_model_for_timestep(self, t, boundary, offload_model): r""" Prepares and returns the required model for the current timestep. Args: t (torch.Tensor): current timestep. boundary (`int`): The timestep threshold. If `t` is at or above this value, the `high_noise_model` is considered as the required model. offload_model (`bool`): A flag intended to control the offloading behavior. Returns: torch.nn.Module: The active model on the target device for the current timestep. """ if t.item() >= boundary: required_model_name = 'high_noise_model' offload_model_name = 'low_noise_model' else: required_model_name = 'low_noise_model' offload_model_name = 'high_noise_model' if offload_model or self.init_on_cpu: if next(getattr( self, offload_model_name).parameters()).device.type == 'cuda': getattr(self, offload_model_name).to('cpu') if next(getattr( self, required_model_name).parameters()).device.type == 'cpu': getattr(self, required_model_name).to(self.device) return getattr(self, required_model_name) def generate(self, input_prompt, img, max_area=720 * 1280, frame_num=81, shift=5.0, sample_solver='unipc', sampling_steps=40, guide_scale=5.0, n_prompt="", seed=-1, offload_model=True): r""" Generates video frames from input image and text prompt using diffusion process. Args: input_prompt (`str`): Text prompt for content generation. img (PIL.Image.Image): Input image tensor. Shape: [3, H, W] max_area (`int`, *optional*, defaults to 720*1280): Maximum pixel area for latent space calculation. Controls video resolution scaling frame_num (`int`, *optional*, defaults to 81): How many frames to sample from a video. The number should be 4n+1 shift (`float`, *optional*, defaults to 5.0): Noise schedule shift parameter. Affects temporal dynamics [NOTE]: If you want to generate a 480p video, it is recommended to set the shift value to 3.0. sample_solver (`str`, *optional*, defaults to 'unipc'): Solver used to sample the video. sampling_steps (`int`, *optional*, defaults to 40): Number of diffusion sampling steps. Higher values improve quality but slow generation guide_scale (`float` or tuple[`float`], *optional*, defaults 5.0): Classifier-free guidance scale. Controls prompt adherence vs. creativity. If tuple, the first guide_scale will be used for low noise model and the second guide_scale will be used for high noise model. n_prompt (`str`, *optional*, defaults to ""): Negative prompt for content exclusion. If not given, use `config.sample_neg_prompt` seed (`int`, *optional*, defaults to -1): Random seed for noise generation. If -1, use random seed offload_model (`bool`, *optional*, defaults to True): If True, offloads models to CPU during generation to save VRAM Returns: torch.Tensor: Generated video frames tensor. Dimensions: (C, N H, W) where: - C: Color channels (3 for RGB) - N: Number of frames (81) - H: Frame height (from max_area) - W: Frame width from max_area) """ # preprocess guide_scale = (guide_scale, guide_scale) if isinstance( guide_scale, float) else guide_scale img = TF.to_tensor(img).sub_(0.5).div_(0.5).to(self.device) F = frame_num h, w = img.shape[1:] aspect_ratio = h / w lat_h = round( np.sqrt(max_area * aspect_ratio) // self.vae_stride[1] // self.patch_size[1] * self.patch_size[1]) lat_w = round( np.sqrt(max_area / aspect_ratio) // self.vae_stride[2] // self.patch_size[2] * self.patch_size[2]) h = lat_h * self.vae_stride[1] w = lat_w * self.vae_stride[2] max_seq_len = ((F - 1) // self.vae_stride[0] + 1) * lat_h * lat_w // ( self.patch_size[1] * self.patch_size[2]) max_seq_len = int(math.ceil(max_seq_len / self.sp_size)) * self.sp_size seed = seed if seed >= 0 else random.randint(0, sys.maxsize) seed_g = torch.Generator(device=self.device) seed_g.manual_seed(seed) noise = torch.randn( 16, (F - 1) // self.vae_stride[0] + 1, lat_h, lat_w, dtype=torch.float32, generator=seed_g, device=self.device) msk = torch.ones(1, F, lat_h, lat_w, device=self.device) msk[:, 1:] = 0 msk = torch.concat([ torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:] ], dim=1) msk = msk.view(1, msk.shape[1] // 4, 4, lat_h, lat_w) msk = msk.transpose(1, 2)[0] if n_prompt == "": n_prompt = self.sample_neg_prompt # preprocess if not self.t5_cpu: self.text_encoder.model.to(self.device) context = self.text_encoder([input_prompt], self.device) context_null = self.text_encoder([n_prompt], self.device) if offload_model: self.text_encoder.model.cpu() else: context = self.text_encoder([input_prompt], torch.device('cpu')) context_null = self.text_encoder([n_prompt], torch.device('cpu')) context = [t.to(self.device) for t in context] context_null = [t.to(self.device) for t in context_null] y = self.vae.encode([ torch.concat([ torch.nn.functional.interpolate( img[None].cpu(), size=(h, w), mode='bicubic').transpose( 0, 1), torch.zeros(3, F - 1, h, w) ], dim=1).to(self.device) ])[0] y = torch.concat([msk, y]) @contextmanager def noop_no_sync(): yield no_sync_low_noise = getattr(self.low_noise_model, 'no_sync', noop_no_sync) no_sync_high_noise = getattr(self.high_noise_model, 'no_sync', noop_no_sync) # evaluation mode with ( torch.amp.autocast('cuda', dtype=self.param_dtype), torch.no_grad(), no_sync_low_noise(), no_sync_high_noise(), ): boundary = self.boundary * self.num_train_timesteps if sample_solver == 'unipc': sample_scheduler = FlowUniPCMultistepScheduler( num_train_timesteps=self.num_train_timesteps, shift=1, use_dynamic_shifting=False) sample_scheduler.set_timesteps( sampling_steps, device=self.device, shift=shift) timesteps = sample_scheduler.timesteps elif sample_solver == 'dpm++': sample_scheduler = FlowDPMSolverMultistepScheduler( num_train_timesteps=self.num_train_timesteps, shift=1, use_dynamic_shifting=False) sampling_sigmas = get_sampling_sigmas(sampling_steps, shift) timesteps, _ = retrieve_timesteps( sample_scheduler, device=self.device, sigmas=sampling_sigmas) else: raise NotImplementedError("Unsupported solver.") # sample videos latent = noise arg_c = { 'context': [context[0]], 'seq_len': max_seq_len, 'y': [y], } arg_null = { 'context': context_null, 'seq_len': max_seq_len, 'y': [y], } if offload_model: torch.cuda.empty_cache() for _, t in enumerate(tqdm(timesteps)): latent_model_input = [latent.to(self.device)] timestep = [t] timestep = torch.stack(timestep).to(self.device) model = self._prepare_model_for_timestep( t, boundary, offload_model) sample_guide_scale = guide_scale[1] if t.item( ) >= boundary else guide_scale[0] noise_pred_cond = model( latent_model_input, t=timestep, **arg_c)[0] if offload_model: torch.cuda.empty_cache() noise_pred_uncond = model( latent_model_input, t=timestep, **arg_null)[0] if offload_model: torch.cuda.empty_cache() noise_pred = noise_pred_uncond + sample_guide_scale * ( noise_pred_cond - noise_pred_uncond) temp_x0 = sample_scheduler.step( noise_pred.unsqueeze(0), t, latent.unsqueeze(0), return_dict=False, generator=seed_g)[0] latent = temp_x0.squeeze(0) x0 = [latent] del latent_model_input, timestep if offload_model: self.low_noise_model.cpu() self.high_noise_model.cpu() torch.cuda.empty_cache() if self.rank == 0: videos = self.vae.decode(x0) del noise, latent, x0 del sample_scheduler if offload_model: gc.collect() torch.cuda.synchronize() if dist.is_initialized(): dist.barrier() return videos[0] if self.rank == 0 else None ================================================ FILE: wan/modules/__init__.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. from .attention import flash_attention from .model import WanModel from .t5 import T5Decoder, T5Encoder, T5EncoderModel, T5Model from .tokenizers import HuggingfaceTokenizer from .vae2_1 import Wan2_1_VAE from .vae2_2 import Wan2_2_VAE __all__ = [ 'Wan2_1_VAE', 'Wan2_2_VAE', 'WanModel', 'T5Model', 'T5Encoder', 'T5Decoder', 'T5EncoderModel', 'HuggingfaceTokenizer', 'flash_attention', ] ================================================ FILE: wan/modules/animate/__init__.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. from .model_animate import WanAnimateModel from .clip import CLIPModel __all__ = ['WanAnimateModel', 'CLIPModel'] ================================================ FILE: wan/modules/animate/animate_utils.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import torch import numbers from peft import LoraConfig def get_loraconfig(transformer, rank=128, alpha=128, init_lora_weights="gaussian"): target_modules = [] for name, module in transformer.named_modules(): if "blocks" in name and "face" not in name and "modulation" not in name and isinstance(module, torch.nn.Linear): target_modules.append(name) transformer_lora_config = LoraConfig( r=rank, lora_alpha=alpha, init_lora_weights=init_lora_weights, target_modules=target_modules, ) return transformer_lora_config class TensorList(object): def __init__(self, tensors): """ tensors: a list of torch.Tensor objects. No need to have uniform shape. """ assert isinstance(tensors, (list, tuple)) assert all(isinstance(u, torch.Tensor) for u in tensors) assert len(set([u.ndim for u in tensors])) == 1 assert len(set([u.dtype for u in tensors])) == 1 assert len(set([u.device for u in tensors])) == 1 self.tensors = tensors def to(self, *args, **kwargs): return TensorList([u.to(*args, **kwargs) for u in self.tensors]) def size(self, dim): assert dim == 0, 'only support get the 0th size' return len(self.tensors) def pow(self, *args, **kwargs): return TensorList([u.pow(*args, **kwargs) for u in self.tensors]) def squeeze(self, dim): assert dim != 0 if dim > 0: dim -= 1 return TensorList([u.squeeze(dim) for u in self.tensors]) def type(self, *args, **kwargs): return TensorList([u.type(*args, **kwargs) for u in self.tensors]) def type_as(self, other): assert isinstance(other, (torch.Tensor, TensorList)) if isinstance(other, torch.Tensor): return TensorList([u.type_as(other) for u in self.tensors]) else: return TensorList([u.type(other.dtype) for u in self.tensors]) @property def dtype(self): return self.tensors[0].dtype @property def device(self): return self.tensors[0].device @property def ndim(self): return 1 + self.tensors[0].ndim def __getitem__(self, index): return self.tensors[index] def __len__(self): return len(self.tensors) def __add__(self, other): return self._apply(other, lambda u, v: u + v) def __radd__(self, other): return self._apply(other, lambda u, v: v + u) def __sub__(self, other): return self._apply(other, lambda u, v: u - v) def __rsub__(self, other): return self._apply(other, lambda u, v: v - u) def __mul__(self, other): return self._apply(other, lambda u, v: u * v) def __rmul__(self, other): return self._apply(other, lambda u, v: v * u) def __floordiv__(self, other): return self._apply(other, lambda u, v: u // v) def __truediv__(self, other): return self._apply(other, lambda u, v: u / v) def __rfloordiv__(self, other): return self._apply(other, lambda u, v: v // u) def __rtruediv__(self, other): return self._apply(other, lambda u, v: v / u) def __pow__(self, other): return self._apply(other, lambda u, v: u ** v) def __rpow__(self, other): return self._apply(other, lambda u, v: v ** u) def __neg__(self): return TensorList([-u for u in self.tensors]) def __iter__(self): for tensor in self.tensors: yield tensor def __repr__(self): return 'TensorList: \n' + repr(self.tensors) def _apply(self, other, op): if isinstance(other, (list, tuple, TensorList)) or ( isinstance(other, torch.Tensor) and ( other.numel() > 1 or other.ndim > 1 ) ): assert len(other) == len(self.tensors) return TensorList([op(u, v) for u, v in zip(self.tensors, other)]) elif isinstance(other, numbers.Number) or ( isinstance(other, torch.Tensor) and ( other.numel() == 1 and other.ndim <= 1 ) ): return TensorList([op(u, other) for u in self.tensors]) else: raise TypeError( f'unsupported operand for *: "TensorList" and "{type(other)}"' ) ================================================ FILE: wan/modules/animate/clip.py ================================================ # Modified from ``https://github.com/openai/CLIP'' and ``https://github.com/mlfoundations/open_clip'' # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import logging import math import torch import torch.nn as nn import torch.nn.functional as F import torchvision.transforms as T from ..attention import flash_attention from ..tokenizers import HuggingfaceTokenizer from .xlm_roberta import XLMRoberta __all__ = [ 'XLMRobertaCLIP', 'clip_xlm_roberta_vit_h_14', 'CLIPModel', ] def pos_interpolate(pos, seq_len): if pos.size(1) == seq_len: return pos else: src_grid = int(math.sqrt(pos.size(1))) tar_grid = int(math.sqrt(seq_len)) n = pos.size(1) - src_grid * src_grid return torch.cat([ pos[:, :n], F.interpolate( pos[:, n:].float().reshape(1, src_grid, src_grid, -1).permute( 0, 3, 1, 2), size=(tar_grid, tar_grid), mode='bicubic', align_corners=False).flatten(2).transpose(1, 2) ], dim=1) class QuickGELU(nn.Module): def forward(self, x): return x * torch.sigmoid(1.702 * x) class LayerNorm(nn.LayerNorm): def forward(self, x): return super().forward(x.float()).type_as(x) class SelfAttention(nn.Module): def __init__(self, dim, num_heads, causal=False, attn_dropout=0.0, proj_dropout=0.0): assert dim % num_heads == 0 super().__init__() self.dim = dim self.num_heads = num_heads self.head_dim = dim // num_heads self.causal = causal self.attn_dropout = attn_dropout self.proj_dropout = proj_dropout # layers self.to_qkv = nn.Linear(dim, dim * 3) self.proj = nn.Linear(dim, dim) def forward(self, x): """ x: [B, L, C]. """ b, s, c, n, d = *x.size(), self.num_heads, self.head_dim # compute query, key, value q, k, v = self.to_qkv(x).view(b, s, 3, n, d).unbind(2) # compute attention p = self.attn_dropout if self.training else 0.0 x = flash_attention(q, k, v, dropout_p=p, causal=self.causal, version=2) x = x.reshape(b, s, c) # output x = self.proj(x) x = F.dropout(x, self.proj_dropout, self.training) return x class SwiGLU(nn.Module): def __init__(self, dim, mid_dim): super().__init__() self.dim = dim self.mid_dim = mid_dim # layers self.fc1 = nn.Linear(dim, mid_dim) self.fc2 = nn.Linear(dim, mid_dim) self.fc3 = nn.Linear(mid_dim, dim) def forward(self, x): x = F.silu(self.fc1(x)) * self.fc2(x) x = self.fc3(x) return x class AttentionBlock(nn.Module): def __init__(self, dim, mlp_ratio, num_heads, post_norm=False, causal=False, activation='quick_gelu', attn_dropout=0.0, proj_dropout=0.0, norm_eps=1e-5): assert activation in ['quick_gelu', 'gelu', 'swi_glu'] super().__init__() self.dim = dim self.mlp_ratio = mlp_ratio self.num_heads = num_heads self.post_norm = post_norm self.causal = causal self.norm_eps = norm_eps # layers self.norm1 = LayerNorm(dim, eps=norm_eps) self.attn = SelfAttention(dim, num_heads, causal, attn_dropout, proj_dropout) self.norm2 = LayerNorm(dim, eps=norm_eps) if activation == 'swi_glu': self.mlp = SwiGLU(dim, int(dim * mlp_ratio)) else: self.mlp = nn.Sequential( nn.Linear(dim, int(dim * mlp_ratio)), QuickGELU() if activation == 'quick_gelu' else nn.GELU(), nn.Linear(int(dim * mlp_ratio), dim), nn.Dropout(proj_dropout)) def forward(self, x): if self.post_norm: x = x + self.norm1(self.attn(x)) x = x + self.norm2(self.mlp(x)) else: x = x + self.attn(self.norm1(x)) x = x + self.mlp(self.norm2(x)) return x class AttentionPool(nn.Module): def __init__(self, dim, mlp_ratio, num_heads, activation='gelu', proj_dropout=0.0, norm_eps=1e-5): assert dim % num_heads == 0 super().__init__() self.dim = dim self.mlp_ratio = mlp_ratio self.num_heads = num_heads self.head_dim = dim // num_heads self.proj_dropout = proj_dropout self.norm_eps = norm_eps # layers gain = 1.0 / math.sqrt(dim) self.cls_embedding = nn.Parameter(gain * torch.randn(1, 1, dim)) self.to_q = nn.Linear(dim, dim) self.to_kv = nn.Linear(dim, dim * 2) self.proj = nn.Linear(dim, dim) self.norm = LayerNorm(dim, eps=norm_eps) self.mlp = nn.Sequential( nn.Linear(dim, int(dim * mlp_ratio)), QuickGELU() if activation == 'quick_gelu' else nn.GELU(), nn.Linear(int(dim * mlp_ratio), dim), nn.Dropout(proj_dropout)) def forward(self, x): """ x: [B, L, C]. """ b, s, c, n, d = *x.size(), self.num_heads, self.head_dim # compute query, key, value q = self.to_q(self.cls_embedding).view(1, 1, n, d).expand(b, -1, -1, -1) k, v = self.to_kv(x).view(b, s, 2, n, d).unbind(2) # compute attention x = flash_attention(q, k, v, version=2) x = x.reshape(b, 1, c) # output x = self.proj(x) x = F.dropout(x, self.proj_dropout, self.training) # mlp x = x + self.mlp(self.norm(x)) return x[:, 0] class VisionTransformer(nn.Module): def __init__(self, image_size=224, patch_size=16, dim=768, mlp_ratio=4, out_dim=512, num_heads=12, num_layers=12, pool_type='token', pre_norm=True, post_norm=False, activation='quick_gelu', attn_dropout=0.0, proj_dropout=0.0, embedding_dropout=0.0, norm_eps=1e-5): if image_size % patch_size != 0: print( '[WARNING] image_size is not divisible by patch_size', flush=True) assert pool_type in ('token', 'token_fc', 'attn_pool') out_dim = out_dim or dim super().__init__() self.image_size = image_size self.patch_size = patch_size self.num_patches = (image_size // patch_size)**2 self.dim = dim self.mlp_ratio = mlp_ratio self.out_dim = out_dim self.num_heads = num_heads self.num_layers = num_layers self.pool_type = pool_type self.post_norm = post_norm self.norm_eps = norm_eps # embeddings gain = 1.0 / math.sqrt(dim) self.patch_embedding = nn.Conv2d( 3, dim, kernel_size=patch_size, stride=patch_size, bias=not pre_norm) if pool_type in ('token', 'token_fc'): self.cls_embedding = nn.Parameter(gain * torch.randn(1, 1, dim)) self.pos_embedding = nn.Parameter(gain * torch.randn( 1, self.num_patches + (1 if pool_type in ('token', 'token_fc') else 0), dim)) self.dropout = nn.Dropout(embedding_dropout) # transformer self.pre_norm = LayerNorm(dim, eps=norm_eps) if pre_norm else None self.transformer = nn.Sequential(*[ AttentionBlock(dim, mlp_ratio, num_heads, post_norm, False, activation, attn_dropout, proj_dropout, norm_eps) for _ in range(num_layers) ]) self.post_norm = LayerNorm(dim, eps=norm_eps) # head if pool_type == 'token': self.head = nn.Parameter(gain * torch.randn(dim, out_dim)) elif pool_type == 'token_fc': self.head = nn.Linear(dim, out_dim) elif pool_type == 'attn_pool': self.head = AttentionPool(dim, mlp_ratio, num_heads, activation, proj_dropout, norm_eps) def forward(self, x, interpolation=False, use_31_block=False): b = x.size(0) # embeddings x = self.patch_embedding(x).flatten(2).permute(0, 2, 1) if self.pool_type in ('token', 'token_fc'): x = torch.cat([self.cls_embedding.expand(b, -1, -1), x], dim=1) if interpolation: e = pos_interpolate(self.pos_embedding, x.size(1)) else: e = self.pos_embedding x = self.dropout(x + e) if self.pre_norm is not None: x = self.pre_norm(x) # transformer if use_31_block: x = self.transformer[:-1](x) return x else: x = self.transformer(x) return x class XLMRobertaWithHead(XLMRoberta): def __init__(self, **kwargs): self.out_dim = kwargs.pop('out_dim') super().__init__(**kwargs) # head mid_dim = (self.dim + self.out_dim) // 2 self.head = nn.Sequential( nn.Linear(self.dim, mid_dim, bias=False), nn.GELU(), nn.Linear(mid_dim, self.out_dim, bias=False)) def forward(self, ids): # xlm-roberta x = super().forward(ids) # average pooling mask = ids.ne(self.pad_id).unsqueeze(-1).to(x) x = (x * mask).sum(dim=1) / mask.sum(dim=1) # head x = self.head(x) return x class XLMRobertaCLIP(nn.Module): def __init__(self, embed_dim=1024, image_size=224, patch_size=14, vision_dim=1280, vision_mlp_ratio=4, vision_heads=16, vision_layers=32, vision_pool='token', vision_pre_norm=True, vision_post_norm=False, activation='gelu', vocab_size=250002, max_text_len=514, type_size=1, pad_id=1, text_dim=1024, text_heads=16, text_layers=24, text_post_norm=True, text_dropout=0.1, attn_dropout=0.0, proj_dropout=0.0, embedding_dropout=0.0, norm_eps=1e-5): super().__init__() self.embed_dim = embed_dim self.image_size = image_size self.patch_size = patch_size self.vision_dim = vision_dim self.vision_mlp_ratio = vision_mlp_ratio self.vision_heads = vision_heads self.vision_layers = vision_layers self.vision_pre_norm = vision_pre_norm self.vision_post_norm = vision_post_norm self.activation = activation self.vocab_size = vocab_size self.max_text_len = max_text_len self.type_size = type_size self.pad_id = pad_id self.text_dim = text_dim self.text_heads = text_heads self.text_layers = text_layers self.text_post_norm = text_post_norm self.norm_eps = norm_eps # models self.visual = VisionTransformer( image_size=image_size, patch_size=patch_size, dim=vision_dim, mlp_ratio=vision_mlp_ratio, out_dim=embed_dim, num_heads=vision_heads, num_layers=vision_layers, pool_type=vision_pool, pre_norm=vision_pre_norm, post_norm=vision_post_norm, activation=activation, attn_dropout=attn_dropout, proj_dropout=proj_dropout, embedding_dropout=embedding_dropout, norm_eps=norm_eps) self.textual = XLMRobertaWithHead( vocab_size=vocab_size, max_seq_len=max_text_len, type_size=type_size, pad_id=pad_id, dim=text_dim, out_dim=embed_dim, num_heads=text_heads, num_layers=text_layers, post_norm=text_post_norm, dropout=text_dropout) self.log_scale = nn.Parameter(math.log(1 / 0.07) * torch.ones([])) def forward(self, imgs, txt_ids): """ imgs: [B, 3, H, W] of torch.float32. - mean: [0.48145466, 0.4578275, 0.40821073] - std: [0.26862954, 0.26130258, 0.27577711] txt_ids: [B, L] of torch.long. Encoded by data.CLIPTokenizer. """ xi = self.visual(imgs) xt = self.textual(txt_ids) return xi, xt def param_groups(self): groups = [{ 'params': [ p for n, p in self.named_parameters() if 'norm' in n or n.endswith('bias') ], 'weight_decay': 0.0 }, { 'params': [ p for n, p in self.named_parameters() if not ('norm' in n or n.endswith('bias')) ] }] return groups def _clip(pretrained=False, pretrained_name=None, model_cls=XLMRobertaCLIP, return_transforms=False, return_tokenizer=False, tokenizer_padding='eos', dtype=torch.float32, device='cpu', **kwargs): # init a model on device with torch.device(device): model = model_cls(**kwargs) # set device model = model.to(dtype=dtype, device=device) output = (model,) # init transforms if return_transforms: # mean and std if 'siglip' in pretrained_name.lower(): mean, std = [0.5, 0.5, 0.5], [0.5, 0.5, 0.5] else: mean = [0.48145466, 0.4578275, 0.40821073] std = [0.26862954, 0.26130258, 0.27577711] # transforms transforms = T.Compose([ T.Resize((model.image_size, model.image_size), interpolation=T.InterpolationMode.BICUBIC), T.ToTensor(), T.Normalize(mean=mean, std=std) ]) output += (transforms,) return output[0] if len(output) == 1 else output def clip_xlm_roberta_vit_h_14( pretrained=False, pretrained_name='open-clip-xlm-roberta-large-vit-huge-14', **kwargs): cfg = dict( embed_dim=1024, image_size=224, patch_size=14, vision_dim=1280, vision_mlp_ratio=4, vision_heads=16, vision_layers=32, vision_pool='token', activation='gelu', vocab_size=250002, max_text_len=514, type_size=1, pad_id=1, text_dim=1024, text_heads=16, text_layers=24, text_post_norm=True, text_dropout=0.1, attn_dropout=0.0, proj_dropout=0.0, embedding_dropout=0.0) cfg.update(**kwargs) return _clip(pretrained, pretrained_name, XLMRobertaCLIP, **cfg) class CLIPModel: def __init__(self, dtype, device, checkpoint_path, tokenizer_path): self.dtype = dtype self.device = device self.checkpoint_path = checkpoint_path self.tokenizer_path = tokenizer_path # init model self.model, self.transforms = clip_xlm_roberta_vit_h_14( pretrained=False, return_transforms=True, return_tokenizer=False, dtype=dtype, device=device) self.model = self.model.eval().requires_grad_(False) logging.info(f'loading {checkpoint_path}') self.model.load_state_dict( torch.load(checkpoint_path, map_location='cpu')) # init tokenizer self.tokenizer = HuggingfaceTokenizer( name=tokenizer_path, seq_len=self.model.max_text_len - 2, clean='whitespace') def visual(self, videos): # preprocess size = (self.model.image_size,) * 2 videos = torch.cat([ F.interpolate( u.transpose(0, 1), size=size, mode='bicubic', align_corners=False) for u in videos ]) videos = self.transforms.transforms[-1](videos.mul_(0.5).add_(0.5)) # forward with torch.cuda.amp.autocast(dtype=self.dtype): out = self.model.visual(videos, use_31_block=True) return out ================================================ FILE: wan/modules/animate/face_blocks.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. from torch import nn import torch from typing import Tuple, Optional from einops import rearrange import torch.nn.functional as F import math from ...distributed.util import gather_forward, get_rank, get_world_size try: from flash_attn import flash_attn_qkvpacked_func, flash_attn_func except ImportError: flash_attn_func = None MEMORY_LAYOUT = { "flash": ( lambda x: x.view(x.shape[0] * x.shape[1], *x.shape[2:]), lambda x: x, ), "torch": ( lambda x: x.transpose(1, 2), lambda x: x.transpose(1, 2), ), "vanilla": ( lambda x: x.transpose(1, 2), lambda x: x.transpose(1, 2), ), } def attention( q, k, v, mode="flash", drop_rate=0, attn_mask=None, causal=False, max_seqlen_q=None, batch_size=1, ): """ Perform QKV self attention. Args: q (torch.Tensor): Query tensor with shape [b, s, a, d], where a is the number of heads. k (torch.Tensor): Key tensor with shape [b, s1, a, d] v (torch.Tensor): Value tensor with shape [b, s1, a, d] mode (str): Attention mode. Choose from 'self_flash', 'cross_flash', 'torch', and 'vanilla'. drop_rate (float): Dropout rate in attention map. (default: 0) attn_mask (torch.Tensor): Attention mask with shape [b, s1] (cross_attn), or [b, a, s, s1] (torch or vanilla). (default: None) causal (bool): Whether to use causal attention. (default: False) cu_seqlens_q (torch.Tensor): dtype torch.int32. The cumulative sequence lengths of the sequences in the batch, used to index into q. cu_seqlens_kv (torch.Tensor): dtype torch.int32. The cumulative sequence lengths of the sequences in the batch, used to index into kv. max_seqlen_q (int): The maximum sequence length in the batch of q. max_seqlen_kv (int): The maximum sequence length in the batch of k and v. Returns: torch.Tensor: Output tensor after self attention with shape [b, s, ad] """ pre_attn_layout, post_attn_layout = MEMORY_LAYOUT[mode] if mode == "torch": if attn_mask is not None and attn_mask.dtype != torch.bool: attn_mask = attn_mask.to(q.dtype) x = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask, dropout_p=drop_rate, is_causal=causal) elif mode == "flash": x = flash_attn_func( q, k, v, ) x = x.view(batch_size, max_seqlen_q, x.shape[-2], x.shape[-1]) # reshape x to [b, s, a, d] elif mode == "vanilla": scale_factor = 1 / math.sqrt(q.size(-1)) b, a, s, _ = q.shape s1 = k.size(2) attn_bias = torch.zeros(b, a, s, s1, dtype=q.dtype, device=q.device) if causal: # Only applied to self attention assert attn_mask is None, "Causal mask and attn_mask cannot be used together" temp_mask = torch.ones(b, a, s, s, dtype=torch.bool, device=q.device).tril(diagonal=0) attn_bias.masked_fill_(temp_mask.logical_not(), float("-inf")) attn_bias.to(q.dtype) if attn_mask is not None: if attn_mask.dtype == torch.bool: attn_bias.masked_fill_(attn_mask.logical_not(), float("-inf")) else: attn_bias += attn_mask attn = (q @ k.transpose(-2, -1)) * scale_factor attn += attn_bias attn = attn.softmax(dim=-1) attn = torch.dropout(attn, p=drop_rate, train=True) x = attn @ v else: raise NotImplementedError(f"Unsupported attention mode: {mode}") x = post_attn_layout(x) b, s, a, d = x.shape out = x.reshape(b, s, -1) return out class CausalConv1d(nn.Module): def __init__(self, chan_in, chan_out, kernel_size=3, stride=1, dilation=1, pad_mode="replicate", **kwargs): super().__init__() self.pad_mode = pad_mode padding = (kernel_size - 1, 0) # T self.time_causal_padding = padding self.conv = nn.Conv1d(chan_in, chan_out, kernel_size, stride=stride, dilation=dilation, **kwargs) def forward(self, x): x = F.pad(x, self.time_causal_padding, mode=self.pad_mode) return self.conv(x) class FaceEncoder(nn.Module): def __init__(self, in_dim: int, hidden_dim: int, num_heads=int, dtype=None, device=None): factory_kwargs = {"dtype": dtype, "device": device} super().__init__() self.num_heads = num_heads self.conv1_local = CausalConv1d(in_dim, 1024 * num_heads, 3, stride=1) self.norm1 = nn.LayerNorm(hidden_dim // 8, elementwise_affine=False, eps=1e-6, **factory_kwargs) self.act = nn.SiLU() self.conv2 = CausalConv1d(1024, 1024, 3, stride=2) self.conv3 = CausalConv1d(1024, 1024, 3, stride=2) self.out_proj = nn.Linear(1024, hidden_dim) self.norm1 = nn.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs) self.norm2 = nn.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs) self.norm3 = nn.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs) self.padding_tokens = nn.Parameter(torch.zeros(1, 1, 1, hidden_dim)) def forward(self, x): x = rearrange(x, "b t c -> b c t") b, c, t = x.shape x = self.conv1_local(x) x = rearrange(x, "b (n c) t -> (b n) t c", n=self.num_heads) x = self.norm1(x) x = self.act(x) x = rearrange(x, "b t c -> b c t") x = self.conv2(x) x = rearrange(x, "b c t -> b t c") x = self.norm2(x) x = self.act(x) x = rearrange(x, "b t c -> b c t") x = self.conv3(x) x = rearrange(x, "b c t -> b t c") x = self.norm3(x) x = self.act(x) x = self.out_proj(x) x = rearrange(x, "(b n) t c -> b t n c", b=b) padding = self.padding_tokens.repeat(b, x.shape[1], 1, 1) x = torch.cat([x, padding], dim=-2) x_local = x.clone() return x_local class RMSNorm(nn.Module): def __init__( self, dim: int, elementwise_affine=True, eps: float = 1e-6, device=None, dtype=None, ): """ Initialize the RMSNorm normalization layer. Args: dim (int): The dimension of the input tensor. eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6. Attributes: eps (float): A small value added to the denominator for numerical stability. weight (nn.Parameter): Learnable scaling parameter. """ factory_kwargs = {"device": device, "dtype": dtype} super().__init__() self.eps = eps if elementwise_affine: self.weight = nn.Parameter(torch.ones(dim, **factory_kwargs)) def _norm(self, x): """ Apply the RMSNorm normalization to the input tensor. Args: x (torch.Tensor): The input tensor. Returns: torch.Tensor: The normalized tensor. """ return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) def forward(self, x): """ Forward pass through the RMSNorm layer. Args: x (torch.Tensor): The input tensor. Returns: torch.Tensor: The output tensor after applying RMSNorm. """ output = self._norm(x.float()).type_as(x) if hasattr(self, "weight"): output = output * self.weight return output def get_norm_layer(norm_layer): """ Get the normalization layer. Args: norm_layer (str): The type of normalization layer. Returns: norm_layer (nn.Module): The normalization layer. """ if norm_layer == "layer": return nn.LayerNorm elif norm_layer == "rms": return RMSNorm else: raise NotImplementedError(f"Norm layer {norm_layer} is not implemented") class FaceAdapter(nn.Module): def __init__( self, hidden_dim: int, heads_num: int, qk_norm: bool = True, qk_norm_type: str = "rms", num_adapter_layers: int = 1, dtype=None, device=None, ): factory_kwargs = {"dtype": dtype, "device": device} super().__init__() self.hidden_size = hidden_dim self.heads_num = heads_num self.fuser_blocks = nn.ModuleList( [ FaceBlock( self.hidden_size, self.heads_num, qk_norm=qk_norm, qk_norm_type=qk_norm_type, **factory_kwargs, ) for _ in range(num_adapter_layers) ] ) def forward( self, x: torch.Tensor, motion_embed: torch.Tensor, idx: int, freqs_cis_q: Tuple[torch.Tensor, torch.Tensor] = None, freqs_cis_k: Tuple[torch.Tensor, torch.Tensor] = None, ) -> torch.Tensor: return self.fuser_blocks[idx](x, motion_embed, freqs_cis_q, freqs_cis_k) class FaceBlock(nn.Module): def __init__( self, hidden_size: int, heads_num: int, qk_norm: bool = True, qk_norm_type: str = "rms", qk_scale: float = None, dtype: Optional[torch.dtype] = None, device: Optional[torch.device] = None, ): factory_kwargs = {"device": device, "dtype": dtype} super().__init__() self.deterministic = False self.hidden_size = hidden_size self.heads_num = heads_num head_dim = hidden_size // heads_num self.scale = qk_scale or head_dim**-0.5 self.linear1_kv = nn.Linear(hidden_size, hidden_size * 2, **factory_kwargs) self.linear1_q = nn.Linear(hidden_size, hidden_size, **factory_kwargs) self.linear2 = nn.Linear(hidden_size, hidden_size, **factory_kwargs) qk_norm_layer = get_norm_layer(qk_norm_type) self.q_norm = ( qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity() ) self.k_norm = ( qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity() ) self.pre_norm_feat = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs) self.pre_norm_motion = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs) def forward( self, x: torch.Tensor, motion_vec: torch.Tensor, motion_mask: Optional[torch.Tensor] = None, use_context_parallel=False, ) -> torch.Tensor: B, T, N, C = motion_vec.shape T_comp = T x_motion = self.pre_norm_motion(motion_vec) x_feat = self.pre_norm_feat(x) kv = self.linear1_kv(x_motion) q = self.linear1_q(x_feat) k, v = rearrange(kv, "B L N (K H D) -> K B L N H D", K=2, H=self.heads_num) q = rearrange(q, "B S (H D) -> B S H D", H=self.heads_num) # Apply QK-Norm if needed. q = self.q_norm(q).to(v) k = self.k_norm(k).to(v) k = rearrange(k, "B L N H D -> (B L) N H D") v = rearrange(v, "B L N H D -> (B L) N H D") if use_context_parallel: q = gather_forward(q, dim=1) q = rearrange(q, "B (L S) H D -> (B L) S H D", L=T_comp) # Compute attention. attn = attention( q, k, v, max_seqlen_q=q.shape[1], batch_size=q.shape[0], ) attn = rearrange(attn, "(B L) S C -> B (L S) C", L=T_comp) if use_context_parallel: attn = torch.chunk(attn, get_world_size(), dim=1)[get_rank()] output = self.linear2(attn) if motion_mask is not None: output = output * rearrange(motion_mask, "B T H W -> B (T H W)").unsqueeze(-1) return output ================================================ FILE: wan/modules/animate/model_animate.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import math import types from copy import deepcopy from einops import rearrange from typing import List import numpy as np import torch import torch.cuda.amp as amp import torch.nn as nn from diffusers.configuration_utils import ConfigMixin, register_to_config from diffusers.models.modeling_utils import ModelMixin from diffusers.loaders import PeftAdapterMixin from ...distributed.sequence_parallel import ( distributed_attention, gather_forward, get_rank, get_world_size, ) from ..model import ( Head, WanAttentionBlock, WanLayerNorm, WanRMSNorm, WanModel, WanSelfAttention, flash_attention, rope_params, sinusoidal_embedding_1d, rope_apply ) from .face_blocks import FaceEncoder, FaceAdapter from .motion_encoder import Generator class HeadAnimate(Head): def forward(self, x, e): """ Args: x(Tensor): Shape [B, L1, C] e(Tensor): Shape [B, L1, C] """ assert e.dtype == torch.float32 with amp.autocast(dtype=torch.float32): e = (self.modulation + e.unsqueeze(1)).chunk(2, dim=1) x = (self.head(self.norm(x) * (1 + e[1]) + e[0])) return x class WanAnimateSelfAttention(WanSelfAttention): def forward(self, x, seq_lens, grid_sizes, freqs): """ Args: x(Tensor): Shape [B, L, num_heads, C / num_heads] seq_lens(Tensor): Shape [B] grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W) freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2] """ b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim # query, key, value function def qkv_fn(x): q = self.norm_q(self.q(x)).view(b, s, n, d) k = self.norm_k(self.k(x)).view(b, s, n, d) v = self.v(x).view(b, s, n, d) return q, k, v q, k, v = qkv_fn(x) x = flash_attention( q=rope_apply(q, grid_sizes, freqs), k=rope_apply(k, grid_sizes, freqs), v=v, k_lens=seq_lens, window_size=self.window_size) # output x = x.flatten(2) x = self.o(x) return x class WanAnimateCrossAttention(WanSelfAttention): def __init__( self, dim, num_heads, window_size=(-1, -1), qk_norm=True, eps=1e-6, use_img_emb=True ): super().__init__( dim, num_heads, window_size, qk_norm, eps ) self.use_img_emb = use_img_emb if use_img_emb: self.k_img = nn.Linear(dim, dim) self.v_img = nn.Linear(dim, dim) self.norm_k_img = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity() def forward(self, x, context, context_lens): """ x: [B, L1, C]. context: [B, L2, C]. context_lens: [B]. """ if self.use_img_emb: context_img = context[:, :257] context = context[:, 257:] else: context = context b, n, d = x.size(0), self.num_heads, self.head_dim # compute query, key, value q = self.norm_q(self.q(x)).view(b, -1, n, d) k = self.norm_k(self.k(context)).view(b, -1, n, d) v = self.v(context).view(b, -1, n, d) if self.use_img_emb: k_img = self.norm_k_img(self.k_img(context_img)).view(b, -1, n, d) v_img = self.v_img(context_img).view(b, -1, n, d) img_x = flash_attention(q, k_img, v_img, k_lens=None) # compute attention x = flash_attention(q, k, v, k_lens=context_lens) # output x = x.flatten(2) if self.use_img_emb: img_x = img_x.flatten(2) x = x + img_x x = self.o(x) return x class WanAnimateAttentionBlock(nn.Module): def __init__(self, dim, ffn_dim, num_heads, window_size=(-1, -1), qk_norm=True, cross_attn_norm=True, eps=1e-6, use_img_emb=True): super().__init__() self.dim = dim self.ffn_dim = ffn_dim self.num_heads = num_heads self.window_size = window_size self.qk_norm = qk_norm self.cross_attn_norm = cross_attn_norm self.eps = eps # layers self.norm1 = WanLayerNorm(dim, eps) self.self_attn = WanAnimateSelfAttention(dim, num_heads, window_size, qk_norm, eps) self.norm3 = WanLayerNorm( dim, eps, elementwise_affine=True ) if cross_attn_norm else nn.Identity() self.cross_attn = WanAnimateCrossAttention(dim, num_heads, (-1, -1), qk_norm, eps, use_img_emb=use_img_emb) self.norm2 = WanLayerNorm(dim, eps) self.ffn = nn.Sequential( nn.Linear(dim, ffn_dim), nn.GELU(approximate='tanh'), nn.Linear(ffn_dim, dim) ) # modulation self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim ** 0.5) def forward( self, x, e, seq_lens, grid_sizes, freqs, context, context_lens, ): """ Args: x(Tensor): Shape [B, L, C] e(Tensor): Shape [B, L1, 6, C] seq_lens(Tensor): Shape [B], length of each sequence in batch grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W) freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2] """ assert e.dtype == torch.float32 with amp.autocast(dtype=torch.float32): e = (self.modulation + e).chunk(6, dim=1) assert e[0].dtype == torch.float32 # self-attention y = self.self_attn( self.norm1(x).float() * (1 + e[1]) + e[0], seq_lens, grid_sizes, freqs ) with amp.autocast(dtype=torch.float32): x = x + y * e[2] # cross-attention & ffn function def cross_attn_ffn(x, context, context_lens, e): x = x + self.cross_attn(self.norm3(x), context, context_lens) y = self.ffn(self.norm2(x).float() * (1 + e[4]) + e[3]) with amp.autocast(dtype=torch.float32): x = x + y * e[5] return x x = cross_attn_ffn(x, context, context_lens, e) return x class MLPProj(torch.nn.Module): def __init__(self, in_dim, out_dim): super().__init__() self.proj = torch.nn.Sequential( torch.nn.LayerNorm(in_dim), torch.nn.Linear(in_dim, in_dim), torch.nn.GELU(), torch.nn.Linear(in_dim, out_dim), torch.nn.LayerNorm(out_dim), ) def forward(self, image_embeds): clip_extra_context_tokens = self.proj(image_embeds) return clip_extra_context_tokens class WanAnimateModel(ModelMixin, ConfigMixin, PeftAdapterMixin): _no_split_modules = ['WanAttentionBlock'] @register_to_config def __init__(self, patch_size=(1, 2, 2), text_len=512, in_dim=36, dim=5120, ffn_dim=13824, freq_dim=256, text_dim=4096, out_dim=16, num_heads=40, num_layers=40, window_size=(-1, -1), qk_norm=True, cross_attn_norm=True, eps=1e-6, motion_encoder_dim=512, use_context_parallel=False, use_img_emb=True): super().__init__() self.patch_size = patch_size self.text_len = text_len self.in_dim = in_dim self.dim = dim self.ffn_dim = ffn_dim self.freq_dim = freq_dim self.text_dim = text_dim self.out_dim = out_dim self.num_heads = num_heads self.num_layers = num_layers self.window_size = window_size self.qk_norm = qk_norm self.cross_attn_norm = cross_attn_norm self.eps = eps self.motion_encoder_dim = motion_encoder_dim self.use_context_parallel = use_context_parallel self.use_img_emb = use_img_emb # embeddings self.patch_embedding = nn.Conv3d( in_dim, dim, kernel_size=patch_size, stride=patch_size) self.pose_patch_embedding = nn.Conv3d( 16, dim, kernel_size=patch_size, stride=patch_size ) self.text_embedding = nn.Sequential( nn.Linear(text_dim, dim), nn.GELU(approximate='tanh'), nn.Linear(dim, dim)) self.time_embedding = nn.Sequential( nn.Linear(freq_dim, dim), nn.SiLU(), nn.Linear(dim, dim)) self.time_projection = nn.Sequential(nn.SiLU(), nn.Linear(dim, dim * 6)) # blocks self.blocks = nn.ModuleList([ WanAnimateAttentionBlock(dim, ffn_dim, num_heads, window_size, qk_norm, cross_attn_norm, eps, use_img_emb) for _ in range(num_layers) ]) # head self.head = HeadAnimate(dim, out_dim, patch_size, eps) # buffers (don't use register_buffer otherwise dtype will be changed in to()) assert (dim % num_heads) == 0 and (dim // num_heads) % 2 == 0 d = dim // num_heads self.freqs = torch.cat([ rope_params(1024, d - 4 * (d // 6)), rope_params(1024, 2 * (d // 6)), rope_params(1024, 2 * (d // 6)) ], dim=1) self.img_emb = MLPProj(1280, dim) # initialize weights self.init_weights() self.motion_encoder = Generator(size=512, style_dim=512, motion_dim=20) self.face_adapter = FaceAdapter( heads_num=self.num_heads, hidden_dim=self.dim, num_adapter_layers=self.num_layers // 5, ) self.face_encoder = FaceEncoder( in_dim=motion_encoder_dim, hidden_dim=self.dim, num_heads=4, ) def after_patch_embedding(self, x: List[torch.Tensor], pose_latents, face_pixel_values): pose_latents = [self.pose_patch_embedding(u.unsqueeze(0)) for u in pose_latents] for x_, pose_latents_ in zip(x, pose_latents): x_[:, :, 1:] += pose_latents_ b,c,T,h,w = face_pixel_values.shape face_pixel_values = rearrange(face_pixel_values, "b c t h w -> (b t) c h w") encode_bs = 8 face_pixel_values_tmp = [] for i in range(math.ceil(face_pixel_values.shape[0]/encode_bs)): face_pixel_values_tmp.append(self.motion_encoder.get_motion(face_pixel_values[i*encode_bs:(i+1)*encode_bs])) motion_vec = torch.cat(face_pixel_values_tmp) motion_vec = rearrange(motion_vec, "(b t) c -> b t c", t=T) motion_vec = self.face_encoder(motion_vec) B, L, H, C = motion_vec.shape pad_face = torch.zeros(B, 1, H, C).type_as(motion_vec) motion_vec = torch.cat([pad_face, motion_vec], dim=1) return x, motion_vec def after_transformer_block(self, block_idx, x, motion_vec, motion_masks=None): if block_idx % 5 == 0: adapter_args = [x, motion_vec, motion_masks, self.use_context_parallel] residual_out = self.face_adapter.fuser_blocks[block_idx // 5](*adapter_args) x = residual_out + x return x def forward( self, x, t, clip_fea, context, seq_len, y=None, pose_latents=None, face_pixel_values=None ): # params device = self.patch_embedding.weight.device if self.freqs.device != device: self.freqs = self.freqs.to(device) if y is not None: x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)] # embeddings x = [self.patch_embedding(u.unsqueeze(0)) for u in x] x, motion_vec = self.after_patch_embedding(x, pose_latents, face_pixel_values) grid_sizes = torch.stack( [torch.tensor(u.shape[2:], dtype=torch.long) for u in x]) x = [u.flatten(2).transpose(1, 2) for u in x] seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.long) assert seq_lens.max() <= seq_len x = torch.cat([ torch.cat([u, u.new_zeros(1, seq_len - u.size(1), u.size(2))], dim=1) for u in x ]) # time embeddings with amp.autocast(dtype=torch.float32): e = self.time_embedding( sinusoidal_embedding_1d(self.freq_dim, t).float() ) e0 = self.time_projection(e).unflatten(1, (6, self.dim)) assert e.dtype == torch.float32 and e0.dtype == torch.float32 # context context_lens = None context = self.text_embedding( torch.stack([ torch.cat( [u, u.new_zeros(self.text_len - u.size(0), u.size(1))]) for u in context ])) if self.use_img_emb: context_clip = self.img_emb(clip_fea) # bs x 257 x dim context = torch.concat([context_clip, context], dim=1) # arguments kwargs = dict( e=e0, seq_lens=seq_lens, grid_sizes=grid_sizes, freqs=self.freqs, context=context, context_lens=context_lens) if self.use_context_parallel: x = torch.chunk(x, get_world_size(), dim=1)[get_rank()] for idx, block in enumerate(self.blocks): x = block(x, **kwargs) x = self.after_transformer_block(idx, x, motion_vec) # head x = self.head(x, e) if self.use_context_parallel: x = gather_forward(x, dim=1) # unpatchify x = self.unpatchify(x, grid_sizes) return [u.float() for u in x] def unpatchify(self, x, grid_sizes): r""" Reconstruct video tensors from patch embeddings. Args: x (List[Tensor]): List of patchified features, each with shape [L, C_out * prod(patch_size)] grid_sizes (Tensor): Original spatial-temporal grid dimensions before patching, shape [B, 3] (3 dimensions correspond to F_patches, H_patches, W_patches) Returns: List[Tensor]: Reconstructed video tensors with shape [C_out, F, H / 8, W / 8] """ c = self.out_dim out = [] for u, v in zip(x, grid_sizes.tolist()): u = u[:math.prod(v)].view(*v, *self.patch_size, c) u = torch.einsum('fhwpqrc->cfphqwr', u) u = u.reshape(c, *[i * j for i, j in zip(v, self.patch_size)]) out.append(u) return out def init_weights(self): r""" Initialize model parameters using Xavier initialization. """ # basic init for m in self.modules(): if isinstance(m, nn.Linear): nn.init.xavier_uniform_(m.weight) if m.bias is not None: nn.init.zeros_(m.bias) # init embeddings nn.init.xavier_uniform_(self.patch_embedding.weight.flatten(1)) for m in self.text_embedding.modules(): if isinstance(m, nn.Linear): nn.init.normal_(m.weight, std=.02) for m in self.time_embedding.modules(): if isinstance(m, nn.Linear): nn.init.normal_(m.weight, std=.02) # init output layer nn.init.zeros_(self.head.head.weight) ================================================ FILE: wan/modules/animate/motion_encoder.py ================================================ # Modified from ``https://github.com/wyhsirius/LIA`` # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import torch import torch.nn as nn from torch.nn import functional as F import math def custom_qr(input_tensor): original_dtype = input_tensor.dtype if original_dtype == torch.bfloat16: q, r = torch.linalg.qr(input_tensor.to(torch.float32)) return q.to(original_dtype), r.to(original_dtype) return torch.linalg.qr(input_tensor) def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5): return F.leaky_relu(input + bias, negative_slope) * scale def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1): _, minor, in_h, in_w = input.shape kernel_h, kernel_w = kernel.shape out = input.view(-1, minor, in_h, 1, in_w, 1) out = F.pad(out, [0, up_x - 1, 0, 0, 0, up_y - 1, 0, 0]) out = out.view(-1, minor, in_h * up_y, in_w * up_x) out = F.pad(out, [max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)]) out = out[:, :, max(-pad_y0, 0): out.shape[2] - max(-pad_y1, 0), max(-pad_x0, 0): out.shape[3] - max(-pad_x1, 0), ] out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]) w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w) out = F.conv2d(out, w) out = out.reshape(-1, minor, in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, ) return out[:, :, ::down_y, ::down_x] def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)): return upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1]) def make_kernel(k): k = torch.tensor(k, dtype=torch.float32) if k.ndim == 1: k = k[None, :] * k[:, None] k /= k.sum() return k class FusedLeakyReLU(nn.Module): def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5): super().__init__() self.bias = nn.Parameter(torch.zeros(1, channel, 1, 1)) self.negative_slope = negative_slope self.scale = scale def forward(self, input): out = fused_leaky_relu(input, self.bias, self.negative_slope, self.scale) return out class Blur(nn.Module): def __init__(self, kernel, pad, upsample_factor=1): super().__init__() kernel = make_kernel(kernel) if upsample_factor > 1: kernel = kernel * (upsample_factor ** 2) self.register_buffer('kernel', kernel) self.pad = pad def forward(self, input): return upfirdn2d(input, self.kernel, pad=self.pad) class ScaledLeakyReLU(nn.Module): def __init__(self, negative_slope=0.2): super().__init__() self.negative_slope = negative_slope def forward(self, input): return F.leaky_relu(input, negative_slope=self.negative_slope) class EqualConv2d(nn.Module): def __init__(self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True): super().__init__() self.weight = nn.Parameter(torch.randn(out_channel, in_channel, kernel_size, kernel_size)) self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2) self.stride = stride self.padding = padding if bias: self.bias = nn.Parameter(torch.zeros(out_channel)) else: self.bias = None def forward(self, input): return F.conv2d(input, self.weight * self.scale, bias=self.bias, stride=self.stride, padding=self.padding) def __repr__(self): return ( f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]},' f' {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})' ) class EqualLinear(nn.Module): def __init__(self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None): super().__init__() self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul)) if bias: self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init)) else: self.bias = None self.activation = activation self.scale = (1 / math.sqrt(in_dim)) * lr_mul self.lr_mul = lr_mul def forward(self, input): if self.activation: out = F.linear(input, self.weight * self.scale) out = fused_leaky_relu(out, self.bias * self.lr_mul) else: out = F.linear(input, self.weight * self.scale, bias=self.bias * self.lr_mul) return out def __repr__(self): return (f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})') class ConvLayer(nn.Sequential): def __init__( self, in_channel, out_channel, kernel_size, downsample=False, blur_kernel=[1, 3, 3, 1], bias=True, activate=True, ): layers = [] if downsample: factor = 2 p = (len(blur_kernel) - factor) + (kernel_size - 1) pad0 = (p + 1) // 2 pad1 = p // 2 layers.append(Blur(blur_kernel, pad=(pad0, pad1))) stride = 2 self.padding = 0 else: stride = 1 self.padding = kernel_size // 2 layers.append(EqualConv2d(in_channel, out_channel, kernel_size, padding=self.padding, stride=stride, bias=bias and not activate)) if activate: if bias: layers.append(FusedLeakyReLU(out_channel)) else: layers.append(ScaledLeakyReLU(0.2)) super().__init__(*layers) class ResBlock(nn.Module): def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1]): super().__init__() self.conv1 = ConvLayer(in_channel, in_channel, 3) self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True) self.skip = ConvLayer(in_channel, out_channel, 1, downsample=True, activate=False, bias=False) def forward(self, input): out = self.conv1(input) out = self.conv2(out) skip = self.skip(input) out = (out + skip) / math.sqrt(2) return out class EncoderApp(nn.Module): def __init__(self, size, w_dim=512): super(EncoderApp, self).__init__() channels = { 4: 512, 8: 512, 16: 512, 32: 512, 64: 256, 128: 128, 256: 64, 512: 32, 1024: 16 } self.w_dim = w_dim log_size = int(math.log(size, 2)) self.convs = nn.ModuleList() self.convs.append(ConvLayer(3, channels[size], 1)) in_channel = channels[size] for i in range(log_size, 2, -1): out_channel = channels[2 ** (i - 1)] self.convs.append(ResBlock(in_channel, out_channel)) in_channel = out_channel self.convs.append(EqualConv2d(in_channel, self.w_dim, 4, padding=0, bias=False)) def forward(self, x): res = [] h = x for conv in self.convs: h = conv(h) res.append(h) return res[-1].squeeze(-1).squeeze(-1), res[::-1][2:] class Encoder(nn.Module): def __init__(self, size, dim=512, dim_motion=20): super(Encoder, self).__init__() # appearance netmork self.net_app = EncoderApp(size, dim) # motion network fc = [EqualLinear(dim, dim)] for i in range(3): fc.append(EqualLinear(dim, dim)) fc.append(EqualLinear(dim, dim_motion)) self.fc = nn.Sequential(*fc) def enc_app(self, x): h_source = self.net_app(x) return h_source def enc_motion(self, x): h, _ = self.net_app(x) h_motion = self.fc(h) return h_motion class Direction(nn.Module): def __init__(self, motion_dim): super(Direction, self).__init__() self.weight = nn.Parameter(torch.randn(512, motion_dim)) def forward(self, input): weight = self.weight + 1e-8 Q, R = custom_qr(weight) if input is None: return Q else: input_diag = torch.diag_embed(input) # alpha, diagonal matrix out = torch.matmul(input_diag, Q.T) out = torch.sum(out, dim=1) return out class Synthesis(nn.Module): def __init__(self, motion_dim): super(Synthesis, self).__init__() self.direction = Direction(motion_dim) class Generator(nn.Module): def __init__(self, size, style_dim=512, motion_dim=20): super().__init__() self.enc = Encoder(size, style_dim, motion_dim) self.dec = Synthesis(motion_dim) def get_motion(self, img): #motion_feat = self.enc.enc_motion(img) motion_feat = torch.utils.checkpoint.checkpoint((self.enc.enc_motion), img, use_reentrant=True) with torch.cuda.amp.autocast(dtype=torch.float32): motion = self.dec.direction(motion_feat) return motion ================================================ FILE: wan/modules/animate/preprocess/UserGuider.md ================================================ # Wan-animate Preprocessing User Guider ## 1. Introductions Wan-animate offers two generation modes: `animation` and `replacement`. While both modes extract the skeleton from the reference video, they each have a distinct preprocessing pipeline. ### 1.1 Animation Mode In this mode, it is highly recommended to enable pose retargeting, especially if the body proportions of the reference and driving characters are dissimilar. - A simplified version of pose retargeting pipeline is provided to help developers quickly implement this functionality. - **NOTE:** Due to the potential complexity of input data, the results from this simplified retargeting version are NOT guaranteed to be perfect. It is strongly advised to verify the preprocessing results before proceeding. - Community contributions to improve on this feature are welcome. ### 1.2 Replacement Mode - Pose retargeting is DISABLED by default in this mode. This is a deliberate choice to account for potential spatial interactions between the character and the environment. - **WARNING**: If there is a significant mismatch in body proportions between the reference and driving characters, artifacts or deformations may appear in the final output. - A simplified version for extracting the character's mask is also provided. - **WARNING:** This mask extraction process is designed for **single-person videos ONLY** and may produce incorrect results or fail in multi-person videos (incorrect pose tracking). For multi-person video, users are required to either develop their own solution or integrate a suitable open-source tool. --- ## 2. Preprocessing Instructions and Recommendations ### 2.1 Basic Usage - The preprocessing process requires some additional models, including pose detection (mandatory), and mask extraction and image editing models (optional, as needed). Place them according to the following directory structure: ``` /path/to/your/ckpt_path/ ├── det/ │ └── yolov10m.onnx ├── pose2d/ │ └── vitpose_h_wholebody.onnx ├── sam2/ │ └── sam2_hiera_large.pt └── FLUX.1-Kontext-dev/ ``` - `video_path`, `refer_path`, and `save_path` correspond to the paths for the input driving video, the character image, and the preprocessed results. - When using `animation` mode, two videos, `src_face.mp4` and `src_pose.mp4`, will be generated in `save_path`. When using `replacement` mode, two additional videos, `src_bg.mp4` and `src_mask.mp4`, will also be generated. - The `resolution_area` parameter determines the resolution for both preprocessing and the generation model. Its size is determined by pixel area. - The `fps` parameter can specify the frame rate for video processing. A lower frame rate can improve generation efficiency, but may cause stuttering or choppiness. --- ### 2.2 Animation Mode - We support three forms: not using pose retargeting, using basic pose retargeting, and using enhanced pose retargeting based on the `FLUX.1-Kontext-dev` image editing model. These are specified via the `retarget_flag` and `use_flux` parameters. - Specifying `retarget_flag` to use basic pose retargeting requires ensuring that both the reference character and the character in the first frame of the driving video are in a front-facing, stretched pose. - Other than that, we recommend using enhanced pose retargeting by specifying both `retarget_flag` and `use_flux`. **NOTE:** Due to the limited capabilities of `FLUX.1-Kontext-dev`, it is NOT guaranteed to produce the expected results (e.g., consistency is not maintained, the pose is incorrect, etc.). It is recommended to check the intermediate results as well as the finally generated pose video; both are stored in `save_path`. Of course, users can also use a better image editing model, or explore the prompts for Flux on their own. --- ### 2.3 Replacement Mode - Specifying `replace_flag` to enable data preprocessing for this mode. The preprocessing will additionally process a mask for the character in the video, and its size and shape can be adjusted by specifying some parameters. - `iterations` and `k` can make the mask larger, covering more area. - `w_len` and `h_len` can adjust the mask's shape. Smaller values will make the outline coarser, while larger values will make it finer. - A smaller, finer-contoured mask can allow for more of the original background to be preserved, but may potentially limit the character's generation area (considering potential appearance differences, this can lead to some shape leakage). A larger, coarser mask can allow the character generation to be more flexible and consistent, but because it includes more of the background, it might affect the background's consistency. We recommend users to adjust the relevant parameters based on their specific input data. ================================================ FILE: wan/modules/animate/preprocess/__init__.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. from .process_pipepline import ProcessPipeline from .video_predictor import SAM2VideoPredictor ================================================ FILE: wan/modules/animate/preprocess/human_visualization.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import os import cv2 import time import math import matplotlib import matplotlib.pyplot as plt import numpy as np from typing import Dict, List import random from pose2d_utils import AAPoseMeta def draw_handpose(canvas, keypoints, hand_score_th=0.6): """ Draw keypoints and connections representing hand pose on a given canvas. Args: canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the hand pose. keypoints (List[Keypoint]| None): A list of Keypoint objects representing the hand keypoints to be drawn or None if no keypoints are present. Returns: np.ndarray: A 3D numpy array representing the modified canvas with the drawn hand pose. Note: The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1. """ eps = 0.01 H, W, C = canvas.shape stickwidth = max(int(min(H, W) / 200), 1) edges = [ [0, 1], [1, 2], [2, 3], [3, 4], [0, 5], [5, 6], [6, 7], [7, 8], [0, 9], [9, 10], [10, 11], [11, 12], [0, 13], [13, 14], [14, 15], [15, 16], [0, 17], [17, 18], [18, 19], [19, 20], ] for ie, (e1, e2) in enumerate(edges): k1 = keypoints[e1] k2 = keypoints[e2] if k1 is None or k2 is None: continue if k1[2] < hand_score_th or k2[2] < hand_score_th: continue x1 = int(k1[0]) y1 = int(k1[1]) x2 = int(k2[0]) y2 = int(k2[1]) if x1 > eps and y1 > eps and x2 > eps and y2 > eps: cv2.line( canvas, (x1, y1), (x2, y2), matplotlib.colors.hsv_to_rgb([ie / float(len(edges)), 1.0, 1.0]) * 255, thickness=stickwidth, ) for keypoint in keypoints: if keypoint is None: continue if keypoint[2] < hand_score_th: continue x, y = keypoint[0], keypoint[1] x = int(x) y = int(y) if x > eps and y > eps: cv2.circle(canvas, (x, y), stickwidth, (0, 0, 255), thickness=-1) return canvas def draw_handpose_new(canvas, keypoints, stickwidth_type='v2', hand_score_th=0.6): """ Draw keypoints and connections representing hand pose on a given canvas. Args: canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the hand pose. keypoints (List[Keypoint]| None): A list of Keypoint objects representing the hand keypoints to be drawn or None if no keypoints are present. Returns: np.ndarray: A 3D numpy array representing the modified canvas with the drawn hand pose. Note: The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1. """ eps = 0.01 H, W, C = canvas.shape if stickwidth_type == 'v1': stickwidth = max(int(min(H, W) / 200), 1) elif stickwidth_type == 'v2': stickwidth = max(max(int(min(H, W) / 200) - 1, 1) // 2, 1) edges = [ [0, 1], [1, 2], [2, 3], [3, 4], [0, 5], [5, 6], [6, 7], [7, 8], [0, 9], [9, 10], [10, 11], [11, 12], [0, 13], [13, 14], [14, 15], [15, 16], [0, 17], [17, 18], [18, 19], [19, 20], ] for ie, (e1, e2) in enumerate(edges): k1 = keypoints[e1] k2 = keypoints[e2] if k1 is None or k2 is None: continue if k1[2] < hand_score_th or k2[2] < hand_score_th: continue x1 = int(k1[0]) y1 = int(k1[1]) x2 = int(k2[0]) y2 = int(k2[1]) if x1 > eps and y1 > eps and x2 > eps and y2 > eps: cv2.line( canvas, (x1, y1), (x2, y2), matplotlib.colors.hsv_to_rgb([ie / float(len(edges)), 1.0, 1.0]) * 255, thickness=stickwidth, ) for keypoint in keypoints: if keypoint is None: continue if keypoint[2] < hand_score_th: continue x, y = keypoint[0], keypoint[1] x = int(x) y = int(y) if x > eps and y > eps: cv2.circle(canvas, (x, y), stickwidth, (0, 0, 255), thickness=-1) return canvas def draw_ellipse_by_2kp(img, keypoint1, keypoint2, color, threshold=0.6): H, W, C = img.shape stickwidth = max(int(min(H, W) / 200), 1) if keypoint1[-1] < threshold or keypoint2[-1] < threshold: return img Y = np.array([keypoint1[0], keypoint2[0]]) X = np.array([keypoint1[1], keypoint2[1]]) mX = np.mean(X) mY = np.mean(Y) length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5 angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1])) polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1) cv2.fillConvexPoly(img, polygon, [int(float(c) * 0.6) for c in color]) return img def split_pose2d_kps_to_aa(kp2ds: np.ndarray) -> List[np.ndarray]: """Convert the 133 keypoints from pose2d to body and hands keypoints. Args: kp2ds (np.ndarray): [133, 2] Returns: List[np.ndarray]: _description_ """ kp2ds_body = ( kp2ds[[0, 6, 6, 8, 10, 5, 7, 9, 12, 14, 16, 11, 13, 15, 2, 1, 4, 3, 17, 20]] + kp2ds[[0, 5, 6, 8, 10, 5, 7, 9, 12, 14, 16, 11, 13, 15, 2, 1, 4, 3, 18, 21]] ) / 2 kp2ds_lhand = kp2ds[91:112] kp2ds_rhand = kp2ds[112:133] return kp2ds_body.copy(), kp2ds_lhand.copy(), kp2ds_rhand.copy() def draw_aapose_by_meta(img, meta: AAPoseMeta, threshold=0.5, stick_width_norm=200, draw_hand=True, draw_head=True): kp2ds = np.concatenate([meta.kps_body, meta.kps_body_p[:, None]], axis=1) kp2ds_lhand = np.concatenate([meta.kps_lhand, meta.kps_lhand_p[:, None]], axis=1) kp2ds_rhand = np.concatenate([meta.kps_rhand, meta.kps_rhand_p[:, None]], axis=1) pose_img = draw_aapose(img, kp2ds, threshold, kp2ds_lhand=kp2ds_lhand, kp2ds_rhand=kp2ds_rhand, stick_width_norm=stick_width_norm, draw_hand=draw_hand, draw_head=draw_head) return pose_img def draw_aapose_by_meta_new(img, meta: AAPoseMeta, threshold=0.5, stickwidth_type='v2', draw_hand=True, draw_head=True): kp2ds = np.concatenate([meta.kps_body, meta.kps_body_p[:, None]], axis=1) kp2ds_lhand = np.concatenate([meta.kps_lhand, meta.kps_lhand_p[:, None]], axis=1) kp2ds_rhand = np.concatenate([meta.kps_rhand, meta.kps_rhand_p[:, None]], axis=1) pose_img = draw_aapose_new(img, kp2ds, threshold, kp2ds_lhand=kp2ds_lhand, kp2ds_rhand=kp2ds_rhand, stickwidth_type=stickwidth_type, draw_hand=draw_hand, draw_head=draw_head) return pose_img def draw_hand_by_meta(img, meta: AAPoseMeta, threshold=0.5, stick_width_norm=200): kp2ds = np.concatenate([meta.kps_body, meta.kps_body_p[:, None] * 0], axis=1) kp2ds_lhand = np.concatenate([meta.kps_lhand, meta.kps_lhand_p[:, None]], axis=1) kp2ds_rhand = np.concatenate([meta.kps_rhand, meta.kps_rhand_p[:, None]], axis=1) pose_img = draw_aapose(img, kp2ds, threshold, kp2ds_lhand=kp2ds_lhand, kp2ds_rhand=kp2ds_rhand, stick_width_norm=stick_width_norm, draw_hand=True, draw_head=False) return pose_img def draw_aaface_by_meta(img, meta: AAPoseMeta, threshold=0.5, stick_width_norm=200, draw_hand=False, draw_head=True): kp2ds = np.concatenate([meta.kps_body, meta.kps_body_p[:, None]], axis=1) # kp2ds_lhand = np.concatenate([meta.kps_lhand, meta.kps_lhand_p[:, None]], axis=1) # kp2ds_rhand = np.concatenate([meta.kps_rhand, meta.kps_rhand_p[:, None]], axis=1) pose_img = draw_M(img, kp2ds, threshold, kp2ds_lhand=None, kp2ds_rhand=None, stick_width_norm=stick_width_norm, draw_hand=draw_hand, draw_head=draw_head) return pose_img def draw_aanose_by_meta(img, meta: AAPoseMeta, threshold=0.5, stick_width_norm=100, draw_hand=False): kp2ds = np.concatenate([meta.kps_body, meta.kps_body_p[:, None]], axis=1) # kp2ds_lhand = np.concatenate([meta.kps_lhand, meta.kps_lhand_p[:, None]], axis=1) # kp2ds_rhand = np.concatenate([meta.kps_rhand, meta.kps_rhand_p[:, None]], axis=1) pose_img = draw_nose(img, kp2ds, threshold, kp2ds_lhand=None, kp2ds_rhand=None, stick_width_norm=stick_width_norm, draw_hand=draw_hand) return pose_img def gen_face_motion_seq(img, metas: List[AAPoseMeta], threshold=0.5, stick_width_norm=200): return def draw_M( img, kp2ds, threshold=0.6, data_to_json=None, idx=-1, kp2ds_lhand=None, kp2ds_rhand=None, draw_hand=False, stick_width_norm=200, draw_head=True ): """ Draw keypoints and connections representing hand pose on a given canvas. Args: canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the hand pose. keypoints (List[Keypoint]| None): A list of Keypoint objects representing the hand keypoints to be drawn or None if no keypoints are present. Returns: np.ndarray: A 3D numpy array representing the modified canvas with the drawn hand pose. Note: The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1. """ new_kep_list = [ "Nose", "Neck", "RShoulder", "RElbow", "RWrist", # No.4 "LShoulder", "LElbow", "LWrist", # No.7 "RHip", "RKnee", "RAnkle", # No.10 "LHip", "LKnee", "LAnkle", # No.13 "REye", "LEye", "REar", "LEar", "LToe", "RToe", ] # kp2ds_body = (kp2ds.copy()[[0, 6, 6, 8, 10, 5, 7, 9, 12, 14, 16, 11, 13, 15, 2, 1, 4, 3, 17, 20]] + \ # kp2ds.copy()[[0, 5, 6, 8, 10, 5, 7, 9, 12, 14, 16, 11, 13, 15, 2, 1, 4, 3, 18, 21]]) / 2 kp2ds = kp2ds.copy() # import ipdb; ipdb.set_trace() kp2ds[[1,2,3,4,5,6,7,8,9,10,11,12,13,18,19], 2] = 0 if not draw_head: kp2ds[[0,14,15,16,17], 2] = 0 kp2ds_body = kp2ds # kp2ds_body = kp2ds_body[:18] # kp2ds_lhand = kp2ds.copy()[91:112] # kp2ds_rhand = kp2ds.copy()[112:133] limbSeq = [ # [2, 3], # [2, 6], # shoulders # [3, 4], # [4, 5], # left arm # [6, 7], # [7, 8], # right arm # [2, 9], # [9, 10], # [10, 11], # right leg # [2, 12], # [12, 13], # [13, 14], # left leg # [2, 1], [1, 15], [15, 17], [1, 16], [16, 18], # face (nose, eyes, ears) # [14, 19], # [11, 20], # foot ] colors = [ # [255, 0, 0], # [255, 85, 0], # [255, 170, 0], # [255, 255, 0], # [170, 255, 0], # [85, 255, 0], # [0, 255, 0], # [0, 255, 85], # [0, 255, 170], # [0, 255, 255], # [0, 170, 255], # [0, 85, 255], # [0, 0, 255], # [85, 0, 255], [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85], # foot # [200, 200, 0], # [100, 100, 0], ] H, W, C = img.shape stickwidth = max(int(min(H, W) / stick_width_norm), 1) for _idx, ((k1_index, k2_index), color) in enumerate(zip(limbSeq, colors)): keypoint1 = kp2ds_body[k1_index - 1] keypoint2 = kp2ds_body[k2_index - 1] if keypoint1[-1] < threshold or keypoint2[-1] < threshold: continue Y = np.array([keypoint1[0], keypoint2[0]]) X = np.array([keypoint1[1], keypoint2[1]]) mX = np.mean(X) mY = np.mean(Y) length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5 angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1])) polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1) cv2.fillConvexPoly(img, polygon, [int(float(c) * 0.6) for c in color]) for _idx, (keypoint, color) in enumerate(zip(kp2ds_body, colors)): if keypoint[-1] < threshold: continue x, y = keypoint[0], keypoint[1] # cv2.circle(canvas, (int(x), int(y)), 4, color, thickness=-1) cv2.circle(img, (int(x), int(y)), stickwidth, color, thickness=-1) if draw_hand: img = draw_handpose(img, kp2ds_lhand, hand_score_th=threshold) img = draw_handpose(img, kp2ds_rhand, hand_score_th=threshold) kp2ds_body[:, 0] /= W kp2ds_body[:, 1] /= H if data_to_json is not None: if idx == -1: data_to_json.append( { "image_id": "frame_{:05d}.jpg".format(len(data_to_json) + 1), "height": H, "width": W, "category_id": 1, "keypoints_body": kp2ds_body.tolist(), "keypoints_left_hand": kp2ds_lhand.tolist(), "keypoints_right_hand": kp2ds_rhand.tolist(), } ) else: data_to_json[idx] = { "image_id": "frame_{:05d}.jpg".format(idx + 1), "height": H, "width": W, "category_id": 1, "keypoints_body": kp2ds_body.tolist(), "keypoints_left_hand": kp2ds_lhand.tolist(), "keypoints_right_hand": kp2ds_rhand.tolist(), } return img def draw_nose( img, kp2ds, threshold=0.6, data_to_json=None, idx=-1, kp2ds_lhand=None, kp2ds_rhand=None, draw_hand=False, stick_width_norm=200, ): """ Draw keypoints and connections representing hand pose on a given canvas. Args: canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the hand pose. keypoints (List[Keypoint]| None): A list of Keypoint objects representing the hand keypoints to be drawn or None if no keypoints are present. Returns: np.ndarray: A 3D numpy array representing the modified canvas with the drawn hand pose. Note: The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1. """ new_kep_list = [ "Nose", "Neck", "RShoulder", "RElbow", "RWrist", # No.4 "LShoulder", "LElbow", "LWrist", # No.7 "RHip", "RKnee", "RAnkle", # No.10 "LHip", "LKnee", "LAnkle", # No.13 "REye", "LEye", "REar", "LEar", "LToe", "RToe", ] # kp2ds_body = (kp2ds.copy()[[0, 6, 6, 8, 10, 5, 7, 9, 12, 14, 16, 11, 13, 15, 2, 1, 4, 3, 17, 20]] + \ # kp2ds.copy()[[0, 5, 6, 8, 10, 5, 7, 9, 12, 14, 16, 11, 13, 15, 2, 1, 4, 3, 18, 21]]) / 2 kp2ds = kp2ds.copy() kp2ds[1:, 2] = 0 # kp2ds[0, 2] = 1 kp2ds_body = kp2ds # kp2ds_body = kp2ds_body[:18] # kp2ds_lhand = kp2ds.copy()[91:112] # kp2ds_rhand = kp2ds.copy()[112:133] limbSeq = [ # [2, 3], # [2, 6], # shoulders # [3, 4], # [4, 5], # left arm # [6, 7], # [7, 8], # right arm # [2, 9], # [9, 10], # [10, 11], # right leg # [2, 12], # [12, 13], # [13, 14], # left leg # [2, 1], [1, 15], [15, 17], [1, 16], [16, 18], # face (nose, eyes, ears) # [14, 19], # [11, 20], # foot ] colors = [ # [255, 0, 0], # [255, 85, 0], # [255, 170, 0], # [255, 255, 0], # [170, 255, 0], # [85, 255, 0], # [0, 255, 0], # [0, 255, 85], # [0, 255, 170], # [0, 255, 255], # [0, 170, 255], # [0, 85, 255], # [0, 0, 255], # [85, 0, 255], [170, 0, 255], # [255, 0, 255], # [255, 0, 170], # [255, 0, 85], # foot # [200, 200, 0], # [100, 100, 0], ] H, W, C = img.shape stickwidth = max(int(min(H, W) / stick_width_norm), 1) # for _idx, ((k1_index, k2_index), color) in enumerate(zip(limbSeq, colors)): # keypoint1 = kp2ds_body[k1_index - 1] # keypoint2 = kp2ds_body[k2_index - 1] # if keypoint1[-1] < threshold or keypoint2[-1] < threshold: # continue # Y = np.array([keypoint1[0], keypoint2[0]]) # X = np.array([keypoint1[1], keypoint2[1]]) # mX = np.mean(X) # mY = np.mean(Y) # length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5 # angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1])) # polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1) # cv2.fillConvexPoly(img, polygon, [int(float(c) * 0.6) for c in color]) for _idx, (keypoint, color) in enumerate(zip(kp2ds_body, colors)): if keypoint[-1] < threshold: continue x, y = keypoint[0], keypoint[1] # cv2.circle(canvas, (int(x), int(y)), 4, color, thickness=-1) cv2.circle(img, (int(x), int(y)), stickwidth, color, thickness=-1) if draw_hand: img = draw_handpose(img, kp2ds_lhand, hand_score_th=threshold) img = draw_handpose(img, kp2ds_rhand, hand_score_th=threshold) kp2ds_body[:, 0] /= W kp2ds_body[:, 1] /= H if data_to_json is not None: if idx == -1: data_to_json.append( { "image_id": "frame_{:05d}.jpg".format(len(data_to_json) + 1), "height": H, "width": W, "category_id": 1, "keypoints_body": kp2ds_body.tolist(), "keypoints_left_hand": kp2ds_lhand.tolist(), "keypoints_right_hand": kp2ds_rhand.tolist(), } ) else: data_to_json[idx] = { "image_id": "frame_{:05d}.jpg".format(idx + 1), "height": H, "width": W, "category_id": 1, "keypoints_body": kp2ds_body.tolist(), "keypoints_left_hand": kp2ds_lhand.tolist(), "keypoints_right_hand": kp2ds_rhand.tolist(), } return img def draw_aapose( img, kp2ds, threshold=0.6, data_to_json=None, idx=-1, kp2ds_lhand=None, kp2ds_rhand=None, draw_hand=False, stick_width_norm=200, draw_head=True ): """ Draw keypoints and connections representing hand pose on a given canvas. Args: canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the hand pose. keypoints (List[Keypoint]| None): A list of Keypoint objects representing the hand keypoints to be drawn or None if no keypoints are present. Returns: np.ndarray: A 3D numpy array representing the modified canvas with the drawn hand pose. Note: The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1. """ new_kep_list = [ "Nose", "Neck", "RShoulder", "RElbow", "RWrist", # No.4 "LShoulder", "LElbow", "LWrist", # No.7 "RHip", "RKnee", "RAnkle", # No.10 "LHip", "LKnee", "LAnkle", # No.13 "REye", "LEye", "REar", "LEar", "LToe", "RToe", ] # kp2ds_body = (kp2ds.copy()[[0, 6, 6, 8, 10, 5, 7, 9, 12, 14, 16, 11, 13, 15, 2, 1, 4, 3, 17, 20]] + \ # kp2ds.copy()[[0, 5, 6, 8, 10, 5, 7, 9, 12, 14, 16, 11, 13, 15, 2, 1, 4, 3, 18, 21]]) / 2 kp2ds = kp2ds.copy() if not draw_head: kp2ds[[0,14,15,16,17], 2] = 0 kp2ds_body = kp2ds # kp2ds_lhand = kp2ds.copy()[91:112] # kp2ds_rhand = kp2ds.copy()[112:133] limbSeq = [ [2, 3], [2, 6], # shoulders [3, 4], [4, 5], # left arm [6, 7], [7, 8], # right arm [2, 9], [9, 10], [10, 11], # right leg [2, 12], [12, 13], [13, 14], # left leg [2, 1], [1, 15], [15, 17], [1, 16], [16, 18], # face (nose, eyes, ears) [14, 19], [11, 20], # foot ] colors = [ [255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85], # foot [200, 200, 0], [100, 100, 0], ] H, W, C = img.shape stickwidth = max(int(min(H, W) / stick_width_norm), 1) for _idx, ((k1_index, k2_index), color) in enumerate(zip(limbSeq, colors)): keypoint1 = kp2ds_body[k1_index - 1] keypoint2 = kp2ds_body[k2_index - 1] if keypoint1[-1] < threshold or keypoint2[-1] < threshold: continue Y = np.array([keypoint1[0], keypoint2[0]]) X = np.array([keypoint1[1], keypoint2[1]]) mX = np.mean(X) mY = np.mean(Y) length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5 angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1])) polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1) cv2.fillConvexPoly(img, polygon, [int(float(c) * 0.6) for c in color]) for _idx, (keypoint, color) in enumerate(zip(kp2ds_body, colors)): if keypoint[-1] < threshold: continue x, y = keypoint[0], keypoint[1] # cv2.circle(canvas, (int(x), int(y)), 4, color, thickness=-1) cv2.circle(img, (int(x), int(y)), stickwidth, color, thickness=-1) if draw_hand: img = draw_handpose(img, kp2ds_lhand, hand_score_th=threshold) img = draw_handpose(img, kp2ds_rhand, hand_score_th=threshold) kp2ds_body[:, 0] /= W kp2ds_body[:, 1] /= H if data_to_json is not None: if idx == -1: data_to_json.append( { "image_id": "frame_{:05d}.jpg".format(len(data_to_json) + 1), "height": H, "width": W, "category_id": 1, "keypoints_body": kp2ds_body.tolist(), "keypoints_left_hand": kp2ds_lhand.tolist(), "keypoints_right_hand": kp2ds_rhand.tolist(), } ) else: data_to_json[idx] = { "image_id": "frame_{:05d}.jpg".format(idx + 1), "height": H, "width": W, "category_id": 1, "keypoints_body": kp2ds_body.tolist(), "keypoints_left_hand": kp2ds_lhand.tolist(), "keypoints_right_hand": kp2ds_rhand.tolist(), } return img def draw_aapose_new( img, kp2ds, threshold=0.6, data_to_json=None, idx=-1, kp2ds_lhand=None, kp2ds_rhand=None, draw_hand=False, stickwidth_type='v2', draw_head=True ): """ Draw keypoints and connections representing hand pose on a given canvas. Args: canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the hand pose. keypoints (List[Keypoint]| None): A list of Keypoint objects representing the hand keypoints to be drawn or None if no keypoints are present. Returns: np.ndarray: A 3D numpy array representing the modified canvas with the drawn hand pose. Note: The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1. """ new_kep_list = [ "Nose", "Neck", "RShoulder", "RElbow", "RWrist", # No.4 "LShoulder", "LElbow", "LWrist", # No.7 "RHip", "RKnee", "RAnkle", # No.10 "LHip", "LKnee", "LAnkle", # No.13 "REye", "LEye", "REar", "LEar", "LToe", "RToe", ] # kp2ds_body = (kp2ds.copy()[[0, 6, 6, 8, 10, 5, 7, 9, 12, 14, 16, 11, 13, 15, 2, 1, 4, 3, 17, 20]] + \ # kp2ds.copy()[[0, 5, 6, 8, 10, 5, 7, 9, 12, 14, 16, 11, 13, 15, 2, 1, 4, 3, 18, 21]]) / 2 kp2ds = kp2ds.copy() if not draw_head: kp2ds[[0,14,15,16,17], 2] = 0 kp2ds_body = kp2ds # kp2ds_lhand = kp2ds.copy()[91:112] # kp2ds_rhand = kp2ds.copy()[112:133] limbSeq = [ [2, 3], [2, 6], # shoulders [3, 4], [4, 5], # left arm [6, 7], [7, 8], # right arm [2, 9], [9, 10], [10, 11], # right leg [2, 12], [12, 13], [13, 14], # left leg [2, 1], [1, 15], [15, 17], [1, 16], [16, 18], # face (nose, eyes, ears) [14, 19], [11, 20], # foot ] colors = [ [255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85], # foot [200, 200, 0], [100, 100, 0], ] H, W, C = img.shape H, W, C = img.shape if stickwidth_type == 'v1': stickwidth = max(int(min(H, W) / 200), 1) elif stickwidth_type == 'v2': stickwidth = max(int(min(H, W) / 200) - 1, 1) else: raise for _idx, ((k1_index, k2_index), color) in enumerate(zip(limbSeq, colors)): keypoint1 = kp2ds_body[k1_index - 1] keypoint2 = kp2ds_body[k2_index - 1] if keypoint1[-1] < threshold or keypoint2[-1] < threshold: continue Y = np.array([keypoint1[0], keypoint2[0]]) X = np.array([keypoint1[1], keypoint2[1]]) mX = np.mean(X) mY = np.mean(Y) length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5 angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1])) polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1) cv2.fillConvexPoly(img, polygon, [int(float(c) * 0.6) for c in color]) for _idx, (keypoint, color) in enumerate(zip(kp2ds_body, colors)): if keypoint[-1] < threshold: continue x, y = keypoint[0], keypoint[1] # cv2.circle(canvas, (int(x), int(y)), 4, color, thickness=-1) cv2.circle(img, (int(x), int(y)), stickwidth, color, thickness=-1) if draw_hand: img = draw_handpose_new(img, kp2ds_lhand, stickwidth_type=stickwidth_type, hand_score_th=threshold) img = draw_handpose_new(img, kp2ds_rhand, stickwidth_type=stickwidth_type, hand_score_th=threshold) kp2ds_body[:, 0] /= W kp2ds_body[:, 1] /= H if data_to_json is not None: if idx == -1: data_to_json.append( { "image_id": "frame_{:05d}.jpg".format(len(data_to_json) + 1), "height": H, "width": W, "category_id": 1, "keypoints_body": kp2ds_body.tolist(), "keypoints_left_hand": kp2ds_lhand.tolist(), "keypoints_right_hand": kp2ds_rhand.tolist(), } ) else: data_to_json[idx] = { "image_id": "frame_{:05d}.jpg".format(idx + 1), "height": H, "width": W, "category_id": 1, "keypoints_body": kp2ds_body.tolist(), "keypoints_left_hand": kp2ds_lhand.tolist(), "keypoints_right_hand": kp2ds_rhand.tolist(), } return img def draw_bbox(img, bbox, color=(255, 0, 0)): img = load_image(img) bbox = [int(bbox_tmp) for bbox_tmp in bbox] cv2.rectangle(img, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color, 2) return img def draw_kp2ds(img, kp2ds, threshold=0, color=(255, 0, 0), skeleton=None, reverse=False): img = load_image(img, reverse) if skeleton is not None: if skeleton == "coco17": skeleton_list = [ [6, 8], [8, 10], [5, 7], [7, 9], [11, 13], [13, 15], [12, 14], [14, 16], [5, 6], [6, 12], [12, 11], [11, 5], ] color_list = [ (255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (255, 0, 255), (0, 255, 255), ] elif skeleton == "cocowholebody": skeleton_list = [ [6, 8], [8, 10], [5, 7], [7, 9], [11, 13], [13, 15], [12, 14], [14, 16], [5, 6], [6, 12], [12, 11], [11, 5], [15, 17], [15, 18], [15, 19], [16, 20], [16, 21], [16, 22], [91, 92, 93, 94, 95], [91, 96, 97, 98, 99], [91, 100, 101, 102, 103], [91, 104, 105, 106, 107], [91, 108, 109, 110, 111], [112, 113, 114, 115, 116], [112, 117, 118, 119, 120], [112, 121, 122, 123, 124], [112, 125, 126, 127, 128], [112, 129, 130, 131, 132], ] color_list = [ (255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (255, 0, 255), (0, 255, 255), ] else: color_list = [color] for _idx, _skeleton in enumerate(skeleton_list): for i in range(len(_skeleton) - 1): cv2.line( img, (int(kp2ds[_skeleton[i], 0]), int(kp2ds[_skeleton[i], 1])), (int(kp2ds[_skeleton[i + 1], 0]), int(kp2ds[_skeleton[i + 1], 1])), color_list[_idx % len(color_list)], 3, ) for _idx, kp2d in enumerate(kp2ds): if kp2d[2] > threshold: cv2.circle(img, (int(kp2d[0]), int(kp2d[1])), 3, color, -1) # cv2.putText(img, # str(_idx), # (int(kp2d[0, i, 0])*1, # int(kp2d[0, i, 1])*1), # cv2.FONT_HERSHEY_SIMPLEX, # 0.75, # color, # 2 # ) return img def draw_mask(img, mask, background=0, return_rgba=False): img = load_image(img) h, w, _ = img.shape if type(background) == int: background = np.ones((h, w, 3)).astype(np.uint8) * 255 * background backgournd = cv2.resize(background, (w, h)) img_rgba = np.concatenate([img, mask], -1) return alphaMerge(img_rgba, background, 0, 0, return_rgba=True) def draw_pcd(pcd_list, save_path=None): fig = plt.figure() ax = fig.add_subplot(111, projection="3d") color_list = ["r", "g", "b", "y", "p"] for _idx, _pcd in enumerate(pcd_list): ax.scatter(_pcd[:, 0], _pcd[:, 1], _pcd[:, 2], c=color_list[_idx], marker="o") ax.set_xlabel("X") ax.set_ylabel("Y") ax.set_zlabel("Z") if save_path is not None: plt.savefig(save_path) else: plt.savefig("tmp.png") def load_image(img, reverse=False): if type(img) == str: img = cv2.imread(img) if reverse: img = img.astype(np.float32) img = img[:, :, ::-1] img = img.astype(np.uint8) return img def draw_skeleten(meta): kps = [] for i, kp in enumerate(meta["keypoints_body"]): if kp is None: # if kp is None: kps.append([0, 0, 0]) else: kps.append([*kp, 1]) kps = np.array(kps) kps[:, 0] *= meta["width"] kps[:, 1] *= meta["height"] pose_img = np.zeros([meta["height"], meta["width"], 3], dtype=np.uint8) pose_img = draw_aapose( pose_img, kps, draw_hand=True, kp2ds_lhand=meta["keypoints_left_hand"], kp2ds_rhand=meta["keypoints_right_hand"], ) return pose_img def draw_skeleten_with_pncc(pncc: np.ndarray, meta: Dict) -> np.ndarray: """ Args: pncc: [H,W,3] meta: required keys: keypoints_body: [N, 3] keypoints_left_hand, keypoints_right_hand Return: np.ndarray [H, W, 3] """ # preprocess keypoints kps = [] for i, kp in enumerate(meta["keypoints_body"]): if kp is None: # if kp is None: kps.append([0, 0, 0]) elif i in [14, 15, 16, 17]: kps.append([0, 0, 0]) else: kps.append([*kp]) kps = np.stack(kps) kps[:, 0] *= pncc.shape[1] kps[:, 1] *= pncc.shape[0] # draw neck canvas = np.zeros_like(pncc) if kps[0][2] > 0.6 and kps[1][2] > 0.6: canvas = draw_ellipse_by_2kp(canvas, kps[0], kps[1], [0, 0, 255]) # draw pncc mask = (pncc > 0).max(axis=2) canvas[mask] = pncc[mask] pncc = canvas # draw other skeleten kps[0] = 0 meta["keypoints_left_hand"][:, 0] *= meta["width"] meta["keypoints_left_hand"][:, 1] *= meta["height"] meta["keypoints_right_hand"][:, 0] *= meta["width"] meta["keypoints_right_hand"][:, 1] *= meta["height"] pose_img = draw_aapose( pncc, kps, draw_hand=True, kp2ds_lhand=meta["keypoints_left_hand"], kp2ds_rhand=meta["keypoints_right_hand"], ) return pose_img FACE_CUSTOM_STYLE = { "eyeball": {"indexs": [68, 69], "color": [255, 255, 255], "connect": False}, "left_eyebrow": {"indexs": [17, 18, 19, 20, 21], "color": [0, 255, 0]}, "right_eyebrow": {"indexs": [22, 23, 24, 25, 26], "color": [0, 0, 255]}, "left_eye": {"indexs": [36, 37, 38, 39, 40, 41], "color": [255, 255, 0], "close": True}, "right_eye": {"indexs": [42, 43, 44, 45, 46, 47], "color": [255, 0, 255], "close": True}, "mouth_outside": {"indexs": list(range(48, 60)), "color": [100, 255, 50], "close": True}, "mouth_inside": {"indexs": [60, 61, 62, 63, 64, 65, 66, 67], "color": [255, 100, 50], "close": True}, } def draw_face_kp(img, kps, thickness=2, style=FACE_CUSTOM_STYLE): """ Args: img: [H, W, 3] kps: [70, 2] """ img = img.copy() for key, item in style.items(): pts = np.array(kps[item["indexs"]]).astype(np.int32) connect = item.get("connect", True) color = item["color"] close = item.get("close", False) if connect: cv2.polylines(img, [pts], close, color, thickness=thickness) else: for kp in pts: kp = np.array(kp).astype(np.int32) cv2.circle(img, kp, thickness * 2, color=color, thickness=-1) return img def draw_traj(metas: List[AAPoseMeta], threshold=0.6): colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \ [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \ [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85], [100, 255, 50], [255, 100, 50], # foot [200, 200, 0], [100, 100, 0] ] limbSeq = [ [1, 2], [1, 5], # shoulders [2, 3], [3, 4], # left arm [5, 6], [6, 7], # right arm [1, 8], [8, 9], [9, 10], # right leg [1, 11], [11, 12], [12, 13], # left leg # face (nose, eyes, ears) [13, 18], [10, 19] # foot ] face_seq = [[1, 0], [0, 14], [14, 16], [0, 15], [15, 17]] kp_body = np.array([meta.kps_body for meta in metas]) kp_body_p = np.array([meta.kps_body_p for meta in metas]) face_seq = random.sample(face_seq, 2) kp_lh = np.array([meta.kps_lhand for meta in metas]) kp_rh = np.array([meta.kps_rhand for meta in metas]) kp_lh_p = np.array([meta.kps_lhand_p for meta in metas]) kp_rh_p = np.array([meta.kps_rhand_p for meta in metas]) # kp_lh = np.concatenate([kp_lh, kp_lh_p], axis=-1) # kp_rh = np.concatenate([kp_rh, kp_rh_p], axis=-1) new_limbSeq = [] key_point_list = [] for _idx, ((k1_index, k2_index)) in enumerate(limbSeq): vis = (kp_body_p[:, k1_index] > threshold) * (kp_body_p[:, k2_index] > threshold) * 1 if vis.sum() * 1.0 / vis.shape[0] > 0.4: new_limbSeq.append([k1_index, k2_index]) for _idx, ((k1_index, k2_index)) in enumerate(limbSeq): keypoint1 = kp_body[:, k1_index - 1] keypoint2 = kp_body[:, k2_index - 1] interleave = random.randint(4, 7) randind = random.randint(0, interleave - 1) # randind = random.rand(range(interleave), sampling_num) Y = np.array([keypoint1[:, 0], keypoint2[:, 0]]) X = np.array([keypoint1[:, 1], keypoint2[:, 1]]) vis = (keypoint1[:, -1] > threshold) * (keypoint2[:, -1] > threshold) * 1 # for randidx in randind: t = randind / interleave x = (1-t)*Y[0, :] + t*Y[1, :] y = (1-t)*X[0, :] + t*X[1, :] # np.array([1]) x = x.astype(int) y = y.astype(int) new_array = np.array([x, y, vis]).T key_point_list.append(new_array) indx_lh = random.randint(0, kp_lh.shape[1] - 1) lh = kp_lh[:, indx_lh, :] lh_p = kp_lh_p[:, indx_lh:indx_lh+1] lh = np.concatenate([lh, lh_p], axis=-1) indx_rh = random.randint(0, kp_rh.shape[1] - 1) rh = kp_rh[:, random.randint(0, kp_rh.shape[1] - 1), :] rh_p = kp_rh_p[:, indx_rh:indx_rh+1] rh = np.concatenate([rh, rh_p], axis=-1) lh[-1, :] = (lh[-1, :] > threshold) * 1 rh[-1, :] = (rh[-1, :] > threshold) * 1 # print(rh.shape, new_array.shape) # exit() key_point_list.append(lh.astype(int)) key_point_list.append(rh.astype(int)) key_points_list = np.stack(key_point_list) num_points = len(key_points_list) sample_colors = random.sample(colors, num_points) stickwidth = max(int(min(metas[0].width, metas[0].height) / 150), 2) image_list_ori = [] for i in range(key_points_list.shape[-2]): _image_vis = np.zeros((metas[0].width, metas[0].height, 3)) points = key_points_list[:, i, :] for idx, point in enumerate(points): x, y, vis = point if vis == 1: cv2.circle(_image_vis, (x, y), stickwidth, sample_colors[idx], thickness=-1) image_list_ori.append(_image_vis) return image_list_ori return [np.zeros([meta.width, meta.height, 3], dtype=np.uint8) for meta in metas] if __name__ == "__main__": meta = { "image_id": "00472.jpg", "height": 540, "width": 414, "category_id": 1, "keypoints_body": [ [0.5084776947463768, 0.11350188078703703], [0.504467655495169, 0.20419560185185184], [0.3982016153381642, 0.198046875], [0.3841664779589372, 0.34869068287037036], [0.3901815368357488, 0.4670536747685185], [0.610733695652174, 0.2103443287037037], [0.6167487545289855, 0.3517650462962963], [0.6448190292874396, 0.4762767650462963], [0.4523371452294686, 0.47320240162037036], [0.4503321256038647, 0.6776475694444445], [0.47639738073671495, 0.8544234664351852], [0.5766483620169082, 0.47320240162037036], [0.5666232638888888, 0.6761103877314815], [0.534542949879227, 0.863646556712963], [0.4864224788647343, 0.09505570023148148], [0.5285278910024155, 0.09351851851851851], [0.46236224335748793, 0.10581597222222222], [0.5586031853864735, 0.10274160879629629], [0.4994551064311594, 0.9405056423611111], [0.4152442821557971, 0.9312825520833333], ], "keypoints_left_hand": [ [267.78515625, 263.830078125, 1.2840936183929443], [265.294921875, 269.640625, 1.2546794414520264], [263.634765625, 277.111328125, 1.2863062620162964], [262.8046875, 285.412109375, 1.267038345336914], [261.14453125, 292.8828125, 1.280144453048706], [273.595703125, 281.26171875, 1.2592815160751343], [271.10546875, 291.22265625, 1.3256099224090576], [265.294921875, 294.54296875, 1.2368024587631226], [261.14453125, 294.54296875, 0.9771889448165894], [274.42578125, 282.091796875, 1.250044584274292], [269.4453125, 291.22265625, 1.2571144104003906], [264.46484375, 292.8828125, 1.177802324295044], [260.314453125, 292.052734375, 0.9283463358879089], [273.595703125, 282.091796875, 1.1834490299224854], [269.4453125, 290.392578125, 1.188171625137329], [265.294921875, 290.392578125, 1.192609429359436], [261.974609375, 289.5625, 0.9366656541824341], [271.935546875, 281.26171875, 1.0946396589279175], [268.615234375, 287.072265625, 0.9906131029129028], [265.294921875, 287.90234375, 1.0219476222991943], [262.8046875, 287.072265625, 0.9240120053291321], ], "keypoints_right_hand": [ [161.53515625, 258.849609375, 1.2069408893585205], [168.17578125, 263.0, 1.1846840381622314], [173.986328125, 269.640625, 1.1435924768447876], [173.986328125, 277.94140625, 1.1802611351013184], [173.986328125, 286.2421875, 1.2599592208862305], [165.685546875, 275.451171875, 1.0633569955825806], [167.345703125, 286.2421875, 1.1693341732025146], [169.8359375, 291.22265625, 1.2698509693145752], [170.666015625, 294.54296875, 1.0619274377822876], [160.705078125, 276.28125, 1.0995020866394043], [163.1953125, 287.90234375, 1.2735884189605713], [166.515625, 291.22265625, 1.339503526687622], [169.005859375, 294.54296875, 1.0835273265838623], [157.384765625, 277.111328125, 1.0866981744766235], [161.53515625, 287.072265625, 1.2468621730804443], [164.025390625, 289.5625, 1.2817761898040771], [166.515625, 292.052734375, 1.099466323852539], [155.724609375, 277.111328125, 1.1065717935562134], [159.044921875, 285.412109375, 1.1924479007720947], [160.705078125, 287.072265625, 1.1304771900177002], [162.365234375, 287.90234375, 1.0040509700775146], ], } demo_meta = AAPoseMeta(meta) res = draw_traj([demo_meta]*5) cv2.imwrite("traj.png", res[0][..., ::-1]) ================================================ FILE: wan/modules/animate/preprocess/pose2d.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import os import cv2 from typing import Union, List import numpy as np import torch import onnxruntime from pose2d_utils import ( read_img, box_convert_simple, bbox_from_detector, crop, keypoints_from_heatmaps, load_pose_metas_from_kp2ds_seq ) class SimpleOnnxInference(object): def __init__(self, checkpoint, device='cuda', reverse_input=False, **kwargs): if isinstance(device, str): device = torch.device(device) if device.type == 'cuda': device = '{}:{}'.format(device.type, device.index) providers = [("CUDAExecutionProvider", {"device_id": device[-1:] if device[-1] in [str(_i) for _i in range(10)] else "0"}), "CPUExecutionProvider"] else: providers = ["CPUExecutionProvider"] self.device = device if not os.path.exists(checkpoint): raise RuntimeError("{} is not existed!".format(checkpoint)) if os.path.isdir(checkpoint): checkpoint = os.path.join(checkpoint, 'end2end.onnx') self.session = onnxruntime.InferenceSession(checkpoint, providers=providers ) self.input_name = self.session.get_inputs()[0].name self.output_name = self.session.get_outputs()[0].name self.input_resolution = self.session.get_inputs()[0].shape[2:] if not reverse_input else self.session.get_inputs()[0].shape[2:][::-1] self.input_resolution = np.array(self.input_resolution) def __call__(self, *args, **kwargs): return self.forward(*args, **kwargs) def get_output_names(self): output_names = [] for node in self.session.get_outputs(): output_names.append(node.name) return output_names def set_device(self, device): if isinstance(device, str): device = torch.device(device) if device.type == 'cuda': device = '{}:{}'.format(device.type, device.index) providers = [("CUDAExecutionProvider", {"device_id": device[-1:] if device[-1] in [str(_i) for _i in range(10)] else "0"}), "CPUExecutionProvider"] else: providers = ["CPUExecutionProvider"] self.session.set_providers(providers) self.device = device class Yolo(SimpleOnnxInference): def __init__(self, checkpoint, device='cuda', threshold_conf=0.05, threshold_multi_persons=0.1, input_resolution=(640, 640), threshold_iou=0.5, threshold_bbox_shape_ratio=0.4, cat_id=[1], select_type='max', strict=True, sorted_func=None, **kwargs): super(Yolo, self).__init__(checkpoint, device=device, **kwargs) model_inputs = self.session.get_inputs() input_shape = model_inputs[0].shape self.input_width = 640 self.input_height = 640 self.threshold_multi_persons = threshold_multi_persons self.threshold_conf = threshold_conf self.threshold_iou = threshold_iou self.threshold_bbox_shape_ratio = threshold_bbox_shape_ratio self.input_resolution = input_resolution self.cat_id = cat_id self.select_type = select_type self.strict = strict self.sorted_func = sorted_func def preprocess(self, input_image): """ Preprocesses the input image before performing inference. Returns: image_data: Preprocessed image data ready for inference. """ img = read_img(input_image) # Get the height and width of the input image img_height, img_width = img.shape[:2] # Resize the image to match the input shape img = cv2.resize(img, (self.input_resolution[1], self.input_resolution[0])) # Normalize the image data by dividing it by 255.0 image_data = np.array(img) / 255.0 # Transpose the image to have the channel dimension as the first dimension image_data = np.transpose(image_data, (2, 0, 1)) # Channel first # Expand the dimensions of the image data to match the expected input shape # image_data = np.expand_dims(image_data, axis=0).astype(np.float32) image_data = image_data.astype(np.float32) # Return the preprocessed image data return image_data, np.array([img_height, img_width]) def postprocess(self, output, shape_raw, cat_id=[1]): """ Performs post-processing on the model's output to extract bounding boxes, scores, and class IDs. Args: input_image (numpy.ndarray): The input image. output (numpy.ndarray): The output of the model. Returns: numpy.ndarray: The input image with detections drawn on it. """ # Transpose and squeeze the output to match the expected shape outputs = np.squeeze(output) if len(outputs.shape) == 1: outputs = outputs[None] if output.shape[-1] != 6 and output.shape[1] == 84: outputs = np.transpose(outputs) # Get the number of rows in the outputs array rows = outputs.shape[0] # Calculate the scaling factors for the bounding box coordinates x_factor = shape_raw[1] / self.input_width y_factor = shape_raw[0] / self.input_height # Lists to store the bounding boxes, scores, and class IDs of the detections boxes = [] scores = [] class_ids = [] if outputs.shape[-1] == 6: max_scores = outputs[:, 4] classid = outputs[:, -1] threshold_conf_masks = max_scores >= self.threshold_conf classid_masks = classid[threshold_conf_masks] != 3.14159 max_scores = max_scores[threshold_conf_masks][classid_masks] classid = classid[threshold_conf_masks][classid_masks] boxes = outputs[:, :4][threshold_conf_masks][classid_masks] boxes[:, [0, 2]] *= x_factor boxes[:, [1, 3]] *= y_factor boxes[:, 2] = boxes[:, 2] - boxes[:, 0] boxes[:, 3] = boxes[:, 3] - boxes[:, 1] boxes = boxes.astype(np.int32) else: classes_scores = outputs[:, 4:] max_scores = np.amax(classes_scores, -1) threshold_conf_masks = max_scores >= self.threshold_conf classid = np.argmax(classes_scores[threshold_conf_masks], -1) classid_masks = classid!=3.14159 classes_scores = classes_scores[threshold_conf_masks][classid_masks] max_scores = max_scores[threshold_conf_masks][classid_masks] classid = classid[classid_masks] xywh = outputs[:, :4][threshold_conf_masks][classid_masks] x = xywh[:, 0:1] y = xywh[:, 1:2] w = xywh[:, 2:3] h = xywh[:, 3:4] left = ((x - w / 2) * x_factor) top = ((y - h / 2) * y_factor) width = (w * x_factor) height = (h * y_factor) boxes = np.concatenate([left, top, width, height], axis=-1).astype(np.int32) boxes = boxes.tolist() scores = max_scores.tolist() class_ids = classid.tolist() # Apply non-maximum suppression to filter out overlapping bounding boxes indices = cv2.dnn.NMSBoxes(boxes, scores, self.threshold_conf, self.threshold_iou) # Iterate over the selected indices after non-maximum suppression results = [] for i in indices: # Get the box, score, and class ID corresponding to the index box = box_convert_simple(boxes[i], 'xywh2xyxy') score = scores[i] class_id = class_ids[i] results.append(box + [score] + [class_id]) # # Draw the detection on the input image # Return the modified input image return np.array(results) def process_results(self, results, shape_raw, cat_id=[1], single_person=True): if isinstance(results, tuple): det_results = results[0] else: det_results = results person_results = [] person_count = 0 if len(results): max_idx = -1 max_bbox_size = shape_raw[0] * shape_raw[1] * -10 max_bbox_shape = -1 bboxes = [] idx_list = [] for i in range(results.shape[0]): bbox = results[i] if (bbox[-1] + 1 in cat_id) and (bbox[-2] > self.threshold_conf): idx_list.append(i) bbox_shape = max((bbox[2] - bbox[0]), ((bbox[3] - bbox[1]))) if bbox_shape > max_bbox_shape: max_bbox_shape = bbox_shape results = results[idx_list] for i in range(results.shape[0]): bbox = results[i] bboxes.append(bbox) if self.select_type == 'max': bbox_size = (bbox[2] - bbox[0]) * ((bbox[3] - bbox[1])) elif self.select_type == 'center': bbox_size = (abs((bbox[2] + bbox[0]) / 2 - shape_raw[1]/2)) * -1 bbox_shape = max((bbox[2] - bbox[0]), ((bbox[3] - bbox[1]))) if bbox_size > max_bbox_size: if (self.strict or max_idx != -1) and bbox_shape < max_bbox_shape * self.threshold_bbox_shape_ratio: continue max_bbox_size = bbox_size max_bbox_shape = bbox_shape max_idx = i if self.sorted_func is not None and len(bboxes) > 0: max_idx = self.sorted_func(bboxes, shape_raw) bbox = bboxes[max_idx] if self.select_type == 'max': max_bbox_size = (bbox[2] - bbox[0]) * ((bbox[3] - bbox[1])) elif self.select_type == 'center': max_bbox_size = (abs((bbox[2] + bbox[0]) / 2 - shape_raw[1]/2)) * -1 if max_idx != -1: person_count = 1 if max_idx != -1: person = {} person['bbox'] = results[max_idx, :5] person['track_id'] = int(0) person_results.append(person) for i in range(results.shape[0]): bbox = results[i] if (bbox[-1] + 1 in cat_id) and (bbox[-2] > self.threshold_conf): if self.select_type == 'max': bbox_size = (bbox[2] - bbox[0]) * ((bbox[3] - bbox[1])) elif self.select_type == 'center': bbox_size = (abs((bbox[2] + bbox[0]) / 2 - shape_raw[1]/2)) * -1 if i != max_idx and bbox_size > max_bbox_size * self.threshold_multi_persons and bbox_size < max_bbox_size: person_count += 1 if not single_person: person = {} person['bbox'] = results[i, :5] person['track_id'] = int(person_count - 1) person_results.append(person) return person_results else: return None def postprocess_threading(self, outputs, shape_raw, person_results, i, single_person=True, **kwargs): result = self.postprocess(outputs[i], shape_raw[i], cat_id=self.cat_id) result = self.process_results(result, shape_raw[i], cat_id=self.cat_id, single_person=single_person) if result is not None and len(result) != 0: person_results[i] = result def forward(self, img, shape_raw, **kwargs): """ Performs inference using an ONNX model and returns the output image with drawn detections. Returns: output_img: The output image with drawn detections. """ if isinstance(img, torch.Tensor): img = img.cpu().numpy() shape_raw = shape_raw.cpu().numpy() outputs = self.session.run(None, {self.session.get_inputs()[0].name: img})[0] person_results = [[{'bbox': np.array([0., 0., 1.*shape_raw[i][1], 1.*shape_raw[i][0], -1]), 'track_id': -1}] for i in range(len(outputs))] for i in range(len(outputs)): self.postprocess_threading(outputs, shape_raw, person_results, i, **kwargs) return person_results class ViTPose(SimpleOnnxInference): def __init__(self, checkpoint, device='cuda', **kwargs): super(ViTPose, self).__init__(checkpoint, device=device) def forward(self, img, center, scale, **kwargs): heatmaps = self.session.run([], {self.session.get_inputs()[0].name: img})[0] points, prob = keypoints_from_heatmaps(heatmaps=heatmaps, center=center, scale=scale*200, unbiased=True, use_udp=False) return np.concatenate([points, prob], axis=2) @staticmethod def preprocess(img, bbox=None, input_resolution=(256, 192), rescale=1.25, mask=None, **kwargs): if bbox is None or bbox[-1] <= 0 or (bbox[2] - bbox[0]) < 10 or (bbox[3] - bbox[1]) < 10: bbox = np.array([0, 0, img.shape[1], img.shape[0]]) bbox_xywh = bbox if mask is not None: img = np.where(mask>128, img, mask) if isinstance(input_resolution, int): center, scale = bbox_from_detector(bbox_xywh, (input_resolution, input_resolution), rescale=rescale) img, new_shape, old_xy, new_xy = crop(img, center, scale, (input_resolution, input_resolution)) else: center, scale = bbox_from_detector(bbox_xywh, input_resolution, rescale=rescale) img, new_shape, old_xy, new_xy = crop(img, center, scale, (input_resolution[0], input_resolution[1])) IMG_NORM_MEAN = np.array([0.485, 0.456, 0.406]) IMG_NORM_STD = np.array([0.229, 0.224, 0.225]) img_norm = (img / 255. - IMG_NORM_MEAN) / IMG_NORM_STD img_norm = img_norm.transpose(2, 0, 1).astype(np.float32) return img_norm, np.array(center), np.array(scale) class Pose2d: def __init__(self, checkpoint, detector_checkpoint=None, device='cuda', **kwargs): if detector_checkpoint is not None: self.detector = Yolo(detector_checkpoint, device) else: self.detector = None self.model = ViTPose(checkpoint, device) self.device = device def load_images(self, inputs): """ Load images from various input types. Args: inputs (Union[str, np.ndarray, List[np.ndarray]]): Input can be file path, single image array, or list of image arrays Returns: List[np.ndarray]: List of RGB image arrays Raises: ValueError: If file format is unsupported or image cannot be read """ if isinstance(inputs, str): if inputs.lower().endswith(('.mp4', '.avi', '.mov', '.mkv')): cap = cv2.VideoCapture(inputs) frames = [] while True: ret, frame = cap.read() if not ret: break frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) cap.release() images = frames elif inputs.lower().endswith(('.jpg', '.jpeg', '.png', '.bmp')): img = cv2.cvtColor(cv2.imread(inputs), cv2.COLOR_BGR2RGB) if img is None: raise ValueError(f"Cannot read image: {inputs}") images = [img] else: raise ValueError(f"Unsupported file format: {inputs}") elif isinstance(inputs, np.ndarray): images = [cv2.cvtColor(image, cv2.COLOR_BGR2RGB) for image in inputs] elif isinstance(inputs, list): images = [cv2.cvtColor(image, cv2.COLOR_BGR2RGB) for image in inputs] return images def __call__( self, inputs: Union[str, np.ndarray, List[np.ndarray]], return_image: bool = False, **kwargs ): """ Process input and estimate 2D keypoints. Args: inputs (Union[str, np.ndarray, List[np.ndarray]]): Input can be file path, single image array, or list of image arrays **kwargs: Additional arguments for processing Returns: np.ndarray: Array of detected 2D keypoints for all input images """ images = self.load_images(inputs) H, W = images[0].shape[:2] if self.detector is not None: bboxes = [] for _image in images: img, shape = self.detector.preprocess(_image) bboxes.append(self.detector(img[None], shape[None])[0][0]["bbox"]) else: bboxes = [None] * len(images) kp2ds = [] for _image, _bbox in zip(images, bboxes): img, center, scale = self.model.preprocess(_image, _bbox) kp2ds.append(self.model(img[None], center[None], scale[None])) kp2ds = np.concatenate(kp2ds, 0) metas = load_pose_metas_from_kp2ds_seq(kp2ds, width=W, height=H) return metas ================================================ FILE: wan/modules/animate/preprocess/pose2d_utils.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import warnings import cv2 import numpy as np from typing import List from PIL import Image def box_convert_simple(box, convert_type='xyxy2xywh'): if convert_type == 'xyxy2xywh': return [box[0], box[1], box[2] - box[0], box[3] - box[1]] elif convert_type == 'xywh2xyxy': return [box[0], box[1], box[2] + box[0], box[3] + box[1]] elif convert_type == 'xyxy2ctwh': return [(box[0] + box[2]) / 2, (box[1] + box[3]) / 2, box[2] - box[0], box[3] - box[1]] elif convert_type == 'ctwh2xyxy': return [box[0] - box[2] // 2, box[1] - box[3] // 2, box[0] + (box[2] - box[2] // 2), box[1] + (box[3] - box[3] // 2)] def read_img(image, convert='RGB', check_exist=False): if isinstance(image, str): if check_exist and not osp.exists(image): return None try: img = Image.open(image) if convert: img = img.convert(convert) except: raise IOError('File error: ', image) return np.asarray(img) else: if isinstance(image, np.ndarray): if convert: return image[..., ::-1] else: if convert: img = img.convert(convert) return np.asarray(img) class AAPoseMeta: def __init__(self, meta=None, kp2ds=None): self.image_id = "" self.height = 0 self.width = 0 self.kps_body: np.ndarray = None self.kps_lhand: np.ndarray = None self.kps_rhand: np.ndarray = None self.kps_face: np.ndarray = None self.kps_body_p: np.ndarray = None self.kps_lhand_p: np.ndarray = None self.kps_rhand_p: np.ndarray = None self.kps_face_p: np.ndarray = None if meta is not None: self.load_from_meta(meta) elif kp2ds is not None: self.load_from_kp2ds(kp2ds) def is_valid(self, kp, p, threshold): x, y = kp if x < 0 or y < 0 or x > self.width or y > self.height or p < threshold: return False else: return True def get_bbox(self, kp, kp_p, threshold=0.5): kps = kp[kp_p > threshold] if kps.size == 0: return 0, 0, 0, 0 x0, y0 = kps.min(axis=0) x1, y1 = kps.max(axis=0) return x0, y0, x1, y1 def crop(self, x0, y0, x1, y1): all_kps = [self.kps_body, self.kps_lhand, self.kps_rhand, self.kps_face] for kps in all_kps: if kps is not None: kps[:, 0] -= x0 kps[:, 1] -= y0 self.width = x1 - x0 self.height = y1 - y0 return self def resize(self, width, height): scale_x = width / self.width scale_y = height / self.height all_kps = [self.kps_body, self.kps_lhand, self.kps_rhand, self.kps_face] for kps in all_kps: if kps is not None: kps[:, 0] *= scale_x kps[:, 1] *= scale_y self.width = width self.height = height return self def get_kps_body_with_p(self, normalize=False): kps_body = self.kps_body.copy() if normalize: kps_body = kps_body / np.array([self.width, self.height]) return np.concatenate([kps_body, self.kps_body_p[:, None]]) @staticmethod def from_kps_face(kps_face: np.ndarray, height: int, width: int): pose_meta = AAPoseMeta() pose_meta.kps_face = kps_face[:, :2] if kps_face.shape[1] == 3: pose_meta.kps_face_p = kps_face[:, 2] else: pose_meta.kps_face_p = kps_face[:, 0] * 0 + 1 pose_meta.height = height pose_meta.width = width return pose_meta @staticmethod def from_kps_body(kps_body: np.ndarray, height: int, width: int): pose_meta = AAPoseMeta() pose_meta.kps_body = kps_body[:, :2] pose_meta.kps_body_p = kps_body[:, 2] pose_meta.height = height pose_meta.width = width return pose_meta @staticmethod def from_humanapi_meta(meta): pose_meta = AAPoseMeta() width, height = meta["width"], meta["height"] pose_meta.width = width pose_meta.height = height pose_meta.kps_body = meta["keypoints_body"][:, :2] * (width, height) pose_meta.kps_body_p = meta["keypoints_body"][:, 2] pose_meta.kps_lhand = meta["keypoints_left_hand"][:, :2] * (width, height) pose_meta.kps_lhand_p = meta["keypoints_left_hand"][:, 2] pose_meta.kps_rhand = meta["keypoints_right_hand"][:, :2] * (width, height) pose_meta.kps_rhand_p = meta["keypoints_right_hand"][:, 2] if 'keypoints_face' in meta: pose_meta.kps_face = meta["keypoints_face"][:, :2] * (width, height) pose_meta.kps_face_p = meta["keypoints_face"][:, 2] return pose_meta def load_from_meta(self, meta, norm_body=True, norm_hand=False): self.image_id = meta.get("image_id", "00000.png") self.height = meta["height"] self.width = meta["width"] kps_body_p = [] kps_body = [] for kp in meta["keypoints_body"]: if kp is None: kps_body.append([0, 0]) kps_body_p.append(0) else: kps_body.append(kp) kps_body_p.append(1) self.kps_body = np.array(kps_body) self.kps_body[:, 0] *= self.width self.kps_body[:, 1] *= self.height self.kps_body_p = np.array(kps_body_p) self.kps_lhand = np.array(meta["keypoints_left_hand"])[:, :2] self.kps_lhand_p = np.array(meta["keypoints_left_hand"])[:, 2] self.kps_rhand = np.array(meta["keypoints_right_hand"])[:, :2] self.kps_rhand_p = np.array(meta["keypoints_right_hand"])[:, 2] @staticmethod def load_from_kp2ds(kp2ds: List[np.ndarray], width: int, height: int): """input 133x3 numpy keypoints and output AAPoseMeta Args: kp2ds (List[np.ndarray]): _description_ width (int): _description_ height (int): _description_ Returns: _type_: _description_ """ pose_meta = AAPoseMeta() pose_meta.width = width pose_meta.height = height kps_body = (kp2ds[[0, 6, 6, 8, 10, 5, 7, 9, 12, 14, 16, 11, 13, 15, 2, 1, 4, 3, 17, 20]] + kp2ds[[0, 5, 6, 8, 10, 5, 7, 9, 12, 14, 16, 11, 13, 15, 2, 1, 4, 3, 18, 21]]) / 2 kps_lhand = kp2ds[91:112] kps_rhand = kp2ds[112:133] kps_face = np.concatenate([kp2ds[23:23+68], kp2ds[1:3]], axis=0) pose_meta.kps_body = kps_body[:, :2] pose_meta.kps_body_p = kps_body[:, 2] pose_meta.kps_lhand = kps_lhand[:, :2] pose_meta.kps_lhand_p = kps_lhand[:, 2] pose_meta.kps_rhand = kps_rhand[:, :2] pose_meta.kps_rhand_p = kps_rhand[:, 2] pose_meta.kps_face = kps_face[:, :2] pose_meta.kps_face_p = kps_face[:, 2] return pose_meta @staticmethod def from_dwpose(dwpose_det_res, height, width): pose_meta = AAPoseMeta() pose_meta.kps_body = dwpose_det_res["bodies"]["candidate"] pose_meta.kps_body_p = dwpose_det_res["bodies"]["score"] pose_meta.kps_body[:, 0] *= width pose_meta.kps_body[:, 1] *= height pose_meta.kps_lhand, pose_meta.kps_rhand = dwpose_det_res["hands"] pose_meta.kps_lhand[:, 0] *= width pose_meta.kps_lhand[:, 1] *= height pose_meta.kps_rhand[:, 0] *= width pose_meta.kps_rhand[:, 1] *= height pose_meta.kps_lhand_p, pose_meta.kps_rhand_p = dwpose_det_res["hands_score"] pose_meta.kps_face = dwpose_det_res["faces"][0] pose_meta.kps_face[:, 0] *= width pose_meta.kps_face[:, 1] *= height pose_meta.kps_face_p = dwpose_det_res["faces_score"][0] return pose_meta def save_json(self): pass def draw_aapose(self, img, threshold=0.5, stick_width_norm=200, draw_hand=True, draw_head=True): from .human_visualization import draw_aapose_by_meta return draw_aapose_by_meta(img, self, threshold, stick_width_norm, draw_hand, draw_head) def translate(self, x0, y0): all_kps = [self.kps_body, self.kps_lhand, self.kps_rhand, self.kps_face] for kps in all_kps: if kps is not None: kps[:, 0] -= x0 kps[:, 1] -= y0 def scale(self, sx, sy): all_kps = [self.kps_body, self.kps_lhand, self.kps_rhand, self.kps_face] for kps in all_kps: if kps is not None: kps[:, 0] *= sx kps[:, 1] *= sy def padding_resize2(self, height=512, width=512): """kps will be changed inplace """ all_kps = [self.kps_body, self.kps_lhand, self.kps_rhand, self.kps_face] ori_height, ori_width = self.height, self.width if (ori_height / ori_width) > (height / width): new_width = int(height / ori_height * ori_width) padding = int((width - new_width) / 2) padding_width = padding padding_height = 0 scale = height / ori_height for kps in all_kps: if kps is not None: kps[:, 0] = kps[:, 0] * scale + padding kps[:, 1] = kps[:, 1] * scale else: new_height = int(width / ori_width * ori_height) padding = int((height - new_height) / 2) padding_width = 0 padding_height = padding scale = width / ori_width for kps in all_kps: if kps is not None: kps[:, 1] = kps[:, 1] * scale + padding kps[:, 0] = kps[:, 0] * scale self.width = width self.height = height return self def transform_preds(coords, center, scale, output_size, use_udp=False): """Get final keypoint predictions from heatmaps and apply scaling and translation to map them back to the image. Note: num_keypoints: K Args: coords (np.ndarray[K, ndims]): * If ndims=2, corrds are predicted keypoint location. * If ndims=4, corrds are composed of (x, y, scores, tags) * If ndims=5, corrds are composed of (x, y, scores, tags, flipped_tags) center (np.ndarray[2, ]): Center of the bounding box (x, y). scale (np.ndarray[2, ]): Scale of the bounding box wrt [width, height]. output_size (np.ndarray[2, ] | list(2,)): Size of the destination heatmaps. use_udp (bool): Use unbiased data processing Returns: np.ndarray: Predicted coordinates in the images. """ assert coords.shape[1] in (2, 4, 5) assert len(center) == 2 assert len(scale) == 2 assert len(output_size) == 2 # Recover the scale which is normalized by a factor of 200. # scale = scale * 200.0 if use_udp: scale_x = scale[0] / (output_size[0] - 1.0) scale_y = scale[1] / (output_size[1] - 1.0) else: scale_x = scale[0] / output_size[0] scale_y = scale[1] / output_size[1] target_coords = np.ones_like(coords) target_coords[:, 0] = coords[:, 0] * scale_x + center[0] - scale[0] * 0.5 target_coords[:, 1] = coords[:, 1] * scale_y + center[1] - scale[1] * 0.5 return target_coords def _calc_distances(preds, targets, mask, normalize): """Calculate the normalized distances between preds and target. Note: batch_size: N num_keypoints: K dimension of keypoints: D (normally, D=2 or D=3) Args: preds (np.ndarray[N, K, D]): Predicted keypoint location. targets (np.ndarray[N, K, D]): Groundtruth keypoint location. mask (np.ndarray[N, K]): Visibility of the target. False for invisible joints, and True for visible. Invisible joints will be ignored for accuracy calculation. normalize (np.ndarray[N, D]): Typical value is heatmap_size Returns: np.ndarray[K, N]: The normalized distances. \ If target keypoints are missing, the distance is -1. """ N, K, _ = preds.shape # set mask=0 when normalize==0 _mask = mask.copy() _mask[np.where((normalize == 0).sum(1))[0], :] = False distances = np.full((N, K), -1, dtype=np.float32) # handle invalid values normalize[np.where(normalize <= 0)] = 1e6 distances[_mask] = np.linalg.norm( ((preds - targets) / normalize[:, None, :])[_mask], axis=-1) return distances.T def _distance_acc(distances, thr=0.5): """Return the percentage below the distance threshold, while ignoring distances values with -1. Note: batch_size: N Args: distances (np.ndarray[N, ]): The normalized distances. thr (float): Threshold of the distances. Returns: float: Percentage of distances below the threshold. \ If all target keypoints are missing, return -1. """ distance_valid = distances != -1 num_distance_valid = distance_valid.sum() if num_distance_valid > 0: return (distances[distance_valid] < thr).sum() / num_distance_valid return -1 def _get_max_preds(heatmaps): """Get keypoint predictions from score maps. Note: batch_size: N num_keypoints: K heatmap height: H heatmap width: W Args: heatmaps (np.ndarray[N, K, H, W]): model predicted heatmaps. Returns: tuple: A tuple containing aggregated results. - preds (np.ndarray[N, K, 2]): Predicted keypoint location. - maxvals (np.ndarray[N, K, 1]): Scores (confidence) of the keypoints. """ assert isinstance(heatmaps, np.ndarray), ('heatmaps should be numpy.ndarray') assert heatmaps.ndim == 4, 'batch_images should be 4-ndim' N, K, _, W = heatmaps.shape heatmaps_reshaped = heatmaps.reshape((N, K, -1)) idx = np.argmax(heatmaps_reshaped, 2).reshape((N, K, 1)) maxvals = np.amax(heatmaps_reshaped, 2).reshape((N, K, 1)) preds = np.tile(idx, (1, 1, 2)).astype(np.float32) preds[:, :, 0] = preds[:, :, 0] % W preds[:, :, 1] = preds[:, :, 1] // W preds = np.where(np.tile(maxvals, (1, 1, 2)) > 0.0, preds, -1) return preds, maxvals def _get_max_preds_3d(heatmaps): """Get keypoint predictions from 3D score maps. Note: batch size: N num keypoints: K heatmap depth size: D heatmap height: H heatmap width: W Args: heatmaps (np.ndarray[N, K, D, H, W]): model predicted heatmaps. Returns: tuple: A tuple containing aggregated results. - preds (np.ndarray[N, K, 3]): Predicted keypoint location. - maxvals (np.ndarray[N, K, 1]): Scores (confidence) of the keypoints. """ assert isinstance(heatmaps, np.ndarray), \ ('heatmaps should be numpy.ndarray') assert heatmaps.ndim == 5, 'heatmaps should be 5-ndim' N, K, D, H, W = heatmaps.shape heatmaps_reshaped = heatmaps.reshape((N, K, -1)) idx = np.argmax(heatmaps_reshaped, 2).reshape((N, K, 1)) maxvals = np.amax(heatmaps_reshaped, 2).reshape((N, K, 1)) preds = np.zeros((N, K, 3), dtype=np.float32) _idx = idx[..., 0] preds[..., 2] = _idx // (H * W) preds[..., 1] = (_idx // W) % H preds[..., 0] = _idx % W preds = np.where(maxvals > 0.0, preds, -1) return preds, maxvals def pose_pck_accuracy(output, target, mask, thr=0.05, normalize=None): """Calculate the pose accuracy of PCK for each individual keypoint and the averaged accuracy across all keypoints from heatmaps. Note: PCK metric measures accuracy of the localization of the body joints. The distances between predicted positions and the ground-truth ones are typically normalized by the bounding box size. The threshold (thr) of the normalized distance is commonly set as 0.05, 0.1 or 0.2 etc. - batch_size: N - num_keypoints: K - heatmap height: H - heatmap width: W Args: output (np.ndarray[N, K, H, W]): Model output heatmaps. target (np.ndarray[N, K, H, W]): Groundtruth heatmaps. mask (np.ndarray[N, K]): Visibility of the target. False for invisible joints, and True for visible. Invisible joints will be ignored for accuracy calculation. thr (float): Threshold of PCK calculation. Default 0.05. normalize (np.ndarray[N, 2]): Normalization factor for H&W. Returns: tuple: A tuple containing keypoint accuracy. - np.ndarray[K]: Accuracy of each keypoint. - float: Averaged accuracy across all keypoints. - int: Number of valid keypoints. """ N, K, H, W = output.shape if K == 0: return None, 0, 0 if normalize is None: normalize = np.tile(np.array([[H, W]]), (N, 1)) pred, _ = _get_max_preds(output) gt, _ = _get_max_preds(target) return keypoint_pck_accuracy(pred, gt, mask, thr, normalize) def keypoint_pck_accuracy(pred, gt, mask, thr, normalize): """Calculate the pose accuracy of PCK for each individual keypoint and the averaged accuracy across all keypoints for coordinates. Note: PCK metric measures accuracy of the localization of the body joints. The distances between predicted positions and the ground-truth ones are typically normalized by the bounding box size. The threshold (thr) of the normalized distance is commonly set as 0.05, 0.1 or 0.2 etc. - batch_size: N - num_keypoints: K Args: pred (np.ndarray[N, K, 2]): Predicted keypoint location. gt (np.ndarray[N, K, 2]): Groundtruth keypoint location. mask (np.ndarray[N, K]): Visibility of the target. False for invisible joints, and True for visible. Invisible joints will be ignored for accuracy calculation. thr (float): Threshold of PCK calculation. normalize (np.ndarray[N, 2]): Normalization factor for H&W. Returns: tuple: A tuple containing keypoint accuracy. - acc (np.ndarray[K]): Accuracy of each keypoint. - avg_acc (float): Averaged accuracy across all keypoints. - cnt (int): Number of valid keypoints. """ distances = _calc_distances(pred, gt, mask, normalize) acc = np.array([_distance_acc(d, thr) for d in distances]) valid_acc = acc[acc >= 0] cnt = len(valid_acc) avg_acc = valid_acc.mean() if cnt > 0 else 0 return acc, avg_acc, cnt def keypoint_auc(pred, gt, mask, normalize, num_step=20): """Calculate the pose accuracy of PCK for each individual keypoint and the averaged accuracy across all keypoints for coordinates. Note: - batch_size: N - num_keypoints: K Args: pred (np.ndarray[N, K, 2]): Predicted keypoint location. gt (np.ndarray[N, K, 2]): Groundtruth keypoint location. mask (np.ndarray[N, K]): Visibility of the target. False for invisible joints, and True for visible. Invisible joints will be ignored for accuracy calculation. normalize (float): Normalization factor. Returns: float: Area under curve. """ nor = np.tile(np.array([[normalize, normalize]]), (pred.shape[0], 1)) x = [1.0 * i / num_step for i in range(num_step)] y = [] for thr in x: _, avg_acc, _ = keypoint_pck_accuracy(pred, gt, mask, thr, nor) y.append(avg_acc) auc = 0 for i in range(num_step): auc += 1.0 / num_step * y[i] return auc def keypoint_nme(pred, gt, mask, normalize_factor): """Calculate the normalized mean error (NME). Note: - batch_size: N - num_keypoints: K Args: pred (np.ndarray[N, K, 2]): Predicted keypoint location. gt (np.ndarray[N, K, 2]): Groundtruth keypoint location. mask (np.ndarray[N, K]): Visibility of the target. False for invisible joints, and True for visible. Invisible joints will be ignored for accuracy calculation. normalize_factor (np.ndarray[N, 2]): Normalization factor. Returns: float: normalized mean error """ distances = _calc_distances(pred, gt, mask, normalize_factor) distance_valid = distances[distances != -1] return distance_valid.sum() / max(1, len(distance_valid)) def keypoint_epe(pred, gt, mask): """Calculate the end-point error. Note: - batch_size: N - num_keypoints: K Args: pred (np.ndarray[N, K, 2]): Predicted keypoint location. gt (np.ndarray[N, K, 2]): Groundtruth keypoint location. mask (np.ndarray[N, K]): Visibility of the target. False for invisible joints, and True for visible. Invisible joints will be ignored for accuracy calculation. Returns: float: Average end-point error. """ distances = _calc_distances( pred, gt, mask, np.ones((pred.shape[0], pred.shape[2]), dtype=np.float32)) distance_valid = distances[distances != -1] return distance_valid.sum() / max(1, len(distance_valid)) def _taylor(heatmap, coord): """Distribution aware coordinate decoding method. Note: - heatmap height: H - heatmap width: W Args: heatmap (np.ndarray[H, W]): Heatmap of a particular joint type. coord (np.ndarray[2,]): Coordinates of the predicted keypoints. Returns: np.ndarray[2,]: Updated coordinates. """ H, W = heatmap.shape[:2] px, py = int(coord[0]), int(coord[1]) if 1 < px < W - 2 and 1 < py < H - 2: dx = 0.5 * (heatmap[py][px + 1] - heatmap[py][px - 1]) dy = 0.5 * (heatmap[py + 1][px] - heatmap[py - 1][px]) dxx = 0.25 * ( heatmap[py][px + 2] - 2 * heatmap[py][px] + heatmap[py][px - 2]) dxy = 0.25 * ( heatmap[py + 1][px + 1] - heatmap[py - 1][px + 1] - heatmap[py + 1][px - 1] + heatmap[py - 1][px - 1]) dyy = 0.25 * ( heatmap[py + 2 * 1][px] - 2 * heatmap[py][px] + heatmap[py - 2 * 1][px]) derivative = np.array([[dx], [dy]]) hessian = np.array([[dxx, dxy], [dxy, dyy]]) if dxx * dyy - dxy**2 != 0: hessianinv = np.linalg.inv(hessian) offset = -hessianinv @ derivative offset = np.squeeze(np.array(offset.T), axis=0) coord += offset return coord def post_dark_udp(coords, batch_heatmaps, kernel=3): """DARK post-pocessing. Implemented by udp. Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation (CVPR 2020). Zhang et al. Distribution-Aware Coordinate Representation for Human Pose Estimation (CVPR 2020). Note: - batch size: B - num keypoints: K - num persons: N - height of heatmaps: H - width of heatmaps: W B=1 for bottom_up paradigm where all persons share the same heatmap. B=N for top_down paradigm where each person has its own heatmaps. Args: coords (np.ndarray[N, K, 2]): Initial coordinates of human pose. batch_heatmaps (np.ndarray[B, K, H, W]): batch_heatmaps kernel (int): Gaussian kernel size (K) for modulation. Returns: np.ndarray([N, K, 2]): Refined coordinates. """ if not isinstance(batch_heatmaps, np.ndarray): batch_heatmaps = batch_heatmaps.cpu().numpy() B, K, H, W = batch_heatmaps.shape N = coords.shape[0] assert (B == 1 or B == N) for heatmaps in batch_heatmaps: for heatmap in heatmaps: cv2.GaussianBlur(heatmap, (kernel, kernel), 0, heatmap) np.clip(batch_heatmaps, 0.001, 50, batch_heatmaps) np.log(batch_heatmaps, batch_heatmaps) batch_heatmaps_pad = np.pad( batch_heatmaps, ((0, 0), (0, 0), (1, 1), (1, 1)), mode='edge').flatten() index = coords[..., 0] + 1 + (coords[..., 1] + 1) * (W + 2) index += (W + 2) * (H + 2) * np.arange(0, B * K).reshape(-1, K) index = index.astype(int).reshape(-1, 1) i_ = batch_heatmaps_pad[index] ix1 = batch_heatmaps_pad[index + 1] iy1 = batch_heatmaps_pad[index + W + 2] ix1y1 = batch_heatmaps_pad[index + W + 3] ix1_y1_ = batch_heatmaps_pad[index - W - 3] ix1_ = batch_heatmaps_pad[index - 1] iy1_ = batch_heatmaps_pad[index - 2 - W] dx = 0.5 * (ix1 - ix1_) dy = 0.5 * (iy1 - iy1_) derivative = np.concatenate([dx, dy], axis=1) derivative = derivative.reshape(N, K, 2, 1) dxx = ix1 - 2 * i_ + ix1_ dyy = iy1 - 2 * i_ + iy1_ dxy = 0.5 * (ix1y1 - ix1 - iy1 + i_ + i_ - ix1_ - iy1_ + ix1_y1_) hessian = np.concatenate([dxx, dxy, dxy, dyy], axis=1) hessian = hessian.reshape(N, K, 2, 2) hessian = np.linalg.inv(hessian + np.finfo(np.float32).eps * np.eye(2)) coords -= np.einsum('ijmn,ijnk->ijmk', hessian, derivative).squeeze() return coords def _gaussian_blur(heatmaps, kernel=11): """Modulate heatmap distribution with Gaussian. sigma = 0.3*((kernel_size-1)*0.5-1)+0.8 sigma~=3 if k=17 sigma=2 if k=11; sigma~=1.5 if k=7; sigma~=1 if k=3; Note: - batch_size: N - num_keypoints: K - heatmap height: H - heatmap width: W Args: heatmaps (np.ndarray[N, K, H, W]): model predicted heatmaps. kernel (int): Gaussian kernel size (K) for modulation, which should match the heatmap gaussian sigma when training. K=17 for sigma=3 and k=11 for sigma=2. Returns: np.ndarray ([N, K, H, W]): Modulated heatmap distribution. """ assert kernel % 2 == 1 border = (kernel - 1) // 2 batch_size = heatmaps.shape[0] num_joints = heatmaps.shape[1] height = heatmaps.shape[2] width = heatmaps.shape[3] for i in range(batch_size): for j in range(num_joints): origin_max = np.max(heatmaps[i, j]) dr = np.zeros((height + 2 * border, width + 2 * border), dtype=np.float32) dr[border:-border, border:-border] = heatmaps[i, j].copy() dr = cv2.GaussianBlur(dr, (kernel, kernel), 0) heatmaps[i, j] = dr[border:-border, border:-border].copy() heatmaps[i, j] *= origin_max / np.max(heatmaps[i, j]) return heatmaps def keypoints_from_regression(regression_preds, center, scale, img_size): """Get final keypoint predictions from regression vectors and transform them back to the image. Note: - batch_size: N - num_keypoints: K Args: regression_preds (np.ndarray[N, K, 2]): model prediction. center (np.ndarray[N, 2]): Center of the bounding box (x, y). scale (np.ndarray[N, 2]): Scale of the bounding box wrt height/width. img_size (list(img_width, img_height)): model input image size. Returns: tuple: - preds (np.ndarray[N, K, 2]): Predicted keypoint location in images. - maxvals (np.ndarray[N, K, 1]): Scores (confidence) of the keypoints. """ N, K, _ = regression_preds.shape preds, maxvals = regression_preds, np.ones((N, K, 1), dtype=np.float32) preds = preds * img_size # Transform back to the image for i in range(N): preds[i] = transform_preds(preds[i], center[i], scale[i], img_size) return preds, maxvals def keypoints_from_heatmaps(heatmaps, center, scale, unbiased=False, post_process='default', kernel=11, valid_radius_factor=0.0546875, use_udp=False, target_type='GaussianHeatmap'): """Get final keypoint predictions from heatmaps and transform them back to the image. Note: - batch size: N - num keypoints: K - heatmap height: H - heatmap width: W Args: heatmaps (np.ndarray[N, K, H, W]): model predicted heatmaps. center (np.ndarray[N, 2]): Center of the bounding box (x, y). scale (np.ndarray[N, 2]): Scale of the bounding box wrt height/width. post_process (str/None): Choice of methods to post-process heatmaps. Currently supported: None, 'default', 'unbiased', 'megvii'. unbiased (bool): Option to use unbiased decoding. Mutually exclusive with megvii. Note: this arg is deprecated and unbiased=True can be replaced by post_process='unbiased' Paper ref: Zhang et al. Distribution-Aware Coordinate Representation for Human Pose Estimation (CVPR 2020). kernel (int): Gaussian kernel size (K) for modulation, which should match the heatmap gaussian sigma when training. K=17 for sigma=3 and k=11 for sigma=2. valid_radius_factor (float): The radius factor of the positive area in classification heatmap for UDP. use_udp (bool): Use unbiased data processing. target_type (str): 'GaussianHeatmap' or 'CombinedTarget'. GaussianHeatmap: Classification target with gaussian distribution. CombinedTarget: The combination of classification target (response map) and regression target (offset map). Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation (CVPR 2020). Returns: tuple: A tuple containing keypoint predictions and scores. - preds (np.ndarray[N, K, 2]): Predicted keypoint location in images. - maxvals (np.ndarray[N, K, 1]): Scores (confidence) of the keypoints. """ # Avoid being affected heatmaps = heatmaps.copy() # detect conflicts if unbiased: assert post_process not in [False, None, 'megvii'] if post_process in ['megvii', 'unbiased']: assert kernel > 0 if use_udp: assert not post_process == 'megvii' # normalize configs if post_process is False: warnings.warn( 'post_process=False is deprecated, ' 'please use post_process=None instead', DeprecationWarning) post_process = None elif post_process is True: if unbiased is True: warnings.warn( 'post_process=True, unbiased=True is deprecated,' " please use post_process='unbiased' instead", DeprecationWarning) post_process = 'unbiased' else: warnings.warn( 'post_process=True, unbiased=False is deprecated, ' "please use post_process='default' instead", DeprecationWarning) post_process = 'default' elif post_process == 'default': if unbiased is True: warnings.warn( 'unbiased=True is deprecated, please use ' "post_process='unbiased' instead", DeprecationWarning) post_process = 'unbiased' # start processing if post_process == 'megvii': heatmaps = _gaussian_blur(heatmaps, kernel=kernel) N, K, H, W = heatmaps.shape if use_udp: if target_type.lower() == 'GaussianHeatMap'.lower(): preds, maxvals = _get_max_preds(heatmaps) preds = post_dark_udp(preds, heatmaps, kernel=kernel) elif target_type.lower() == 'CombinedTarget'.lower(): for person_heatmaps in heatmaps: for i, heatmap in enumerate(person_heatmaps): kt = 2 * kernel + 1 if i % 3 == 0 else kernel cv2.GaussianBlur(heatmap, (kt, kt), 0, heatmap) # valid radius is in direct proportion to the height of heatmap. valid_radius = valid_radius_factor * H offset_x = heatmaps[:, 1::3, :].flatten() * valid_radius offset_y = heatmaps[:, 2::3, :].flatten() * valid_radius heatmaps = heatmaps[:, ::3, :] preds, maxvals = _get_max_preds(heatmaps) index = preds[..., 0] + preds[..., 1] * W index += W * H * np.arange(0, N * K / 3) index = index.astype(int).reshape(N, K // 3, 1) preds += np.concatenate((offset_x[index], offset_y[index]), axis=2) else: raise ValueError('target_type should be either ' "'GaussianHeatmap' or 'CombinedTarget'") else: preds, maxvals = _get_max_preds(heatmaps) if post_process == 'unbiased': # alleviate biased coordinate # apply Gaussian distribution modulation. heatmaps = np.log( np.maximum(_gaussian_blur(heatmaps, kernel), 1e-10)) for n in range(N): for k in range(K): preds[n][k] = _taylor(heatmaps[n][k], preds[n][k]) elif post_process is not None: # add +/-0.25 shift to the predicted locations for higher acc. for n in range(N): for k in range(K): heatmap = heatmaps[n][k] px = int(preds[n][k][0]) py = int(preds[n][k][1]) if 1 < px < W - 1 and 1 < py < H - 1: diff = np.array([ heatmap[py][px + 1] - heatmap[py][px - 1], heatmap[py + 1][px] - heatmap[py - 1][px] ]) preds[n][k] += np.sign(diff) * .25 if post_process == 'megvii': preds[n][k] += 0.5 # Transform back to the image for i in range(N): preds[i] = transform_preds( preds[i], center[i], scale[i], [W, H], use_udp=use_udp) if post_process == 'megvii': maxvals = maxvals / 255.0 + 0.5 return preds, maxvals def keypoints_from_heatmaps3d(heatmaps, center, scale): """Get final keypoint predictions from 3d heatmaps and transform them back to the image. Note: - batch size: N - num keypoints: K - heatmap depth size: D - heatmap height: H - heatmap width: W Args: heatmaps (np.ndarray[N, K, D, H, W]): model predicted heatmaps. center (np.ndarray[N, 2]): Center of the bounding box (x, y). scale (np.ndarray[N, 2]): Scale of the bounding box wrt height/width. Returns: tuple: A tuple containing keypoint predictions and scores. - preds (np.ndarray[N, K, 3]): Predicted 3d keypoint location \ in images. - maxvals (np.ndarray[N, K, 1]): Scores (confidence) of the keypoints. """ N, K, D, H, W = heatmaps.shape preds, maxvals = _get_max_preds_3d(heatmaps) # Transform back to the image for i in range(N): preds[i, :, :2] = transform_preds(preds[i, :, :2], center[i], scale[i], [W, H]) return preds, maxvals def multilabel_classification_accuracy(pred, gt, mask, thr=0.5): """Get multi-label classification accuracy. Note: - batch size: N - label number: L Args: pred (np.ndarray[N, L, 2]): model predicted labels. gt (np.ndarray[N, L, 2]): ground-truth labels. mask (np.ndarray[N, 1] or np.ndarray[N, L] ): reliability of ground-truth labels. Returns: float: multi-label classification accuracy. """ # we only compute accuracy on the samples with ground-truth of all labels. valid = (mask > 0).min(axis=1) if mask.ndim == 2 else (mask > 0) pred, gt = pred[valid], gt[valid] if pred.shape[0] == 0: acc = 0.0 # when no sample is with gt labels, set acc to 0. else: # The classification of a sample is regarded as correct # only if it's correct for all labels. acc = (((pred - thr) * (gt - thr)) > 0).all(axis=1).mean() return acc def get_transform(center, scale, res, rot=0): """Generate transformation matrix.""" # res: (height, width), (rows, cols) crop_aspect_ratio = res[0] / float(res[1]) h = 200 * scale w = h / crop_aspect_ratio t = np.zeros((3, 3)) t[0, 0] = float(res[1]) / w t[1, 1] = float(res[0]) / h t[0, 2] = res[1] * (-float(center[0]) / w + .5) t[1, 2] = res[0] * (-float(center[1]) / h + .5) t[2, 2] = 1 if not rot == 0: rot = -rot # To match direction of rotation from cropping rot_mat = np.zeros((3, 3)) rot_rad = rot * np.pi / 180 sn, cs = np.sin(rot_rad), np.cos(rot_rad) rot_mat[0, :2] = [cs, -sn] rot_mat[1, :2] = [sn, cs] rot_mat[2, 2] = 1 # Need to rotate around center t_mat = np.eye(3) t_mat[0, 2] = -res[1] / 2 t_mat[1, 2] = -res[0] / 2 t_inv = t_mat.copy() t_inv[:2, 2] *= -1 t = np.dot(t_inv, np.dot(rot_mat, np.dot(t_mat, t))) return t def transform(pt, center, scale, res, invert=0, rot=0): """Transform pixel location to different reference.""" t = get_transform(center, scale, res, rot=rot) if invert: t = np.linalg.inv(t) new_pt = np.array([pt[0] - 1, pt[1] - 1, 1.]).T new_pt = np.dot(t, new_pt) return np.array([round(new_pt[0]), round(new_pt[1])], dtype=int) + 1 def bbox_from_detector(bbox, input_resolution=(224, 224), rescale=1.25): """ Get center and scale of bounding box from bounding box. The expected format is [min_x, min_y, max_x, max_y]. """ CROP_IMG_HEIGHT, CROP_IMG_WIDTH = input_resolution CROP_ASPECT_RATIO = CROP_IMG_HEIGHT / float(CROP_IMG_WIDTH) # center center_x = (bbox[0] + bbox[2]) / 2.0 center_y = (bbox[1] + bbox[3]) / 2.0 center = np.array([center_x, center_y]) # scale bbox_w = bbox[2] - bbox[0] bbox_h = bbox[3] - bbox[1] bbox_size = max(bbox_w * CROP_ASPECT_RATIO, bbox_h) scale = np.array([bbox_size / CROP_ASPECT_RATIO, bbox_size]) / 200.0 # scale = bbox_size / 200.0 # adjust bounding box tightness scale *= rescale return center, scale def crop(img, center, scale, res): """ Crop image according to the supplied bounding box. res: [rows, cols] """ # Upper left point ul = np.array(transform([1, 1], center, max(scale), res, invert=1)) - 1 # Bottom right point br = np.array(transform([res[1] + 1, res[0] + 1], center, max(scale), res, invert=1)) - 1 # Padding so that when rotated proper amount of context is included pad = int(np.linalg.norm(br - ul) / 2 - float(br[1] - ul[1]) / 2) new_shape = [br[1] - ul[1], br[0] - ul[0]] if len(img.shape) > 2: new_shape += [img.shape[2]] new_img = np.zeros(new_shape, dtype=np.float32) # Range to fill new array new_x = max(0, -ul[0]), min(br[0], len(img[0])) - ul[0] new_y = max(0, -ul[1]), min(br[1], len(img)) - ul[1] # Range to sample from original image old_x = max(0, ul[0]), min(len(img[0]), br[0]) old_y = max(0, ul[1]), min(len(img), br[1]) try: new_img[new_y[0]:new_y[1], new_x[0]:new_x[1]] = img[old_y[0]:old_y[1], old_x[0]:old_x[1]] except Exception as e: print(e) new_img = cv2.resize(new_img, (res[1], res[0])) # (cols, rows) return new_img, new_shape, (old_x, old_y), (new_x, new_y) # , ul, br def split_kp2ds_for_aa(kp2ds, ret_face=False): kp2ds_body = (kp2ds[[0, 6, 6, 8, 10, 5, 7, 9, 12, 14, 16, 11, 13, 15, 2, 1, 4, 3, 17, 20]] + kp2ds[[0, 5, 6, 8, 10, 5, 7, 9, 12, 14, 16, 11, 13, 15, 2, 1, 4, 3, 18, 21]]) / 2 kp2ds_lhand = kp2ds[91:112] kp2ds_rhand = kp2ds[112:133] kp2ds_face = kp2ds[22:91] if ret_face: return kp2ds_body.copy(), kp2ds_lhand.copy(), kp2ds_rhand.copy(), kp2ds_face.copy() return kp2ds_body.copy(), kp2ds_lhand.copy(), kp2ds_rhand.copy() def load_pose_metas_from_kp2ds_seq_list(kp2ds_seq, width, height): metas = [] for kps in kp2ds_seq: if len(kps) != 1: return None kps = kps[0].copy() kps[:, 0] /= width kps[:, 1] /= height kp2ds_body, kp2ds_lhand, kp2ds_rhand, kp2ds_face = split_kp2ds_for_aa(kps, ret_face=True) if kp2ds_body[:, :2].min(axis=1).max() < 0: kp2ds_body = last_kp2ds_body last_kp2ds_body = kp2ds_body meta = { "width": width, "height": height, "keypoints_body": kp2ds_body.tolist(), "keypoints_left_hand": kp2ds_lhand.tolist(), "keypoints_right_hand": kp2ds_rhand.tolist(), "keypoints_face": kp2ds_face.tolist(), } metas.append(meta) return metas def load_pose_metas_from_kp2ds_seq(kp2ds_seq, width, height): metas = [] for kps in kp2ds_seq: kps = kps.copy() kps[:, 0] /= width kps[:, 1] /= height kp2ds_body, kp2ds_lhand, kp2ds_rhand, kp2ds_face = split_kp2ds_for_aa(kps, ret_face=True) # 排除全部小于0的情况 if kp2ds_body[:, :2].min(axis=1).max() < 0: kp2ds_body = last_kp2ds_body last_kp2ds_body = kp2ds_body meta = { "width": width, "height": height, "keypoints_body": kp2ds_body, "keypoints_left_hand": kp2ds_lhand, "keypoints_right_hand": kp2ds_rhand, "keypoints_face": kp2ds_face, } metas.append(meta) return metas ================================================ FILE: wan/modules/animate/preprocess/preprocess_data.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import os import argparse from process_pipepline import ProcessPipeline def _parse_args(): parser = argparse.ArgumentParser( description="The preprocessing pipeline for Wan-animate." ) parser.add_argument( "--ckpt_path", type=str, default=None, help="The path to the preprocessing model's checkpoint directory. ") parser.add_argument( "--video_path", type=str, default=None, help="The path to the driving video.") parser.add_argument( "--refer_path", type=str, default=None, help="The path to the refererence image.") parser.add_argument( "--save_path", type=str, default=None, help="The path to save the processed results.") parser.add_argument( "--resolution_area", type=int, nargs=2, default=[1280, 720], help="The target resolution for processing, specified as [width, height]. To handle different aspect ratios, the video is resized to have a total area equivalent to width * height, while preserving the original aspect ratio." ) parser.add_argument( "--fps", type=int, default=30, help="The target FPS for processing the driving video. Set to -1 to use the video's original FPS." ) parser.add_argument( "--replace_flag", action="store_true", default=False, help="Whether to use replacement mode.") parser.add_argument( "--retarget_flag", action="store_true", default=False, help="Whether to use pose retargeting. Currently only supported in animation mode") parser.add_argument( "--use_flux", action="store_true", default=False, help="Whether to use image editing in pose retargeting. Recommended if the character in the reference image or the first frame of the driving video is not in a standard, front-facing pose") # Parameters for the mask strategy in replacement mode. These control the mask's size and shape. Refer to https://arxiv.org/pdf/2502.06145 parser.add_argument( "--iterations", type=int, default=3, help="Number of iterations for mask dilation." ) parser.add_argument( "--k", type=int, default=7, help="Number of kernel size for mask dilation." ) parser.add_argument( "--w_len", type=int, default=1, help="The number of subdivisions for the grid along the 'w' dimension. A higher value results in a more detailed contour. A value of 1 means no subdivision is performed." ) parser.add_argument( "--h_len", type=int, default=1, help="The number of subdivisions for the grid along the 'h' dimension. A higher value results in a more detailed contour. A value of 1 means no subdivision is performed." ) args = parser.parse_args() return args if __name__ == '__main__': args = _parse_args() args_dict = vars(args) print(args_dict) assert len(args.resolution_area) == 2, "resolution_area should be a list of two integers [width, height]" assert not args.use_flux or args.retarget_flag, "Image editing with FLUX can only be used when pose retargeting is enabled." pose2d_checkpoint_path = os.path.join(args.ckpt_path, 'pose2d/vitpose_h_wholebody.onnx') det_checkpoint_path = os.path.join(args.ckpt_path, 'det/yolov10m.onnx') sam2_checkpoint_path = os.path.join(args.ckpt_path, 'sam2/sam2_hiera_large.pt') if args.replace_flag else None flux_kontext_path = os.path.join(args.ckpt_path, 'FLUX.1-Kontext-dev') if args.use_flux else None process_pipeline = ProcessPipeline(det_checkpoint_path=det_checkpoint_path, pose2d_checkpoint_path=pose2d_checkpoint_path, sam_checkpoint_path=sam2_checkpoint_path, flux_kontext_path=flux_kontext_path) os.makedirs(args.save_path, exist_ok=True) process_pipeline(video_path=args.video_path, refer_image_path=args.refer_path, output_path=args.save_path, resolution_area=args.resolution_area, fps=args.fps, iterations=args.iterations, k=args.k, w_len=args.w_len, h_len=args.h_len, retarget_flag=args.retarget_flag, use_flux=args.use_flux, replace_flag=args.replace_flag) ================================================ FILE: wan/modules/animate/preprocess/process_pipepline.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import os import numpy as np import shutil import torch from diffusers import FluxKontextPipeline import cv2 from loguru import logger from PIL import Image try: import moviepy.editor as mpy except: import moviepy as mpy from decord import VideoReader from pose2d import Pose2d from pose2d_utils import AAPoseMeta from utils import resize_by_area, get_frame_indices, padding_resize, get_face_bboxes, get_aug_mask, get_mask_body_img from human_visualization import draw_aapose_by_meta_new from retarget_pose import get_retarget_pose import sam2.modeling.sam.transformer as transformer transformer.USE_FLASH_ATTN = False transformer.MATH_KERNEL_ON = True transformer.OLD_GPU = True from sam_utils import build_sam2_video_predictor class ProcessPipeline(): def __init__(self, det_checkpoint_path, pose2d_checkpoint_path, sam_checkpoint_path, flux_kontext_path): self.pose2d = Pose2d(checkpoint=pose2d_checkpoint_path, detector_checkpoint=det_checkpoint_path) model_cfg = "sam2_hiera_l.yaml" if sam_checkpoint_path is not None: self.predictor = build_sam2_video_predictor(model_cfg, sam_checkpoint_path) if flux_kontext_path is not None: self.flux_kontext = FluxKontextPipeline.from_pretrained(flux_kontext_path, torch_dtype=torch.bfloat16).to("cuda") def __call__(self, video_path, refer_image_path, output_path, resolution_area=[1280, 720], fps=30, iterations=3, k=7, w_len=1, h_len=1, retarget_flag=False, use_flux=False, replace_flag=False): if replace_flag: video_reader = VideoReader(video_path) frame_num = len(video_reader) print('frame_num: {}'.format(frame_num)) video_fps = video_reader.get_avg_fps() print('video_fps: {}'.format(video_fps)) print('fps: {}'.format(fps)) # TODO: Maybe we can switch to PyAV later, which can get accurate frame num duration = video_reader.get_frame_timestamp(-1)[-1] expected_frame_num = int(duration * video_fps + 0.5) ratio = abs((frame_num - expected_frame_num)/frame_num) if ratio > 0.1: print("Warning: The difference between the actual number of frames and the expected number of frames is two large") frame_num = expected_frame_num if fps == -1: fps = video_fps target_num = int(frame_num / video_fps * fps) print('target_num: {}'.format(target_num)) idxs = get_frame_indices(frame_num, video_fps, target_num, fps) frames = video_reader.get_batch(idxs).asnumpy() frames = [resize_by_area(frame, resolution_area[0] * resolution_area[1], divisor=16) for frame in frames] height, width = frames[0].shape[:2] logger.info(f"Processing pose meta") tpl_pose_metas = self.pose2d(frames) face_images = [] for idx, meta in enumerate(tpl_pose_metas): face_bbox_for_image = get_face_bboxes(meta['keypoints_face'][:, :2], scale=1.3, image_shape=(frames[0].shape[0], frames[0].shape[1])) x1, x2, y1, y2 = face_bbox_for_image face_image = frames[idx][y1:y2, x1:x2] face_image = cv2.resize(face_image, (512, 512)) face_images.append(face_image) logger.info(f"Processing reference image: {refer_image_path}") refer_img = cv2.imread(refer_image_path) src_ref_path = os.path.join(output_path, 'src_ref.png') shutil.copy(refer_image_path, src_ref_path) refer_img = refer_img[..., ::-1] refer_img = padding_resize(refer_img, height, width) logger.info(f"Processing template video: {video_path}") tpl_retarget_pose_metas = [AAPoseMeta.from_humanapi_meta(meta) for meta in tpl_pose_metas] cond_images = [] for idx, meta in enumerate(tpl_retarget_pose_metas): canvas = np.zeros_like(refer_img) conditioning_image = draw_aapose_by_meta_new(canvas, meta) cond_images.append(conditioning_image) masks = self.get_mask(frames, 400, tpl_pose_metas) bg_images = [] aug_masks = [] for frame, mask in zip(frames, masks): if iterations > 0: _, each_mask = get_mask_body_img(frame, mask, iterations=iterations, k=k) each_aug_mask = get_aug_mask(each_mask, w_len=w_len, h_len=h_len) else: each_aug_mask = mask each_bg_image = frame * (1 - each_aug_mask[:, :, None]) bg_images.append(each_bg_image) aug_masks.append(each_aug_mask) src_face_path = os.path.join(output_path, 'src_face.mp4') mpy.ImageSequenceClip(face_images, fps=fps).write_videofile(src_face_path) src_pose_path = os.path.join(output_path, 'src_pose.mp4') mpy.ImageSequenceClip(cond_images, fps=fps).write_videofile(src_pose_path) src_bg_path = os.path.join(output_path, 'src_bg.mp4') mpy.ImageSequenceClip(bg_images, fps=fps).write_videofile(src_bg_path) aug_masks_new = [np.stack([mask * 255, mask * 255, mask * 255], axis=2) for mask in aug_masks] src_mask_path = os.path.join(output_path, 'src_mask.mp4') mpy.ImageSequenceClip(aug_masks_new, fps=fps).write_videofile(src_mask_path) return True else: logger.info(f"Processing reference image: {refer_image_path}") refer_img = cv2.imread(refer_image_path) src_ref_path = os.path.join(output_path, 'src_ref.png') shutil.copy(refer_image_path, src_ref_path) refer_img = refer_img[..., ::-1] refer_img = resize_by_area(refer_img, resolution_area[0] * resolution_area[1], divisor=16) refer_pose_meta = self.pose2d([refer_img])[0] logger.info(f"Processing template video: {video_path}") video_reader = VideoReader(video_path) frame_num = len(video_reader) print('frame_num: {}'.format(frame_num)) video_fps = video_reader.get_avg_fps() print('video_fps: {}'.format(video_fps)) print('fps: {}'.format(fps)) # TODO: Maybe we can switch to PyAV later, which can get accurate frame num duration = video_reader.get_frame_timestamp(-1)[-1] expected_frame_num = int(duration * video_fps + 0.5) ratio = abs((frame_num - expected_frame_num)/frame_num) if ratio > 0.1: print("Warning: The difference between the actual number of frames and the expected number of frames is two large") frame_num = expected_frame_num if fps == -1: fps = video_fps target_num = int(frame_num / video_fps * fps) print('target_num: {}'.format(target_num)) idxs = get_frame_indices(frame_num, video_fps, target_num, fps) frames = video_reader.get_batch(idxs).asnumpy() logger.info(f"Processing pose meta") tpl_pose_meta0 = self.pose2d(frames[:1])[0] tpl_pose_metas = self.pose2d(frames) face_images = [] for idx, meta in enumerate(tpl_pose_metas): face_bbox_for_image = get_face_bboxes(meta['keypoints_face'][:, :2], scale=1.3, image_shape=(frames[0].shape[0], frames[0].shape[1])) x1, x2, y1, y2 = face_bbox_for_image face_image = frames[idx][y1:y2, x1:x2] face_image = cv2.resize(face_image, (512, 512)) face_images.append(face_image) if retarget_flag: if use_flux: tpl_prompt, refer_prompt = self.get_editing_prompts(tpl_pose_metas, refer_pose_meta) refer_input = Image.fromarray(refer_img) refer_edit = self.flux_kontext( image=refer_input, height=refer_img.shape[0], width=refer_img.shape[1], prompt=refer_prompt, guidance_scale=2.5, num_inference_steps=28, ).images[0] refer_edit = Image.fromarray(padding_resize(np.array(refer_edit), refer_img.shape[0], refer_img.shape[1])) refer_edit_path = os.path.join(output_path, 'refer_edit.png') refer_edit.save(refer_edit_path) refer_edit_pose_meta = self.pose2d([np.array(refer_edit)])[0] tpl_img = frames[1] tpl_input = Image.fromarray(tpl_img) tpl_edit = self.flux_kontext( image=tpl_input, height=tpl_img.shape[0], width=tpl_img.shape[1], prompt=tpl_prompt, guidance_scale=2.5, num_inference_steps=28, ).images[0] tpl_edit = Image.fromarray(padding_resize(np.array(tpl_edit), tpl_img.shape[0], tpl_img.shape[1])) tpl_edit_path = os.path.join(output_path, 'tpl_edit.png') tpl_edit.save(tpl_edit_path) tpl_edit_pose_meta0 = self.pose2d([np.array(tpl_edit)])[0] tpl_retarget_pose_metas = get_retarget_pose(tpl_pose_meta0, refer_pose_meta, tpl_pose_metas, tpl_edit_pose_meta0, refer_edit_pose_meta) else: tpl_retarget_pose_metas = get_retarget_pose(tpl_pose_meta0, refer_pose_meta, tpl_pose_metas, None, None) else: tpl_retarget_pose_metas = [AAPoseMeta.from_humanapi_meta(meta) for meta in tpl_pose_metas] cond_images = [] for idx, meta in enumerate(tpl_retarget_pose_metas): if retarget_flag: canvas = np.zeros_like(refer_img) conditioning_image = draw_aapose_by_meta_new(canvas, meta) else: canvas = np.zeros_like(frames[0]) conditioning_image = draw_aapose_by_meta_new(canvas, meta) conditioning_image = padding_resize(conditioning_image, refer_img.shape[0], refer_img.shape[1]) cond_images.append(conditioning_image) src_face_path = os.path.join(output_path, 'src_face.mp4') mpy.ImageSequenceClip(face_images, fps=fps).write_videofile(src_face_path) src_pose_path = os.path.join(output_path, 'src_pose.mp4') mpy.ImageSequenceClip(cond_images, fps=fps).write_videofile(src_pose_path) return True def get_editing_prompts(self, tpl_pose_metas, refer_pose_meta): arm_visible = False leg_visible = False for tpl_pose_meta in tpl_pose_metas: tpl_keypoints = tpl_pose_meta['keypoints_body'] if tpl_keypoints[3].all() != 0 or tpl_keypoints[4].all() != 0 or tpl_keypoints[6].all() != 0 or tpl_keypoints[7].all() != 0: if (tpl_keypoints[3][0] <= 1 and tpl_keypoints[3][1] <= 1 and tpl_keypoints[3][2] >= 0.75) or (tpl_keypoints[4][0] <= 1 and tpl_keypoints[4][1] <= 1 and tpl_keypoints[4][2] >= 0.75) or \ (tpl_keypoints[6][0] <= 1 and tpl_keypoints[6][1] <= 1 and tpl_keypoints[6][2] >= 0.75) or (tpl_keypoints[7][0] <= 1 and tpl_keypoints[7][1] <= 1 and tpl_keypoints[7][2] >= 0.75): arm_visible = True if tpl_keypoints[9].all() != 0 or tpl_keypoints[12].all() != 0 or tpl_keypoints[10].all() != 0 or tpl_keypoints[13].all() != 0: if (tpl_keypoints[9][0] <= 1 and tpl_keypoints[9][1] <= 1 and tpl_keypoints[9][2] >= 0.75) or (tpl_keypoints[12][0] <= 1 and tpl_keypoints[12][1] <= 1 and tpl_keypoints[12][2] >= 0.75) or \ (tpl_keypoints[10][0] <= 1 and tpl_keypoints[10][1] <= 1 and tpl_keypoints[10][2] >= 0.75) or (tpl_keypoints[13][0] <= 1 and tpl_keypoints[13][1] <= 1 and tpl_keypoints[13][2] >= 0.75): leg_visible = True if arm_visible and leg_visible: break if leg_visible: if tpl_pose_meta['width'] > tpl_pose_meta['height']: tpl_prompt = "Change the person to a standard T-pose (facing forward with arms extended). The person is standing. Feet and Hands are visible in the image." else: tpl_prompt = "Change the person to a standard pose with the face oriented forward and arms extending straight down by the sides. The person is standing. Feet and Hands are visible in the image." if refer_pose_meta['width'] > refer_pose_meta['height']: refer_prompt = "Change the person to a standard T-pose (facing forward with arms extended). The person is standing. Feet and Hands are visible in the image." else: refer_prompt = "Change the person to a standard pose with the face oriented forward and arms extending straight down by the sides. The person is standing. Feet and Hands are visible in the image." elif arm_visible: if tpl_pose_meta['width'] > tpl_pose_meta['height']: tpl_prompt = "Change the person to a standard T-pose (facing forward with arms extended). Hands are visible in the image." else: tpl_prompt = "Change the person to a standard pose with the face oriented forward and arms extending straight down by the sides. Hands are visible in the image." if refer_pose_meta['width'] > refer_pose_meta['height']: refer_prompt = "Change the person to a standard T-pose (facing forward with arms extended). Hands are visible in the image." else: refer_prompt = "Change the person to a standard pose with the face oriented forward and arms extending straight down by the sides. Hands are visible in the image." else: tpl_prompt = "Change the person to face forward." refer_prompt = "Change the person to face forward." return tpl_prompt, refer_prompt def get_mask(self, frames, th_step, kp2ds_all): frame_num = len(frames) if frame_num < th_step: num_step = 1 else: num_step = (frame_num + th_step) // th_step all_mask = [] for index in range(num_step): each_frames = frames[index * th_step:(index + 1) * th_step] kp2ds = kp2ds_all[index * th_step:(index + 1) * th_step] if len(each_frames) > 4: key_frame_num = 4 elif 4 >= len(each_frames) > 0: key_frame_num = 1 else: continue key_frame_step = len(kp2ds) // key_frame_num key_frame_index_list = list(range(0, len(kp2ds), key_frame_step)) key_points_index = [0, 1, 2, 5, 8, 11, 10, 13] key_frame_body_points_list = [] for key_frame_index in key_frame_index_list: keypoints_body_list = [] body_key_points = kp2ds[key_frame_index]['keypoints_body'] for each_index in key_points_index: each_keypoint = body_key_points[each_index] if None is each_keypoint: continue keypoints_body_list.append(each_keypoint) keypoints_body = np.array(keypoints_body_list)[:, :2] wh = np.array([[kp2ds[0]['width'], kp2ds[0]['height']]]) points = (keypoints_body * wh).astype(np.int32) key_frame_body_points_list.append(points) inference_state = self.predictor.init_state_v2(frames=each_frames) self.predictor.reset_state(inference_state) ann_obj_id = 1 for ann_frame_idx, points in zip(key_frame_index_list, key_frame_body_points_list): labels = np.array([1] * points.shape[0], np.int32) _, out_obj_ids, out_mask_logits = self.predictor.add_new_points( inference_state=inference_state, frame_idx=ann_frame_idx, obj_id=ann_obj_id, points=points, labels=labels, ) video_segments = {} for out_frame_idx, out_obj_ids, out_mask_logits in self.predictor.propagate_in_video(inference_state): video_segments[out_frame_idx] = { out_obj_id: (out_mask_logits[i] > 0.0).cpu().numpy() for i, out_obj_id in enumerate(out_obj_ids) } for out_frame_idx in range(len(video_segments)): for out_obj_id, out_mask in video_segments[out_frame_idx].items(): out_mask = out_mask[0].astype(np.uint8) all_mask.append(out_mask) return all_mask def convert_list_to_array(self, metas): metas_list = [] for meta in metas: for key, value in meta.items(): if type(value) is list: value = np.array(value) meta[key] = value metas_list.append(meta) return metas_list ================================================ FILE: wan/modules/animate/preprocess/retarget_pose.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import os import cv2 import numpy as np import json from tqdm import tqdm import math from typing import NamedTuple, List import copy from pose2d_utils import AAPoseMeta # load skeleton name and bone lines keypoint_list = [ "Nose", "Neck", "RShoulder", "RElbow", "RWrist", # No.4 "LShoulder", "LElbow", "LWrist", # No.7 "RHip", "RKnee", "RAnkle", # No.10 "LHip", "LKnee", "LAnkle", # No.13 "REye", "LEye", "REar", "LEar", "LToe", "RToe", ] limbSeq = [ [2, 3], [2, 6], # shoulders [3, 4], [4, 5], # left arm [6, 7], [7, 8], # right arm [2, 9], [9, 10], [10, 11], # right leg [2, 12], [12, 13], [13, 14], # left leg [2, 1], [1, 15], [15, 17], [1, 16], [16, 18], # face (nose, eyes, ears) [14, 19], # left foot [11, 20] # right foot ] eps = 0.01 class Keypoint(NamedTuple): x: float y: float score: float = 1.0 id: int = -1 # for each limb, calculate src & dst bone's length # and calculate their ratios def get_length(skeleton, limb): k1_index, k2_index = limb H, W = skeleton['height'], skeleton['width'] keypoints = skeleton['keypoints_body'] keypoint1 = keypoints[k1_index - 1] keypoint2 = keypoints[k2_index - 1] if keypoint1 is None or keypoint2 is None: return None, None, None X = np.array([keypoint1[0], keypoint2[0]]) * float(W) Y = np.array([keypoint1[1], keypoint2[1]]) * float(H) length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5 return X, Y, length def get_handpose_meta(keypoints, delta, src_H, src_W): new_keypoints = [] for idx, keypoint in enumerate(keypoints): if keypoint is None: new_keypoints.append(None) continue if keypoint.score == 0: new_keypoints.append(None) continue x, y = keypoint.x, keypoint.y x = int(x * src_W + delta[0]) y = int(y * src_H + delta[1]) new_keypoints.append( Keypoint( x=x, y=y, score=keypoint.score, )) return new_keypoints def deal_hand_keypoints(hand_res, r_ratio, l_ratio, hand_score_th = 0.5): left_hand = [] right_hand = [] left_delta_x = hand_res['left'][0][0] * (l_ratio - 1) left_delta_y = hand_res['left'][0][1] * (l_ratio - 1) right_delta_x = hand_res['right'][0][0] * (r_ratio - 1) right_delta_y = hand_res['right'][0][1] * (r_ratio - 1) length = len(hand_res['left']) for i in range(length): # left hand if hand_res['left'][i][2] < hand_score_th: left_hand.append( Keypoint( x=-1, y=-1, score=0, ) ) else: left_hand.append( Keypoint( x=hand_res['left'][i][0] * l_ratio - left_delta_x, y=hand_res['left'][i][1] * l_ratio - left_delta_y, score = hand_res['left'][i][2] ) ) # right hand if hand_res['right'][i][2] < hand_score_th: right_hand.append( Keypoint( x=-1, y=-1, score=0, ) ) else: right_hand.append( Keypoint( x=hand_res['right'][i][0] * r_ratio - right_delta_x, y=hand_res['right'][i][1] * r_ratio - right_delta_y, score = hand_res['right'][i][2] ) ) return right_hand, left_hand def get_scaled_pose(canvas, src_canvas, keypoints, keypoints_hand, bone_ratio_list, delta_ground_x, delta_ground_y, rescaled_src_ground_x, body_flag, id, scale_min, threshold = 0.4): H, W = canvas src_H, src_W = src_canvas new_length_list = [ ] angle_list = [ ] # keypoints from 0-1 to H/W range for idx in range(len(keypoints)): if keypoints[idx] is None or len(keypoints[idx]) == 0: continue keypoints[idx] = [keypoints[idx][0] * src_W, keypoints[idx][1] * src_H, keypoints[idx][2]] # first traverse, get new_length_list and angle_list for idx, (k1_index, k2_index) in enumerate(limbSeq): keypoint1 = keypoints[k1_index - 1] keypoint2 = keypoints[k2_index - 1] if keypoint1 is None or keypoint2 is None or len(keypoint1) == 0 or len(keypoint2) == 0: new_length_list.append(None) angle_list.append(None) continue Y = np.array([keypoint1[0], keypoint2[0]]) #* float(W) X = np.array([keypoint1[1], keypoint2[1]]) #* float(H) length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5 new_length = length * bone_ratio_list[idx] angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1])) new_length_list.append(new_length) angle_list.append(angle) # Keep foot length within 0.5x calf length foot_lower_leg_ratio = 0.5 if new_length_list[8] != None and new_length_list[18] != None: if new_length_list[18] > new_length_list[8] * foot_lower_leg_ratio: new_length_list[18] = new_length_list[8] * foot_lower_leg_ratio if new_length_list[11] != None and new_length_list[17] != None: if new_length_list[17] > new_length_list[11] * foot_lower_leg_ratio: new_length_list[17] = new_length_list[11] * foot_lower_leg_ratio # second traverse, calculate new keypoints rescale_keypoints = keypoints.copy() for idx, (k1_index, k2_index) in enumerate(limbSeq): # update dst_keypoints start_keypoint = rescale_keypoints[k1_index - 1] new_length = new_length_list[idx] angle = angle_list[idx] if rescale_keypoints[k1_index - 1] is None or rescale_keypoints[k2_index - 1] is None or \ len(rescale_keypoints[k1_index - 1]) == 0 or len(rescale_keypoints[k2_index - 1]) == 0: continue # calculate end_keypoint delta_x = new_length * math.cos(math.radians(angle)) delta_y = new_length * math.sin(math.radians(angle)) end_keypoint_x = start_keypoint[0] - delta_x end_keypoint_y = start_keypoint[1] - delta_y # update keypoints rescale_keypoints[k2_index - 1] = [end_keypoint_x, end_keypoint_y, rescale_keypoints[k2_index - 1][2]] if id == 0: if body_flag == 'full_body' and rescale_keypoints[8] != None and rescale_keypoints[11] != None: delta_ground_x_offset_first_frame = (rescale_keypoints[8][0] + rescale_keypoints[11][0]) / 2 - rescaled_src_ground_x delta_ground_x += delta_ground_x_offset_first_frame elif body_flag == 'half_body' and rescale_keypoints[1] != None: delta_ground_x_offset_first_frame = rescale_keypoints[1][0] - rescaled_src_ground_x delta_ground_x += delta_ground_x_offset_first_frame # offset all keypoints for idx in range(len(rescale_keypoints)): if rescale_keypoints[idx] is None or len(rescale_keypoints[idx]) == 0 : continue rescale_keypoints[idx][0] -= delta_ground_x rescale_keypoints[idx][1] -= delta_ground_y # rescale keypoints to original size rescale_keypoints[idx][0] /= scale_min rescale_keypoints[idx][1] /= scale_min # Scale hand proportions based on body skeletal ratios r_ratio = max(bone_ratio_list[0], bone_ratio_list[1]) / scale_min l_ratio = max(bone_ratio_list[0], bone_ratio_list[1]) / scale_min left_hand, right_hand = deal_hand_keypoints(keypoints_hand, r_ratio, l_ratio, hand_score_th = threshold) left_hand_new = left_hand.copy() right_hand_new = right_hand.copy() if rescale_keypoints[4] == None and rescale_keypoints[7] == None: pass elif rescale_keypoints[4] == None and rescale_keypoints[7] != None: right_hand_delta = np.array(rescale_keypoints[7][:2]) - np.array(keypoints[7][:2]) right_hand_new = get_handpose_meta(right_hand, right_hand_delta, src_H, src_W) elif rescale_keypoints[4] != None and rescale_keypoints[7] == None: left_hand_delta = np.array(rescale_keypoints[4][:2]) - np.array(keypoints[4][:2]) left_hand_new = get_handpose_meta(left_hand, left_hand_delta, src_H, src_W) else: # get left_hand and right_hand offset left_hand_delta = np.array(rescale_keypoints[4][:2]) - np.array(keypoints[4][:2]) right_hand_delta = np.array(rescale_keypoints[7][:2]) - np.array(keypoints[7][:2]) if keypoints[4][0] != None and left_hand[0].x != -1: left_hand_root_offset = np.array( ( keypoints[4][0] - left_hand[0].x * src_W, keypoints[4][1] - left_hand[0].y * src_H)) left_hand_delta += left_hand_root_offset if keypoints[7][0] != None and right_hand[0].x != -1: right_hand_root_offset = np.array( ( keypoints[7][0] - right_hand[0].x * src_W, keypoints[7][1] - right_hand[0].y * src_H)) right_hand_delta += right_hand_root_offset dis_left_hand = ((keypoints[4][0] - left_hand[0].x * src_W) ** 2 + (keypoints[4][1] - left_hand[0].y * src_H) ** 2) ** 0.5 dis_right_hand = ((keypoints[7][0] - left_hand[0].x * src_W) ** 2 + (keypoints[7][1] - left_hand[0].y * src_H) ** 2) ** 0.5 if dis_left_hand > dis_right_hand: right_hand_new = get_handpose_meta(left_hand, right_hand_delta, src_H, src_W) left_hand_new = get_handpose_meta(right_hand, left_hand_delta, src_H, src_W) else: left_hand_new = get_handpose_meta(left_hand, left_hand_delta, src_H, src_W) right_hand_new = get_handpose_meta(right_hand, right_hand_delta, src_H, src_W) # get normalized keypoints_body norm_body_keypoints = [ ] for body_keypoint in rescale_keypoints: if body_keypoint != None: norm_body_keypoints.append([body_keypoint[0] / W , body_keypoint[1] / H, body_keypoint[2]]) else: norm_body_keypoints.append(None) frame_info = { 'height': H, 'width': W, 'keypoints_body': norm_body_keypoints, 'keypoints_left_hand' : left_hand_new, 'keypoints_right_hand' : right_hand_new, } return frame_info def rescale_skeleton(H, W, keypoints, bone_ratio_list): rescale_keypoints = keypoints.copy() new_length_list = [ ] angle_list = [ ] # keypoints from 0-1 to H/W range for idx in range(len(rescale_keypoints)): if rescale_keypoints[idx] is None or len(rescale_keypoints[idx]) == 0: continue rescale_keypoints[idx] = [rescale_keypoints[idx][0] * W, rescale_keypoints[idx][1] * H] # first traverse, get new_length_list and angle_list for idx, (k1_index, k2_index) in enumerate(limbSeq): keypoint1 = rescale_keypoints[k1_index - 1] keypoint2 = rescale_keypoints[k2_index - 1] if keypoint1 is None or keypoint2 is None or len(keypoint1) == 0 or len(keypoint2) == 0: new_length_list.append(None) angle_list.append(None) continue Y = np.array([keypoint1[0], keypoint2[0]]) #* float(W) X = np.array([keypoint1[1], keypoint2[1]]) #* float(H) length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5 new_length = length * bone_ratio_list[idx] angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1])) new_length_list.append(new_length) angle_list.append(angle) # # second traverse, calculate new keypoints for idx, (k1_index, k2_index) in enumerate(limbSeq): # update dst_keypoints start_keypoint = rescale_keypoints[k1_index - 1] new_length = new_length_list[idx] angle = angle_list[idx] if rescale_keypoints[k1_index - 1] is None or rescale_keypoints[k2_index - 1] is None or \ len(rescale_keypoints[k1_index - 1]) == 0 or len(rescale_keypoints[k2_index - 1]) == 0: continue # calculate end_keypoint delta_x = new_length * math.cos(math.radians(angle)) delta_y = new_length * math.sin(math.radians(angle)) end_keypoint_x = start_keypoint[0] - delta_x end_keypoint_y = start_keypoint[1] - delta_y # update keypoints rescale_keypoints[k2_index - 1] = [end_keypoint_x, end_keypoint_y] return rescale_keypoints def fix_lack_keypoints_use_sym(skeleton): keypoints = skeleton['keypoints_body'] H, W = skeleton['height'], skeleton['width'] limb_points_list = [ [3, 4, 5], [6, 7, 8], [12, 13, 14, 19], [9, 10, 11, 20], ] for limb_points in limb_points_list: miss_flag = False for point in limb_points: if keypoints[point - 1] is None: miss_flag = True continue if miss_flag: skeleton['keypoints_body'][point - 1] = None repair_limb_seq_left = [ [3, 4], [4, 5], # left arm [12, 13], [13, 14], # left leg [14, 19] # left foot ] repair_limb_seq_right = [ [6, 7], [7, 8], # right arm [9, 10], [10, 11], # right leg [11, 20] # right foot ] repair_limb_seq = [repair_limb_seq_left, repair_limb_seq_right] for idx_part, part in enumerate(repair_limb_seq): for idx, limb in enumerate(part): k1_index, k2_index = limb keypoint1 = keypoints[k1_index - 1] keypoint2 = keypoints[k2_index - 1] if keypoint1 != None and keypoint2 is None: # reference to symmetric limb sym_limb = repair_limb_seq[1-idx_part][idx] k1_index_sym, k2_index_sym = sym_limb keypoint1_sym = keypoints[k1_index_sym - 1] keypoint2_sym = keypoints[k2_index_sym - 1] ref_length = 0 if keypoint1_sym != None and keypoint2_sym != None: X = np.array([keypoint1_sym[0], keypoint2_sym[0]]) * float(W) Y = np.array([keypoint1_sym[1], keypoint2_sym[1]]) * float(H) ref_length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5 else: ref_length_left, ref_length_right = 0, 0 if keypoints[1] != None and keypoints[8] != None: X = np.array([keypoints[1][0], keypoints[8][0]]) * float(W) Y = np.array([keypoints[1][1], keypoints[8][1]]) * float(H) ref_length_left = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5 if idx <= 1: # arms ref_length_left /= 2 if keypoints[1] != None and keypoints[11] != None: X = np.array([keypoints[1][0], keypoints[11][0]]) * float(W) Y = np.array([keypoints[1][1], keypoints[11][1]]) * float(H) ref_length_right = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5 if idx <= 1: # arms ref_length_right /= 2 elif idx == 4: # foot ref_length_right /= 5 ref_length = max(ref_length_left, ref_length_right) if ref_length != 0: skeleton['keypoints_body'][k2_index - 1] = [0, 0] #init skeleton['keypoints_body'][k2_index - 1][0] = skeleton['keypoints_body'][k1_index - 1][0] skeleton['keypoints_body'][k2_index - 1][1] = skeleton['keypoints_body'][k1_index - 1][1] + ref_length / H return skeleton def rescale_shorten_skeleton(ratio_list, src_length_list, dst_length_list): modify_bone_list = [ [0, 1], [2, 4], [3, 5], [6, 9], [7, 10], [8, 11], [17, 18] ] for modify_bone in modify_bone_list: new_ratio = max(ratio_list[modify_bone[0]], ratio_list[modify_bone[1]]) ratio_list[modify_bone[0]] = new_ratio ratio_list[modify_bone[1]] = new_ratio if ratio_list[13]!= None and ratio_list[15]!= None: ratio_eye_avg = (ratio_list[13] + ratio_list[15]) / 2 ratio_list[13] = ratio_eye_avg ratio_list[15] = ratio_eye_avg if ratio_list[14]!= None and ratio_list[16]!= None: ratio_eye_avg = (ratio_list[14] + ratio_list[16]) / 2 ratio_list[14] = ratio_eye_avg ratio_list[16] = ratio_eye_avg return ratio_list, src_length_list, dst_length_list def check_full_body(keypoints, threshold = 0.4): body_flag = 'half_body' # 1. If ankle points exist, confidence is greater than the threshold, and points do not exceed the frame, return full_body if keypoints[10] != None and keypoints[13] != None and keypoints[8] != None and keypoints[11] != None: if (keypoints[10][1] <= 1 and keypoints[13][1] <= 1) and (keypoints[10][2] >= threshold and keypoints[13][2] >= threshold) and \ (keypoints[8][1] <= 1 and keypoints[11][1] <= 1) and (keypoints[8][2] >= threshold and keypoints[11][2] >= threshold): body_flag = 'full_body' return body_flag # 2. If hip points exist, return three_quarter_body if (keypoints[8] != None and keypoints[11] != None): if (keypoints[8][1] <= 1 and keypoints[11][1] <= 1) and (keypoints[8][2] >= threshold and keypoints[11][2] >= threshold): body_flag = 'three_quarter_body' return body_flag return body_flag def check_full_body_both(flag1, flag2): body_flag_dict = { 'full_body': 2, 'three_quarter_body' : 1, 'half_body': 0 } body_flag_dict_reverse = { 2: 'full_body', 1: 'three_quarter_body', 0: 'half_body' } flag1_num = body_flag_dict[flag1] flag2_num = body_flag_dict[flag2] flag_both_num = min(flag1_num, flag2_num) return body_flag_dict_reverse[flag_both_num] def write_to_poses(data_to_json, none_idx, dst_shape, bone_ratio_list, delta_ground_x, delta_ground_y, rescaled_src_ground_x, body_flag, scale_min): outputs = [] length = len(data_to_json) for id in tqdm(range(length)): src_height, src_width = data_to_json[id]['height'], data_to_json[id]['width'] width, height = dst_shape keypoints = data_to_json[id]['keypoints_body'] for idx in range(len(keypoints)): if idx in none_idx: keypoints[idx] = None new_keypoints = keypoints.copy() # get hand keypoints keypoints_hand = {'left' : data_to_json[id]['keypoints_left_hand'], 'right' : data_to_json[id]['keypoints_right_hand']} # Normalize hand coordinates to 0-1 range for hand_idx in range(len(data_to_json[id]['keypoints_left_hand'])): data_to_json[id]['keypoints_left_hand'][hand_idx][0] = data_to_json[id]['keypoints_left_hand'][hand_idx][0] / src_width data_to_json[id]['keypoints_left_hand'][hand_idx][1] = data_to_json[id]['keypoints_left_hand'][hand_idx][1] / src_height for hand_idx in range(len(data_to_json[id]['keypoints_right_hand'])): data_to_json[id]['keypoints_right_hand'][hand_idx][0] = data_to_json[id]['keypoints_right_hand'][hand_idx][0] / src_width data_to_json[id]['keypoints_right_hand'][hand_idx][1] = data_to_json[id]['keypoints_right_hand'][hand_idx][1] / src_height frame_info = get_scaled_pose((height, width), (src_height, src_width), new_keypoints, keypoints_hand, bone_ratio_list, delta_ground_x, delta_ground_y, rescaled_src_ground_x, body_flag, id, scale_min) outputs.append(frame_info) return outputs def calculate_scale_ratio(skeleton, skeleton_edit, scale_ratio_flag): if scale_ratio_flag: headw = max(skeleton['keypoints_body'][0][0], skeleton['keypoints_body'][14][0], skeleton['keypoints_body'][15][0], skeleton['keypoints_body'][16][0], skeleton['keypoints_body'][17][0]) - \ min(skeleton['keypoints_body'][0][0], skeleton['keypoints_body'][14][0], skeleton['keypoints_body'][15][0], skeleton['keypoints_body'][16][0], skeleton['keypoints_body'][17][0]) headw_edit = max(skeleton_edit['keypoints_body'][0][0], skeleton_edit['keypoints_body'][14][0], skeleton_edit['keypoints_body'][15][0], skeleton_edit['keypoints_body'][16][0], skeleton_edit['keypoints_body'][17][0]) - \ min(skeleton_edit['keypoints_body'][0][0], skeleton_edit['keypoints_body'][14][0], skeleton_edit['keypoints_body'][15][0], skeleton_edit['keypoints_body'][16][0], skeleton_edit['keypoints_body'][17][0]) headw_ratio = headw / headw_edit _, _, shoulder = get_length(skeleton, [6,3]) _, _, shoulder_edit = get_length(skeleton_edit, [6,3]) shoulder_ratio = shoulder / shoulder_edit return max(headw_ratio, shoulder_ratio) else: return 1 def retarget_pose(src_skeleton, dst_skeleton, all_src_skeleton, src_skeleton_edit, dst_skeleton_edit, threshold=0.4): if src_skeleton_edit is not None and dst_skeleton_edit is not None: use_edit_for_base = True else: use_edit_for_base = False src_skeleton_ori = copy.deepcopy(src_skeleton) dst_skeleton_ori_h, dst_skeleton_ori_w = dst_skeleton['height'], dst_skeleton['width'] if src_skeleton['keypoints_body'][0] != None and src_skeleton['keypoints_body'][10] != None and src_skeleton['keypoints_body'][13] != None and \ dst_skeleton['keypoints_body'][0] != None and dst_skeleton['keypoints_body'][10] != None and dst_skeleton['keypoints_body'][13] != None and \ src_skeleton['keypoints_body'][0][2] > 0.5 and src_skeleton['keypoints_body'][10][2] > 0.5 and src_skeleton['keypoints_body'][13][2] > 0.5 and \ dst_skeleton['keypoints_body'][0][2] > 0.5 and dst_skeleton['keypoints_body'][10][2] > 0.5 and dst_skeleton['keypoints_body'][13][2] > 0.5: src_height = src_skeleton['height'] * abs( (src_skeleton['keypoints_body'][10][1] + src_skeleton['keypoints_body'][13][1]) / 2 - src_skeleton['keypoints_body'][0][1]) dst_height = dst_skeleton['height'] * abs( (dst_skeleton['keypoints_body'][10][1] + dst_skeleton['keypoints_body'][13][1]) / 2 - dst_skeleton['keypoints_body'][0][1]) scale_min = 1.0 * src_height / dst_height elif src_skeleton['keypoints_body'][0] != None and src_skeleton['keypoints_body'][8] != None and src_skeleton['keypoints_body'][11] != None and \ dst_skeleton['keypoints_body'][0] != None and dst_skeleton['keypoints_body'][8] != None and dst_skeleton['keypoints_body'][11] != None and \ src_skeleton['keypoints_body'][0][2] > 0.5 and src_skeleton['keypoints_body'][8][2] > 0.5 and src_skeleton['keypoints_body'][11][2] > 0.5 and \ dst_skeleton['keypoints_body'][0][2] > 0.5 and dst_skeleton['keypoints_body'][8][2] > 0.5 and dst_skeleton['keypoints_body'][11][2] > 0.5: src_height = src_skeleton['height'] * abs( (src_skeleton['keypoints_body'][8][1] + src_skeleton['keypoints_body'][11][1]) / 2 - src_skeleton['keypoints_body'][0][1]) dst_height = dst_skeleton['height'] * abs( (dst_skeleton['keypoints_body'][8][1] + dst_skeleton['keypoints_body'][11][1]) / 2 - dst_skeleton['keypoints_body'][0][1]) scale_min = 1.0 * src_height / dst_height else: scale_min = np.sqrt(src_skeleton['height'] * src_skeleton['width']) / np.sqrt(dst_skeleton['height'] * dst_skeleton['width']) if use_edit_for_base: scale_ratio_flag = False if src_skeleton_edit['keypoints_body'][0] != None and src_skeleton_edit['keypoints_body'][10] != None and src_skeleton_edit['keypoints_body'][13] != None and \ dst_skeleton_edit['keypoints_body'][0] != None and dst_skeleton_edit['keypoints_body'][10] != None and dst_skeleton_edit['keypoints_body'][13] != None and \ src_skeleton_edit['keypoints_body'][0][2] > 0.5 and src_skeleton_edit['keypoints_body'][10][2] > 0.5 and src_skeleton_edit['keypoints_body'][13][2] > 0.5 and \ dst_skeleton_edit['keypoints_body'][0][2] > 0.5 and dst_skeleton_edit['keypoints_body'][10][2] > 0.5 and dst_skeleton_edit['keypoints_body'][13][2] > 0.5: src_height_edit = src_skeleton_edit['height'] * abs( (src_skeleton_edit['keypoints_body'][10][1] + src_skeleton_edit['keypoints_body'][13][1]) / 2 - src_skeleton_edit['keypoints_body'][0][1]) dst_height_edit = dst_skeleton_edit['height'] * abs( (dst_skeleton_edit['keypoints_body'][10][1] + dst_skeleton_edit['keypoints_body'][13][1]) / 2 - dst_skeleton_edit['keypoints_body'][0][1]) scale_min_edit = 1.0 * src_height_edit / dst_height_edit elif src_skeleton_edit['keypoints_body'][0] != None and src_skeleton_edit['keypoints_body'][8] != None and src_skeleton_edit['keypoints_body'][11] != None and \ dst_skeleton_edit['keypoints_body'][0] != None and dst_skeleton_edit['keypoints_body'][8] != None and dst_skeleton_edit['keypoints_body'][11] != None and \ src_skeleton_edit['keypoints_body'][0][2] > 0.5 and src_skeleton_edit['keypoints_body'][8][2] > 0.5 and src_skeleton_edit['keypoints_body'][11][2] > 0.5 and \ dst_skeleton_edit['keypoints_body'][0][2] > 0.5 and dst_skeleton_edit['keypoints_body'][8][2] > 0.5 and dst_skeleton_edit['keypoints_body'][11][2] > 0.5: src_height_edit = src_skeleton_edit['height'] * abs( (src_skeleton_edit['keypoints_body'][8][1] + src_skeleton_edit['keypoints_body'][11][1]) / 2 - src_skeleton_edit['keypoints_body'][0][1]) dst_height_edit = dst_skeleton_edit['height'] * abs( (dst_skeleton_edit['keypoints_body'][8][1] + dst_skeleton_edit['keypoints_body'][11][1]) / 2 - dst_skeleton_edit['keypoints_body'][0][1]) scale_min_edit = 1.0 * src_height_edit / dst_height_edit else: scale_min_edit = np.sqrt(src_skeleton_edit['height'] * src_skeleton_edit['width']) / np.sqrt(dst_skeleton_edit['height'] * dst_skeleton_edit['width']) scale_ratio_flag = True # Flux may change the scale, compensate for it here ratio_src = calculate_scale_ratio(src_skeleton, src_skeleton_edit, scale_ratio_flag) ratio_dst = calculate_scale_ratio(dst_skeleton, dst_skeleton_edit, scale_ratio_flag) dst_skeleton_edit['height'] = int(dst_skeleton_edit['height'] * scale_min_edit) dst_skeleton_edit['width'] = int(dst_skeleton_edit['width'] * scale_min_edit) for idx in range(len(dst_skeleton_edit['keypoints_left_hand'])): dst_skeleton_edit['keypoints_left_hand'][idx][0] *= scale_min_edit dst_skeleton_edit['keypoints_left_hand'][idx][1] *= scale_min_edit for idx in range(len(dst_skeleton_edit['keypoints_right_hand'])): dst_skeleton_edit['keypoints_right_hand'][idx][0] *= scale_min_edit dst_skeleton_edit['keypoints_right_hand'][idx][1] *= scale_min_edit dst_skeleton['height'] = int(dst_skeleton['height'] * scale_min) dst_skeleton['width'] = int(dst_skeleton['width'] * scale_min) for idx in range(len(dst_skeleton['keypoints_left_hand'])): dst_skeleton['keypoints_left_hand'][idx][0] *= scale_min dst_skeleton['keypoints_left_hand'][idx][1] *= scale_min for idx in range(len(dst_skeleton['keypoints_right_hand'])): dst_skeleton['keypoints_right_hand'][idx][0] *= scale_min dst_skeleton['keypoints_right_hand'][idx][1] *= scale_min dst_body_flag = check_full_body(dst_skeleton['keypoints_body'], threshold) src_body_flag = check_full_body(src_skeleton_ori['keypoints_body'], threshold) body_flag = check_full_body_both(dst_body_flag, src_body_flag) #print('body_flag: ', body_flag) if use_edit_for_base: src_skeleton_edit = fix_lack_keypoints_use_sym(src_skeleton_edit) dst_skeleton_edit = fix_lack_keypoints_use_sym(dst_skeleton_edit) else: src_skeleton = fix_lack_keypoints_use_sym(src_skeleton) dst_skeleton = fix_lack_keypoints_use_sym(dst_skeleton) none_idx = [] for idx in range(len(dst_skeleton['keypoints_body'])): if dst_skeleton['keypoints_body'][idx] == None or src_skeleton['keypoints_body'][idx] == None: src_skeleton['keypoints_body'][idx] = None dst_skeleton['keypoints_body'][idx] = None none_idx.append(idx) # get bone ratio list ratio_list, src_length_list, dst_length_list = [], [], [] for idx, limb in enumerate(limbSeq): if use_edit_for_base: src_X, src_Y, src_length = get_length(src_skeleton_edit, limb) dst_X, dst_Y, dst_length = get_length(dst_skeleton_edit, limb) if src_X is None or src_Y is None or dst_X is None or dst_Y is None: ratio = -1 else: ratio = 1.0 * dst_length * ratio_dst / src_length / ratio_src else: src_X, src_Y, src_length = get_length(src_skeleton, limb) dst_X, dst_Y, dst_length = get_length(dst_skeleton, limb) if src_X is None or src_Y is None or dst_X is None or dst_Y is None: ratio = -1 else: ratio = 1.0 * dst_length / src_length ratio_list.append(ratio) src_length_list.append(src_length) dst_length_list.append(dst_length) for idx, ratio in enumerate(ratio_list): if ratio == -1: if ratio_list[0] != -1 and ratio_list[1] != -1: ratio_list[idx] = (ratio_list[0] + ratio_list[1]) / 2 # Consider adding constraints when Flux fails to correct head pose, causing neck issues. # if ratio_list[12] > (ratio_list[0]+ratio_list[1])/2*1.25: # ratio_list[12] = (ratio_list[0]+ratio_list[1])/2*1.25 ratio_list, src_length_list, dst_length_list = rescale_shorten_skeleton(ratio_list, src_length_list, dst_length_list) rescaled_src_skeleton_ori = rescale_skeleton(src_skeleton_ori['height'], src_skeleton_ori['width'], src_skeleton_ori['keypoints_body'], ratio_list) # get global translation offset_x and offset_y if body_flag == 'full_body': #print('use foot mark.') dst_ground_y = max(dst_skeleton['keypoints_body'][10][1], dst_skeleton['keypoints_body'][13][1]) * dst_skeleton[ 'height'] # The midpoint between toe and ankle if dst_skeleton['keypoints_body'][18] != None and dst_skeleton['keypoints_body'][19] != None: right_foot_mid = (dst_skeleton['keypoints_body'][10][1] + dst_skeleton['keypoints_body'][19][1]) / 2 left_foot_mid = (dst_skeleton['keypoints_body'][13][1] + dst_skeleton['keypoints_body'][18][1]) / 2 dst_ground_y = max(left_foot_mid, right_foot_mid) * dst_skeleton['height'] rescaled_src_ground_y = max(rescaled_src_skeleton_ori[10][1], rescaled_src_skeleton_ori[13][1]) delta_ground_y = rescaled_src_ground_y - dst_ground_y dst_ground_x = (dst_skeleton['keypoints_body'][8][0] + dst_skeleton['keypoints_body'][11][0]) * dst_skeleton[ 'width'] / 2 rescaled_src_ground_x = (rescaled_src_skeleton_ori[8][0] + rescaled_src_skeleton_ori[11][0]) / 2 delta_ground_x = rescaled_src_ground_x - dst_ground_x delta_x, delta_y = delta_ground_x, delta_ground_y else: #print('use neck mark.') # use neck keypoint as mark src_neck_y = rescaled_src_skeleton_ori[1][1] dst_neck_y = dst_skeleton['keypoints_body'][1][1] delta_neck_y = src_neck_y - dst_neck_y * dst_skeleton['height'] src_neck_x = rescaled_src_skeleton_ori[1][0] dst_neck_x = dst_skeleton['keypoints_body'][1][0] delta_neck_x = src_neck_x - dst_neck_x * dst_skeleton['width'] delta_x, delta_y = delta_neck_x, delta_neck_y rescaled_src_ground_x = src_neck_x dst_shape = (dst_skeleton_ori_w, dst_skeleton_ori_h) output = write_to_poses(all_src_skeleton, none_idx, dst_shape, ratio_list, delta_x, delta_y, rescaled_src_ground_x, body_flag, scale_min) return output def get_retarget_pose(tpl_pose_meta0, refer_pose_meta, tpl_pose_metas, tql_edit_pose_meta0, refer_edit_pose_meta): for key, value in tpl_pose_meta0.items(): if type(value) is np.ndarray: if key in ['keypoints_left_hand', 'keypoints_right_hand']: value = value * np.array([[tpl_pose_meta0["width"], tpl_pose_meta0["height"], 1.0]]) if not isinstance(value, list): value = value.tolist() tpl_pose_meta0[key] = value for key, value in refer_pose_meta.items(): if type(value) is np.ndarray: if key in ['keypoints_left_hand', 'keypoints_right_hand']: value = value * np.array([[refer_pose_meta["width"], refer_pose_meta["height"], 1.0]]) if not isinstance(value, list): value = value.tolist() refer_pose_meta[key] = value tpl_pose_metas_new = [] for meta in tpl_pose_metas: for key, value in meta.items(): if type(value) is np.ndarray: if key in ['keypoints_left_hand', 'keypoints_right_hand']: value = value * np.array([[meta["width"], meta["height"], 1.0]]) if not isinstance(value, list): value = value.tolist() meta[key] = value tpl_pose_metas_new.append(meta) if tql_edit_pose_meta0 is not None: for key, value in tql_edit_pose_meta0.items(): if type(value) is np.ndarray: if key in ['keypoints_left_hand', 'keypoints_right_hand']: value = value * np.array([[tql_edit_pose_meta0["width"], tql_edit_pose_meta0["height"], 1.0]]) if not isinstance(value, list): value = value.tolist() tql_edit_pose_meta0[key] = value if refer_edit_pose_meta is not None: for key, value in refer_edit_pose_meta.items(): if type(value) is np.ndarray: if key in ['keypoints_left_hand', 'keypoints_right_hand']: value = value * np.array([[refer_edit_pose_meta["width"], refer_edit_pose_meta["height"], 1.0]]) if not isinstance(value, list): value = value.tolist() refer_edit_pose_meta[key] = value retarget_tpl_pose_metas = retarget_pose(tpl_pose_meta0, refer_pose_meta, tpl_pose_metas_new, tql_edit_pose_meta0, refer_edit_pose_meta) pose_metas = [] for meta in retarget_tpl_pose_metas: pose_meta = AAPoseMeta() width, height = meta["width"], meta["height"] pose_meta.width = width pose_meta.height = height pose_meta.kps_body = np.array(meta["keypoints_body"])[:, :2] * (width, height) pose_meta.kps_body_p = np.array(meta["keypoints_body"])[:, 2] kps_lhand = [] kps_lhand_p = [] for each_kps_lhand in meta["keypoints_left_hand"]: if each_kps_lhand is not None: kps_lhand.append([each_kps_lhand.x, each_kps_lhand.y]) kps_lhand_p.append(each_kps_lhand.score) else: kps_lhand.append([None, None]) kps_lhand_p.append(0.0) pose_meta.kps_lhand = np.array(kps_lhand) pose_meta.kps_lhand_p = np.array(kps_lhand_p) kps_rhand = [] kps_rhand_p = [] for each_kps_rhand in meta["keypoints_right_hand"]: if each_kps_rhand is not None: kps_rhand.append([each_kps_rhand.x, each_kps_rhand.y]) kps_rhand_p.append(each_kps_rhand.score) else: kps_rhand.append([None, None]) kps_rhand_p.append(0.0) pose_meta.kps_rhand = np.array(kps_rhand) pose_meta.kps_rhand_p = np.array(kps_rhand_p) pose_metas.append(pose_meta) return pose_metas ================================================ FILE: wan/modules/animate/preprocess/sam_utils.py ================================================ # Copyright (c) 2025. Your modifications here. # This file wraps and extends sam2.utils.misc for custom modifications. from sam2.utils import misc as sam2_misc from sam2.utils.misc import * from PIL import Image import numpy as np import torch from tqdm import tqdm import os import logging import torch from hydra import compose from hydra.utils import instantiate from omegaconf import OmegaConf from sam2.utils.misc import AsyncVideoFrameLoader, _load_img_as_tensor from sam2.build_sam import _load_checkpoint def _load_img_v2_as_tensor(img, image_size): img_pil = Image.fromarray(img.astype(np.uint8)) img_np = np.array(img_pil.convert("RGB").resize((image_size, image_size))) if img_np.dtype == np.uint8: # np.uint8 is expected for JPEG images img_np = img_np / 255.0 else: raise RuntimeError(f"Unknown image dtype: {img_np.dtype}") img = torch.from_numpy(img_np).permute(2, 0, 1) video_width, video_height = img_pil.size # the original video size return img, video_height, video_width def load_video_frames( video_path, image_size, offload_video_to_cpu, img_mean=(0.485, 0.456, 0.406), img_std=(0.229, 0.224, 0.225), async_loading_frames=False, frame_names=None, ): """ Load the video frames from a directory of JPEG files (".jpg" format). The frames are resized to image_size x image_size and are loaded to GPU if `offload_video_to_cpu` is `False` and to CPU if `offload_video_to_cpu` is `True`. You can load a frame asynchronously by setting `async_loading_frames` to `True`. """ if isinstance(video_path, str) and os.path.isdir(video_path): jpg_folder = video_path else: raise NotImplementedError("Only JPEG frames are supported at this moment") if frame_names is None: frame_names = [ p for p in os.listdir(jpg_folder) if os.path.splitext(p)[-1] in [".jpg", ".jpeg", ".JPG", ".JPEG", ".png"] ] frame_names.sort(key=lambda p: int(os.path.splitext(p)[0])) num_frames = len(frame_names) if num_frames == 0: raise RuntimeError(f"no images found in {jpg_folder}") img_paths = [os.path.join(jpg_folder, frame_name) for frame_name in frame_names] img_mean = torch.tensor(img_mean, dtype=torch.float32)[:, None, None] img_std = torch.tensor(img_std, dtype=torch.float32)[:, None, None] if async_loading_frames: lazy_images = AsyncVideoFrameLoader( img_paths, image_size, offload_video_to_cpu, img_mean, img_std ) return lazy_images, lazy_images.video_height, lazy_images.video_width images = torch.zeros(num_frames, 3, image_size, image_size, dtype=torch.float32) for n, img_path in enumerate(tqdm(img_paths, desc="frame loading (JPEG)")): images[n], video_height, video_width = _load_img_as_tensor(img_path, image_size) if not offload_video_to_cpu: images = images.cuda() img_mean = img_mean.cuda() img_std = img_std.cuda() # normalize by mean and std images -= img_mean images /= img_std return images, video_height, video_width def load_video_frames_v2( frames, image_size, offload_video_to_cpu, img_mean=(0.485, 0.456, 0.406), img_std=(0.229, 0.224, 0.225), async_loading_frames=False, frame_names=None, ): """ Load the video frames from a directory of JPEG files (".jpg" format). The frames are resized to image_size x image_size and are loaded to GPU if `offload_video_to_cpu` is `False` and to CPU if `offload_video_to_cpu` is `True`. You can load a frame asynchronously by setting `async_loading_frames` to `True`. """ num_frames = len(frames) img_mean = torch.tensor(img_mean, dtype=torch.float32)[:, None, None] img_std = torch.tensor(img_std, dtype=torch.float32)[:, None, None] images = torch.zeros(num_frames, 3, image_size, image_size, dtype=torch.float32) for n, frame in enumerate(tqdm(frames, desc="video frame")): images[n], video_height, video_width = _load_img_v2_as_tensor(frame, image_size) if not offload_video_to_cpu: images = images.cuda() img_mean = img_mean.cuda() img_std = img_std.cuda() # normalize by mean and std images -= img_mean images /= img_std return images, video_height, video_width def build_sam2_video_predictor( config_file, ckpt_path=None, device="cuda", mode="eval", hydra_overrides_extra=[], apply_postprocessing=True, ): hydra_overrides = [ "++model._target_=video_predictor.SAM2VideoPredictor", ] if apply_postprocessing: hydra_overrides_extra = hydra_overrides_extra.copy() hydra_overrides_extra += [ # dynamically fall back to multi-mask if the single mask is not stable "++model.sam_mask_decoder_extra_args.dynamic_multimask_via_stability=true", "++model.sam_mask_decoder_extra_args.dynamic_multimask_stability_delta=0.05", "++model.sam_mask_decoder_extra_args.dynamic_multimask_stability_thresh=0.98", # the sigmoid mask logits on interacted frames with clicks in the memory encoder so that the encoded masks are exactly as what users see from clicking "++model.binarize_mask_from_pts_for_mem_enc=true", # fill small holes in the low-res masks up to `fill_hole_area` (before resizing them to the original video resolution) "++model.fill_hole_area=8", ] hydra_overrides.extend(hydra_overrides_extra) # Read config and init model cfg = compose(config_name=config_file, overrides=hydra_overrides) OmegaConf.resolve(cfg) model = instantiate(cfg.model, _recursive_=True) _load_checkpoint(model, ckpt_path) model = model.to(device) if mode == "eval": model.eval() return model ================================================ FILE: wan/modules/animate/preprocess/utils.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import os import cv2 import math import random import numpy as np def get_mask_boxes(mask): """ Args: mask: [h, w] Returns: """ y_coords, x_coords = np.nonzero(mask) x_min = x_coords.min() x_max = x_coords.max() y_min = y_coords.min() y_max = y_coords.max() bbox = np.array([x_min, y_min, x_max, y_max]).astype(np.int32) return bbox def get_aug_mask(body_mask, w_len=10, h_len=20): body_bbox = get_mask_boxes(body_mask) bbox_wh = body_bbox[2:4] - body_bbox[0:2] w_slice = np.int32(bbox_wh[0] / w_len) h_slice = np.int32(bbox_wh[1] / h_len) for each_w in range(body_bbox[0], body_bbox[2], w_slice): w_start = min(each_w, body_bbox[2]) w_end = min((each_w + w_slice), body_bbox[2]) # print(w_start, w_end) for each_h in range(body_bbox[1], body_bbox[3], h_slice): h_start = min(each_h, body_bbox[3]) h_end = min((each_h + h_slice), body_bbox[3]) if body_mask[h_start:h_end, w_start:w_end].sum() > 0: body_mask[h_start:h_end, w_start:w_end] = 1 return body_mask def get_mask_body_img(img_copy, hand_mask, k=7, iterations=1): kernel = np.ones((k, k), np.uint8) dilation = cv2.dilate(hand_mask, kernel, iterations=iterations) mask_hand_img = img_copy * (1 - dilation[:, :, None]) return mask_hand_img, dilation def get_face_bboxes(kp2ds, scale, image_shape, ratio_aug): h, w = image_shape kp2ds_face = kp2ds.copy()[23:91, :2] min_x, min_y = np.min(kp2ds_face, axis=0) max_x, max_y = np.max(kp2ds_face, axis=0) initial_width = max_x - min_x initial_height = max_y - min_y initial_area = initial_width * initial_height expanded_area = initial_area * scale new_width = np.sqrt(expanded_area * (initial_width / initial_height)) new_height = np.sqrt(expanded_area * (initial_height / initial_width)) delta_width = (new_width - initial_width) / 2 delta_height = (new_height - initial_height) / 4 if ratio_aug: if random.random() > 0.5: delta_width += random.uniform(0, initial_width // 10) else: delta_height += random.uniform(0, initial_height // 10) expanded_min_x = max(min_x - delta_width, 0) expanded_max_x = min(max_x + delta_width, w) expanded_min_y = max(min_y - 3 * delta_height, 0) expanded_max_y = min(max_y + delta_height, h) return [int(expanded_min_x), int(expanded_max_x), int(expanded_min_y), int(expanded_max_y)] def calculate_new_size(orig_w, orig_h, target_area, divisor=64): target_ratio = orig_w / orig_h def check_valid(w, h): if w <= 0 or h <= 0: return False return (w * h <= target_area and w % divisor == 0 and h % divisor == 0) def get_ratio_diff(w, h): return abs(w / h - target_ratio) def round_to_64(value, round_up=False, divisor=64): if round_up: return divisor * ((value + (divisor - 1)) // divisor) return divisor * (value // divisor) possible_sizes = [] max_area_h = int(np.sqrt(target_area / target_ratio)) max_area_w = int(max_area_h * target_ratio) max_h = round_to_64(max_area_h, round_up=True, divisor=divisor) max_w = round_to_64(max_area_w, round_up=True, divisor=divisor) for h in range(divisor, max_h + divisor, divisor): ideal_w = h * target_ratio w_down = round_to_64(ideal_w) w_up = round_to_64(ideal_w, round_up=True) for w in [w_down, w_up]: if check_valid(w, h, divisor): possible_sizes.append((w, h, get_ratio_diff(w, h))) if not possible_sizes: raise ValueError("Can not find suitable size") possible_sizes.sort(key=lambda x: (-x[0] * x[1], x[2])) best_w, best_h, _ = possible_sizes[0] return int(best_w), int(best_h) def resize_by_area(image, target_area, keep_aspect_ratio=True, divisor=64, padding_color=(0, 0, 0)): h, w = image.shape[:2] try: new_w, new_h = calculate_new_size(w, h, target_area, divisor) except: aspect_ratio = w / h if keep_aspect_ratio: new_h = math.sqrt(target_area / aspect_ratio) new_w = target_area / new_h else: new_w = new_h = math.sqrt(target_area) new_w, new_h = int((new_w // divisor) * divisor), int((new_h // divisor) * divisor) interpolation = cv2.INTER_AREA if (new_w * new_h < w * h) else cv2.INTER_LINEAR resized_image = padding_resize(image, height=new_h, width=new_w, padding_color=padding_color, interpolation=interpolation) return resized_image def padding_resize(img_ori, height=512, width=512, padding_color=(0, 0, 0), interpolation=cv2.INTER_LINEAR): ori_height = img_ori.shape[0] ori_width = img_ori.shape[1] channel = img_ori.shape[2] img_pad = np.zeros((height, width, channel)) if channel == 1: img_pad[:, :, 0] = padding_color[0] else: img_pad[:, :, 0] = padding_color[0] img_pad[:, :, 1] = padding_color[1] img_pad[:, :, 2] = padding_color[2] if (ori_height / ori_width) > (height / width): new_width = int(height / ori_height * ori_width) img = cv2.resize(img_ori, (new_width, height), interpolation=interpolation) padding = int((width - new_width) / 2) if len(img.shape) == 2: img = img[:, :, np.newaxis] img_pad[:, padding: padding + new_width, :] = img else: new_height = int(width / ori_width * ori_height) img = cv2.resize(img_ori, (width, new_height), interpolation=interpolation) padding = int((height - new_height) / 2) if len(img.shape) == 2: img = img[:, :, np.newaxis] img_pad[padding: padding + new_height, :, :] = img img_pad = np.uint8(img_pad) return img_pad def get_frame_indices(frame_num, video_fps, clip_length, train_fps): start_frame = 0 times = np.arange(0, clip_length) / train_fps frame_indices = start_frame + np.round(times * video_fps).astype(int) frame_indices = np.clip(frame_indices, 0, frame_num - 1) return frame_indices.tolist() def get_face_bboxes(kp2ds, scale, image_shape): h, w = image_shape kp2ds_face = kp2ds.copy()[1:] * (w, h) min_x, min_y = np.min(kp2ds_face, axis=0) max_x, max_y = np.max(kp2ds_face, axis=0) initial_width = max_x - min_x initial_height = max_y - min_y initial_area = initial_width * initial_height expanded_area = initial_area * scale new_width = np.sqrt(expanded_area * (initial_width / initial_height)) new_height = np.sqrt(expanded_area * (initial_height / initial_width)) delta_width = (new_width - initial_width) / 2 delta_height = (new_height - initial_height) / 4 expanded_min_x = max(min_x - delta_width, 0) expanded_max_x = min(max_x + delta_width, w) expanded_min_y = max(min_y - 3 * delta_height, 0) expanded_max_y = min(max_y + delta_height, h) return [int(expanded_min_x), int(expanded_max_x), int(expanded_min_y), int(expanded_max_y)] ================================================ FILE: wan/modules/animate/preprocess/video_predictor.py ================================================ # Copyright (c) 2025. Your modifications here. # A wrapper for sam2 functions from collections import OrderedDict import torch from tqdm import tqdm from sam2.modeling.sam2_base import NO_OBJ_SCORE, SAM2Base from sam2.sam2_video_predictor import SAM2VideoPredictor as _SAM2VideoPredictor from sam2.utils.misc import concat_points, fill_holes_in_mask_scores from sam_utils import load_video_frames_v2, load_video_frames class SAM2VideoPredictor(_SAM2VideoPredictor): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) @torch.inference_mode() def init_state( self, video_path, offload_video_to_cpu=False, offload_state_to_cpu=False, async_loading_frames=False, frame_names=None ): """Initialize a inference state.""" images, video_height, video_width = load_video_frames( video_path=video_path, image_size=self.image_size, offload_video_to_cpu=offload_video_to_cpu, async_loading_frames=async_loading_frames, frame_names=frame_names ) inference_state = {} inference_state["images"] = images inference_state["num_frames"] = len(images) # whether to offload the video frames to CPU memory # turning on this option saves the GPU memory with only a very small overhead inference_state["offload_video_to_cpu"] = offload_video_to_cpu # whether to offload the inference state to CPU memory # turning on this option saves the GPU memory at the cost of a lower tracking fps # (e.g. in a test case of 768x768 model, fps dropped from 27 to 24 when tracking one object # and from 24 to 21 when tracking two objects) inference_state["offload_state_to_cpu"] = offload_state_to_cpu # the original video height and width, used for resizing final output scores inference_state["video_height"] = video_height inference_state["video_width"] = video_width inference_state["device"] = torch.device("cuda") if offload_state_to_cpu: inference_state["storage_device"] = torch.device("cpu") else: inference_state["storage_device"] = torch.device("cuda") # inputs on each frame inference_state["point_inputs_per_obj"] = {} inference_state["mask_inputs_per_obj"] = {} # visual features on a small number of recently visited frames for quick interactions inference_state["cached_features"] = {} # values that don't change across frames (so we only need to hold one copy of them) inference_state["constants"] = {} # mapping between client-side object id and model-side object index inference_state["obj_id_to_idx"] = OrderedDict() inference_state["obj_idx_to_id"] = OrderedDict() inference_state["obj_ids"] = [] # A storage to hold the model's tracking results and states on each frame inference_state["output_dict"] = { "cond_frame_outputs": {}, # dict containing {frame_idx: } "non_cond_frame_outputs": {}, # dict containing {frame_idx: } } # Slice (view) of each object tracking results, sharing the same memory with "output_dict" inference_state["output_dict_per_obj"] = {} # A temporary storage to hold new outputs when user interact with a frame # to add clicks or mask (it's merged into "output_dict" before propagation starts) inference_state["temp_output_dict_per_obj"] = {} # Frames that already holds consolidated outputs from click or mask inputs # (we directly use their consolidated outputs during tracking) inference_state["consolidated_frame_inds"] = { "cond_frame_outputs": set(), # set containing frame indices "non_cond_frame_outputs": set(), # set containing frame indices } # metadata for each tracking frame (e.g. which direction it's tracked) inference_state["tracking_has_started"] = False inference_state["frames_already_tracked"] = {} # Warm up the visual backbone and cache the image feature on frame 0 self._get_image_feature(inference_state, frame_idx=0, batch_size=1) return inference_state @torch.inference_mode() def init_state_v2( self, frames, offload_video_to_cpu=False, offload_state_to_cpu=False, async_loading_frames=False, frame_names=None ): """Initialize a inference state.""" images, video_height, video_width = load_video_frames_v2( frames=frames, image_size=self.image_size, offload_video_to_cpu=offload_video_to_cpu, async_loading_frames=async_loading_frames, frame_names=frame_names ) inference_state = {} inference_state["images"] = images inference_state["num_frames"] = len(images) # whether to offload the video frames to CPU memory # turning on this option saves the GPU memory with only a very small overhead inference_state["offload_video_to_cpu"] = offload_video_to_cpu # whether to offload the inference state to CPU memory # turning on this option saves the GPU memory at the cost of a lower tracking fps # (e.g. in a test case of 768x768 model, fps dropped from 27 to 24 when tracking one object # and from 24 to 21 when tracking two objects) inference_state["offload_state_to_cpu"] = offload_state_to_cpu # the original video height and width, used for resizing final output scores inference_state["video_height"] = video_height inference_state["video_width"] = video_width inference_state["device"] = torch.device("cuda") if offload_state_to_cpu: inference_state["storage_device"] = torch.device("cpu") else: inference_state["storage_device"] = torch.device("cuda") # inputs on each frame inference_state["point_inputs_per_obj"] = {} inference_state["mask_inputs_per_obj"] = {} # visual features on a small number of recently visited frames for quick interactions inference_state["cached_features"] = {} # values that don't change across frames (so we only need to hold one copy of them) inference_state["constants"] = {} # mapping between client-side object id and model-side object index inference_state["obj_id_to_idx"] = OrderedDict() inference_state["obj_idx_to_id"] = OrderedDict() inference_state["obj_ids"] = [] # A storage to hold the model's tracking results and states on each frame inference_state["output_dict"] = { "cond_frame_outputs": {}, # dict containing {frame_idx: } "non_cond_frame_outputs": {}, # dict containing {frame_idx: } } # Slice (view) of each object tracking results, sharing the same memory with "output_dict" inference_state["output_dict_per_obj"] = {} # A temporary storage to hold new outputs when user interact with a frame # to add clicks or mask (it's merged into "output_dict" before propagation starts) inference_state["temp_output_dict_per_obj"] = {} # Frames that already holds consolidated outputs from click or mask inputs # (we directly use their consolidated outputs during tracking) inference_state["consolidated_frame_inds"] = { "cond_frame_outputs": set(), # set containing frame indices "non_cond_frame_outputs": set(), # set containing frame indices } # metadata for each tracking frame (e.g. which direction it's tracked) inference_state["tracking_has_started"] = False inference_state["frames_already_tracked"] = {} # resolves KeyError: 'frames_tracked_per_obj' when using newer SAM-2 versions for running preprocessing in 'replacement mode' inference_state["frames_tracked_per_obj"] = {} # Warm up the visual backbone and cache the image feature on frame 0 self._get_image_feature(inference_state, frame_idx=0, batch_size=1) return inference_state ================================================ FILE: wan/modules/animate/xlm_roberta.py ================================================ # Modified from transformers.models.xlm_roberta.modeling_xlm_roberta # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import torch import torch.nn as nn import torch.nn.functional as F __all__ = ['XLMRoberta', 'xlm_roberta_large'] class SelfAttention(nn.Module): def __init__(self, dim, num_heads, dropout=0.1, eps=1e-5): assert dim % num_heads == 0 super().__init__() self.dim = dim self.num_heads = num_heads self.head_dim = dim // num_heads self.eps = eps # layers self.q = nn.Linear(dim, dim) self.k = nn.Linear(dim, dim) self.v = nn.Linear(dim, dim) self.o = nn.Linear(dim, dim) self.dropout = nn.Dropout(dropout) def forward(self, x, mask): """ x: [B, L, C]. """ b, s, c, n, d = *x.size(), self.num_heads, self.head_dim # compute query, key, value q = self.q(x).reshape(b, s, n, d).permute(0, 2, 1, 3) k = self.k(x).reshape(b, s, n, d).permute(0, 2, 1, 3) v = self.v(x).reshape(b, s, n, d).permute(0, 2, 1, 3) # compute attention p = self.dropout.p if self.training else 0.0 x = F.scaled_dot_product_attention(q, k, v, mask, p) x = x.permute(0, 2, 1, 3).reshape(b, s, c) # output x = self.o(x) x = self.dropout(x) return x class AttentionBlock(nn.Module): def __init__(self, dim, num_heads, post_norm, dropout=0.1, eps=1e-5): super().__init__() self.dim = dim self.num_heads = num_heads self.post_norm = post_norm self.eps = eps # layers self.attn = SelfAttention(dim, num_heads, dropout, eps) self.norm1 = nn.LayerNorm(dim, eps=eps) self.ffn = nn.Sequential( nn.Linear(dim, dim * 4), nn.GELU(), nn.Linear(dim * 4, dim), nn.Dropout(dropout)) self.norm2 = nn.LayerNorm(dim, eps=eps) def forward(self, x, mask): if self.post_norm: x = self.norm1(x + self.attn(x, mask)) x = self.norm2(x + self.ffn(x)) else: x = x + self.attn(self.norm1(x), mask) x = x + self.ffn(self.norm2(x)) return x class XLMRoberta(nn.Module): """ XLMRobertaModel with no pooler and no LM head. """ def __init__(self, vocab_size=250002, max_seq_len=514, type_size=1, pad_id=1, dim=1024, num_heads=16, num_layers=24, post_norm=True, dropout=0.1, eps=1e-5): super().__init__() self.vocab_size = vocab_size self.max_seq_len = max_seq_len self.type_size = type_size self.pad_id = pad_id self.dim = dim self.num_heads = num_heads self.num_layers = num_layers self.post_norm = post_norm self.eps = eps # embeddings self.token_embedding = nn.Embedding(vocab_size, dim, padding_idx=pad_id) self.type_embedding = nn.Embedding(type_size, dim) self.pos_embedding = nn.Embedding(max_seq_len, dim, padding_idx=pad_id) self.dropout = nn.Dropout(dropout) # blocks self.blocks = nn.ModuleList([ AttentionBlock(dim, num_heads, post_norm, dropout, eps) for _ in range(num_layers) ]) # norm layer self.norm = nn.LayerNorm(dim, eps=eps) def forward(self, ids): """ ids: [B, L] of torch.LongTensor. """ b, s = ids.shape mask = ids.ne(self.pad_id).long() # embeddings x = self.token_embedding(ids) + \ self.type_embedding(torch.zeros_like(ids)) + \ self.pos_embedding(self.pad_id + torch.cumsum(mask, dim=1) * mask) if self.post_norm: x = self.norm(x) x = self.dropout(x) # blocks mask = torch.where( mask.view(b, 1, 1, s).gt(0), 0.0, torch.finfo(x.dtype).min) for block in self.blocks: x = block(x, mask) # output if not self.post_norm: x = self.norm(x) return x def xlm_roberta_large(pretrained=False, return_tokenizer=False, device='cpu', **kwargs): """ XLMRobertaLarge adapted from Huggingface. """ # params cfg = dict( vocab_size=250002, max_seq_len=514, type_size=1, pad_id=1, dim=1024, num_heads=16, num_layers=24, post_norm=True, dropout=0.1, eps=1e-5) cfg.update(**kwargs) # init a model on device with torch.device(device): model = XLMRoberta(**cfg) return model ================================================ FILE: wan/modules/attention.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import torch try: import flash_attn_interface FLASH_ATTN_3_AVAILABLE = True except ModuleNotFoundError: FLASH_ATTN_3_AVAILABLE = False try: import flash_attn FLASH_ATTN_2_AVAILABLE = True except ModuleNotFoundError: FLASH_ATTN_2_AVAILABLE = False import warnings __all__ = [ 'flash_attention', 'attention', ] def flash_attention( q, k, v, q_lens=None, k_lens=None, dropout_p=0., softmax_scale=None, q_scale=None, causal=False, window_size=(-1, -1), deterministic=False, dtype=torch.bfloat16, version=None, ): """ q: [B, Lq, Nq, C1]. k: [B, Lk, Nk, C1]. v: [B, Lk, Nk, C2]. Nq must be divisible by Nk. q_lens: [B]. k_lens: [B]. dropout_p: float. Dropout probability. softmax_scale: float. The scaling of QK^T before applying softmax. causal: bool. Whether to apply causal attention mask. window_size: (left right). If not (-1, -1), apply sliding window local attention. deterministic: bool. If True, slightly slower and uses more memory. dtype: torch.dtype. Apply when dtype of q/k/v is not float16/bfloat16. """ half_dtypes = (torch.float16, torch.bfloat16) assert dtype in half_dtypes assert q.device.type == 'cuda' and q.size(-1) <= 256 # params b, lq, lk, out_dtype = q.size(0), q.size(1), k.size(1), q.dtype def half(x): return x if x.dtype in half_dtypes else x.to(dtype) # preprocess query if q_lens is None: q = half(q.flatten(0, 1)) q_lens = torch.tensor( [lq] * b, dtype=torch.int32).to( device=q.device, non_blocking=True) else: q = half(torch.cat([u[:v] for u, v in zip(q, q_lens)])) # preprocess key, value if k_lens is None: k = half(k.flatten(0, 1)) v = half(v.flatten(0, 1)) k_lens = torch.tensor( [lk] * b, dtype=torch.int32).to( device=k.device, non_blocking=True) else: k = half(torch.cat([u[:v] for u, v in zip(k, k_lens)])) v = half(torch.cat([u[:v] for u, v in zip(v, k_lens)])) q = q.to(v.dtype) k = k.to(v.dtype) if q_scale is not None: q = q * q_scale if version is not None and version == 3 and not FLASH_ATTN_3_AVAILABLE: warnings.warn( 'Flash attention 3 is not available, use flash attention 2 instead.' ) # apply attention if (version is None or version == 3) and FLASH_ATTN_3_AVAILABLE: # Note: dropout_p, window_size are not supported in FA3 now. x = flash_attn_interface.flash_attn_varlen_func( q=q, k=k, v=v, cu_seqlens_q=torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum( 0, dtype=torch.int32).to(q.device, non_blocking=True), cu_seqlens_k=torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum( 0, dtype=torch.int32).to(q.device, non_blocking=True), seqused_q=None, seqused_k=None, max_seqlen_q=lq, max_seqlen_k=lk, softmax_scale=softmax_scale, causal=causal, deterministic=deterministic)[0].unflatten(0, (b, lq)) else: assert FLASH_ATTN_2_AVAILABLE x = flash_attn.flash_attn_varlen_func( q=q, k=k, v=v, cu_seqlens_q=torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum( 0, dtype=torch.int32).to(q.device, non_blocking=True), cu_seqlens_k=torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum( 0, dtype=torch.int32).to(q.device, non_blocking=True), max_seqlen_q=lq, max_seqlen_k=lk, dropout_p=dropout_p, softmax_scale=softmax_scale, causal=causal, window_size=window_size, deterministic=deterministic).unflatten(0, (b, lq)) # output return x.type(out_dtype) def attention( q, k, v, q_lens=None, k_lens=None, dropout_p=0., softmax_scale=None, q_scale=None, causal=False, window_size=(-1, -1), deterministic=False, dtype=torch.bfloat16, fa_version=None, ): if FLASH_ATTN_2_AVAILABLE or FLASH_ATTN_3_AVAILABLE: return flash_attention( q=q, k=k, v=v, q_lens=q_lens, k_lens=k_lens, dropout_p=dropout_p, softmax_scale=softmax_scale, q_scale=q_scale, causal=causal, window_size=window_size, deterministic=deterministic, dtype=dtype, version=fa_version, ) else: if q_lens is not None or k_lens is not None: warnings.warn( 'Padding mask is disabled when using scaled_dot_product_attention. It can have a significant impact on performance.' ) attn_mask = None q = q.transpose(1, 2).to(dtype) k = k.transpose(1, 2).to(dtype) v = v.transpose(1, 2).to(dtype) out = torch.nn.functional.scaled_dot_product_attention( q, k, v, attn_mask=attn_mask, is_causal=causal, dropout_p=dropout_p) out = out.transpose(1, 2).contiguous() return out ================================================ FILE: wan/modules/model.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import math import torch import torch.nn as nn from diffusers.configuration_utils import ConfigMixin, register_to_config from diffusers.models.modeling_utils import ModelMixin from .attention import flash_attention __all__ = ['WanModel'] def sinusoidal_embedding_1d(dim, position): # preprocess assert dim % 2 == 0 half = dim // 2 position = position.type(torch.float64) # calculation sinusoid = torch.outer( position, torch.pow(10000, -torch.arange(half).to(position).div(half))) x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1) return x @torch.amp.autocast('cuda', enabled=False) def rope_params(max_seq_len, dim, theta=10000): assert dim % 2 == 0 freqs = torch.outer( torch.arange(max_seq_len), 1.0 / torch.pow(theta, torch.arange(0, dim, 2).to(torch.float64).div(dim))) freqs = torch.polar(torch.ones_like(freqs), freqs) return freqs @torch.amp.autocast('cuda', enabled=False) def rope_apply(x, grid_sizes, freqs): n, c = x.size(2), x.size(3) // 2 # split freqs freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1) # loop over samples output = [] for i, (f, h, w) in enumerate(grid_sizes.tolist()): seq_len = f * h * w # precompute multipliers x_i = torch.view_as_complex(x[i, :seq_len].to(torch.float64).reshape( seq_len, n, -1, 2)) freqs_i = torch.cat([ freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1), freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1), freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1) ], dim=-1).reshape(seq_len, 1, -1) # apply rotary embedding x_i = torch.view_as_real(x_i * freqs_i).flatten(2) x_i = torch.cat([x_i, x[i, seq_len:]]) # append to collection output.append(x_i) return torch.stack(output).float() class WanRMSNorm(nn.Module): def __init__(self, dim, eps=1e-5): super().__init__() self.dim = dim self.eps = eps self.weight = nn.Parameter(torch.ones(dim)) def forward(self, x): r""" Args: x(Tensor): Shape [B, L, C] """ return self._norm(x.float()).type_as(x) * self.weight def _norm(self, x): return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps) class WanLayerNorm(nn.LayerNorm): def __init__(self, dim, eps=1e-6, elementwise_affine=False): super().__init__(dim, elementwise_affine=elementwise_affine, eps=eps) def forward(self, x): r""" Args: x(Tensor): Shape [B, L, C] """ return super().forward(x.float()).type_as(x) class WanSelfAttention(nn.Module): def __init__(self, dim, num_heads, window_size=(-1, -1), qk_norm=True, eps=1e-6): assert dim % num_heads == 0 super().__init__() self.dim = dim self.num_heads = num_heads self.head_dim = dim // num_heads self.window_size = window_size self.qk_norm = qk_norm self.eps = eps # layers self.q = nn.Linear(dim, dim) self.k = nn.Linear(dim, dim) self.v = nn.Linear(dim, dim) self.o = nn.Linear(dim, dim) self.norm_q = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity() self.norm_k = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity() def forward(self, x, seq_lens, grid_sizes, freqs): r""" Args: x(Tensor): Shape [B, L, num_heads, C / num_heads] seq_lens(Tensor): Shape [B] grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W) freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2] """ b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim # query, key, value function def qkv_fn(x): q = self.norm_q(self.q(x)).view(b, s, n, d) k = self.norm_k(self.k(x)).view(b, s, n, d) v = self.v(x).view(b, s, n, d) return q, k, v q, k, v = qkv_fn(x) x = flash_attention( q=rope_apply(q, grid_sizes, freqs), k=rope_apply(k, grid_sizes, freqs), v=v, k_lens=seq_lens, window_size=self.window_size) # output x = x.flatten(2) x = self.o(x) return x class WanCrossAttention(WanSelfAttention): def forward(self, x, context, context_lens): r""" Args: x(Tensor): Shape [B, L1, C] context(Tensor): Shape [B, L2, C] context_lens(Tensor): Shape [B] """ b, n, d = x.size(0), self.num_heads, self.head_dim # compute query, key, value q = self.norm_q(self.q(x)).view(b, -1, n, d) k = self.norm_k(self.k(context)).view(b, -1, n, d) v = self.v(context).view(b, -1, n, d) # compute attention x = flash_attention(q, k, v, k_lens=context_lens) # output x = x.flatten(2) x = self.o(x) return x class WanAttentionBlock(nn.Module): def __init__(self, dim, ffn_dim, num_heads, window_size=(-1, -1), qk_norm=True, cross_attn_norm=False, eps=1e-6): super().__init__() self.dim = dim self.ffn_dim = ffn_dim self.num_heads = num_heads self.window_size = window_size self.qk_norm = qk_norm self.cross_attn_norm = cross_attn_norm self.eps = eps # layers self.norm1 = WanLayerNorm(dim, eps) self.self_attn = WanSelfAttention(dim, num_heads, window_size, qk_norm, eps) self.norm3 = WanLayerNorm( dim, eps, elementwise_affine=True) if cross_attn_norm else nn.Identity() self.cross_attn = WanCrossAttention(dim, num_heads, (-1, -1), qk_norm, eps) self.norm2 = WanLayerNorm(dim, eps) self.ffn = nn.Sequential( nn.Linear(dim, ffn_dim), nn.GELU(approximate='tanh'), nn.Linear(ffn_dim, dim)) # modulation self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5) def forward( self, x, e, seq_lens, grid_sizes, freqs, context, context_lens, ): r""" Args: x(Tensor): Shape [B, L, C] e(Tensor): Shape [B, L1, 6, C] seq_lens(Tensor): Shape [B], length of each sequence in batch grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W) freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2] """ assert e.dtype == torch.float32 with torch.amp.autocast('cuda', dtype=torch.float32): e = (self.modulation.unsqueeze(0) + e).chunk(6, dim=2) assert e[0].dtype == torch.float32 # self-attention y = self.self_attn( self.norm1(x).float() * (1 + e[1].squeeze(2)) + e[0].squeeze(2), seq_lens, grid_sizes, freqs) with torch.amp.autocast('cuda', dtype=torch.float32): x = x + y * e[2].squeeze(2) # cross-attention & ffn function def cross_attn_ffn(x, context, context_lens, e): x = x + self.cross_attn(self.norm3(x), context, context_lens) y = self.ffn( self.norm2(x).float() * (1 + e[4].squeeze(2)) + e[3].squeeze(2)) with torch.amp.autocast('cuda', dtype=torch.float32): x = x + y * e[5].squeeze(2) return x x = cross_attn_ffn(x, context, context_lens, e) return x class Head(nn.Module): def __init__(self, dim, out_dim, patch_size, eps=1e-6): super().__init__() self.dim = dim self.out_dim = out_dim self.patch_size = patch_size self.eps = eps # layers out_dim = math.prod(patch_size) * out_dim self.norm = WanLayerNorm(dim, eps) self.head = nn.Linear(dim, out_dim) # modulation self.modulation = nn.Parameter(torch.randn(1, 2, dim) / dim**0.5) def forward(self, x, e): r""" Args: x(Tensor): Shape [B, L1, C] e(Tensor): Shape [B, L1, C] """ assert e.dtype == torch.float32 with torch.amp.autocast('cuda', dtype=torch.float32): e = (self.modulation.unsqueeze(0) + e.unsqueeze(2)).chunk(2, dim=2) x = ( self.head( self.norm(x) * (1 + e[1].squeeze(2)) + e[0].squeeze(2))) return x class WanModel(ModelMixin, ConfigMixin): r""" Wan diffusion backbone supporting both text-to-video and image-to-video. """ ignore_for_config = [ 'patch_size', 'cross_attn_norm', 'qk_norm', 'text_dim', 'window_size' ] _no_split_modules = ['WanAttentionBlock'] @register_to_config def __init__(self, model_type='t2v', patch_size=(1, 2, 2), text_len=512, in_dim=16, dim=2048, ffn_dim=8192, freq_dim=256, text_dim=4096, out_dim=16, num_heads=16, num_layers=32, window_size=(-1, -1), qk_norm=True, cross_attn_norm=True, eps=1e-6): r""" Initialize the diffusion model backbone. Args: model_type (`str`, *optional*, defaults to 't2v'): Model variant - 't2v' (text-to-video) or 'i2v' (image-to-video) patch_size (`tuple`, *optional*, defaults to (1, 2, 2)): 3D patch dimensions for video embedding (t_patch, h_patch, w_patch) text_len (`int`, *optional*, defaults to 512): Fixed length for text embeddings in_dim (`int`, *optional*, defaults to 16): Input video channels (C_in) dim (`int`, *optional*, defaults to 2048): Hidden dimension of the transformer ffn_dim (`int`, *optional*, defaults to 8192): Intermediate dimension in feed-forward network freq_dim (`int`, *optional*, defaults to 256): Dimension for sinusoidal time embeddings text_dim (`int`, *optional*, defaults to 4096): Input dimension for text embeddings out_dim (`int`, *optional*, defaults to 16): Output video channels (C_out) num_heads (`int`, *optional*, defaults to 16): Number of attention heads num_layers (`int`, *optional*, defaults to 32): Number of transformer blocks window_size (`tuple`, *optional*, defaults to (-1, -1)): Window size for local attention (-1 indicates global attention) qk_norm (`bool`, *optional*, defaults to True): Enable query/key normalization cross_attn_norm (`bool`, *optional*, defaults to False): Enable cross-attention normalization eps (`float`, *optional*, defaults to 1e-6): Epsilon value for normalization layers """ super().__init__() assert model_type in ['t2v', 'i2v', 'ti2v', 's2v'] self.model_type = model_type self.patch_size = patch_size self.text_len = text_len self.in_dim = in_dim self.dim = dim self.ffn_dim = ffn_dim self.freq_dim = freq_dim self.text_dim = text_dim self.out_dim = out_dim self.num_heads = num_heads self.num_layers = num_layers self.window_size = window_size self.qk_norm = qk_norm self.cross_attn_norm = cross_attn_norm self.eps = eps # embeddings self.patch_embedding = nn.Conv3d( in_dim, dim, kernel_size=patch_size, stride=patch_size) self.text_embedding = nn.Sequential( nn.Linear(text_dim, dim), nn.GELU(approximate='tanh'), nn.Linear(dim, dim)) self.time_embedding = nn.Sequential( nn.Linear(freq_dim, dim), nn.SiLU(), nn.Linear(dim, dim)) self.time_projection = nn.Sequential(nn.SiLU(), nn.Linear(dim, dim * 6)) # blocks self.blocks = nn.ModuleList([ WanAttentionBlock(dim, ffn_dim, num_heads, window_size, qk_norm, cross_attn_norm, eps) for _ in range(num_layers) ]) # head self.head = Head(dim, out_dim, patch_size, eps) # buffers (don't use register_buffer otherwise dtype will be changed in to()) assert (dim % num_heads) == 0 and (dim // num_heads) % 2 == 0 d = dim // num_heads self.freqs = torch.cat([ rope_params(1024, d - 4 * (d // 6)), rope_params(1024, 2 * (d // 6)), rope_params(1024, 2 * (d // 6)) ], dim=1) # initialize weights self.init_weights() def forward( self, x, t, context, seq_len, y=None, ): r""" Forward pass through the diffusion model Args: x (List[Tensor]): List of input video tensors, each with shape [C_in, F, H, W] t (Tensor): Diffusion timesteps tensor of shape [B] context (List[Tensor]): List of text embeddings each with shape [L, C] seq_len (`int`): Maximum sequence length for positional encoding y (List[Tensor], *optional*): Conditional video inputs for image-to-video mode, same shape as x Returns: List[Tensor]: List of denoised video tensors with original input shapes [C_out, F, H / 8, W / 8] """ if self.model_type == 'i2v': assert y is not None # params device = self.patch_embedding.weight.device if self.freqs.device != device: self.freqs = self.freqs.to(device) if y is not None: x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)] # embeddings x = [self.patch_embedding(u.unsqueeze(0)) for u in x] grid_sizes = torch.stack( [torch.tensor(u.shape[2:], dtype=torch.long) for u in x]) x = [u.flatten(2).transpose(1, 2) for u in x] seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.long) assert seq_lens.max() <= seq_len x = torch.cat([ torch.cat([u, u.new_zeros(1, seq_len - u.size(1), u.size(2))], dim=1) for u in x ]) # time embeddings if t.dim() == 1: t = t.expand(t.size(0), seq_len) with torch.amp.autocast('cuda', dtype=torch.float32): bt = t.size(0) t = t.flatten() e = self.time_embedding( sinusoidal_embedding_1d(self.freq_dim, t).unflatten(0, (bt, seq_len)).float()) e0 = self.time_projection(e).unflatten(2, (6, self.dim)) assert e.dtype == torch.float32 and e0.dtype == torch.float32 # context context_lens = None context = self.text_embedding( torch.stack([ torch.cat( [u, u.new_zeros(self.text_len - u.size(0), u.size(1))]) for u in context ])) # arguments kwargs = dict( e=e0, seq_lens=seq_lens, grid_sizes=grid_sizes, freqs=self.freqs, context=context, context_lens=context_lens) for block in self.blocks: x = block(x, **kwargs) # head x = self.head(x, e) # unpatchify x = self.unpatchify(x, grid_sizes) return [u.float() for u in x] def unpatchify(self, x, grid_sizes): r""" Reconstruct video tensors from patch embeddings. Args: x (List[Tensor]): List of patchified features, each with shape [L, C_out * prod(patch_size)] grid_sizes (Tensor): Original spatial-temporal grid dimensions before patching, shape [B, 3] (3 dimensions correspond to F_patches, H_patches, W_patches) Returns: List[Tensor]: Reconstructed video tensors with shape [C_out, F, H / 8, W / 8] """ c = self.out_dim out = [] for u, v in zip(x, grid_sizes.tolist()): u = u[:math.prod(v)].view(*v, *self.patch_size, c) u = torch.einsum('fhwpqrc->cfphqwr', u) u = u.reshape(c, *[i * j for i, j in zip(v, self.patch_size)]) out.append(u) return out def init_weights(self): r""" Initialize model parameters using Xavier initialization. """ # basic init for m in self.modules(): if isinstance(m, nn.Linear): nn.init.xavier_uniform_(m.weight) if m.bias is not None: nn.init.zeros_(m.bias) # init embeddings nn.init.xavier_uniform_(self.patch_embedding.weight.flatten(1)) for m in self.text_embedding.modules(): if isinstance(m, nn.Linear): nn.init.normal_(m.weight, std=.02) for m in self.time_embedding.modules(): if isinstance(m, nn.Linear): nn.init.normal_(m.weight, std=.02) # init output layer nn.init.zeros_(self.head.head.weight) ================================================ FILE: wan/modules/s2v/__init__.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. from .audio_encoder import AudioEncoder from .model_s2v import WanModel_S2V __all__ = ['WanModel_S2V', 'AudioEncoder'] ================================================ FILE: wan/modules/s2v/audio_encoder.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import math import librosa import numpy as np import torch import torch.nn.functional as F from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor def get_sample_indices(original_fps, total_frames, target_fps, num_sample, fixed_start=None): required_duration = num_sample / target_fps required_origin_frames = int(np.ceil(required_duration * original_fps)) if required_duration > total_frames / original_fps: raise ValueError("required_duration must be less than video length") if not fixed_start is None and fixed_start >= 0: start_frame = fixed_start else: max_start = total_frames - required_origin_frames if max_start < 0: raise ValueError("video length is too short") start_frame = np.random.randint(0, max_start + 1) start_time = start_frame / original_fps end_time = start_time + required_duration time_points = np.linspace(start_time, end_time, num_sample, endpoint=False) frame_indices = np.round(np.array(time_points) * original_fps).astype(int) frame_indices = np.clip(frame_indices, 0, total_frames - 1) return frame_indices def linear_interpolation(features, input_fps, output_fps, output_len=None): """ features: shape=[1, T, 512] input_fps: fps for audio, f_a output_fps: fps for video, f_m output_len: video length """ features = features.transpose(1, 2) # [1, 512, T] seq_len = features.shape[2] / float(input_fps) # T/f_a if output_len is None: output_len = int(seq_len * output_fps) # f_m*T/f_a output_features = F.interpolate( features, size=output_len, align_corners=True, mode='linear') # [1, 512, output_len] return output_features.transpose(1, 2) # [1, output_len, 512] class AudioEncoder(): def __init__(self, device='cpu', model_id="facebook/wav2vec2-base-960h"): # load pretrained model self.processor = Wav2Vec2Processor.from_pretrained(model_id) self.model = Wav2Vec2ForCTC.from_pretrained(model_id) self.model = self.model.to(device) self.video_rate = 30 def extract_audio_feat(self, audio_path, return_all_layers=False, dtype=torch.float32): audio_input, sample_rate = librosa.load(audio_path, sr=16000) input_values = self.processor( audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values # INFERENCE # retrieve logits & take argmax res = self.model( input_values.to(self.model.device), output_hidden_states=True) if return_all_layers: feat = torch.cat(res.hidden_states) else: feat = res.hidden_states[-1] feat = linear_interpolation( feat, input_fps=50, output_fps=self.video_rate) z = feat.to(dtype) # Encoding for the motion return z def get_audio_embed_bucket(self, audio_embed, stride=2, batch_frames=12, m=2): num_layers, audio_frame_num, audio_dim = audio_embed.shape if num_layers > 1: return_all_layers = True else: return_all_layers = False min_batch_num = int(audio_frame_num / (batch_frames * stride)) + 1 bucket_num = min_batch_num * batch_frames batch_idx = [stride * i for i in range(bucket_num)] batch_audio_eb = [] for bi in batch_idx: if bi < audio_frame_num: audio_sample_stride = 2 chosen_idx = list( range(bi - m * audio_sample_stride, bi + (m + 1) * audio_sample_stride, audio_sample_stride)) chosen_idx = [0 if c < 0 else c for c in chosen_idx] chosen_idx = [ audio_frame_num - 1 if c >= audio_frame_num else c for c in chosen_idx ] if return_all_layers: frame_audio_embed = audio_embed[:, chosen_idx].flatten( start_dim=-2, end_dim=-1) else: frame_audio_embed = audio_embed[0][chosen_idx].flatten() else: frame_audio_embed = \ torch.zeros([audio_dim * (2 * m + 1)], device=audio_embed.device) if not return_all_layers \ else torch.zeros([num_layers, audio_dim * (2 * m + 1)], device=audio_embed.device) batch_audio_eb.append(frame_audio_embed) batch_audio_eb = torch.cat([c.unsqueeze(0) for c in batch_audio_eb], dim=0) return batch_audio_eb, min_batch_num def get_audio_embed_bucket_fps(self, audio_embed, fps=16, batch_frames=81, m=0): num_layers, audio_frame_num, audio_dim = audio_embed.shape if num_layers > 1: return_all_layers = True else: return_all_layers = False scale = self.video_rate / fps min_batch_num = int(audio_frame_num / (batch_frames * scale)) + 1 bucket_num = min_batch_num * batch_frames padd_audio_num = math.ceil(min_batch_num * batch_frames / fps * self.video_rate) - audio_frame_num batch_idx = get_sample_indices( original_fps=self.video_rate, total_frames=audio_frame_num + padd_audio_num, target_fps=fps, num_sample=bucket_num, fixed_start=0) batch_audio_eb = [] audio_sample_stride = int(self.video_rate / fps) for bi in batch_idx: if bi < audio_frame_num: chosen_idx = list( range(bi - m * audio_sample_stride, bi + (m + 1) * audio_sample_stride, audio_sample_stride)) chosen_idx = [0 if c < 0 else c for c in chosen_idx] chosen_idx = [ audio_frame_num - 1 if c >= audio_frame_num else c for c in chosen_idx ] if return_all_layers: frame_audio_embed = audio_embed[:, chosen_idx].flatten( start_dim=-2, end_dim=-1) else: frame_audio_embed = audio_embed[0][chosen_idx].flatten() else: frame_audio_embed = \ torch.zeros([audio_dim * (2 * m + 1)], device=audio_embed.device) if not return_all_layers \ else torch.zeros([num_layers, audio_dim * (2 * m + 1)], device=audio_embed.device) batch_audio_eb.append(frame_audio_embed) batch_audio_eb = torch.cat([c.unsqueeze(0) for c in batch_audio_eb], dim=0) return batch_audio_eb, min_batch_num ================================================ FILE: wan/modules/s2v/audio_utils.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import math from typing import Tuple, Union import torch import torch.cuda.amp as amp import torch.nn as nn from diffusers.models.attention import AdaLayerNorm from ..model import WanAttentionBlock, WanCrossAttention from .auxi_blocks import MotionEncoder_tc class CausalAudioEncoder(nn.Module): def __init__(self, dim=5120, num_layers=25, out_dim=2048, video_rate=8, num_token=4, need_global=False): super().__init__() self.encoder = MotionEncoder_tc( in_dim=dim, hidden_dim=out_dim, num_heads=num_token, need_global=need_global) weight = torch.ones((1, num_layers, 1, 1)) * 0.01 self.weights = torch.nn.Parameter(weight) self.act = torch.nn.SiLU() def forward(self, features): with amp.autocast(dtype=torch.float32): # features B * num_layers * dim * video_length weights = self.act(self.weights) weights_sum = weights.sum(dim=1, keepdims=True) weighted_feat = ((features * weights) / weights_sum).sum( dim=1) # b dim f weighted_feat = weighted_feat.permute(0, 2, 1) # b f dim res = self.encoder(weighted_feat) # b f n dim return res # b f n dim class AudioCrossAttention(WanCrossAttention): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) class AudioInjector_WAN(nn.Module): def __init__(self, all_modules, all_modules_names, dim=2048, num_heads=32, inject_layer=[0, 27], root_net=None, enable_adain=False, adain_dim=2048, need_adain_ont=False): super().__init__() num_injector_layers = len(inject_layer) self.injected_block_id = {} audio_injector_id = 0 for mod_name, mod in zip(all_modules_names, all_modules): if isinstance(mod, WanAttentionBlock): for inject_id in inject_layer: if f'transformer_blocks.{inject_id}' in mod_name: self.injected_block_id[inject_id] = audio_injector_id audio_injector_id += 1 self.injector = nn.ModuleList([ AudioCrossAttention( dim=dim, num_heads=num_heads, qk_norm=True, ) for _ in range(audio_injector_id) ]) self.injector_pre_norm_feat = nn.ModuleList([ nn.LayerNorm( dim, elementwise_affine=False, eps=1e-6, ) for _ in range(audio_injector_id) ]) self.injector_pre_norm_vec = nn.ModuleList([ nn.LayerNorm( dim, elementwise_affine=False, eps=1e-6, ) for _ in range(audio_injector_id) ]) if enable_adain: self.injector_adain_layers = nn.ModuleList([ AdaLayerNorm( output_dim=dim * 2, embedding_dim=adain_dim, chunk_dim=1) for _ in range(audio_injector_id) ]) if need_adain_ont: self.injector_adain_output_layers = nn.ModuleList( [nn.Linear(dim, dim) for _ in range(audio_injector_id)]) ================================================ FILE: wan/modules/s2v/auxi_blocks.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import importlib.metadata import math from typing import Any, Dict, List, Optional, Tuple, Union import torch import torch.nn as nn import torch.nn.functional as F from diffusers.configuration_utils import ConfigMixin, register_to_config from diffusers.models import ModelMixin from diffusers.utils import is_torch_version, logging from einops import rearrange try: from flash_attn import flash_attn_func, flash_attn_qkvpacked_func except ImportError: flash_attn_func = None MEMORY_LAYOUT = { "flash": ( lambda x: x.view(x.shape[0] * x.shape[1], *x.shape[2:]), lambda x: x, ), "torch": ( lambda x: x.transpose(1, 2), lambda x: x.transpose(1, 2), ), "vanilla": ( lambda x: x.transpose(1, 2), lambda x: x.transpose(1, 2), ), } def attention( q, k, v, mode="flash", drop_rate=0, attn_mask=None, causal=False, max_seqlen_q=None, batch_size=1, ): """ Perform QKV self attention. Args: q (torch.Tensor): Query tensor with shape [b, s, a, d], where a is the number of heads. k (torch.Tensor): Key tensor with shape [b, s1, a, d] v (torch.Tensor): Value tensor with shape [b, s1, a, d] mode (str): Attention mode. Choose from 'self_flash', 'cross_flash', 'torch', and 'vanilla'. drop_rate (float): Dropout rate in attention map. (default: 0) attn_mask (torch.Tensor): Attention mask with shape [b, s1] (cross_attn), or [b, a, s, s1] (torch or vanilla). (default: None) causal (bool): Whether to use causal attention. (default: False) cu_seqlens_q (torch.Tensor): dtype torch.int32. The cumulative sequence lengths of the sequences in the batch, used to index into q. cu_seqlens_kv (torch.Tensor): dtype torch.int32. The cumulative sequence lengths of the sequences in the batch, used to index into kv. max_seqlen_q (int): The maximum sequence length in the batch of q. max_seqlen_kv (int): The maximum sequence length in the batch of k and v. Returns: torch.Tensor: Output tensor after self attention with shape [b, s, ad] """ pre_attn_layout, post_attn_layout = MEMORY_LAYOUT[mode] if mode == "torch": if attn_mask is not None and attn_mask.dtype != torch.bool: attn_mask = attn_mask.to(q.dtype) x = F.scaled_dot_product_attention( q, k, v, attn_mask=attn_mask, dropout_p=drop_rate, is_causal=causal) elif mode == "flash": x = flash_attn_func( q, k, v, ) # x with shape [(bxs), a, d] x = x.view(batch_size, max_seqlen_q, x.shape[-2], x.shape[-1]) # reshape x to [b, s, a, d] elif mode == "vanilla": scale_factor = 1 / math.sqrt(q.size(-1)) b, a, s, _ = q.shape s1 = k.size(2) attn_bias = torch.zeros(b, a, s, s1, dtype=q.dtype, device=q.device) if causal: # Only applied to self attention assert ( attn_mask is None), "Causal mask and attn_mask cannot be used together" temp_mask = torch.ones( b, a, s, s, dtype=torch.bool, device=q.device).tril(diagonal=0) attn_bias.masked_fill_(temp_mask.logical_not(), float("-inf")) attn_bias.to(q.dtype) if attn_mask is not None: if attn_mask.dtype == torch.bool: attn_bias.masked_fill_(attn_mask.logical_not(), float("-inf")) else: attn_bias += attn_mask # TODO: Maybe force q and k to be float32 to avoid numerical overflow attn = (q @ k.transpose(-2, -1)) * scale_factor attn += attn_bias attn = attn.softmax(dim=-1) attn = torch.dropout(attn, p=drop_rate, train=True) x = attn @ v else: raise NotImplementedError(f"Unsupported attention mode: {mode}") x = post_attn_layout(x) b, s, a, d = x.shape out = x.reshape(b, s, -1) return out class CausalConv1d(nn.Module): def __init__(self, chan_in, chan_out, kernel_size=3, stride=1, dilation=1, pad_mode='replicate', **kwargs): super().__init__() self.pad_mode = pad_mode padding = (kernel_size - 1, 0) # T self.time_causal_padding = padding self.conv = nn.Conv1d( chan_in, chan_out, kernel_size, stride=stride, dilation=dilation, **kwargs) def forward(self, x): x = F.pad(x, self.time_causal_padding, mode=self.pad_mode) return self.conv(x) class MotionEncoder_tc(nn.Module): def __init__(self, in_dim: int, hidden_dim: int, num_heads=int, need_global=True, dtype=None, device=None): factory_kwargs = {"dtype": dtype, "device": device} super().__init__() self.num_heads = num_heads self.need_global = need_global self.conv1_local = CausalConv1d( in_dim, hidden_dim // 4 * num_heads, 3, stride=1) if need_global: self.conv1_global = CausalConv1d( in_dim, hidden_dim // 4, 3, stride=1) self.norm1 = nn.LayerNorm( hidden_dim // 4, elementwise_affine=False, eps=1e-6, **factory_kwargs) self.act = nn.SiLU() self.conv2 = CausalConv1d(hidden_dim // 4, hidden_dim // 2, 3, stride=2) self.conv3 = CausalConv1d(hidden_dim // 2, hidden_dim, 3, stride=2) if need_global: self.final_linear = nn.Linear(hidden_dim, hidden_dim, **factory_kwargs) self.norm1 = nn.LayerNorm( hidden_dim // 4, elementwise_affine=False, eps=1e-6, **factory_kwargs) self.norm2 = nn.LayerNorm( hidden_dim // 2, elementwise_affine=False, eps=1e-6, **factory_kwargs) self.norm3 = nn.LayerNorm( hidden_dim, elementwise_affine=False, eps=1e-6, **factory_kwargs) self.padding_tokens = nn.Parameter(torch.zeros(1, 1, 1, hidden_dim)) def forward(self, x): x = rearrange(x, 'b t c -> b c t') x_ori = x.clone() b, c, t = x.shape x = self.conv1_local(x) x = rearrange(x, 'b (n c) t -> (b n) t c', n=self.num_heads) x = self.norm1(x) x = self.act(x) x = rearrange(x, 'b t c -> b c t') x = self.conv2(x) x = rearrange(x, 'b c t -> b t c') x = self.norm2(x) x = self.act(x) x = rearrange(x, 'b t c -> b c t') x = self.conv3(x) x = rearrange(x, 'b c t -> b t c') x = self.norm3(x) x = self.act(x) x = rearrange(x, '(b n) t c -> b t n c', b=b) padding = self.padding_tokens.repeat(b, x.shape[1], 1, 1) x = torch.cat([x, padding], dim=-2) x_local = x.clone() if not self.need_global: return x_local x = self.conv1_global(x_ori) x = rearrange(x, 'b c t -> b t c') x = self.norm1(x) x = self.act(x) x = rearrange(x, 'b t c -> b c t') x = self.conv2(x) x = rearrange(x, 'b c t -> b t c') x = self.norm2(x) x = self.act(x) x = rearrange(x, 'b t c -> b c t') x = self.conv3(x) x = rearrange(x, 'b c t -> b t c') x = self.norm3(x) x = self.act(x) x = self.final_linear(x) x = rearrange(x, '(b n) t c -> b t n c', b=b) return x, x_local ================================================ FILE: wan/modules/s2v/model_s2v.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import math import types from copy import deepcopy import numpy as np import torch import torch.cuda.amp as amp import torch.nn as nn from diffusers.configuration_utils import ConfigMixin, register_to_config from diffusers.models.modeling_utils import ModelMixin from einops import rearrange from ...distributed.sequence_parallel import ( distributed_attention, gather_forward, get_rank, get_world_size, ) from ..model import ( Head, WanAttentionBlock, WanLayerNorm, WanModel, WanSelfAttention, flash_attention, rope_params, sinusoidal_embedding_1d, ) from .audio_utils import AudioInjector_WAN, CausalAudioEncoder from .motioner import FramePackMotioner, MotionerTransformers from .s2v_utils import rope_precompute def zero_module(module): """ Zero out the parameters of a module and return it. """ for p in module.parameters(): p.detach().zero_() return module def torch_dfs(model: nn.Module, parent_name='root'): module_names, modules = [], [] current_name = parent_name if parent_name else 'root' module_names.append(current_name) modules.append(model) for name, child in model.named_children(): if parent_name: child_name = f'{parent_name}.{name}' else: child_name = name child_modules, child_names = torch_dfs(child, child_name) module_names += child_names modules += child_modules return modules, module_names @amp.autocast(enabled=False) def rope_apply(x, grid_sizes, freqs, start=None): n, c = x.size(2), x.size(3) // 2 # loop over samples output = [] for i, _ in enumerate(x): s = x.size(1) x_i = torch.view_as_complex(x[i, :s].to(torch.float64).reshape( s, n, -1, 2)) freqs_i = freqs[i, :s] # apply rotary embedding x_i = torch.view_as_real(x_i * freqs_i).flatten(2) x_i = torch.cat([x_i, x[i, s:]]) # append to collection output.append(x_i) return torch.stack(output).float() @amp.autocast(enabled=False) def rope_apply_usp(x, grid_sizes, freqs): s, n, c = x.size(1), x.size(2), x.size(3) // 2 # loop over samples output = [] for i, _ in enumerate(x): s = x.size(1) # precompute multipliers x_i = torch.view_as_complex(x[i, :s].to(torch.float64).reshape( s, n, -1, 2)) freqs_i = freqs[i] freqs_i_rank = freqs_i x_i = torch.view_as_real(x_i * freqs_i_rank).flatten(2) x_i = torch.cat([x_i, x[i, s:]]) # append to collection output.append(x_i) return torch.stack(output).float() def sp_attn_forward_s2v(self, x, seq_lens, grid_sizes, freqs, dtype=torch.bfloat16): b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim half_dtypes = (torch.float16, torch.bfloat16) def half(x): return x if x.dtype in half_dtypes else x.to(dtype) # query, key, value function def qkv_fn(x): q = self.norm_q(self.q(x)).view(b, s, n, d) k = self.norm_k(self.k(x)).view(b, s, n, d) v = self.v(x).view(b, s, n, d) return q, k, v q, k, v = qkv_fn(x) q = rope_apply_usp(q, grid_sizes, freqs) k = rope_apply_usp(k, grid_sizes, freqs) x = distributed_attention( half(q), half(k), half(v), seq_lens, window_size=self.window_size, ) # output x = x.flatten(2) x = self.o(x) return x class Head_S2V(Head): def forward(self, x, e): """ Args: x(Tensor): Shape [B, L1, C] e(Tensor): Shape [B, L1, C] """ assert e.dtype == torch.float32 with amp.autocast(dtype=torch.float32): e = (self.modulation + e.unsqueeze(1)).chunk(2, dim=1) x = (self.head(self.norm(x) * (1 + e[1]) + e[0])) return x class WanS2VSelfAttention(WanSelfAttention): def forward(self, x, seq_lens, grid_sizes, freqs): """ Args: x(Tensor): Shape [B, L, num_heads, C / num_heads] seq_lens(Tensor): Shape [B] grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W) freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2] """ b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim # query, key, value function def qkv_fn(x): q = self.norm_q(self.q(x)).view(b, s, n, d) k = self.norm_k(self.k(x)).view(b, s, n, d) v = self.v(x).view(b, s, n, d) return q, k, v q, k, v = qkv_fn(x) x = flash_attention( q=rope_apply(q, grid_sizes, freqs), k=rope_apply(k, grid_sizes, freqs), v=v, k_lens=seq_lens, window_size=self.window_size) # output x = x.flatten(2) x = self.o(x) return x class WanS2VAttentionBlock(WanAttentionBlock): def __init__(self, dim, ffn_dim, num_heads, window_size=(-1, -1), qk_norm=True, cross_attn_norm=False, eps=1e-6): super().__init__(dim, ffn_dim, num_heads, window_size, qk_norm, cross_attn_norm, eps) self.self_attn = WanS2VSelfAttention(dim, num_heads, window_size, qk_norm, eps) def forward(self, x, e, seq_lens, grid_sizes, freqs, context, context_lens): assert e[0].dtype == torch.float32 seg_idx = e[1].item() seg_idx = min(max(0, seg_idx), x.size(1)) seg_idx = [0, seg_idx, x.size(1)] e = e[0] modulation = self.modulation.unsqueeze(2) with amp.autocast(dtype=torch.float32): e = (modulation + e).chunk(6, dim=1) assert e[0].dtype == torch.float32 e = [element.squeeze(1) for element in e] norm_x = self.norm1(x).float() parts = [] for i in range(2): parts.append(norm_x[:, seg_idx[i]:seg_idx[i + 1]] * (1 + e[1][:, i:i + 1]) + e[0][:, i:i + 1]) norm_x = torch.cat(parts, dim=1) # self-attention y = self.self_attn(norm_x, seq_lens, grid_sizes, freqs) with amp.autocast(dtype=torch.float32): z = [] for i in range(2): z.append(y[:, seg_idx[i]:seg_idx[i + 1]] * e[2][:, i:i + 1]) y = torch.cat(z, dim=1) x = x + y # cross-attention & ffn function def cross_attn_ffn(x, context, context_lens, e): x = x + self.cross_attn(self.norm3(x), context, context_lens) norm2_x = self.norm2(x).float() parts = [] for i in range(2): parts.append(norm2_x[:, seg_idx[i]:seg_idx[i + 1]] * (1 + e[4][:, i:i + 1]) + e[3][:, i:i + 1]) norm2_x = torch.cat(parts, dim=1) y = self.ffn(norm2_x) with amp.autocast(dtype=torch.float32): z = [] for i in range(2): z.append(y[:, seg_idx[i]:seg_idx[i + 1]] * e[5][:, i:i + 1]) y = torch.cat(z, dim=1) x = x + y return x x = cross_attn_ffn(x, context, context_lens, e) return x class WanModel_S2V(ModelMixin, ConfigMixin): ignore_for_config = [ 'args', 'kwargs', 'patch_size', 'cross_attn_norm', 'qk_norm', 'text_dim', 'window_size' ] _no_split_modules = ['WanS2VAttentionBlock'] @register_to_config def __init__( self, cond_dim=0, audio_dim=5120, num_audio_token=4, enable_adain=False, adain_mode="attn_norm", audio_inject_layers=[0, 4, 8, 12, 16, 20, 24, 27], zero_init=False, zero_timestep=False, enable_motioner=True, add_last_motion=True, enable_tsm=False, trainable_token_pos_emb=False, motion_token_num=1024, enable_framepack=False, # Mutually exclusive with enable_motioner framepack_drop_mode="drop", model_type='s2v', patch_size=(1, 2, 2), text_len=512, in_dim=16, dim=2048, ffn_dim=8192, freq_dim=256, text_dim=4096, out_dim=16, num_heads=16, num_layers=32, window_size=(-1, -1), qk_norm=True, cross_attn_norm=True, eps=1e-6, *args, **kwargs): super().__init__() assert model_type == 's2v' self.model_type = model_type self.patch_size = patch_size self.text_len = text_len self.in_dim = in_dim self.dim = dim self.ffn_dim = ffn_dim self.freq_dim = freq_dim self.text_dim = text_dim self.out_dim = out_dim self.num_heads = num_heads self.num_layers = num_layers self.window_size = window_size self.qk_norm = qk_norm self.cross_attn_norm = cross_attn_norm self.eps = eps # embeddings self.patch_embedding = nn.Conv3d( in_dim, dim, kernel_size=patch_size, stride=patch_size) self.text_embedding = nn.Sequential( nn.Linear(text_dim, dim), nn.GELU(approximate='tanh'), nn.Linear(dim, dim)) self.time_embedding = nn.Sequential( nn.Linear(freq_dim, dim), nn.SiLU(), nn.Linear(dim, dim)) self.time_projection = nn.Sequential(nn.SiLU(), nn.Linear(dim, dim * 6)) # blocks self.blocks = nn.ModuleList([ WanS2VAttentionBlock(dim, ffn_dim, num_heads, window_size, qk_norm, cross_attn_norm, eps) for _ in range(num_layers) ]) # head self.head = Head_S2V(dim, out_dim, patch_size, eps) # buffers (don't use register_buffer otherwise dtype will be changed in to()) assert (dim % num_heads) == 0 and (dim // num_heads) % 2 == 0 d = dim // num_heads self.freqs = torch.cat([ rope_params(1024, d - 4 * (d // 6)), rope_params(1024, 2 * (d // 6)), rope_params(1024, 2 * (d // 6)) ], dim=1) # initialize weights self.init_weights() self.use_context_parallel = False # will modify in _configure_model func if cond_dim > 0: self.cond_encoder = nn.Conv3d( cond_dim, self.dim, kernel_size=self.patch_size, stride=self.patch_size) self.enbale_adain = enable_adain self.casual_audio_encoder = CausalAudioEncoder( dim=audio_dim, out_dim=self.dim, num_token=num_audio_token, need_global=enable_adain) all_modules, all_modules_names = torch_dfs( self.blocks, parent_name="root.transformer_blocks") self.audio_injector = AudioInjector_WAN( all_modules, all_modules_names, dim=self.dim, num_heads=self.num_heads, inject_layer=audio_inject_layers, root_net=self, enable_adain=enable_adain, adain_dim=self.dim, need_adain_ont=adain_mode != "attn_norm", ) self.adain_mode = adain_mode self.trainable_cond_mask = nn.Embedding(3, self.dim) if zero_init: self.zero_init_weights() self.zero_timestep = zero_timestep # Whether to assign 0 value timestep to ref/motion # init motioner if enable_motioner and enable_framepack: raise ValueError( "enable_motioner and enable_framepack are mutually exclusive, please set one of them to False" ) self.enable_motioner = enable_motioner self.add_last_motion = add_last_motion if enable_motioner: motioner_dim = 2048 self.motioner = MotionerTransformers( patch_size=(2, 4, 4), dim=motioner_dim, ffn_dim=motioner_dim, freq_dim=256, out_dim=16, num_heads=16, num_layers=13, window_size=(-1, -1), qk_norm=True, cross_attn_norm=False, eps=1e-6, motion_token_num=motion_token_num, enable_tsm=enable_tsm, motion_stride=4, expand_ratio=2, trainable_token_pos_emb=trainable_token_pos_emb, ) self.zip_motion_out = torch.nn.Sequential( WanLayerNorm(motioner_dim), zero_module(nn.Linear(motioner_dim, self.dim))) self.trainable_token_pos_emb = trainable_token_pos_emb if trainable_token_pos_emb: d = self.dim // self.num_heads x = torch.zeros([1, motion_token_num, self.num_heads, d]) x[..., ::2] = 1 gride_sizes = [[ torch.tensor([0, 0, 0]).unsqueeze(0).repeat(1, 1), torch.tensor([ 1, self.motioner.motion_side_len, self.motioner.motion_side_len ]).unsqueeze(0).repeat(1, 1), torch.tensor([ 1, self.motioner.motion_side_len, self.motioner.motion_side_len ]).unsqueeze(0).repeat(1, 1), ]] token_freqs = rope_apply(x, gride_sizes, self.freqs) token_freqs = token_freqs[0, :, 0].reshape(motion_token_num, -1, 2) token_freqs = token_freqs * 0.01 self.token_freqs = torch.nn.Parameter(token_freqs) self.enable_framepack = enable_framepack if enable_framepack: self.frame_packer = FramePackMotioner( inner_dim=self.dim, num_heads=self.num_heads, zip_frame_buckets=[1, 2, 16], drop_mode=framepack_drop_mode) def zero_init_weights(self): with torch.no_grad(): self.trainable_cond_mask = zero_module(self.trainable_cond_mask) if hasattr(self, "cond_encoder"): self.cond_encoder = zero_module(self.cond_encoder) for i in range(self.audio_injector.injector.__len__()): self.audio_injector.injector[i].o = zero_module( self.audio_injector.injector[i].o) if self.enbale_adain: self.audio_injector.injector_adain_layers[ i].linear = zero_module( self.audio_injector.injector_adain_layers[i].linear) def process_motion(self, motion_latents, drop_motion_frames=False): if drop_motion_frames or motion_latents[0].shape[1] == 0: return [], [] self.lat_motion_frames = motion_latents[0].shape[1] mot = [self.patch_embedding(m.unsqueeze(0)) for m in motion_latents] batch_size = len(mot) mot_remb = [] flattern_mot = [] for bs in range(batch_size): height, width = mot[bs].shape[3], mot[bs].shape[4] flat_mot = mot[bs].flatten(2).transpose(1, 2).contiguous() motion_grid_sizes = [[ torch.tensor([-self.lat_motion_frames, 0, 0]).unsqueeze(0).repeat(1, 1), torch.tensor([0, height, width]).unsqueeze(0).repeat(1, 1), torch.tensor([self.lat_motion_frames, height, width]).unsqueeze(0).repeat(1, 1) ]] motion_rope_emb = rope_precompute( flat_mot.detach().view(1, flat_mot.shape[1], self.num_heads, self.dim // self.num_heads), motion_grid_sizes, self.freqs, start=None) mot_remb.append(motion_rope_emb) flattern_mot.append(flat_mot) return flattern_mot, mot_remb def process_motion_frame_pack(self, motion_latents, drop_motion_frames=False, add_last_motion=2): flattern_mot, mot_remb = self.frame_packer(motion_latents, add_last_motion) if drop_motion_frames: return [m[:, :0] for m in flattern_mot ], [m[:, :0] for m in mot_remb] else: return flattern_mot, mot_remb def process_motion_transformer_motioner(self, motion_latents, drop_motion_frames=False, add_last_motion=True): batch_size, height, width = len( motion_latents), motion_latents[0].shape[2] // self.patch_size[ 1], motion_latents[0].shape[3] // self.patch_size[2] freqs = self.freqs device = self.patch_embedding.weight.device if freqs.device != device: freqs = freqs.to(device) if self.trainable_token_pos_emb: with amp.autocast(dtype=torch.float64): token_freqs = self.token_freqs.to(torch.float64) token_freqs = token_freqs / token_freqs.norm( dim=-1, keepdim=True) freqs = [freqs, torch.view_as_complex(token_freqs)] if not drop_motion_frames and add_last_motion: last_motion_latent = [u[:, -1:] for u in motion_latents] last_mot = [ self.patch_embedding(m.unsqueeze(0)) for m in last_motion_latent ] last_mot = [m.flatten(2).transpose(1, 2) for m in last_mot] last_mot = torch.cat(last_mot) gride_sizes = [[ torch.tensor([-1, 0, 0]).unsqueeze(0).repeat(batch_size, 1), torch.tensor([0, height, width]).unsqueeze(0).repeat(batch_size, 1), torch.tensor([1, height, width]).unsqueeze(0).repeat(batch_size, 1) ]] else: last_mot = torch.zeros([batch_size, 0, self.dim], device=motion_latents[0].device, dtype=motion_latents[0].dtype) gride_sizes = [] zip_motion = self.motioner(motion_latents) zip_motion = self.zip_motion_out(zip_motion) if drop_motion_frames: zip_motion = zip_motion * 0.0 zip_motion_grid_sizes = [[ torch.tensor([-1, 0, 0]).unsqueeze(0).repeat(batch_size, 1), torch.tensor([ 0, self.motioner.motion_side_len, self.motioner.motion_side_len ]).unsqueeze(0).repeat(batch_size, 1), torch.tensor( [1 if not self.trainable_token_pos_emb else -1, height, width]).unsqueeze(0).repeat(batch_size, 1), ]] mot = torch.cat([last_mot, zip_motion], dim=1) gride_sizes = gride_sizes + zip_motion_grid_sizes motion_rope_emb = rope_precompute( mot.detach().view(batch_size, mot.shape[1], self.num_heads, self.dim // self.num_heads), gride_sizes, freqs, start=None) return [m.unsqueeze(0) for m in mot ], [r.unsqueeze(0) for r in motion_rope_emb] def inject_motion(self, x, seq_lens, rope_embs, mask_input, motion_latents, drop_motion_frames=False, add_last_motion=True): # inject the motion frames token to the hidden states if self.enable_motioner: mot, mot_remb = self.process_motion_transformer_motioner( motion_latents, drop_motion_frames=drop_motion_frames, add_last_motion=add_last_motion) elif self.enable_framepack: mot, mot_remb = self.process_motion_frame_pack( motion_latents, drop_motion_frames=drop_motion_frames, add_last_motion=add_last_motion) else: mot, mot_remb = self.process_motion( motion_latents, drop_motion_frames=drop_motion_frames) if len(mot) > 0: x = [torch.cat([u, m], dim=1) for u, m in zip(x, mot)] seq_lens = seq_lens + torch.tensor([r.size(1) for r in mot], dtype=torch.long) rope_embs = [ torch.cat([u, m], dim=1) for u, m in zip(rope_embs, mot_remb) ] mask_input = [ torch.cat([ m, 2 * torch.ones([1, u.shape[1] - m.shape[1]], device=m.device, dtype=m.dtype) ], dim=1) for m, u in zip(mask_input, x) ] return x, seq_lens, rope_embs, mask_input def after_transformer_block(self, block_idx, hidden_states): if block_idx in self.audio_injector.injected_block_id.keys(): audio_attn_id = self.audio_injector.injected_block_id[block_idx] audio_emb = self.merged_audio_emb # b f n c num_frames = audio_emb.shape[1] if self.use_context_parallel: hidden_states = gather_forward(hidden_states, dim=1) input_hidden_states = hidden_states[:, :self. original_seq_len].clone( ) # b (f h w) c input_hidden_states = rearrange( input_hidden_states, "b (t n) c -> (b t) n c", t=num_frames) if self.enbale_adain and self.adain_mode == "attn_norm": audio_emb_global = self.audio_emb_global audio_emb_global = rearrange(audio_emb_global, "b t n c -> (b t) n c") adain_hidden_states = self.audio_injector.injector_adain_layers[ audio_attn_id]( input_hidden_states, temb=audio_emb_global[:, 0]) attn_hidden_states = adain_hidden_states else: attn_hidden_states = self.audio_injector.injector_pre_norm_feat[ audio_attn_id]( input_hidden_states) audio_emb = rearrange( audio_emb, "b t n c -> (b t) n c", t=num_frames) attn_audio_emb = audio_emb residual_out = self.audio_injector.injector[audio_attn_id]( x=attn_hidden_states, context=attn_audio_emb, context_lens=torch.ones( attn_hidden_states.shape[0], dtype=torch.long, device=attn_hidden_states.device) * attn_audio_emb.shape[1]) residual_out = rearrange( residual_out, "(b t) n c -> b (t n) c", t=num_frames) hidden_states[:, :self. original_seq_len] = hidden_states[:, :self. original_seq_len] + residual_out if self.use_context_parallel: hidden_states = torch.chunk( hidden_states, get_world_size(), dim=1)[get_rank()] return hidden_states def forward( self, x, t, context, seq_len, ref_latents, motion_latents, cond_states, audio_input=None, motion_frames=[17, 5], add_last_motion=2, drop_motion_frames=False, *extra_args, **extra_kwargs): """ x: A list of videos each with shape [C, T, H, W]. t: [B]. context: A list of text embeddings each with shape [L, C]. seq_len: A list of video token lens, no need for this model. ref_latents A list of reference image for each video with shape [C, 1, H, W]. motion_latents A list of motion frames for each video with shape [C, T_m, H, W]. cond_states A list of condition frames (i.e. pose) each with shape [C, T, H, W]. audio_input The input audio embedding [B, num_wav2vec_layer, C_a, T_a]. motion_frames The number of motion frames and motion latents frames encoded by vae, i.e. [17, 5] add_last_motion For the motioner, if add_last_motion > 0, it means that the most recent frame (i.e., the last frame) will be added. For frame packing, the behavior depends on the value of add_last_motion: add_last_motion = 0: Only the farthest part of the latent (i.e., clean_latents_4x) is included. add_last_motion = 1: Both clean_latents_2x and clean_latents_4x are included. add_last_motion = 2: All motion-related latents are used. drop_motion_frames Bool, whether drop the motion frames info """ add_last_motion = self.add_last_motion * add_last_motion audio_input = torch.cat([ audio_input[..., 0:1].repeat(1, 1, 1, motion_frames[0]), audio_input ], dim=-1) audio_emb_res = self.casual_audio_encoder(audio_input) if self.enbale_adain: audio_emb_global, audio_emb = audio_emb_res self.audio_emb_global = audio_emb_global[:, motion_frames[1]:].clone() else: audio_emb = audio_emb_res self.merged_audio_emb = audio_emb[:, motion_frames[1]:, :] device = self.patch_embedding.weight.device # embeddings x = [self.patch_embedding(u.unsqueeze(0)) for u in x] # cond states cond = [self.cond_encoder(c.unsqueeze(0)) for c in cond_states] x = [x_ + pose for x_, pose in zip(x, cond)] grid_sizes = torch.stack( [torch.tensor(u.shape[2:], dtype=torch.long) for u in x]) x = [u.flatten(2).transpose(1, 2) for u in x] seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.long) original_grid_sizes = deepcopy(grid_sizes) grid_sizes = [[torch.zeros_like(grid_sizes), grid_sizes, grid_sizes]] # ref and motion self.lat_motion_frames = motion_latents[0].shape[1] ref = [self.patch_embedding(r.unsqueeze(0)) for r in ref_latents] batch_size = len(ref) height, width = ref[0].shape[3], ref[0].shape[4] ref_grid_sizes = [[ torch.tensor([30, 0, 0]).unsqueeze(0).repeat(batch_size, 1), # the start index torch.tensor([31, height, width]).unsqueeze(0).repeat(batch_size, 1), # the end index torch.tensor([1, height, width]).unsqueeze(0).repeat(batch_size, 1), ] # the range ] ref = [r.flatten(2).transpose(1, 2) for r in ref] # r: 1 c f h w self.original_seq_len = seq_lens[0] seq_lens = seq_lens + torch.tensor([r.size(1) for r in ref], dtype=torch.long) grid_sizes = grid_sizes + ref_grid_sizes x = [torch.cat([u, r], dim=1) for u, r in zip(x, ref)] # Initialize masks to indicate noisy latent, ref latent, and motion latent. # However, at this point, only the first two (noisy and ref latents) are marked; # the marking of motion latent will be implemented inside `inject_motion`. mask_input = [ torch.zeros([1, u.shape[1]], dtype=torch.long, device=x[0].device) for u in x ] for i in range(len(mask_input)): mask_input[i][:, self.original_seq_len:] = 1 # compute the rope embeddings for the input x = torch.cat(x) b, s, n, d = x.size(0), x.size( 1), self.num_heads, self.dim // self.num_heads self.pre_compute_freqs = rope_precompute( x.detach().view(b, s, n, d), grid_sizes, self.freqs, start=None) x = [u.unsqueeze(0) for u in x] self.pre_compute_freqs = [ u.unsqueeze(0) for u in self.pre_compute_freqs ] x, seq_lens, self.pre_compute_freqs, mask_input = self.inject_motion( x, seq_lens, self.pre_compute_freqs, mask_input, motion_latents, drop_motion_frames=drop_motion_frames, add_last_motion=add_last_motion) x = torch.cat(x, dim=0) self.pre_compute_freqs = torch.cat(self.pre_compute_freqs, dim=0) mask_input = torch.cat(mask_input, dim=0) x = x + self.trainable_cond_mask(mask_input).to(x.dtype) # time embeddings if self.zero_timestep: t = torch.cat([t, torch.zeros([1], dtype=t.dtype, device=t.device)]) with amp.autocast(dtype=torch.float32): e = self.time_embedding( sinusoidal_embedding_1d(self.freq_dim, t).float()) e0 = self.time_projection(e).unflatten(1, (6, self.dim)) assert e.dtype == torch.float32 and e0.dtype == torch.float32 if self.zero_timestep: e = e[:-1] zero_e0 = e0[-1:] e0 = e0[:-1] token_len = x.shape[1] e0 = torch.cat([ e0.unsqueeze(2), zero_e0.unsqueeze(2).repeat(e0.size(0), 1, 1, 1) ], dim=2) e0 = [e0, self.original_seq_len] else: e0 = e0.unsqueeze(2).repeat(1, 1, 2, 1) e0 = [e0, 0] # context context_lens = None context = self.text_embedding( torch.stack([ torch.cat( [u, u.new_zeros(self.text_len - u.size(0), u.size(1))]) for u in context ])) # grad ckpt args def create_custom_forward(module, return_dict=None): def custom_forward(*inputs, **kwargs): if return_dict is not None: return module(*inputs, **kwargs, return_dict=return_dict) else: return module(*inputs, **kwargs) return custom_forward if self.use_context_parallel: # sharded tensors for long context attn sp_rank = get_rank() x = torch.chunk(x, get_world_size(), dim=1) sq_size = [u.shape[1] for u in x] sq_start_size = sum(sq_size[:sp_rank]) x = x[sp_rank] # Confirm the application range of the time embedding in e0[0] for each sequence: # - For tokens before seg_id: apply e0[0][:, :, 0] # - For tokens after seg_id: apply e0[0][:, :, 1] sp_size = x.shape[1] seg_idx = e0[1] - sq_start_size e0[1] = seg_idx self.pre_compute_freqs = torch.chunk( self.pre_compute_freqs, get_world_size(), dim=1) self.pre_compute_freqs = self.pre_compute_freqs[sp_rank] # arguments kwargs = dict( e=e0, seq_lens=seq_lens, grid_sizes=grid_sizes, freqs=self.pre_compute_freqs, context=context, context_lens=context_lens) for idx, block in enumerate(self.blocks): x = block(x, **kwargs) x = self.after_transformer_block(idx, x) # Context Parallel if self.use_context_parallel: x = gather_forward(x.contiguous(), dim=1) # unpatchify x = x[:, :self.original_seq_len] # head x = self.head(x, e) x = self.unpatchify(x, original_grid_sizes) return [u.float() for u in x] def unpatchify(self, x, grid_sizes): """ Reconstruct video tensors from patch embeddings. Args: x (List[Tensor]): List of patchified features, each with shape [L, C_out * prod(patch_size)] grid_sizes (Tensor): Original spatial-temporal grid dimensions before patching, shape [B, 3] (3 dimensions correspond to F_patches, H_patches, W_patches) Returns: List[Tensor]: Reconstructed video tensors with shape [C_out, F, H / 8, W / 8] """ c = self.out_dim out = [] for u, v in zip(x, grid_sizes.tolist()): u = u[:math.prod(v)].view(*v, *self.patch_size, c) u = torch.einsum('fhwpqrc->cfphqwr', u) u = u.reshape(c, *[i * j for i, j in zip(v, self.patch_size)]) out.append(u) return out def init_weights(self): r""" Initialize model parameters using Xavier initialization. """ # basic init for m in self.modules(): if isinstance(m, nn.Linear): nn.init.xavier_uniform_(m.weight) if m.bias is not None: nn.init.zeros_(m.bias) # init embeddings nn.init.xavier_uniform_(self.patch_embedding.weight.flatten(1)) for m in self.text_embedding.modules(): if isinstance(m, nn.Linear): nn.init.normal_(m.weight, std=.02) for m in self.time_embedding.modules(): if isinstance(m, nn.Linear): nn.init.normal_(m.weight, std=.02) # init output layer nn.init.zeros_(self.head.head.weight) ================================================ FILE: wan/modules/s2v/motioner.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import math from typing import Any, Dict, List, Literal, Optional, Union import numpy as np import torch import torch.cuda.amp as amp import torch.nn as nn from diffusers.loaders import FromOriginalModelMixin, PeftAdapterMixin from diffusers.utils import BaseOutput, is_torch_version from einops import rearrange, repeat from ..model import flash_attention from .s2v_utils import rope_precompute def sinusoidal_embedding_1d(dim, position): # preprocess assert dim % 2 == 0 half = dim // 2 position = position.type(torch.float64) # calculation sinusoid = torch.outer( position, torch.pow(10000, -torch.arange(half).to(position).div(half))) x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1) return x @amp.autocast(enabled=False) def rope_params(max_seq_len, dim, theta=10000): assert dim % 2 == 0 freqs = torch.outer( torch.arange(max_seq_len), 1.0 / torch.pow(theta, torch.arange(0, dim, 2).to(torch.float64).div(dim))) freqs = torch.polar(torch.ones_like(freqs), freqs) return freqs @amp.autocast(enabled=False) def rope_apply(x, grid_sizes, freqs, start=None): n, c = x.size(2), x.size(3) // 2 # split freqs if type(freqs) is list: trainable_freqs = freqs[1] freqs = freqs[0] freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1) # loop over samples output = [] output = x.clone() seq_bucket = [0] if not type(grid_sizes) is list: grid_sizes = [grid_sizes] for g in grid_sizes: if not type(g) is list: g = [torch.zeros_like(g), g] batch_size = g[0].shape[0] for i in range(batch_size): if start is None: f_o, h_o, w_o = g[0][i] else: f_o, h_o, w_o = start[i] f, h, w = g[1][i] t_f, t_h, t_w = g[2][i] seq_f, seq_h, seq_w = f - f_o, h - h_o, w - w_o seq_len = int(seq_f * seq_h * seq_w) if seq_len > 0: if t_f > 0: factor_f, factor_h, factor_w = (t_f / seq_f).item(), ( t_h / seq_h).item(), (t_w / seq_w).item() if f_o >= 0: f_sam = np.linspace(f_o.item(), (t_f + f_o).item() - 1, seq_f).astype(int).tolist() else: f_sam = np.linspace(-f_o.item(), (-t_f - f_o).item() + 1, seq_f).astype(int).tolist() h_sam = np.linspace(h_o.item(), (t_h + h_o).item() - 1, seq_h).astype(int).tolist() w_sam = np.linspace(w_o.item(), (t_w + w_o).item() - 1, seq_w).astype(int).tolist() assert f_o * f >= 0 and h_o * h >= 0 and w_o * w >= 0 freqs_0 = freqs[0][f_sam] if f_o >= 0 else freqs[0][ f_sam].conj() freqs_0 = freqs_0.view(seq_f, 1, 1, -1) freqs_i = torch.cat([ freqs_0.expand(seq_f, seq_h, seq_w, -1), freqs[1][h_sam].view(1, seq_h, 1, -1).expand( seq_f, seq_h, seq_w, -1), freqs[2][w_sam].view(1, 1, seq_w, -1).expand( seq_f, seq_h, seq_w, -1), ], dim=-1).reshape(seq_len, 1, -1) elif t_f < 0: freqs_i = trainable_freqs.unsqueeze(1) # apply rotary embedding # precompute multipliers x_i = torch.view_as_complex( x[i, seq_bucket[-1]:seq_bucket[-1] + seq_len].to( torch.float64).reshape(seq_len, n, -1, 2)) x_i = torch.view_as_real(x_i * freqs_i).flatten(2) output[i, seq_bucket[-1]:seq_bucket[-1] + seq_len] = x_i seq_bucket.append(seq_bucket[-1] + seq_len) return output.float() class RMSNorm(nn.Module): def __init__(self, dim, eps=1e-5): super().__init__() self.dim = dim self.eps = eps self.weight = nn.Parameter(torch.ones(dim)) def forward(self, x): return self._norm(x.float()).type_as(x) * self.weight def _norm(self, x): return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps) class LayerNorm(nn.LayerNorm): def __init__(self, dim, eps=1e-6, elementwise_affine=False): super().__init__(dim, elementwise_affine=elementwise_affine, eps=eps) def forward(self, x): return super().forward(x.float()).type_as(x) class SelfAttention(nn.Module): def __init__(self, dim, num_heads, window_size=(-1, -1), qk_norm=True, eps=1e-6): assert dim % num_heads == 0 super().__init__() self.dim = dim self.num_heads = num_heads self.head_dim = dim // num_heads self.window_size = window_size self.qk_norm = qk_norm self.eps = eps # layers self.q = nn.Linear(dim, dim) self.k = nn.Linear(dim, dim) self.v = nn.Linear(dim, dim) self.o = nn.Linear(dim, dim) self.norm_q = RMSNorm(dim, eps=eps) if qk_norm else nn.Identity() self.norm_k = RMSNorm(dim, eps=eps) if qk_norm else nn.Identity() def forward(self, x, seq_lens, grid_sizes, freqs): b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim # query, key, value function def qkv_fn(x): q = self.norm_q(self.q(x)).view(b, s, n, d) k = self.norm_k(self.k(x)).view(b, s, n, d) v = self.v(x).view(b, s, n, d) return q, k, v q, k, v = qkv_fn(x) x = flash_attention( q=rope_apply(q, grid_sizes, freqs), k=rope_apply(k, grid_sizes, freqs), v=v, k_lens=seq_lens, window_size=self.window_size) # output x = x.flatten(2) x = self.o(x) return x class SwinSelfAttention(SelfAttention): def forward(self, x, seq_lens, grid_sizes, freqs): b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim assert b == 1, 'Only support batch_size 1' # query, key, value function def qkv_fn(x): q = self.norm_q(self.q(x)).view(b, s, n, d) k = self.norm_k(self.k(x)).view(b, s, n, d) v = self.v(x).view(b, s, n, d) return q, k, v q, k, v = qkv_fn(x) q = rope_apply(q, grid_sizes, freqs) k = rope_apply(k, grid_sizes, freqs) T, H, W = grid_sizes[0].tolist() q = rearrange(q, 'b (t h w) n d -> (b t) (h w) n d', t=T, h=H, w=W) k = rearrange(k, 'b (t h w) n d -> (b t) (h w) n d', t=T, h=H, w=W) v = rearrange(v, 'b (t h w) n d -> (b t) (h w) n d', t=T, h=H, w=W) ref_q = q[-1:] q = q[:-1] ref_k = repeat( k[-1:], "1 s n d -> t s n d", t=k.shape[0] - 1) # t hw n d k = k[:-1] k = torch.cat([k[:1], k, k[-1:]]) k = torch.cat([k[1:-1], k[2:], k[:-2], ref_k], dim=1) # (bt) (3hw) n d ref_v = repeat(v[-1:], "1 s n d -> t s n d", t=v.shape[0] - 1) v = v[:-1] v = torch.cat([v[:1], v, v[-1:]]) v = torch.cat([v[1:-1], v[2:], v[:-2], ref_v], dim=1) # q: b (t h w) n d # k: b (t h w) n d out = flash_attention( q=q, k=k, v=v, # k_lens=torch.tensor([k.shape[1]] * k.shape[0], device=x.device, dtype=torch.long), window_size=self.window_size) out = torch.cat([out, ref_v[:1]], axis=0) out = rearrange(out, '(b t) (h w) n d -> b (t h w) n d', t=T, h=H, w=W) x = out # output x = x.flatten(2) x = self.o(x) return x #Fix the reference frame RoPE to 1,H,W. #Set the current frame RoPE to 1. #Set the previous frame RoPE to 0. class CasualSelfAttention(SelfAttention): def forward(self, x, seq_lens, grid_sizes, freqs): shifting = 3 b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim assert b == 1, 'Only support batch_size 1' # query, key, value function def qkv_fn(x): q = self.norm_q(self.q(x)).view(b, s, n, d) k = self.norm_k(self.k(x)).view(b, s, n, d) v = self.v(x).view(b, s, n, d) return q, k, v q, k, v = qkv_fn(x) T, H, W = grid_sizes[0].tolist() q = rearrange(q, 'b (t h w) n d -> (b t) (h w) n d', t=T, h=H, w=W) k = rearrange(k, 'b (t h w) n d -> (b t) (h w) n d', t=T, h=H, w=W) v = rearrange(v, 'b (t h w) n d -> (b t) (h w) n d', t=T, h=H, w=W) ref_q = q[-1:] q = q[:-1] grid_sizes = torch.tensor([[1, H, W]] * q.shape[0], dtype=torch.long) start = [[shifting, 0, 0]] * q.shape[0] q = rope_apply(q, grid_sizes, freqs, start=start) ref_k = k[-1:] grid_sizes = torch.tensor([[1, H, W]], dtype=torch.long) # start = [[shifting, H, W]] start = [[shifting + 10, 0, 0]] ref_k = rope_apply(ref_k, grid_sizes, freqs, start) ref_k = repeat( ref_k, "1 s n d -> t s n d", t=k.shape[0] - 1) # t hw n d k = k[:-1] k = torch.cat([*([k[:1]] * shifting), k]) cat_k = [] for i in range(shifting): cat_k.append(k[i:i - shifting]) cat_k.append(k[shifting:]) k = torch.cat(cat_k, dim=1) # (bt) (3hw) n d grid_sizes = torch.tensor( [[shifting + 1, H, W]] * q.shape[0], dtype=torch.long) k = rope_apply(k, grid_sizes, freqs) k = torch.cat([k, ref_k], dim=1) ref_v = repeat(v[-1:], "1 s n d -> t s n d", t=q.shape[0]) # t hw n d v = v[:-1] v = torch.cat([*([v[:1]] * shifting), v]) cat_v = [] for i in range(shifting): cat_v.append(v[i:i - shifting]) cat_v.append(v[shifting:]) v = torch.cat(cat_v, dim=1) # (bt) (3hw) n d v = torch.cat([v, ref_v], dim=1) # q: b (t h w) n d # k: b (t h w) n d outs = [] for i in range(q.shape[0]): out = flash_attention( q=q[i:i + 1], k=k[i:i + 1], v=v[i:i + 1], window_size=self.window_size) outs.append(out) out = torch.cat(outs, dim=0) out = torch.cat([out, ref_v[:1]], axis=0) out = rearrange(out, '(b t) (h w) n d -> b (t h w) n d', t=T, h=H, w=W) x = out # output x = x.flatten(2) x = self.o(x) return x class MotionerAttentionBlock(nn.Module): def __init__(self, dim, ffn_dim, num_heads, window_size=(-1, -1), qk_norm=True, cross_attn_norm=False, eps=1e-6, self_attn_block="SelfAttention"): super().__init__() self.dim = dim self.ffn_dim = ffn_dim self.num_heads = num_heads self.window_size = window_size self.qk_norm = qk_norm self.cross_attn_norm = cross_attn_norm self.eps = eps # layers self.norm1 = LayerNorm(dim, eps) if self_attn_block == "SelfAttention": self.self_attn = SelfAttention(dim, num_heads, window_size, qk_norm, eps) elif self_attn_block == "SwinSelfAttention": self.self_attn = SwinSelfAttention(dim, num_heads, window_size, qk_norm, eps) elif self_attn_block == "CasualSelfAttention": self.self_attn = CasualSelfAttention(dim, num_heads, window_size, qk_norm, eps) self.norm2 = LayerNorm(dim, eps) self.ffn = nn.Sequential( nn.Linear(dim, ffn_dim), nn.GELU(approximate='tanh'), nn.Linear(ffn_dim, dim)) def forward( self, x, seq_lens, grid_sizes, freqs, ): # self-attention y = self.self_attn(self.norm1(x).float(), seq_lens, grid_sizes, freqs) x = x + y y = self.ffn(self.norm2(x).float()) x = x + y return x class Head(nn.Module): def __init__(self, dim, out_dim, patch_size, eps=1e-6): super().__init__() self.dim = dim self.out_dim = out_dim self.patch_size = patch_size self.eps = eps # layers out_dim = math.prod(patch_size) * out_dim self.norm = LayerNorm(dim, eps) self.head = nn.Linear(dim, out_dim) def forward(self, x): x = self.head(self.norm(x)) return x class MotionerTransformers(nn.Module, PeftAdapterMixin): def __init__( self, patch_size=(1, 2, 2), in_dim=16, dim=2048, ffn_dim=8192, freq_dim=256, out_dim=16, num_heads=16, num_layers=32, window_size=(-1, -1), qk_norm=True, cross_attn_norm=False, eps=1e-6, self_attn_block="SelfAttention", motion_token_num=1024, enable_tsm=False, motion_stride=4, expand_ratio=2, trainable_token_pos_emb=False, ): super().__init__() self.patch_size = patch_size self.in_dim = in_dim self.dim = dim self.ffn_dim = ffn_dim self.freq_dim = freq_dim self.out_dim = out_dim self.num_heads = num_heads self.num_layers = num_layers self.window_size = window_size self.qk_norm = qk_norm self.cross_attn_norm = cross_attn_norm self.eps = eps self.enable_tsm = enable_tsm self.motion_stride = motion_stride self.expand_ratio = expand_ratio self.sample_c = self.patch_size[0] # embeddings self.patch_embedding = nn.Conv3d( in_dim, dim, kernel_size=patch_size, stride=patch_size) # blocks self.blocks = nn.ModuleList([ MotionerAttentionBlock( dim, ffn_dim, num_heads, window_size, qk_norm, cross_attn_norm, eps, self_attn_block=self_attn_block) for _ in range(num_layers) ]) # buffers (don't use register_buffer otherwise dtype will be changed in to()) assert (dim % num_heads) == 0 and (dim // num_heads) % 2 == 0 d = dim // num_heads self.freqs = torch.cat([ rope_params(1024, d - 4 * (d // 6)), rope_params(1024, 2 * (d // 6)), rope_params(1024, 2 * (d // 6)) ], dim=1) self.gradient_checkpointing = False self.motion_side_len = int(math.sqrt(motion_token_num)) assert self.motion_side_len**2 == motion_token_num self.token = nn.Parameter( torch.zeros(1, motion_token_num, dim).contiguous()) self.trainable_token_pos_emb = trainable_token_pos_emb if trainable_token_pos_emb: x = torch.zeros([1, motion_token_num, num_heads, d]) x[..., ::2] = 1 gride_sizes = [[ torch.tensor([0, 0, 0]).unsqueeze(0).repeat(1, 1), torch.tensor([1, self.motion_side_len, self.motion_side_len]).unsqueeze(0).repeat(1, 1), torch.tensor([1, self.motion_side_len, self.motion_side_len]).unsqueeze(0).repeat(1, 1), ]] token_freqs = rope_apply(x, gride_sizes, self.freqs) token_freqs = token_freqs[0, :, 0].reshape(motion_token_num, -1, 2) token_freqs = token_freqs * 0.01 self.token_freqs = torch.nn.Parameter(token_freqs) def after_patch_embedding(self, x): return x def forward( self, x, ): """ x: A list of videos each with shape [C, T, H, W]. t: [B]. context: A list of text embeddings each with shape [L, C]. """ # params motion_frames = x[0].shape[1] device = self.patch_embedding.weight.device freqs = self.freqs if freqs.device != device: freqs = freqs.to(device) if self.trainable_token_pos_emb: with amp.autocast(dtype=torch.float64): token_freqs = self.token_freqs.to(torch.float64) token_freqs = token_freqs / token_freqs.norm( dim=-1, keepdim=True) freqs = [freqs, torch.view_as_complex(token_freqs)] if self.enable_tsm: sample_idx = [ sample_indices( u.shape[1], stride=self.motion_stride, expand_ratio=self.expand_ratio, c=self.sample_c) for u in x ] x = [ torch.flip(torch.flip(u, [1])[:, idx], [1]) for idx, u in zip(sample_idx, x) ] # embeddings x = [self.patch_embedding(u.unsqueeze(0)) for u in x] x = self.after_patch_embedding(x) seq_f, seq_h, seq_w = x[0].shape[-3:] batch_size = len(x) if not self.enable_tsm: grid_sizes = torch.stack( [torch.tensor(u.shape[2:], dtype=torch.long) for u in x]) grid_sizes = [[ torch.zeros_like(grid_sizes), grid_sizes, grid_sizes ]] seq_f = 0 else: grid_sizes = [] for idx in sample_idx[0][::-1][::self.sample_c]: tsm_frame_grid_sizes = [[ torch.tensor([idx, 0, 0]).unsqueeze(0).repeat(batch_size, 1), torch.tensor([idx + 1, seq_h, seq_w]).unsqueeze(0).repeat(batch_size, 1), torch.tensor([1, seq_h, seq_w]).unsqueeze(0).repeat(batch_size, 1), ]] grid_sizes += tsm_frame_grid_sizes seq_f = sample_idx[0][-1] + 1 x = [u.flatten(2).transpose(1, 2) for u in x] seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.long) x = torch.cat([u for u in x]) batch_size = len(x) token_grid_sizes = [[ torch.tensor([seq_f, 0, 0]).unsqueeze(0).repeat(batch_size, 1), torch.tensor( [seq_f + 1, self.motion_side_len, self.motion_side_len]).unsqueeze(0).repeat(batch_size, 1), torch.tensor( [1 if not self.trainable_token_pos_emb else -1, seq_h, seq_w]).unsqueeze(0).repeat(batch_size, 1), ] # 第三行代表rope emb的想要覆盖到的范围 ] grid_sizes = grid_sizes + token_grid_sizes token_unpatch_grid_sizes = torch.stack([ torch.tensor([1, 32, 32], dtype=torch.long) for b in range(batch_size) ]) token_len = self.token.shape[1] token = self.token.clone().repeat(x.shape[0], 1, 1).contiguous() seq_lens = seq_lens + torch.tensor([t.size(0) for t in token], dtype=torch.long) x = torch.cat([x, token], dim=1) # arguments kwargs = dict( seq_lens=seq_lens, grid_sizes=grid_sizes, freqs=freqs, ) # grad ckpt args def create_custom_forward(module, return_dict=None): def custom_forward(*inputs, **kwargs): if return_dict is not None: return module(*inputs, **kwargs, return_dict=return_dict) else: return module(*inputs, **kwargs) return custom_forward ckpt_kwargs: Dict[str, Any] = ({ "use_reentrant": False } if is_torch_version(">=", "1.11.0") else {}) for idx, block in enumerate(self.blocks): if self.training and self.gradient_checkpointing: x = torch.utils.checkpoint.checkpoint( create_custom_forward(block), x, **kwargs, **ckpt_kwargs, ) else: x = block(x, **kwargs) # head out = x[:, -token_len:] return out def unpatchify(self, x, grid_sizes): c = self.out_dim out = [] for u, v in zip(x, grid_sizes.tolist()): u = u[:math.prod(v)].view(*v, *self.patch_size, c) u = torch.einsum('fhwpqrc->cfphqwr', u) u = u.reshape(c, *[i * j for i, j in zip(v, self.patch_size)]) out.append(u) return out def init_weights(self): # basic init for m in self.modules(): if isinstance(m, nn.Linear): nn.init.xavier_uniform_(m.weight) if m.bias is not None: nn.init.zeros_(m.bias) # init embeddings nn.init.xavier_uniform_(self.patch_embedding.weight.flatten(1)) class FramePackMotioner(nn.Module): def __init__( self, inner_dim=1024, num_heads=16, # Used to indicate the number of heads in the backbone network; unrelated to this module's design zip_frame_buckets=[ 1, 2, 16 ], # Three numbers representing the number of frames sampled for patch operations from the nearest to the farthest frames drop_mode="drop", # If not "drop", it will use "padd", meaning padding instead of deletion *args, **kwargs): super().__init__(*args, **kwargs) self.proj = nn.Conv3d( 16, inner_dim, kernel_size=(1, 2, 2), stride=(1, 2, 2)) self.proj_2x = nn.Conv3d( 16, inner_dim, kernel_size=(2, 4, 4), stride=(2, 4, 4)) self.proj_4x = nn.Conv3d( 16, inner_dim, kernel_size=(4, 8, 8), stride=(4, 8, 8)) self.zip_frame_buckets = torch.tensor( zip_frame_buckets, dtype=torch.long) self.inner_dim = inner_dim self.num_heads = num_heads assert (inner_dim % num_heads) == 0 and (inner_dim // num_heads) % 2 == 0 d = inner_dim // num_heads self.freqs = torch.cat([ rope_params(1024, d - 4 * (d // 6)), rope_params(1024, 2 * (d // 6)), rope_params(1024, 2 * (d // 6)) ], dim=1) self.drop_mode = drop_mode def forward(self, motion_latents, add_last_motion=2): motion_frames = motion_latents[0].shape[1] mot = [] mot_remb = [] for m in motion_latents: lat_height, lat_width = m.shape[2], m.shape[3] padd_lat = torch.zeros(16, self.zip_frame_buckets.sum(), lat_height, lat_width).to( device=m.device, dtype=m.dtype) overlap_frame = min(padd_lat.shape[1], m.shape[1]) if overlap_frame > 0: padd_lat[:, -overlap_frame:] = m[:, -overlap_frame:] if add_last_motion < 2 and self.drop_mode != "drop": zero_end_frame = self.zip_frame_buckets[:self.zip_frame_buckets. __len__() - add_last_motion - 1].sum() padd_lat[:, -zero_end_frame:] = 0 padd_lat = padd_lat.unsqueeze(0) clean_latents_4x, clean_latents_2x, clean_latents_post = padd_lat[:, :, -self.zip_frame_buckets.sum( ):, :, :].split( list(self.zip_frame_buckets)[::-1], dim=2) # 16, 2 ,1 # patchfy clean_latents_post = self.proj(clean_latents_post).flatten( 2).transpose(1, 2) clean_latents_2x = self.proj_2x(clean_latents_2x).flatten( 2).transpose(1, 2) clean_latents_4x = self.proj_4x(clean_latents_4x).flatten( 2).transpose(1, 2) if add_last_motion < 2 and self.drop_mode == "drop": clean_latents_post = clean_latents_post[:, : 0] if add_last_motion < 2 else clean_latents_post clean_latents_2x = clean_latents_2x[:, : 0] if add_last_motion < 1 else clean_latents_2x motion_lat = torch.cat( [clean_latents_post, clean_latents_2x, clean_latents_4x], dim=1) # rope start_time_id = -(self.zip_frame_buckets[:1].sum()) end_time_id = start_time_id + self.zip_frame_buckets[0] grid_sizes = [] if add_last_motion < 2 and self.drop_mode == "drop" else \ [ [torch.tensor([start_time_id, 0, 0]).unsqueeze(0).repeat(1, 1), torch.tensor([end_time_id, lat_height // 2, lat_width // 2]).unsqueeze(0).repeat(1, 1), torch.tensor([self.zip_frame_buckets[0], lat_height // 2, lat_width // 2]).unsqueeze(0).repeat(1, 1), ] ] start_time_id = -(self.zip_frame_buckets[:2].sum()) end_time_id = start_time_id + self.zip_frame_buckets[1] // 2 grid_sizes_2x = [] if add_last_motion < 1 and self.drop_mode == "drop" else \ [ [torch.tensor([start_time_id, 0, 0]).unsqueeze(0).repeat(1, 1), torch.tensor([end_time_id, lat_height // 4, lat_width // 4]).unsqueeze(0).repeat(1, 1), torch.tensor([self.zip_frame_buckets[1], lat_height // 2, lat_width // 2]).unsqueeze(0).repeat(1, 1), ] ] start_time_id = -(self.zip_frame_buckets[:3].sum()) end_time_id = start_time_id + self.zip_frame_buckets[2] // 4 grid_sizes_4x = [[ torch.tensor([start_time_id, 0, 0]).unsqueeze(0).repeat(1, 1), torch.tensor([end_time_id, lat_height // 8, lat_width // 8]).unsqueeze(0).repeat(1, 1), torch.tensor([ self.zip_frame_buckets[2], lat_height // 2, lat_width // 2 ]).unsqueeze(0).repeat(1, 1), ]] grid_sizes = grid_sizes + grid_sizes_2x + grid_sizes_4x motion_rope_emb = rope_precompute( motion_lat.detach().view(1, motion_lat.shape[1], self.num_heads, self.inner_dim // self.num_heads), grid_sizes, self.freqs, start=None) mot.append(motion_lat) mot_remb.append(motion_rope_emb) return mot, mot_remb def sample_indices(N, stride, expand_ratio, c): indices = [] current_start = 0 while current_start < N: bucket_width = int(stride * (expand_ratio**(len(indices) / stride))) interval = int(bucket_width / stride * c) current_end = min(N, current_start + bucket_width) bucket_samples = [] for i in range(current_end - 1, current_start - 1, -interval): for near in range(c): bucket_samples.append(i - near) indices += bucket_samples[::-1] current_start += bucket_width return indices if __name__ == '__main__': device = "cuda" model = FramePackMotioner(inner_dim=1024) batch_size = 2 num_frame, height, width = (28, 32, 32) single_input = torch.ones([16, num_frame, height, width], device=device) for i in range(num_frame): single_input[:, num_frame - 1 - i] *= i x = [single_input] * batch_size model.forward(x) ================================================ FILE: wan/modules/s2v/s2v_utils.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import numpy as np import torch def rope_precompute(x, grid_sizes, freqs, start=None): b, s, n, c = x.size(0), x.size(1), x.size(2), x.size(3) // 2 # split freqs if type(freqs) is list: trainable_freqs = freqs[1] freqs = freqs[0] freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1) # loop over samples output = torch.view_as_complex(x.detach().reshape(b, s, n, -1, 2).to(torch.float64)) seq_bucket = [0] if not type(grid_sizes) is list: grid_sizes = [grid_sizes] for g in grid_sizes: if not type(g) is list: g = [torch.zeros_like(g), g] batch_size = g[0].shape[0] for i in range(batch_size): if start is None: f_o, h_o, w_o = g[0][i] else: f_o, h_o, w_o = start[i] f, h, w = g[1][i] t_f, t_h, t_w = g[2][i] seq_f, seq_h, seq_w = f - f_o, h - h_o, w - w_o seq_len = int(seq_f * seq_h * seq_w) if seq_len > 0: if t_f > 0: factor_f, factor_h, factor_w = (t_f / seq_f).item(), ( t_h / seq_h).item(), (t_w / seq_w).item() # Generate a list of seq_f integers starting from f_o and ending at math.ceil(factor_f * seq_f.item() + f_o.item()) if f_o >= 0: f_sam = np.linspace(f_o.item(), (t_f + f_o).item() - 1, seq_f).astype(int).tolist() else: f_sam = np.linspace(-f_o.item(), (-t_f - f_o).item() + 1, seq_f).astype(int).tolist() h_sam = np.linspace(h_o.item(), (t_h + h_o).item() - 1, seq_h).astype(int).tolist() w_sam = np.linspace(w_o.item(), (t_w + w_o).item() - 1, seq_w).astype(int).tolist() assert f_o * f >= 0 and h_o * h >= 0 and w_o * w >= 0 freqs_0 = freqs[0][f_sam] if f_o >= 0 else freqs[0][ f_sam].conj() freqs_0 = freqs_0.view(seq_f, 1, 1, -1) freqs_i = torch.cat([ freqs_0.expand(seq_f, seq_h, seq_w, -1), freqs[1][h_sam].view(1, seq_h, 1, -1).expand( seq_f, seq_h, seq_w, -1), freqs[2][w_sam].view(1, 1, seq_w, -1).expand( seq_f, seq_h, seq_w, -1), ], dim=-1).reshape(seq_len, 1, -1) elif t_f < 0: freqs_i = trainable_freqs.unsqueeze(1) # apply rotary embedding output[i, seq_bucket[-1]:seq_bucket[-1] + seq_len] = freqs_i seq_bucket.append(seq_bucket[-1] + seq_len) return output ================================================ FILE: wan/modules/t5.py ================================================ # Modified from transformers.models.t5.modeling_t5 # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import logging import math import torch import torch.nn as nn import torch.nn.functional as F from .tokenizers import HuggingfaceTokenizer __all__ = [ 'T5Model', 'T5Encoder', 'T5Decoder', 'T5EncoderModel', ] def fp16_clamp(x): if x.dtype == torch.float16 and torch.isinf(x).any(): clamp = torch.finfo(x.dtype).max - 1000 x = torch.clamp(x, min=-clamp, max=clamp) return x def init_weights(m): if isinstance(m, T5LayerNorm): nn.init.ones_(m.weight) elif isinstance(m, T5Model): nn.init.normal_(m.token_embedding.weight, std=1.0) elif isinstance(m, T5FeedForward): nn.init.normal_(m.gate[0].weight, std=m.dim**-0.5) nn.init.normal_(m.fc1.weight, std=m.dim**-0.5) nn.init.normal_(m.fc2.weight, std=m.dim_ffn**-0.5) elif isinstance(m, T5Attention): nn.init.normal_(m.q.weight, std=(m.dim * m.dim_attn)**-0.5) nn.init.normal_(m.k.weight, std=m.dim**-0.5) nn.init.normal_(m.v.weight, std=m.dim**-0.5) nn.init.normal_(m.o.weight, std=(m.num_heads * m.dim_attn)**-0.5) elif isinstance(m, T5RelativeEmbedding): nn.init.normal_( m.embedding.weight, std=(2 * m.num_buckets * m.num_heads)**-0.5) class GELU(nn.Module): def forward(self, x): return 0.5 * x * (1.0 + torch.tanh( math.sqrt(2.0 / math.pi) * (x + 0.044715 * torch.pow(x, 3.0)))) class T5LayerNorm(nn.Module): def __init__(self, dim, eps=1e-6): super(T5LayerNorm, self).__init__() self.dim = dim self.eps = eps self.weight = nn.Parameter(torch.ones(dim)) def forward(self, x): x = x * torch.rsqrt(x.float().pow(2).mean(dim=-1, keepdim=True) + self.eps) if self.weight.dtype in [torch.float16, torch.bfloat16]: x = x.type_as(self.weight) return self.weight * x class T5Attention(nn.Module): def __init__(self, dim, dim_attn, num_heads, dropout=0.1): assert dim_attn % num_heads == 0 super(T5Attention, self).__init__() self.dim = dim self.dim_attn = dim_attn self.num_heads = num_heads self.head_dim = dim_attn // num_heads # layers self.q = nn.Linear(dim, dim_attn, bias=False) self.k = nn.Linear(dim, dim_attn, bias=False) self.v = nn.Linear(dim, dim_attn, bias=False) self.o = nn.Linear(dim_attn, dim, bias=False) self.dropout = nn.Dropout(dropout) def forward(self, x, context=None, mask=None, pos_bias=None): """ x: [B, L1, C]. context: [B, L2, C] or None. mask: [B, L2] or [B, L1, L2] or None. """ # check inputs context = x if context is None else context b, n, c = x.size(0), self.num_heads, self.head_dim # compute query, key, value q = self.q(x).view(b, -1, n, c) k = self.k(context).view(b, -1, n, c) v = self.v(context).view(b, -1, n, c) # attention bias attn_bias = x.new_zeros(b, n, q.size(1), k.size(1)) if pos_bias is not None: attn_bias += pos_bias if mask is not None: assert mask.ndim in [2, 3] mask = mask.view(b, 1, 1, -1) if mask.ndim == 2 else mask.unsqueeze(1) attn_bias.masked_fill_(mask == 0, torch.finfo(x.dtype).min) # compute attention (T5 does not use scaling) attn = torch.einsum('binc,bjnc->bnij', q, k) + attn_bias attn = F.softmax(attn.float(), dim=-1).type_as(attn) x = torch.einsum('bnij,bjnc->binc', attn, v) # output x = x.reshape(b, -1, n * c) x = self.o(x) x = self.dropout(x) return x class T5FeedForward(nn.Module): def __init__(self, dim, dim_ffn, dropout=0.1): super(T5FeedForward, self).__init__() self.dim = dim self.dim_ffn = dim_ffn # layers self.gate = nn.Sequential(nn.Linear(dim, dim_ffn, bias=False), GELU()) self.fc1 = nn.Linear(dim, dim_ffn, bias=False) self.fc2 = nn.Linear(dim_ffn, dim, bias=False) self.dropout = nn.Dropout(dropout) def forward(self, x): x = self.fc1(x) * self.gate(x) x = self.dropout(x) x = self.fc2(x) x = self.dropout(x) return x class T5SelfAttention(nn.Module): def __init__(self, dim, dim_attn, dim_ffn, num_heads, num_buckets, shared_pos=True, dropout=0.1): super(T5SelfAttention, self).__init__() self.dim = dim self.dim_attn = dim_attn self.dim_ffn = dim_ffn self.num_heads = num_heads self.num_buckets = num_buckets self.shared_pos = shared_pos # layers self.norm1 = T5LayerNorm(dim) self.attn = T5Attention(dim, dim_attn, num_heads, dropout) self.norm2 = T5LayerNorm(dim) self.ffn = T5FeedForward(dim, dim_ffn, dropout) self.pos_embedding = None if shared_pos else T5RelativeEmbedding( num_buckets, num_heads, bidirectional=True) def forward(self, x, mask=None, pos_bias=None): e = pos_bias if self.shared_pos else self.pos_embedding( x.size(1), x.size(1)) x = fp16_clamp(x + self.attn(self.norm1(x), mask=mask, pos_bias=e)) x = fp16_clamp(x + self.ffn(self.norm2(x))) return x class T5CrossAttention(nn.Module): def __init__(self, dim, dim_attn, dim_ffn, num_heads, num_buckets, shared_pos=True, dropout=0.1): super(T5CrossAttention, self).__init__() self.dim = dim self.dim_attn = dim_attn self.dim_ffn = dim_ffn self.num_heads = num_heads self.num_buckets = num_buckets self.shared_pos = shared_pos # layers self.norm1 = T5LayerNorm(dim) self.self_attn = T5Attention(dim, dim_attn, num_heads, dropout) self.norm2 = T5LayerNorm(dim) self.cross_attn = T5Attention(dim, dim_attn, num_heads, dropout) self.norm3 = T5LayerNorm(dim) self.ffn = T5FeedForward(dim, dim_ffn, dropout) self.pos_embedding = None if shared_pos else T5RelativeEmbedding( num_buckets, num_heads, bidirectional=False) def forward(self, x, mask=None, encoder_states=None, encoder_mask=None, pos_bias=None): e = pos_bias if self.shared_pos else self.pos_embedding( x.size(1), x.size(1)) x = fp16_clamp(x + self.self_attn(self.norm1(x), mask=mask, pos_bias=e)) x = fp16_clamp(x + self.cross_attn( self.norm2(x), context=encoder_states, mask=encoder_mask)) x = fp16_clamp(x + self.ffn(self.norm3(x))) return x class T5RelativeEmbedding(nn.Module): def __init__(self, num_buckets, num_heads, bidirectional, max_dist=128): super(T5RelativeEmbedding, self).__init__() self.num_buckets = num_buckets self.num_heads = num_heads self.bidirectional = bidirectional self.max_dist = max_dist # layers self.embedding = nn.Embedding(num_buckets, num_heads) def forward(self, lq, lk): device = self.embedding.weight.device # rel_pos = torch.arange(lk).unsqueeze(0).to(device) - \ # torch.arange(lq).unsqueeze(1).to(device) rel_pos = torch.arange(lk, device=device).unsqueeze(0) - \ torch.arange(lq, device=device).unsqueeze(1) rel_pos = self._relative_position_bucket(rel_pos) rel_pos_embeds = self.embedding(rel_pos) rel_pos_embeds = rel_pos_embeds.permute(2, 0, 1).unsqueeze( 0) # [1, N, Lq, Lk] return rel_pos_embeds.contiguous() def _relative_position_bucket(self, rel_pos): # preprocess if self.bidirectional: num_buckets = self.num_buckets // 2 rel_buckets = (rel_pos > 0).long() * num_buckets rel_pos = torch.abs(rel_pos) else: num_buckets = self.num_buckets rel_buckets = 0 rel_pos = -torch.min(rel_pos, torch.zeros_like(rel_pos)) # embeddings for small and large positions max_exact = num_buckets // 2 rel_pos_large = max_exact + (torch.log(rel_pos.float() / max_exact) / math.log(self.max_dist / max_exact) * (num_buckets - max_exact)).long() rel_pos_large = torch.min( rel_pos_large, torch.full_like(rel_pos_large, num_buckets - 1)) rel_buckets += torch.where(rel_pos < max_exact, rel_pos, rel_pos_large) return rel_buckets class T5Encoder(nn.Module): def __init__(self, vocab, dim, dim_attn, dim_ffn, num_heads, num_layers, num_buckets, shared_pos=True, dropout=0.1): super(T5Encoder, self).__init__() self.dim = dim self.dim_attn = dim_attn self.dim_ffn = dim_ffn self.num_heads = num_heads self.num_layers = num_layers self.num_buckets = num_buckets self.shared_pos = shared_pos # layers self.token_embedding = vocab if isinstance(vocab, nn.Embedding) \ else nn.Embedding(vocab, dim) self.pos_embedding = T5RelativeEmbedding( num_buckets, num_heads, bidirectional=True) if shared_pos else None self.dropout = nn.Dropout(dropout) self.blocks = nn.ModuleList([ T5SelfAttention(dim, dim_attn, dim_ffn, num_heads, num_buckets, shared_pos, dropout) for _ in range(num_layers) ]) self.norm = T5LayerNorm(dim) # initialize weights self.apply(init_weights) def forward(self, ids, mask=None): x = self.token_embedding(ids) x = self.dropout(x) e = self.pos_embedding(x.size(1), x.size(1)) if self.shared_pos else None for block in self.blocks: x = block(x, mask, pos_bias=e) x = self.norm(x) x = self.dropout(x) return x class T5Decoder(nn.Module): def __init__(self, vocab, dim, dim_attn, dim_ffn, num_heads, num_layers, num_buckets, shared_pos=True, dropout=0.1): super(T5Decoder, self).__init__() self.dim = dim self.dim_attn = dim_attn self.dim_ffn = dim_ffn self.num_heads = num_heads self.num_layers = num_layers self.num_buckets = num_buckets self.shared_pos = shared_pos # layers self.token_embedding = vocab if isinstance(vocab, nn.Embedding) \ else nn.Embedding(vocab, dim) self.pos_embedding = T5RelativeEmbedding( num_buckets, num_heads, bidirectional=False) if shared_pos else None self.dropout = nn.Dropout(dropout) self.blocks = nn.ModuleList([ T5CrossAttention(dim, dim_attn, dim_ffn, num_heads, num_buckets, shared_pos, dropout) for _ in range(num_layers) ]) self.norm = T5LayerNorm(dim) # initialize weights self.apply(init_weights) def forward(self, ids, mask=None, encoder_states=None, encoder_mask=None): b, s = ids.size() # causal mask if mask is None: mask = torch.tril(torch.ones(1, s, s).to(ids.device)) elif mask.ndim == 2: mask = torch.tril(mask.unsqueeze(1).expand(-1, s, -1)) # layers x = self.token_embedding(ids) x = self.dropout(x) e = self.pos_embedding(x.size(1), x.size(1)) if self.shared_pos else None for block in self.blocks: x = block(x, mask, encoder_states, encoder_mask, pos_bias=e) x = self.norm(x) x = self.dropout(x) return x class T5Model(nn.Module): def __init__(self, vocab_size, dim, dim_attn, dim_ffn, num_heads, encoder_layers, decoder_layers, num_buckets, shared_pos=True, dropout=0.1): super(T5Model, self).__init__() self.vocab_size = vocab_size self.dim = dim self.dim_attn = dim_attn self.dim_ffn = dim_ffn self.num_heads = num_heads self.encoder_layers = encoder_layers self.decoder_layers = decoder_layers self.num_buckets = num_buckets # layers self.token_embedding = nn.Embedding(vocab_size, dim) self.encoder = T5Encoder(self.token_embedding, dim, dim_attn, dim_ffn, num_heads, encoder_layers, num_buckets, shared_pos, dropout) self.decoder = T5Decoder(self.token_embedding, dim, dim_attn, dim_ffn, num_heads, decoder_layers, num_buckets, shared_pos, dropout) self.head = nn.Linear(dim, vocab_size, bias=False) # initialize weights self.apply(init_weights) def forward(self, encoder_ids, encoder_mask, decoder_ids, decoder_mask): x = self.encoder(encoder_ids, encoder_mask) x = self.decoder(decoder_ids, decoder_mask, x, encoder_mask) x = self.head(x) return x def _t5(name, encoder_only=False, decoder_only=False, return_tokenizer=False, tokenizer_kwargs={}, dtype=torch.float32, device='cpu', **kwargs): # sanity check assert not (encoder_only and decoder_only) # params if encoder_only: model_cls = T5Encoder kwargs['vocab'] = kwargs.pop('vocab_size') kwargs['num_layers'] = kwargs.pop('encoder_layers') _ = kwargs.pop('decoder_layers') elif decoder_only: model_cls = T5Decoder kwargs['vocab'] = kwargs.pop('vocab_size') kwargs['num_layers'] = kwargs.pop('decoder_layers') _ = kwargs.pop('encoder_layers') else: model_cls = T5Model # init model with torch.device(device): model = model_cls(**kwargs) # set device model = model.to(dtype=dtype, device=device) # init tokenizer if return_tokenizer: from .tokenizers import HuggingfaceTokenizer tokenizer = HuggingfaceTokenizer(f'google/{name}', **tokenizer_kwargs) return model, tokenizer else: return model def umt5_xxl(**kwargs): cfg = dict( vocab_size=256384, dim=4096, dim_attn=4096, dim_ffn=10240, num_heads=64, encoder_layers=24, decoder_layers=24, num_buckets=32, shared_pos=False, dropout=0.1) cfg.update(**kwargs) return _t5('umt5-xxl', **cfg) class T5EncoderModel: def __init__( self, text_len, dtype=torch.bfloat16, device=torch.cuda.current_device(), checkpoint_path=None, tokenizer_path=None, shard_fn=None, ): self.text_len = text_len self.dtype = dtype self.device = device self.checkpoint_path = checkpoint_path self.tokenizer_path = tokenizer_path # init model model = umt5_xxl( encoder_only=True, return_tokenizer=False, dtype=dtype, device=device).eval().requires_grad_(False) logging.info(f'loading {checkpoint_path}') model.load_state_dict(torch.load(checkpoint_path, map_location='cpu')) self.model = model if shard_fn is not None: self.model = shard_fn(self.model, sync_module_states=False) else: self.model.to(self.device) # init tokenizer self.tokenizer = HuggingfaceTokenizer( name=tokenizer_path, seq_len=text_len, clean='whitespace') def __call__(self, texts, device): ids, mask = self.tokenizer( texts, return_mask=True, add_special_tokens=True) ids = ids.to(device) mask = mask.to(device) seq_lens = mask.gt(0).sum(dim=1).long() context = self.model(ids, mask) return [u[:v] for u, v in zip(context, seq_lens)] ================================================ FILE: wan/modules/tokenizers.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import html import string import ftfy import regex as re from transformers import AutoTokenizer __all__ = ['HuggingfaceTokenizer'] def basic_clean(text): text = ftfy.fix_text(text) text = html.unescape(html.unescape(text)) return text.strip() def whitespace_clean(text): text = re.sub(r'\s+', ' ', text) text = text.strip() return text def canonicalize(text, keep_punctuation_exact_string=None): text = text.replace('_', ' ') if keep_punctuation_exact_string: text = keep_punctuation_exact_string.join( part.translate(str.maketrans('', '', string.punctuation)) for part in text.split(keep_punctuation_exact_string)) else: text = text.translate(str.maketrans('', '', string.punctuation)) text = text.lower() text = re.sub(r'\s+', ' ', text) return text.strip() class HuggingfaceTokenizer: def __init__(self, name, seq_len=None, clean=None, **kwargs): assert clean in (None, 'whitespace', 'lower', 'canonicalize') self.name = name self.seq_len = seq_len self.clean = clean # init tokenizer self.tokenizer = AutoTokenizer.from_pretrained(name, **kwargs) self.vocab_size = self.tokenizer.vocab_size def __call__(self, sequence, **kwargs): return_mask = kwargs.pop('return_mask', False) # arguments _kwargs = {'return_tensors': 'pt'} if self.seq_len is not None: _kwargs.update({ 'padding': 'max_length', 'truncation': True, 'max_length': self.seq_len }) _kwargs.update(**kwargs) # tokenization if isinstance(sequence, str): sequence = [sequence] if self.clean: sequence = [self._clean(u) for u in sequence] ids = self.tokenizer(sequence, **_kwargs) # output if return_mask: return ids.input_ids, ids.attention_mask else: return ids.input_ids def _clean(self, text): if self.clean == 'whitespace': text = whitespace_clean(basic_clean(text)) elif self.clean == 'lower': text = whitespace_clean(basic_clean(text)).lower() elif self.clean == 'canonicalize': text = canonicalize(basic_clean(text)) return text ================================================ FILE: wan/modules/vae2_1.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import logging import torch import torch.cuda.amp as amp import torch.nn as nn import torch.nn.functional as F from einops import rearrange __all__ = [ 'Wan2_1_VAE', ] CACHE_T = 2 class CausalConv3d(nn.Conv3d): """ Causal 3d convolusion. """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self._padding = (self.padding[2], self.padding[2], self.padding[1], self.padding[1], 2 * self.padding[0], 0) self.padding = (0, 0, 0) def forward(self, x, cache_x=None): padding = list(self._padding) if cache_x is not None and self._padding[4] > 0: cache_x = cache_x.to(x.device) x = torch.cat([cache_x, x], dim=2) padding[4] -= cache_x.shape[2] x = F.pad(x, padding) return super().forward(x) class RMS_norm(nn.Module): def __init__(self, dim, channel_first=True, images=True, bias=False): super().__init__() broadcastable_dims = (1, 1, 1) if not images else (1, 1) shape = (dim, *broadcastable_dims) if channel_first else (dim,) self.channel_first = channel_first self.scale = dim**0.5 self.gamma = nn.Parameter(torch.ones(shape)) self.bias = nn.Parameter(torch.zeros(shape)) if bias else 0. def forward(self, x): return F.normalize( x, dim=(1 if self.channel_first else -1)) * self.scale * self.gamma + self.bias class Upsample(nn.Upsample): def forward(self, x): """ Fix bfloat16 support for nearest neighbor interpolation. """ return super().forward(x.float()).type_as(x) class Resample(nn.Module): def __init__(self, dim, mode): assert mode in ('none', 'upsample2d', 'upsample3d', 'downsample2d', 'downsample3d') super().__init__() self.dim = dim self.mode = mode # layers if mode == 'upsample2d': self.resample = nn.Sequential( Upsample(scale_factor=(2., 2.), mode='nearest-exact'), nn.Conv2d(dim, dim // 2, 3, padding=1)) elif mode == 'upsample3d': self.resample = nn.Sequential( Upsample(scale_factor=(2., 2.), mode='nearest-exact'), nn.Conv2d(dim, dim // 2, 3, padding=1)) self.time_conv = CausalConv3d( dim, dim * 2, (3, 1, 1), padding=(1, 0, 0)) elif mode == 'downsample2d': self.resample = nn.Sequential( nn.ZeroPad2d((0, 1, 0, 1)), nn.Conv2d(dim, dim, 3, stride=(2, 2))) elif mode == 'downsample3d': self.resample = nn.Sequential( nn.ZeroPad2d((0, 1, 0, 1)), nn.Conv2d(dim, dim, 3, stride=(2, 2))) self.time_conv = CausalConv3d( dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0)) else: self.resample = nn.Identity() def forward(self, x, feat_cache=None, feat_idx=[0]): b, c, t, h, w = x.size() if self.mode == 'upsample3d': if feat_cache is not None: idx = feat_idx[0] if feat_cache[idx] is None: feat_cache[idx] = 'Rep' feat_idx[0] += 1 else: cache_x = x[:, :, -CACHE_T:, :, :].clone() if cache_x.shape[2] < 2 and feat_cache[ idx] is not None and feat_cache[idx] != 'Rep': # cache last frame of last two chunk cache_x = torch.cat([ feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( cache_x.device), cache_x ], dim=2) if cache_x.shape[2] < 2 and feat_cache[ idx] is not None and feat_cache[idx] == 'Rep': cache_x = torch.cat([ torch.zeros_like(cache_x).to(cache_x.device), cache_x ], dim=2) if feat_cache[idx] == 'Rep': x = self.time_conv(x) else: x = self.time_conv(x, feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 x = x.reshape(b, 2, c, t, h, w) x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]), 3) x = x.reshape(b, c, t * 2, h, w) t = x.shape[2] x = rearrange(x, 'b c t h w -> (b t) c h w') x = self.resample(x) x = rearrange(x, '(b t) c h w -> b c t h w', t=t) if self.mode == 'downsample3d': if feat_cache is not None: idx = feat_idx[0] if feat_cache[idx] is None: feat_cache[idx] = x.clone() feat_idx[0] += 1 else: cache_x = x[:, :, -1:, :, :].clone() # if cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx]!='Rep': # # cache last frame of last two chunk # cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2) x = self.time_conv( torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2)) feat_cache[idx] = cache_x feat_idx[0] += 1 return x def init_weight(self, conv): conv_weight = conv.weight nn.init.zeros_(conv_weight) c1, c2, t, h, w = conv_weight.size() one_matrix = torch.eye(c1, c2) init_matrix = one_matrix nn.init.zeros_(conv_weight) #conv_weight.data[:,:,-1,1,1] = init_matrix * 0.5 conv_weight.data[:, :, 1, 0, 0] = init_matrix #* 0.5 conv.weight.data.copy_(conv_weight) nn.init.zeros_(conv.bias.data) def init_weight2(self, conv): conv_weight = conv.weight.data nn.init.zeros_(conv_weight) c1, c2, t, h, w = conv_weight.size() init_matrix = torch.eye(c1 // 2, c2) #init_matrix = repeat(init_matrix, 'o ... -> (o 2) ...').permute(1,0,2).contiguous().reshape(c1,c2) conv_weight[:c1 // 2, :, -1, 0, 0] = init_matrix conv_weight[c1 // 2:, :, -1, 0, 0] = init_matrix conv.weight.data.copy_(conv_weight) nn.init.zeros_(conv.bias.data) class ResidualBlock(nn.Module): def __init__(self, in_dim, out_dim, dropout=0.0): super().__init__() self.in_dim = in_dim self.out_dim = out_dim # layers self.residual = nn.Sequential( RMS_norm(in_dim, images=False), nn.SiLU(), CausalConv3d(in_dim, out_dim, 3, padding=1), RMS_norm(out_dim, images=False), nn.SiLU(), nn.Dropout(dropout), CausalConv3d(out_dim, out_dim, 3, padding=1)) self.shortcut = CausalConv3d(in_dim, out_dim, 1) \ if in_dim != out_dim else nn.Identity() def forward(self, x, feat_cache=None, feat_idx=[0]): h = self.shortcut(x) for layer in self.residual: if isinstance(layer, CausalConv3d) and feat_cache is not None: idx = feat_idx[0] cache_x = x[:, :, -CACHE_T:, :, :].clone() if cache_x.shape[2] < 2 and feat_cache[idx] is not None: # cache last frame of last two chunk cache_x = torch.cat([ feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( cache_x.device), cache_x ], dim=2) x = layer(x, feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 else: x = layer(x) return x + h class AttentionBlock(nn.Module): """ Causal self-attention with a single head. """ def __init__(self, dim): super().__init__() self.dim = dim # layers self.norm = RMS_norm(dim) self.to_qkv = nn.Conv2d(dim, dim * 3, 1) self.proj = nn.Conv2d(dim, dim, 1) # zero out the last layer params nn.init.zeros_(self.proj.weight) def forward(self, x): identity = x b, c, t, h, w = x.size() x = rearrange(x, 'b c t h w -> (b t) c h w') x = self.norm(x) # compute query, key, value q, k, v = self.to_qkv(x).reshape(b * t, 1, c * 3, -1).permute(0, 1, 3, 2).contiguous().chunk( 3, dim=-1) # apply attention x = F.scaled_dot_product_attention( q, k, v, ) x = x.squeeze(1).permute(0, 2, 1).reshape(b * t, c, h, w) # output x = self.proj(x) x = rearrange(x, '(b t) c h w-> b c t h w', t=t) return x + identity class Encoder3d(nn.Module): def __init__(self, dim=128, z_dim=4, dim_mult=[1, 2, 4, 4], num_res_blocks=2, attn_scales=[], temperal_downsample=[True, True, False], dropout=0.0): super().__init__() self.dim = dim self.z_dim = z_dim self.dim_mult = dim_mult self.num_res_blocks = num_res_blocks self.attn_scales = attn_scales self.temperal_downsample = temperal_downsample # dimensions dims = [dim * u for u in [1] + dim_mult] scale = 1.0 # init block self.conv1 = CausalConv3d(3, dims[0], 3, padding=1) # downsample blocks downsamples = [] for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): # residual (+attention) blocks for _ in range(num_res_blocks): downsamples.append(ResidualBlock(in_dim, out_dim, dropout)) if scale in attn_scales: downsamples.append(AttentionBlock(out_dim)) in_dim = out_dim # downsample block if i != len(dim_mult) - 1: mode = 'downsample3d' if temperal_downsample[ i] else 'downsample2d' downsamples.append(Resample(out_dim, mode=mode)) scale /= 2.0 self.downsamples = nn.Sequential(*downsamples) # middle blocks self.middle = nn.Sequential( ResidualBlock(out_dim, out_dim, dropout), AttentionBlock(out_dim), ResidualBlock(out_dim, out_dim, dropout)) # output blocks self.head = nn.Sequential( RMS_norm(out_dim, images=False), nn.SiLU(), CausalConv3d(out_dim, z_dim, 3, padding=1)) def forward(self, x, feat_cache=None, feat_idx=[0]): if feat_cache is not None: idx = feat_idx[0] cache_x = x[:, :, -CACHE_T:, :, :].clone() if cache_x.shape[2] < 2 and feat_cache[idx] is not None: # cache last frame of last two chunk cache_x = torch.cat([ feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( cache_x.device), cache_x ], dim=2) x = self.conv1(x, feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 else: x = self.conv1(x) ## downsamples for layer in self.downsamples: if feat_cache is not None: x = layer(x, feat_cache, feat_idx) else: x = layer(x) ## middle for layer in self.middle: if isinstance(layer, ResidualBlock) and feat_cache is not None: x = layer(x, feat_cache, feat_idx) else: x = layer(x) ## head for layer in self.head: if isinstance(layer, CausalConv3d) and feat_cache is not None: idx = feat_idx[0] cache_x = x[:, :, -CACHE_T:, :, :].clone() if cache_x.shape[2] < 2 and feat_cache[idx] is not None: # cache last frame of last two chunk cache_x = torch.cat([ feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( cache_x.device), cache_x ], dim=2) x = layer(x, feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 else: x = layer(x) return x class Decoder3d(nn.Module): def __init__(self, dim=128, z_dim=4, dim_mult=[1, 2, 4, 4], num_res_blocks=2, attn_scales=[], temperal_upsample=[False, True, True], dropout=0.0): super().__init__() self.dim = dim self.z_dim = z_dim self.dim_mult = dim_mult self.num_res_blocks = num_res_blocks self.attn_scales = attn_scales self.temperal_upsample = temperal_upsample # dimensions dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]] scale = 1.0 / 2**(len(dim_mult) - 2) # init block self.conv1 = CausalConv3d(z_dim, dims[0], 3, padding=1) # middle blocks self.middle = nn.Sequential( ResidualBlock(dims[0], dims[0], dropout), AttentionBlock(dims[0]), ResidualBlock(dims[0], dims[0], dropout)) # upsample blocks upsamples = [] for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): # residual (+attention) blocks if i == 1 or i == 2 or i == 3: in_dim = in_dim // 2 for _ in range(num_res_blocks + 1): upsamples.append(ResidualBlock(in_dim, out_dim, dropout)) if scale in attn_scales: upsamples.append(AttentionBlock(out_dim)) in_dim = out_dim # upsample block if i != len(dim_mult) - 1: mode = 'upsample3d' if temperal_upsample[i] else 'upsample2d' upsamples.append(Resample(out_dim, mode=mode)) scale *= 2.0 self.upsamples = nn.Sequential(*upsamples) # output blocks self.head = nn.Sequential( RMS_norm(out_dim, images=False), nn.SiLU(), CausalConv3d(out_dim, 3, 3, padding=1)) def forward(self, x, feat_cache=None, feat_idx=[0]): ## conv1 if feat_cache is not None: idx = feat_idx[0] cache_x = x[:, :, -CACHE_T:, :, :].clone() if cache_x.shape[2] < 2 and feat_cache[idx] is not None: # cache last frame of last two chunk cache_x = torch.cat([ feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( cache_x.device), cache_x ], dim=2) x = self.conv1(x, feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 else: x = self.conv1(x) ## middle for layer in self.middle: if isinstance(layer, ResidualBlock) and feat_cache is not None: x = layer(x, feat_cache, feat_idx) else: x = layer(x) ## upsamples for layer in self.upsamples: if feat_cache is not None: x = layer(x, feat_cache, feat_idx) else: x = layer(x) ## head for layer in self.head: if isinstance(layer, CausalConv3d) and feat_cache is not None: idx = feat_idx[0] cache_x = x[:, :, -CACHE_T:, :, :].clone() if cache_x.shape[2] < 2 and feat_cache[idx] is not None: # cache last frame of last two chunk cache_x = torch.cat([ feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( cache_x.device), cache_x ], dim=2) x = layer(x, feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 else: x = layer(x) return x def count_conv3d(model): count = 0 for m in model.modules(): if isinstance(m, CausalConv3d): count += 1 return count class WanVAE_(nn.Module): def __init__(self, dim=128, z_dim=4, dim_mult=[1, 2, 4, 4], num_res_blocks=2, attn_scales=[], temperal_downsample=[True, True, False], dropout=0.0): super().__init__() self.dim = dim self.z_dim = z_dim self.dim_mult = dim_mult self.num_res_blocks = num_res_blocks self.attn_scales = attn_scales self.temperal_downsample = temperal_downsample self.temperal_upsample = temperal_downsample[::-1] # modules self.encoder = Encoder3d(dim, z_dim * 2, dim_mult, num_res_blocks, attn_scales, self.temperal_downsample, dropout) self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1) self.conv2 = CausalConv3d(z_dim, z_dim, 1) self.decoder = Decoder3d(dim, z_dim, dim_mult, num_res_blocks, attn_scales, self.temperal_upsample, dropout) def forward(self, x): mu, log_var = self.encode(x) z = self.reparameterize(mu, log_var) x_recon = self.decode(z) return x_recon, mu, log_var def encode(self, x, scale): self.clear_cache() ## cache t = x.shape[2] iter_ = 1 + (t - 1) // 4 ## 对encode输入的x，按时间拆分为1、4、4、4.... for i in range(iter_): self._enc_conv_idx = [0] if i == 0: out = self.encoder( x[:, :, :1, :, :], feat_cache=self._enc_feat_map, feat_idx=self._enc_conv_idx) else: out_ = self.encoder( x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :], feat_cache=self._enc_feat_map, feat_idx=self._enc_conv_idx) out = torch.cat([out, out_], 2) mu, log_var = self.conv1(out).chunk(2, dim=1) if isinstance(scale[0], torch.Tensor): mu = (mu - scale[0].view(1, self.z_dim, 1, 1, 1)) * scale[1].view( 1, self.z_dim, 1, 1, 1) else: mu = (mu - scale[0]) * scale[1] self.clear_cache() return mu def decode(self, z, scale): self.clear_cache() # z: [b,c,t,h,w] if isinstance(scale[0], torch.Tensor): z = z / scale[1].view(1, self.z_dim, 1, 1, 1) + scale[0].view( 1, self.z_dim, 1, 1, 1) else: z = z / scale[1] + scale[0] iter_ = z.shape[2] x = self.conv2(z) for i in range(iter_): self._conv_idx = [0] if i == 0: out = self.decoder( x[:, :, i:i + 1, :, :], feat_cache=self._feat_map, feat_idx=self._conv_idx) else: out_ = self.decoder( x[:, :, i:i + 1, :, :], feat_cache=self._feat_map, feat_idx=self._conv_idx) out = torch.cat([out, out_], 2) self.clear_cache() return out def reparameterize(self, mu, log_var): std = torch.exp(0.5 * log_var) eps = torch.randn_like(std) return eps * std + mu def sample(self, imgs, deterministic=False): mu, log_var = self.encode(imgs) if deterministic: return mu std = torch.exp(0.5 * log_var.clamp(-30.0, 20.0)) return mu + std * torch.randn_like(std) def clear_cache(self): self._conv_num = count_conv3d(self.decoder) self._conv_idx = [0] self._feat_map = [None] * self._conv_num #cache encode self._enc_conv_num = count_conv3d(self.encoder) self._enc_conv_idx = [0] self._enc_feat_map = [None] * self._enc_conv_num def _video_vae(pretrained_path=None, z_dim=None, device='cpu', **kwargs): """ Autoencoder3d adapted from Stable Diffusion 1.x, 2.x and XL. """ # params cfg = dict( dim=96, z_dim=z_dim, dim_mult=[1, 2, 4, 4], num_res_blocks=2, attn_scales=[], temperal_downsample=[False, True, True], dropout=0.0) cfg.update(**kwargs) # init model with torch.device('meta'): model = WanVAE_(**cfg) # load checkpoint logging.info(f'loading {pretrained_path}') model.load_state_dict( torch.load(pretrained_path, map_location=device), assign=True) return model class Wan2_1_VAE: def __init__(self, z_dim=16, vae_pth='cache/vae_step_411000.pth', dtype=torch.float, device="cuda"): self.dtype = dtype self.device = device mean = [ -0.7571, -0.7089, -0.9113, 0.1075, -0.1745, 0.9653, -0.1517, 1.5508, 0.4134, -0.0715, 0.5517, -0.3632, -0.1922, -0.9497, 0.2503, -0.2921 ] std = [ 2.8184, 1.4541, 2.3275, 2.6558, 1.2196, 1.7708, 2.6052, 2.0743, 3.2687, 2.1526, 2.8652, 1.5579, 1.6382, 1.1253, 2.8251, 1.9160 ] self.mean = torch.tensor(mean, dtype=dtype, device=device) self.std = torch.tensor(std, dtype=dtype, device=device) self.scale = [self.mean, 1.0 / self.std] # init model self.model = _video_vae( pretrained_path=vae_pth, z_dim=z_dim, ).eval().requires_grad_(False).to(device) def encode(self, videos): """ videos: A list of videos each with shape [C, T, H, W]. """ with amp.autocast(dtype=self.dtype): return [ self.model.encode(u.unsqueeze(0), self.scale).float().squeeze(0) for u in videos ] def decode(self, zs): with amp.autocast(dtype=self.dtype): return [ self.model.decode(u.unsqueeze(0), self.scale).float().clamp_(-1, 1).squeeze(0) for u in zs ] ================================================ FILE: wan/modules/vae2_2.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import logging import torch import torch.cuda.amp as amp import torch.nn as nn import torch.nn.functional as F from einops import rearrange __all__ = [ "Wan2_2_VAE", ] CACHE_T = 2 class CausalConv3d(nn.Conv3d): """ Causal 3d convolusion. """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self._padding = ( self.padding[2], self.padding[2], self.padding[1], self.padding[1], 2 * self.padding[0], 0, ) self.padding = (0, 0, 0) def forward(self, x, cache_x=None): padding = list(self._padding) if cache_x is not None and self._padding[4] > 0: cache_x = cache_x.to(x.device) x = torch.cat([cache_x, x], dim=2) padding[4] -= cache_x.shape[2] x = F.pad(x, padding) return super().forward(x) class RMS_norm(nn.Module): def __init__(self, dim, channel_first=True, images=True, bias=False): super().__init__() broadcastable_dims = (1, 1, 1) if not images else (1, 1) shape = (dim, *broadcastable_dims) if channel_first else (dim,) self.channel_first = channel_first self.scale = dim**0.5 self.gamma = nn.Parameter(torch.ones(shape)) self.bias = nn.Parameter(torch.zeros(shape)) if bias else 0.0 def forward(self, x): return (F.normalize(x, dim=(1 if self.channel_first else -1)) * self.scale * self.gamma + self.bias) class Upsample(nn.Upsample): def forward(self, x): """ Fix bfloat16 support for nearest neighbor interpolation. """ return super().forward(x.float()).type_as(x) class Resample(nn.Module): def __init__(self, dim, mode): assert mode in ( "none", "upsample2d", "upsample3d", "downsample2d", "downsample3d", ) super().__init__() self.dim = dim self.mode = mode # layers if mode == "upsample2d": self.resample = nn.Sequential( Upsample(scale_factor=(2.0, 2.0), mode="nearest-exact"), nn.Conv2d(dim, dim, 3, padding=1), ) elif mode == "upsample3d": self.resample = nn.Sequential( Upsample(scale_factor=(2.0, 2.0), mode="nearest-exact"), nn.Conv2d(dim, dim, 3, padding=1), # nn.Conv2d(dim, dim//2, 3, padding=1) ) self.time_conv = CausalConv3d( dim, dim * 2, (3, 1, 1), padding=(1, 0, 0)) elif mode == "downsample2d": self.resample = nn.Sequential( nn.ZeroPad2d((0, 1, 0, 1)), nn.Conv2d(dim, dim, 3, stride=(2, 2))) elif mode == "downsample3d": self.resample = nn.Sequential( nn.ZeroPad2d((0, 1, 0, 1)), nn.Conv2d(dim, dim, 3, stride=(2, 2))) self.time_conv = CausalConv3d( dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0)) else: self.resample = nn.Identity() def forward(self, x, feat_cache=None, feat_idx=[0]): b, c, t, h, w = x.size() if self.mode == "upsample3d": if feat_cache is not None: idx = feat_idx[0] if feat_cache[idx] is None: feat_cache[idx] = "Rep" feat_idx[0] += 1 else: cache_x = x[:, :, -CACHE_T:, :, :].clone() if (cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx] != "Rep"): # cache last frame of last two chunk cache_x = torch.cat( [ feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( cache_x.device), cache_x, ], dim=2, ) if (cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx] == "Rep"): cache_x = torch.cat( [ torch.zeros_like(cache_x).to(cache_x.device), cache_x ], dim=2, ) if feat_cache[idx] == "Rep": x = self.time_conv(x) else: x = self.time_conv(x, feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 x = x.reshape(b, 2, c, t, h, w) x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]), 3) x = x.reshape(b, c, t * 2, h, w) t = x.shape[2] x = rearrange(x, "b c t h w -> (b t) c h w") x = self.resample(x) x = rearrange(x, "(b t) c h w -> b c t h w", t=t) if self.mode == "downsample3d": if feat_cache is not None: idx = feat_idx[0] if feat_cache[idx] is None: feat_cache[idx] = x.clone() feat_idx[0] += 1 else: cache_x = x[:, :, -1:, :, :].clone() x = self.time_conv( torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2)) feat_cache[idx] = cache_x feat_idx[0] += 1 return x def init_weight(self, conv): conv_weight = conv.weight.detach().clone() nn.init.zeros_(conv_weight) c1, c2, t, h, w = conv_weight.size() one_matrix = torch.eye(c1, c2) init_matrix = one_matrix nn.init.zeros_(conv_weight) conv_weight.data[:, :, 1, 0, 0] = init_matrix # * 0.5 conv.weight = nn.Parameter(conv_weight) nn.init.zeros_(conv.bias.data) def init_weight2(self, conv): conv_weight = conv.weight.data.detach().clone() nn.init.zeros_(conv_weight) c1, c2, t, h, w = conv_weight.size() init_matrix = torch.eye(c1 // 2, c2) conv_weight[:c1 // 2, :, -1, 0, 0] = init_matrix conv_weight[c1 // 2:, :, -1, 0, 0] = init_matrix conv.weight = nn.Parameter(conv_weight) nn.init.zeros_(conv.bias.data) class ResidualBlock(nn.Module): def __init__(self, in_dim, out_dim, dropout=0.0): super().__init__() self.in_dim = in_dim self.out_dim = out_dim # layers self.residual = nn.Sequential( RMS_norm(in_dim, images=False), nn.SiLU(), CausalConv3d(in_dim, out_dim, 3, padding=1), RMS_norm(out_dim, images=False), nn.SiLU(), nn.Dropout(dropout), CausalConv3d(out_dim, out_dim, 3, padding=1), ) self.shortcut = ( CausalConv3d(in_dim, out_dim, 1) if in_dim != out_dim else nn.Identity()) def forward(self, x, feat_cache=None, feat_idx=[0]): h = self.shortcut(x) for layer in self.residual: if isinstance(layer, CausalConv3d) and feat_cache is not None: idx = feat_idx[0] cache_x = x[:, :, -CACHE_T:, :, :].clone() if cache_x.shape[2] < 2 and feat_cache[idx] is not None: # cache last frame of last two chunk cache_x = torch.cat( [ feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( cache_x.device), cache_x, ], dim=2, ) x = layer(x, feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 else: x = layer(x) return x + h class AttentionBlock(nn.Module): """ Causal self-attention with a single head. """ def __init__(self, dim): super().__init__() self.dim = dim # layers self.norm = RMS_norm(dim) self.to_qkv = nn.Conv2d(dim, dim * 3, 1) self.proj = nn.Conv2d(dim, dim, 1) # zero out the last layer params nn.init.zeros_(self.proj.weight) def forward(self, x): identity = x b, c, t, h, w = x.size() x = rearrange(x, "b c t h w -> (b t) c h w") x = self.norm(x) # compute query, key, value q, k, v = ( self.to_qkv(x).reshape(b * t, 1, c * 3, -1).permute(0, 1, 3, 2).contiguous().chunk(3, dim=-1)) # apply attention x = F.scaled_dot_product_attention( q, k, v, ) x = x.squeeze(1).permute(0, 2, 1).reshape(b * t, c, h, w) # output x = self.proj(x) x = rearrange(x, "(b t) c h w-> b c t h w", t=t) return x + identity def patchify(x, patch_size): if patch_size == 1: return x if x.dim() == 4: x = rearrange( x, "b c (h q) (w r) -> b (c r q) h w", q=patch_size, r=patch_size) elif x.dim() == 5: x = rearrange( x, "b c f (h q) (w r) -> b (c r q) f h w", q=patch_size, r=patch_size, ) else: raise ValueError(f"Invalid input shape: {x.shape}") return x def unpatchify(x, patch_size): if patch_size == 1: return x if x.dim() == 4: x = rearrange( x, "b (c r q) h w -> b c (h q) (w r)", q=patch_size, r=patch_size) elif x.dim() == 5: x = rearrange( x, "b (c r q) f h w -> b c f (h q) (w r)", q=patch_size, r=patch_size, ) return x class AvgDown3D(nn.Module): def __init__( self, in_channels, out_channels, factor_t, factor_s=1, ): super().__init__() self.in_channels = in_channels self.out_channels = out_channels self.factor_t = factor_t self.factor_s = factor_s self.factor = self.factor_t * self.factor_s * self.factor_s assert in_channels * self.factor % out_channels == 0 self.group_size = in_channels * self.factor // out_channels def forward(self, x: torch.Tensor) -> torch.Tensor: pad_t = (self.factor_t - x.shape[2] % self.factor_t) % self.factor_t pad = (0, 0, 0, 0, pad_t, 0) x = F.pad(x, pad) B, C, T, H, W = x.shape x = x.view( B, C, T // self.factor_t, self.factor_t, H // self.factor_s, self.factor_s, W // self.factor_s, self.factor_s, ) x = x.permute(0, 1, 3, 5, 7, 2, 4, 6).contiguous() x = x.view( B, C * self.factor, T // self.factor_t, H // self.factor_s, W // self.factor_s, ) x = x.view( B, self.out_channels, self.group_size, T // self.factor_t, H // self.factor_s, W // self.factor_s, ) x = x.mean(dim=2) return x class DupUp3D(nn.Module): def __init__( self, in_channels: int, out_channels: int, factor_t, factor_s=1, ): super().__init__() self.in_channels = in_channels self.out_channels = out_channels self.factor_t = factor_t self.factor_s = factor_s self.factor = self.factor_t * self.factor_s * self.factor_s assert out_channels * self.factor % in_channels == 0 self.repeats = out_channels * self.factor // in_channels def forward(self, x: torch.Tensor, first_chunk=False) -> torch.Tensor: x = x.repeat_interleave(self.repeats, dim=1) x = x.view( x.size(0), self.out_channels, self.factor_t, self.factor_s, self.factor_s, x.size(2), x.size(3), x.size(4), ) x = x.permute(0, 1, 5, 2, 6, 3, 7, 4).contiguous() x = x.view( x.size(0), self.out_channels, x.size(2) * self.factor_t, x.size(4) * self.factor_s, x.size(6) * self.factor_s, ) if first_chunk: x = x[:, :, self.factor_t - 1:, :, :] return x class Down_ResidualBlock(nn.Module): def __init__(self, in_dim, out_dim, dropout, mult, temperal_downsample=False, down_flag=False): super().__init__() # Shortcut path with downsample self.avg_shortcut = AvgDown3D( in_dim, out_dim, factor_t=2 if temperal_downsample else 1, factor_s=2 if down_flag else 1, ) # Main path with residual blocks and downsample downsamples = [] for _ in range(mult): downsamples.append(ResidualBlock(in_dim, out_dim, dropout)) in_dim = out_dim # Add the final downsample block if down_flag: mode = "downsample3d" if temperal_downsample else "downsample2d" downsamples.append(Resample(out_dim, mode=mode)) self.downsamples = nn.Sequential(*downsamples) def forward(self, x, feat_cache=None, feat_idx=[0]): x_copy = x.clone() for module in self.downsamples: x = module(x, feat_cache, feat_idx) return x + self.avg_shortcut(x_copy) class Up_ResidualBlock(nn.Module): def __init__(self, in_dim, out_dim, dropout, mult, temperal_upsample=False, up_flag=False): super().__init__() # Shortcut path with upsample if up_flag: self.avg_shortcut = DupUp3D( in_dim, out_dim, factor_t=2 if temperal_upsample else 1, factor_s=2 if up_flag else 1, ) else: self.avg_shortcut = None # Main path with residual blocks and upsample upsamples = [] for _ in range(mult): upsamples.append(ResidualBlock(in_dim, out_dim, dropout)) in_dim = out_dim # Add the final upsample block if up_flag: mode = "upsample3d" if temperal_upsample else "upsample2d" upsamples.append(Resample(out_dim, mode=mode)) self.upsamples = nn.Sequential(*upsamples) def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=False): x_main = x.clone() for module in self.upsamples: x_main = module(x_main, feat_cache, feat_idx) if self.avg_shortcut is not None: x_shortcut = self.avg_shortcut(x, first_chunk) return x_main + x_shortcut else: return x_main class Encoder3d(nn.Module): def __init__( self, dim=128, z_dim=4, dim_mult=[1, 2, 4, 4], num_res_blocks=2, attn_scales=[], temperal_downsample=[True, True, False], dropout=0.0, ): super().__init__() self.dim = dim self.z_dim = z_dim self.dim_mult = dim_mult self.num_res_blocks = num_res_blocks self.attn_scales = attn_scales self.temperal_downsample = temperal_downsample # dimensions dims = [dim * u for u in [1] + dim_mult] scale = 1.0 # init block self.conv1 = CausalConv3d(12, dims[0], 3, padding=1) # downsample blocks downsamples = [] for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): t_down_flag = ( temperal_downsample[i] if i < len(temperal_downsample) else False) downsamples.append( Down_ResidualBlock( in_dim=in_dim, out_dim=out_dim, dropout=dropout, mult=num_res_blocks, temperal_downsample=t_down_flag, down_flag=i != len(dim_mult) - 1, )) scale /= 2.0 self.downsamples = nn.Sequential(*downsamples) # middle blocks self.middle = nn.Sequential( ResidualBlock(out_dim, out_dim, dropout), AttentionBlock(out_dim), ResidualBlock(out_dim, out_dim, dropout), ) # # output blocks self.head = nn.Sequential( RMS_norm(out_dim, images=False), nn.SiLU(), CausalConv3d(out_dim, z_dim, 3, padding=1), ) def forward(self, x, feat_cache=None, feat_idx=[0]): if feat_cache is not None: idx = feat_idx[0] cache_x = x[:, :, -CACHE_T:, :, :].clone() if cache_x.shape[2] < 2 and feat_cache[idx] is not None: cache_x = torch.cat( [ feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( cache_x.device), cache_x, ], dim=2, ) x = self.conv1(x, feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 else: x = self.conv1(x) ## downsamples for layer in self.downsamples: if feat_cache is not None: x = layer(x, feat_cache, feat_idx) else: x = layer(x) ## middle for layer in self.middle: if isinstance(layer, ResidualBlock) and feat_cache is not None: x = layer(x, feat_cache, feat_idx) else: x = layer(x) ## head for layer in self.head: if isinstance(layer, CausalConv3d) and feat_cache is not None: idx = feat_idx[0] cache_x = x[:, :, -CACHE_T:, :, :].clone() if cache_x.shape[2] < 2 and feat_cache[idx] is not None: cache_x = torch.cat( [ feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( cache_x.device), cache_x, ], dim=2, ) x = layer(x, feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 else: x = layer(x) return x class Decoder3d(nn.Module): def __init__( self, dim=128, z_dim=4, dim_mult=[1, 2, 4, 4], num_res_blocks=2, attn_scales=[], temperal_upsample=[False, True, True], dropout=0.0, ): super().__init__() self.dim = dim self.z_dim = z_dim self.dim_mult = dim_mult self.num_res_blocks = num_res_blocks self.attn_scales = attn_scales self.temperal_upsample = temperal_upsample # dimensions dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]] scale = 1.0 / 2**(len(dim_mult) - 2) # init block self.conv1 = CausalConv3d(z_dim, dims[0], 3, padding=1) # middle blocks self.middle = nn.Sequential( ResidualBlock(dims[0], dims[0], dropout), AttentionBlock(dims[0]), ResidualBlock(dims[0], dims[0], dropout), ) # upsample blocks upsamples = [] for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): t_up_flag = temperal_upsample[i] if i < len( temperal_upsample) else False upsamples.append( Up_ResidualBlock( in_dim=in_dim, out_dim=out_dim, dropout=dropout, mult=num_res_blocks + 1, temperal_upsample=t_up_flag, up_flag=i != len(dim_mult) - 1, )) self.upsamples = nn.Sequential(*upsamples) # output blocks self.head = nn.Sequential( RMS_norm(out_dim, images=False), nn.SiLU(), CausalConv3d(out_dim, 12, 3, padding=1), ) def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=False): if feat_cache is not None: idx = feat_idx[0] cache_x = x[:, :, -CACHE_T:, :, :].clone() if cache_x.shape[2] < 2 and feat_cache[idx] is not None: cache_x = torch.cat( [ feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( cache_x.device), cache_x, ], dim=2, ) x = self.conv1(x, feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 else: x = self.conv1(x) for layer in self.middle: if isinstance(layer, ResidualBlock) and feat_cache is not None: x = layer(x, feat_cache, feat_idx) else: x = layer(x) ## upsamples for layer in self.upsamples: if feat_cache is not None: x = layer(x, feat_cache, feat_idx, first_chunk) else: x = layer(x) ## head for layer in self.head: if isinstance(layer, CausalConv3d) and feat_cache is not None: idx = feat_idx[0] cache_x = x[:, :, -CACHE_T:, :, :].clone() if cache_x.shape[2] < 2 and feat_cache[idx] is not None: cache_x = torch.cat( [ feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( cache_x.device), cache_x, ], dim=2, ) x = layer(x, feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 else: x = layer(x) return x def count_conv3d(model): count = 0 for m in model.modules(): if isinstance(m, CausalConv3d): count += 1 return count class WanVAE_(nn.Module): def __init__( self, dim=160, dec_dim=256, z_dim=16, dim_mult=[1, 2, 4, 4], num_res_blocks=2, attn_scales=[], temperal_downsample=[True, True, False], dropout=0.0, ): super().__init__() self.dim = dim self.z_dim = z_dim self.dim_mult = dim_mult self.num_res_blocks = num_res_blocks self.attn_scales = attn_scales self.temperal_downsample = temperal_downsample self.temperal_upsample = temperal_downsample[::-1] # modules self.encoder = Encoder3d( dim, z_dim * 2, dim_mult, num_res_blocks, attn_scales, self.temperal_downsample, dropout, ) self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1) self.conv2 = CausalConv3d(z_dim, z_dim, 1) self.decoder = Decoder3d( dec_dim, z_dim, dim_mult, num_res_blocks, attn_scales, self.temperal_upsample, dropout, ) def forward(self, x, scale=[0, 1]): mu = self.encode(x, scale) x_recon = self.decode(mu, scale) return x_recon, mu def encode(self, x, scale): self.clear_cache() x = patchify(x, patch_size=2) t = x.shape[2] iter_ = 1 + (t - 1) // 4 for i in range(iter_): self._enc_conv_idx = [0] if i == 0: out = self.encoder( x[:, :, :1, :, :], feat_cache=self._enc_feat_map, feat_idx=self._enc_conv_idx, ) else: out_ = self.encoder( x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :], feat_cache=self._enc_feat_map, feat_idx=self._enc_conv_idx, ) out = torch.cat([out, out_], 2) mu, log_var = self.conv1(out).chunk(2, dim=1) if isinstance(scale[0], torch.Tensor): mu = (mu - scale[0].view(1, self.z_dim, 1, 1, 1)) * scale[1].view( 1, self.z_dim, 1, 1, 1) else: mu = (mu - scale[0]) * scale[1] self.clear_cache() return mu def decode(self, z, scale): self.clear_cache() if isinstance(scale[0], torch.Tensor): z = z / scale[1].view(1, self.z_dim, 1, 1, 1) + scale[0].view( 1, self.z_dim, 1, 1, 1) else: z = z / scale[1] + scale[0] iter_ = z.shape[2] x = self.conv2(z) for i in range(iter_): self._conv_idx = [0] if i == 0: out = self.decoder( x[:, :, i:i + 1, :, :], feat_cache=self._feat_map, feat_idx=self._conv_idx, first_chunk=True, ) else: out_ = self.decoder( x[:, :, i:i + 1, :, :], feat_cache=self._feat_map, feat_idx=self._conv_idx, ) out = torch.cat([out, out_], 2) out = unpatchify(out, patch_size=2) self.clear_cache() return out def reparameterize(self, mu, log_var): std = torch.exp(0.5 * log_var) eps = torch.randn_like(std) return eps * std + mu def sample(self, imgs, deterministic=False): mu, log_var = self.encode(imgs) if deterministic: return mu std = torch.exp(0.5 * log_var.clamp(-30.0, 20.0)) return mu + std * torch.randn_like(std) def clear_cache(self): self._conv_num = count_conv3d(self.decoder) self._conv_idx = [0] self._feat_map = [None] * self._conv_num # cache encode self._enc_conv_num = count_conv3d(self.encoder) self._enc_conv_idx = [0] self._enc_feat_map = [None] * self._enc_conv_num def _video_vae(pretrained_path=None, z_dim=16, dim=160, device="cpu", **kwargs): # params cfg = dict( dim=dim, z_dim=z_dim, dim_mult=[1, 2, 4, 4], num_res_blocks=2, attn_scales=[], temperal_downsample=[True, True, True], dropout=0.0, ) cfg.update(**kwargs) # init model with torch.device("meta"): model = WanVAE_(**cfg) # load checkpoint logging.info(f"loading {pretrained_path}") model.load_state_dict( torch.load(pretrained_path, map_location=device), assign=True) return model class Wan2_2_VAE: def __init__( self, z_dim=48, c_dim=160, vae_pth=None, dim_mult=[1, 2, 4, 4], temperal_downsample=[False, True, True], dtype=torch.float, device="cuda", ): self.dtype = dtype self.device = device mean = torch.tensor( [ -0.2289, -0.0052, -0.1323, -0.2339, -0.2799, 0.0174, 0.1838, 0.1557, -0.1382, 0.0542, 0.2813, 0.0891, 0.1570, -0.0098, 0.0375, -0.1825, -0.2246, -0.1207, -0.0698, 0.5109, 0.2665, -0.2108, -0.2158, 0.2502, -0.2055, -0.0322, 0.1109, 0.1567, -0.0729, 0.0899, -0.2799, -0.1230, -0.0313, -0.1649, 0.0117, 0.0723, -0.2839, -0.2083, -0.0520, 0.3748, 0.0152, 0.1957, 0.1433, -0.2944, 0.3573, -0.0548, -0.1681, -0.0667, ], dtype=dtype, device=device, ) std = torch.tensor( [ 0.4765, 1.0364, 0.4514, 1.1677, 0.5313, 0.4990, 0.4818, 0.5013, 0.8158, 1.0344, 0.5894, 1.0901, 0.6885, 0.6165, 0.8454, 0.4978, 0.5759, 0.3523, 0.7135, 0.6804, 0.5833, 1.4146, 0.8986, 0.5659, 0.7069, 0.5338, 0.4889, 0.4917, 0.4069, 0.4999, 0.6866, 0.4093, 0.5709, 0.6065, 0.6415, 0.4944, 0.5726, 1.2042, 0.5458, 1.6887, 0.3971, 1.0600, 0.3943, 0.5537, 0.5444, 0.4089, 0.7468, 0.7744, ], dtype=dtype, device=device, ) self.scale = [mean, 1.0 / std] # init model self.model = ( _video_vae( pretrained_path=vae_pth, z_dim=z_dim, dim=c_dim, dim_mult=dim_mult, temperal_downsample=temperal_downsample, ).eval().requires_grad_(False).to(device)) def encode(self, videos): try: if not isinstance(videos, list): raise TypeError("videos should be a list") with amp.autocast(dtype=self.dtype): return [ self.model.encode(u.unsqueeze(0), self.scale).float().squeeze(0) for u in videos ] except TypeError as e: logging.info(e) return None def decode(self, zs): try: if not isinstance(zs, list): raise TypeError("zs should be a list") with amp.autocast(dtype=self.dtype): return [ self.model.decode(u.unsqueeze(0), self.scale).float().clamp_(-1, 1).squeeze(0) for u in zs ] except TypeError as e: logging.info(e) return None ================================================ FILE: wan/speech2video.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import gc import logging import math import os import random import sys import types from contextlib import contextmanager from copy import deepcopy from functools import partial import numpy as np import torch import torch.cuda.amp as amp import torch.distributed as dist import torchvision.transforms.functional as TF from decord import VideoReader from PIL import Image from safetensors import safe_open from torchvision import transforms from tqdm import tqdm from .distributed.fsdp import shard_model from .distributed.sequence_parallel import sp_attn_forward, sp_dit_forward from .distributed.util import get_world_size from .modules.s2v.audio_encoder import AudioEncoder from .modules.s2v.model_s2v import WanModel_S2V, sp_attn_forward_s2v from .modules.t5 import T5EncoderModel from .modules.vae2_1 import Wan2_1_VAE from .utils.fm_solvers import ( FlowDPMSolverMultistepScheduler, get_sampling_sigmas, retrieve_timesteps, ) from .utils.fm_solvers_unipc import FlowUniPCMultistepScheduler def load_safetensors(path): tensors = {} with safe_open(path, framework="pt", device="cpu") as f: for key in f.keys(): tensors[key] = f.get_tensor(key) return tensors class WanS2V: def __init__( self, config, checkpoint_dir, device_id=0, rank=0, t5_fsdp=False, dit_fsdp=False, use_sp=False, t5_cpu=False, init_on_cpu=True, convert_model_dtype=False, ): r""" Initializes the image-to-video generation model components. Args: config (EasyDict): Object containing model parameters initialized from config.py checkpoint_dir (`str`): Path to directory containing model checkpoints device_id (`int`, *optional*, defaults to 0): Id of target GPU device rank (`int`, *optional*, defaults to 0): Process rank for distributed training t5_fsdp (`bool`, *optional*, defaults to False): Enable FSDP sharding for T5 model dit_fsdp (`bool`, *optional*, defaults to False): Enable FSDP sharding for DiT model use_sp (`bool`, *optional*, defaults to False): Enable distribution strategy of sequence parallel. t5_cpu (`bool`, *optional*, defaults to False): Whether to place T5 model on CPU. Only works without t5_fsdp. init_on_cpu (`bool`, *optional*, defaults to True): Enable initializing Transformer Model on CPU. Only works without FSDP or USP. convert_model_dtype (`bool`, *optional*, defaults to False): Convert DiT model parameters dtype to 'config.param_dtype'. Only works without FSDP. """ self.device = torch.device(f"cuda:{device_id}") self.config = config self.rank = rank self.t5_cpu = t5_cpu self.init_on_cpu = init_on_cpu self.num_train_timesteps = config.num_train_timesteps self.param_dtype = config.param_dtype if t5_fsdp or dit_fsdp or use_sp: self.init_on_cpu = False shard_fn = partial(shard_model, device_id=device_id) self.text_encoder = T5EncoderModel( text_len=config.text_len, dtype=config.t5_dtype, device=torch.device('cpu'), checkpoint_path=os.path.join(checkpoint_dir, config.t5_checkpoint), tokenizer_path=os.path.join(checkpoint_dir, config.t5_tokenizer), shard_fn=shard_fn if t5_fsdp else None, ) self.vae = Wan2_1_VAE( vae_pth=os.path.join(checkpoint_dir, config.vae_checkpoint), device=self.device) logging.info(f"Creating WanModel from {checkpoint_dir}") if not dit_fsdp: self.noise_model = WanModel_S2V.from_pretrained( checkpoint_dir, torch_dtype=self.param_dtype, device_map=self.device) else: self.noise_model = WanModel_S2V.from_pretrained( checkpoint_dir, torch_dtype=self.param_dtype) self.noise_model = self._configure_model( model=self.noise_model, use_sp=use_sp, dit_fsdp=dit_fsdp, shard_fn=shard_fn, convert_model_dtype=convert_model_dtype) self.audio_encoder = AudioEncoder( model_id=os.path.join(checkpoint_dir, "wav2vec2-large-xlsr-53-english")) if use_sp: self.sp_size = get_world_size() else: self.sp_size = 1 self.sample_neg_prompt = config.sample_neg_prompt self.motion_frames = config.transformer.motion_frames self.drop_first_motion = config.drop_first_motion self.fps = config.sample_fps self.audio_sample_m = 0 def _configure_model(self, model, use_sp, dit_fsdp, shard_fn, convert_model_dtype): """ Configures a model object. This includes setting evaluation modes, applying distributed parallel strategy, and handling device placement. Args: model (torch.nn.Module): The model instance to configure. use_sp (`bool`): Enable distribution strategy of sequence parallel. dit_fsdp (`bool`): Enable FSDP sharding for DiT model. shard_fn (callable): The function to apply FSDP sharding. convert_model_dtype (`bool`): Convert DiT model parameters dtype to 'config.param_dtype'. Only works without FSDP. Returns: torch.nn.Module: The configured model. """ model.eval().requires_grad_(False) if use_sp: for block in model.blocks: block.self_attn.forward = types.MethodType( sp_attn_forward_s2v, block.self_attn) model.use_context_parallel = True if dist.is_initialized(): dist.barrier() if dit_fsdp: model = shard_fn(model) else: if convert_model_dtype: model.to(self.param_dtype) if not self.init_on_cpu: model.to(self.device) return model def get_size_less_than_area(self, height, width, target_area=1024 * 704, divisor=64): if height * width <= target_area: # If the original image area is already less than or equal to the target, # no resizing is needed—just padding. Still need to ensure that the padded area doesn't exceed the target. max_upper_area = target_area min_scale = 0.1 max_scale = 1.0 else: # Resize to fit within the target area and then pad to multiples of `divisor` max_upper_area = target_area # Maximum allowed total pixel count after padding d = divisor - 1 b = d * (height + width) a = height * width c = d**2 - max_upper_area # Calculate scale boundaries using quadratic equation min_scale = (-b + math.sqrt(b**2 - 2 * a * c)) / ( 2 * a) # Scale when maximum padding is applied max_scale = math.sqrt(max_upper_area / (height * width)) # Scale without any padding # We want to choose the largest possible scale such that the final padded area does not exceed max_upper_area # Use binary search-like iteration to find this scale find_it = False for i in range(100): scale = max_scale - (max_scale - min_scale) * i / 100 new_height, new_width = int(height * scale), int(width * scale) # Pad to make dimensions divisible by 64 pad_height = (64 - new_height % 64) % 64 pad_width = (64 - new_width % 64) % 64 pad_top = pad_height // 2 pad_bottom = pad_height - pad_top pad_left = pad_width // 2 pad_right = pad_width - pad_left padded_height, padded_width = new_height + pad_height, new_width + pad_width if padded_height * padded_width <= max_upper_area: find_it = True break if find_it: return padded_height, padded_width else: # Fallback: calculate target dimensions based on aspect ratio and divisor alignment aspect_ratio = width / height target_width = int( (target_area * aspect_ratio)**0.5 // divisor * divisor) target_height = int( (target_area / aspect_ratio)**0.5 // divisor * divisor) # Ensure the result is not larger than the original resolution if target_width >= width or target_height >= height: target_width = int(width // divisor * divisor) target_height = int(height // divisor * divisor) return target_height, target_width def prepare_default_cond_input(self, map_shape=[3, 12, 64, 64], motion_frames=5, lat_motion_frames=2, enable_mano=False, enable_kp=False, enable_pose=False): default_value = [1.0, -1.0, -1.0] cond_enable = [enable_mano, enable_kp, enable_pose] cond = [] for d, c in zip(default_value, cond_enable): if c: map_value = torch.ones( map_shape, dtype=self.param_dtype, device=self.device) * d cond_lat = torch.cat([ map_value[:, :, 0:1].repeat(1, 1, motion_frames, 1, 1), map_value ], dim=2) cond_lat = torch.stack( self.vae.encode(cond_lat.to( self.param_dtype)))[:, :, lat_motion_frames:].to( self.param_dtype) cond.append(cond_lat) if len(cond) >= 1: cond = torch.cat(cond, dim=1) else: cond = None return cond def encode_audio(self, audio_path, infer_frames): z = self.audio_encoder.extract_audio_feat( audio_path, return_all_layers=True) audio_embed_bucket, num_repeat = self.audio_encoder.get_audio_embed_bucket_fps( z, fps=self.fps, batch_frames=infer_frames, m=self.audio_sample_m) audio_embed_bucket = audio_embed_bucket.to(self.device, self.param_dtype) audio_embed_bucket = audio_embed_bucket.unsqueeze(0) if len(audio_embed_bucket.shape) == 3: audio_embed_bucket = audio_embed_bucket.permute(0, 2, 1) elif len(audio_embed_bucket.shape) == 4: audio_embed_bucket = audio_embed_bucket.permute(0, 2, 3, 1) return audio_embed_bucket, num_repeat def read_last_n_frames(self, video_path, n_frames, target_fps=16, reverse=False): """ Read the last `n_frames` from a video at the specified frame rate. Parameters: video_path (str): Path to the video file. n_frames (int): Number of frames to read. target_fps (int, optional): Target sampling frame rate. Defaults to 16. reverse (bool, optional): Whether to read frames in reverse order. If True, reads the first `n_frames` instead of the last ones. Returns: np.ndarray: A NumPy array of shape [n_frames, H, W, 3], representing the sampled video frames. """ vr = VideoReader(video_path) original_fps = vr.get_avg_fps() total_frames = len(vr) interval = max(1, round(original_fps / target_fps)) required_span = (n_frames - 1) * interval start_frame = max(0, total_frames - required_span - 1) if not reverse else 0 sampled_indices = [] for i in range(n_frames): indice = start_frame + i * interval if indice >= total_frames: break else: sampled_indices.append(indice) return vr.get_batch(sampled_indices).asnumpy() def load_pose_cond(self, pose_video, num_repeat, infer_frames, size): HEIGHT, WIDTH = size if not pose_video is None: pose_seq = self.read_last_n_frames( pose_video, n_frames=infer_frames * num_repeat, target_fps=self.fps, reverse=True) resize_opreat = transforms.Resize(min(HEIGHT, WIDTH)) crop_opreat = transforms.CenterCrop((HEIGHT, WIDTH)) tensor_trans = transforms.ToTensor() cond_tensor = torch.from_numpy(pose_seq) cond_tensor = cond_tensor.permute(0, 3, 1, 2) / 255.0 * 2 - 1.0 cond_tensor = crop_opreat(resize_opreat(cond_tensor)).permute( 1, 0, 2, 3).unsqueeze(0) padding_frame_num = num_repeat * infer_frames - cond_tensor.shape[2] cond_tensor = torch.cat([ cond_tensor, - torch.ones([1, 3, padding_frame_num, HEIGHT, WIDTH]) ], dim=2) cond_tensors = torch.chunk(cond_tensor, num_repeat, dim=2) else: cond_tensors = [-torch.ones([1, 3, infer_frames, HEIGHT, WIDTH])] COND = [] for r in range(len(cond_tensors)): cond = cond_tensors[r] cond = torch.cat([cond[:, :, 0:1].repeat(1, 1, 1, 1, 1), cond], dim=2) cond_lat = torch.stack( self.vae.encode( cond.to(dtype=self.param_dtype, device=self.device)))[:, :, 1:].cpu() # for mem save COND.append(cond_lat) return COND def get_gen_size(self, size, max_area, ref_image_path, pre_video_path): if not size is None: HEIGHT, WIDTH = size else: if pre_video_path: ref_image = self.read_last_n_frames( pre_video_path, n_frames=1)[0] else: ref_image = np.array(Image.open(ref_image_path).convert('RGB')) HEIGHT, WIDTH = ref_image.shape[:2] HEIGHT, WIDTH = self.get_size_less_than_area( HEIGHT, WIDTH, target_area=max_area) return (HEIGHT, WIDTH) def generate( self, input_prompt, ref_image_path, audio_path, enable_tts, tts_prompt_audio, tts_prompt_text, tts_text, num_repeat=1, pose_video=None, max_area=720 * 1280, infer_frames=80, shift=5.0, sample_solver='unipc', sampling_steps=40, guide_scale=5.0, n_prompt="", seed=-1, offload_model=True, init_first_frame=False, ): r""" Generates video frames from input image and text prompt using diffusion process. Args: input_prompt (`str`): Text prompt for content generation. ref_image_path ('str'): Input image path audio_path ('str'): Audio for video driven num_repeat ('int'): Number of clips to generate; will be automatically adjusted based on the audio length pose_video ('str'): If provided, uses a sequence of poses to drive the generated video max_area (`int`, *optional*, defaults to 720*1280): Maximum pixel area for latent space calculation. Controls video resolution scaling infer_frames (`int`, *optional*, defaults to 80): How many frames to generate per clips. The number should be 4n shift (`float`, *optional*, defaults to 5.0): Noise schedule shift parameter. Affects temporal dynamics [NOTE]: If you want to generate a 480p video, it is recommended to set the shift value to 3.0. sample_solver (`str`, *optional*, defaults to 'unipc'): Solver used to sample the video. sampling_steps (`int`, *optional*, defaults to 40): Number of diffusion sampling steps. Higher values improve quality but slow generation guide_scale (`float` or tuple[`float`], *optional*, defaults 5.0): Classifier-free guidance scale. Controls prompt adherence vs. creativity. If tuple, the first guide_scale will be used for low noise model and the second guide_scale will be used for high noise model. n_prompt (`str`, *optional*, defaults to ""): Negative prompt for content exclusion. If not given, use `config.sample_neg_prompt` seed (`int`, *optional*, defaults to -1): Random seed for noise generation. If -1, use random seed offload_model (`bool`, *optional*, defaults to True): If True, offloads models to CPU during generation to save VRAM init_first_frame (`bool`, *optional*, defaults to False): Whether to use the reference image as the first frame (i.e., standard image-to-video generation) Returns: torch.Tensor: Generated video frames tensor. Dimensions: (C, N H, W) where: - C: Color channels (3 for RGB) - N: Number of frames (81) - H: Frame height (from max_area) - W: Frame width from max_area) """ # preprocess size = self.get_gen_size( size=None, max_area=max_area, ref_image_path=ref_image_path, pre_video_path=None) HEIGHT, WIDTH = size channel = 3 resize_opreat = transforms.Resize(min(HEIGHT, WIDTH)) crop_opreat = transforms.CenterCrop((HEIGHT, WIDTH)) tensor_trans = transforms.ToTensor() ref_image = None motion_latents = None if ref_image is None: ref_image = np.array(Image.open(ref_image_path).convert('RGB')) if motion_latents is None: motion_latents = torch.zeros( [1, channel, self.motion_frames, HEIGHT, WIDTH], dtype=self.param_dtype, device=self.device) # extract audio emb if enable_tts is True: audio_path = self.tts(tts_prompt_audio, tts_prompt_text, tts_text) audio_emb, nr = self.encode_audio(audio_path, infer_frames=infer_frames) if num_repeat is None or num_repeat > nr: num_repeat = nr lat_motion_frames = (self.motion_frames + 3) // 4 model_pic = crop_opreat(resize_opreat(Image.fromarray(ref_image))) ref_pixel_values = tensor_trans(model_pic) ref_pixel_values = ref_pixel_values.unsqueeze(1).unsqueeze( 0) * 2 - 1.0 # b c 1 h w ref_pixel_values = ref_pixel_values.to( dtype=self.vae.dtype, device=self.vae.device) ref_latents = torch.stack(self.vae.encode(ref_pixel_values)) # encode the motion latents videos_last_frames = motion_latents.detach() drop_first_motion = self.drop_first_motion if init_first_frame: drop_first_motion = False motion_latents[:, :, -6:] = ref_pixel_values motion_latents = torch.stack(self.vae.encode(motion_latents)) # get pose cond input if need COND = self.load_pose_cond( pose_video=pose_video, num_repeat=num_repeat, infer_frames=infer_frames, size=size) seed = seed if seed >= 0 else random.randint(0, sys.maxsize) if n_prompt == "": n_prompt = self.sample_neg_prompt # preprocess if not self.t5_cpu: self.text_encoder.model.to(self.device) context = self.text_encoder([input_prompt], self.device) context_null = self.text_encoder([n_prompt], self.device) if offload_model: self.text_encoder.model.cpu() else: context = self.text_encoder([input_prompt], torch.device('cpu')) context_null = self.text_encoder([n_prompt], torch.device('cpu')) context = [t.to(self.device) for t in context] context_null = [t.to(self.device) for t in context_null] out = [] # evaluation mode with ( torch.amp.autocast('cuda', dtype=self.param_dtype), torch.no_grad(), ): for r in range(num_repeat): seed_g = torch.Generator(device=self.device) seed_g.manual_seed(seed + r) lat_target_frames = (infer_frames + 3 + self.motion_frames ) // 4 - lat_motion_frames target_shape = [lat_target_frames, HEIGHT // 8, WIDTH // 8] noise = [ torch.randn( 16, target_shape[0], target_shape[1], target_shape[2], dtype=self.param_dtype, device=self.device, generator=seed_g) ] max_seq_len = np.prod(target_shape) // 4 if sample_solver == 'unipc': sample_scheduler = FlowUniPCMultistepScheduler( num_train_timesteps=self.num_train_timesteps, shift=1, use_dynamic_shifting=False) sample_scheduler.set_timesteps( sampling_steps, device=self.device, shift=shift) timesteps = sample_scheduler.timesteps elif sample_solver == 'dpm++': sample_scheduler = FlowDPMSolverMultistepScheduler( num_train_timesteps=self.num_train_timesteps, shift=1, use_dynamic_shifting=False) sampling_sigmas = get_sampling_sigmas(sampling_steps, shift) timesteps, _ = retrieve_timesteps( sample_scheduler, device=self.device, sigmas=sampling_sigmas) else: raise NotImplementedError("Unsupported solver.") latents = deepcopy(noise) with torch.no_grad(): left_idx = r * infer_frames right_idx = r * infer_frames + infer_frames cond_latents = COND[r] if pose_video else COND[0] * 0 cond_latents = cond_latents.to( dtype=self.param_dtype, device=self.device) audio_input = audio_emb[..., left_idx:right_idx] input_motion_latents = motion_latents.clone() arg_c = { 'context': context[0:1], 'seq_len': max_seq_len, 'cond_states': cond_latents, "motion_latents": input_motion_latents, 'ref_latents': ref_latents, "audio_input": audio_input, "motion_frames": [self.motion_frames, lat_motion_frames], "drop_motion_frames": drop_first_motion and r == 0, } if guide_scale > 1: arg_null = { 'context': context_null[0:1], 'seq_len': max_seq_len, 'cond_states': cond_latents, "motion_latents": input_motion_latents, 'ref_latents': ref_latents, "audio_input": 0.0 * audio_input, "motion_frames": [ self.motion_frames, lat_motion_frames ], "drop_motion_frames": drop_first_motion and r == 0, } if offload_model or self.init_on_cpu: self.noise_model.to(self.device) torch.cuda.empty_cache() for i, t in enumerate(tqdm(timesteps)): latent_model_input = latents[0:1] timestep = [t] timestep = torch.stack(timestep).to(self.device) noise_pred_cond = self.noise_model( latent_model_input, t=timestep, **arg_c) if guide_scale > 1: noise_pred_uncond = self.noise_model( latent_model_input, t=timestep, **arg_null) noise_pred = [ u + guide_scale * (c - u) for c, u in zip(noise_pred_cond, noise_pred_uncond) ] else: noise_pred = noise_pred_cond temp_x0 = sample_scheduler.step( noise_pred[0].unsqueeze(0), t, latents[0].unsqueeze(0), return_dict=False, generator=seed_g)[0] latents[0] = temp_x0.squeeze(0) if offload_model: self.noise_model.cpu() torch.cuda.synchronize() torch.cuda.empty_cache() latents = torch.stack(latents) if not (drop_first_motion and r == 0): decode_latents = torch.cat([motion_latents, latents], dim=2) else: decode_latents = torch.cat([ref_latents, latents], dim=2) image = torch.stack(self.vae.decode(decode_latents)) image = image[:, :, -(infer_frames):] if (drop_first_motion and r == 0): image = image[:, :, 3:] overlap_frames_num = min(self.motion_frames, image.shape[2]) videos_last_frames = torch.cat([ videos_last_frames[:, :, overlap_frames_num:], image[:, :, -overlap_frames_num:] ], dim=2) videos_last_frames = videos_last_frames.to( dtype=motion_latents.dtype, device=motion_latents.device) motion_latents = torch.stack( self.vae.encode(videos_last_frames)) out.append(image.cpu()) videos = torch.cat(out, dim=2) del noise, latents del sample_scheduler if offload_model: gc.collect() torch.cuda.synchronize() if dist.is_initialized(): dist.barrier() return videos[0] if self.rank == 0 else None def tts(self, tts_prompt_audio, tts_prompt_text, tts_text): if not hasattr(self, 'cosyvoice'): self.load_tts() speech_list = [] from cosyvoice.utils.file_utils import load_wav import torchaudio prompt_speech_16k = load_wav(tts_prompt_audio, 16000) if tts_prompt_text is not None: for i in self.cosyvoice.inference_zero_shot(tts_text, tts_prompt_text, prompt_speech_16k): speech_list.append(i['tts_speech']) else: for i in self.cosyvoice.inference_cross_lingual(tts_text, prompt_speech_16k): speech_list.append(i['tts_speech']) torchaudio.save('tts.wav', torch.concat(speech_list, dim=1), self.cosyvoice.sample_rate) return 'tts.wav' def load_tts(self): if not os.path.exists('CosyVoice'): from wan.utils.utils import download_cosyvoice_repo download_cosyvoice_repo('CosyVoice') if not os.path.exists('CosyVoice2-0.5B'): from wan.utils.utils import download_cosyvoice_model download_cosyvoice_model('CosyVoice2-0.5B', 'CosyVoice2-0.5B') sys.path.append('CosyVoice') sys.path.append('CosyVoice/third_party/Matcha-TTS') from cosyvoice.cli.cosyvoice import CosyVoice2 self.cosyvoice = CosyVoice2('CosyVoice2-0.5B') ================================================ FILE: wan/text2video.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import gc import logging import math import os import random import sys import types from contextlib import contextmanager from functools import partial import torch import torch.cuda.amp as amp import torch.distributed as dist from tqdm import tqdm from .distributed.fsdp import shard_model from .distributed.sequence_parallel import sp_attn_forward, sp_dit_forward from .distributed.util import get_world_size from .modules.model import WanModel from .modules.t5 import T5EncoderModel from .modules.vae2_1 import Wan2_1_VAE from .utils.fm_solvers import ( FlowDPMSolverMultistepScheduler, get_sampling_sigmas, retrieve_timesteps, ) from .utils.fm_solvers_unipc import FlowUniPCMultistepScheduler class WanT2V: def __init__( self, config, checkpoint_dir, device_id=0, rank=0, t5_fsdp=False, dit_fsdp=False, use_sp=False, t5_cpu=False, init_on_cpu=True, convert_model_dtype=False, ): r""" Initializes the Wan text-to-video generation model components. Args: config (EasyDict): Object containing model parameters initialized from config.py checkpoint_dir (`str`): Path to directory containing model checkpoints device_id (`int`, *optional*, defaults to 0): Id of target GPU device rank (`int`, *optional*, defaults to 0): Process rank for distributed training t5_fsdp (`bool`, *optional*, defaults to False): Enable FSDP sharding for T5 model dit_fsdp (`bool`, *optional*, defaults to False): Enable FSDP sharding for DiT model use_sp (`bool`, *optional*, defaults to False): Enable distribution strategy of sequence parallel. t5_cpu (`bool`, *optional*, defaults to False): Whether to place T5 model on CPU. Only works without t5_fsdp. init_on_cpu (`bool`, *optional*, defaults to True): Enable initializing Transformer Model on CPU. Only works without FSDP or USP. convert_model_dtype (`bool`, *optional*, defaults to False): Convert DiT model parameters dtype to 'config.param_dtype'. Only works without FSDP. """ self.device = torch.device(f"cuda:{device_id}") self.config = config self.rank = rank self.t5_cpu = t5_cpu self.init_on_cpu = init_on_cpu self.num_train_timesteps = config.num_train_timesteps self.boundary = config.boundary self.param_dtype = config.param_dtype if t5_fsdp or dit_fsdp or use_sp: self.init_on_cpu = False shard_fn = partial(shard_model, device_id=device_id) self.text_encoder = T5EncoderModel( text_len=config.text_len, dtype=config.t5_dtype, device=torch.device('cpu'), checkpoint_path=os.path.join(checkpoint_dir, config.t5_checkpoint), tokenizer_path=os.path.join(checkpoint_dir, config.t5_tokenizer), shard_fn=shard_fn if t5_fsdp else None) self.vae_stride = config.vae_stride self.patch_size = config.patch_size self.vae = Wan2_1_VAE( vae_pth=os.path.join(checkpoint_dir, config.vae_checkpoint), device=self.device) logging.info(f"Creating WanModel from {checkpoint_dir}") self.low_noise_model = WanModel.from_pretrained( checkpoint_dir, subfolder=config.low_noise_checkpoint) self.low_noise_model = self._configure_model( model=self.low_noise_model, use_sp=use_sp, dit_fsdp=dit_fsdp, shard_fn=shard_fn, convert_model_dtype=convert_model_dtype) self.high_noise_model = WanModel.from_pretrained( checkpoint_dir, subfolder=config.high_noise_checkpoint) self.high_noise_model = self._configure_model( model=self.high_noise_model, use_sp=use_sp, dit_fsdp=dit_fsdp, shard_fn=shard_fn, convert_model_dtype=convert_model_dtype) if use_sp: self.sp_size = get_world_size() else: self.sp_size = 1 self.sample_neg_prompt = config.sample_neg_prompt def _configure_model(self, model, use_sp, dit_fsdp, shard_fn, convert_model_dtype): """ Configures a model object. This includes setting evaluation modes, applying distributed parallel strategy, and handling device placement. Args: model (torch.nn.Module): The model instance to configure. use_sp (`bool`): Enable distribution strategy of sequence parallel. dit_fsdp (`bool`): Enable FSDP sharding for DiT model. shard_fn (callable): The function to apply FSDP sharding. convert_model_dtype (`bool`): Convert DiT model parameters dtype to 'config.param_dtype'. Only works without FSDP. Returns: torch.nn.Module: The configured model. """ model.eval().requires_grad_(False) if use_sp: for block in model.blocks: block.self_attn.forward = types.MethodType( sp_attn_forward, block.self_attn) model.forward = types.MethodType(sp_dit_forward, model) if dist.is_initialized(): dist.barrier() if dit_fsdp: model = shard_fn(model) else: if convert_model_dtype: model.to(self.param_dtype) if not self.init_on_cpu: model.to(self.device) return model def _prepare_model_for_timestep(self, t, boundary, offload_model): r""" Prepares and returns the required model for the current timestep. Args: t (torch.Tensor): current timestep. boundary (`int`): The timestep threshold. If `t` is at or above this value, the `high_noise_model` is considered as the required model. offload_model (`bool`): A flag intended to control the offloading behavior. Returns: torch.nn.Module: The active model on the target device for the current timestep. """ if t.item() >= boundary: required_model_name = 'high_noise_model' offload_model_name = 'low_noise_model' else: required_model_name = 'low_noise_model' offload_model_name = 'high_noise_model' if offload_model or self.init_on_cpu: if next(getattr( self, offload_model_name).parameters()).device.type == 'cuda': getattr(self, offload_model_name).to('cpu') if next(getattr( self, required_model_name).parameters()).device.type == 'cpu': getattr(self, required_model_name).to(self.device) return getattr(self, required_model_name) def generate(self, input_prompt, size=(1280, 720), frame_num=81, shift=5.0, sample_solver='unipc', sampling_steps=50, guide_scale=5.0, n_prompt="", seed=-1, offload_model=True): r""" Generates video frames from text prompt using diffusion process. Args: input_prompt (`str`): Text prompt for content generation size (`tuple[int]`, *optional*, defaults to (1280,720)): Controls video resolution, (width,height). frame_num (`int`, *optional*, defaults to 81): How many frames to sample from a video. The number should be 4n+1 shift (`float`, *optional*, defaults to 5.0): Noise schedule shift parameter. Affects temporal dynamics sample_solver (`str`, *optional*, defaults to 'unipc'): Solver used to sample the video. sampling_steps (`int`, *optional*, defaults to 50): Number of diffusion sampling steps. Higher values improve quality but slow generation guide_scale (`float` or tuple[`float`], *optional*, defaults 5.0): Classifier-free guidance scale. Controls prompt adherence vs. creativity. If tuple, the first guide_scale will be used for low noise model and the second guide_scale will be used for high noise model. n_prompt (`str`, *optional*, defaults to ""): Negative prompt for content exclusion. If not given, use `config.sample_neg_prompt` seed (`int`, *optional*, defaults to -1): Random seed for noise generation. If -1, use random seed. offload_model (`bool`, *optional*, defaults to True): If True, offloads models to CPU during generation to save VRAM Returns: torch.Tensor: Generated video frames tensor. Dimensions: (C, N H, W) where: - C: Color channels (3 for RGB) - N: Number of frames (81) - H: Frame height (from size) - W: Frame width from size) """ # preprocess guide_scale = (guide_scale, guide_scale) if isinstance( guide_scale, float) else guide_scale F = frame_num target_shape = (self.vae.model.z_dim, (F - 1) // self.vae_stride[0] + 1, size[1] // self.vae_stride[1], size[0] // self.vae_stride[2]) seq_len = math.ceil((target_shape[2] * target_shape[3]) / (self.patch_size[1] * self.patch_size[2]) * target_shape[1] / self.sp_size) * self.sp_size if n_prompt == "": n_prompt = self.sample_neg_prompt seed = seed if seed >= 0 else random.randint(0, sys.maxsize) seed_g = torch.Generator(device=self.device) seed_g.manual_seed(seed) if not self.t5_cpu: self.text_encoder.model.to(self.device) context = self.text_encoder([input_prompt], self.device) context_null = self.text_encoder([n_prompt], self.device) if offload_model: self.text_encoder.model.cpu() else: context = self.text_encoder([input_prompt], torch.device('cpu')) context_null = self.text_encoder([n_prompt], torch.device('cpu')) context = [t.to(self.device) for t in context] context_null = [t.to(self.device) for t in context_null] noise = [ torch.randn( target_shape[0], target_shape[1], target_shape[2], target_shape[3], dtype=torch.float32, device=self.device, generator=seed_g) ] @contextmanager def noop_no_sync(): yield no_sync_low_noise = getattr(self.low_noise_model, 'no_sync', noop_no_sync) no_sync_high_noise = getattr(self.high_noise_model, 'no_sync', noop_no_sync) # evaluation mode with ( torch.amp.autocast('cuda', dtype=self.param_dtype), torch.no_grad(), no_sync_low_noise(), no_sync_high_noise(), ): boundary = self.boundary * self.num_train_timesteps if sample_solver == 'unipc': sample_scheduler = FlowUniPCMultistepScheduler( num_train_timesteps=self.num_train_timesteps, shift=1, use_dynamic_shifting=False) sample_scheduler.set_timesteps( sampling_steps, device=self.device, shift=shift) timesteps = sample_scheduler.timesteps elif sample_solver == 'dpm++': sample_scheduler = FlowDPMSolverMultistepScheduler( num_train_timesteps=self.num_train_timesteps, shift=1, use_dynamic_shifting=False) sampling_sigmas = get_sampling_sigmas(sampling_steps, shift) timesteps, _ = retrieve_timesteps( sample_scheduler, device=self.device, sigmas=sampling_sigmas) else: raise NotImplementedError("Unsupported solver.") # sample videos latents = noise arg_c = {'context': context, 'seq_len': seq_len} arg_null = {'context': context_null, 'seq_len': seq_len} for _, t in enumerate(tqdm(timesteps)): latent_model_input = latents timestep = [t] timestep = torch.stack(timestep) model = self._prepare_model_for_timestep( t, boundary, offload_model) sample_guide_scale = guide_scale[1] if t.item( ) >= boundary else guide_scale[0] noise_pred_cond = model( latent_model_input, t=timestep, **arg_c)[0] noise_pred_uncond = model( latent_model_input, t=timestep, **arg_null)[0] noise_pred = noise_pred_uncond + sample_guide_scale * ( noise_pred_cond - noise_pred_uncond) temp_x0 = sample_scheduler.step( noise_pred.unsqueeze(0), t, latents[0].unsqueeze(0), return_dict=False, generator=seed_g)[0] latents = [temp_x0.squeeze(0)] x0 = latents if offload_model: self.low_noise_model.cpu() self.high_noise_model.cpu() torch.cuda.empty_cache() if self.rank == 0: videos = self.vae.decode(x0) del noise, latents del sample_scheduler if offload_model: gc.collect() torch.cuda.synchronize() if dist.is_initialized(): dist.barrier() return videos[0] if self.rank == 0 else None ================================================ FILE: wan/textimage2video.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import gc import logging import math import os import random import sys import types from contextlib import contextmanager from functools import partial import torch import torch.cuda.amp as amp import torch.distributed as dist import torchvision.transforms.functional as TF from PIL import Image from tqdm import tqdm from .distributed.fsdp import shard_model from .distributed.sequence_parallel import sp_attn_forward, sp_dit_forward from .distributed.util import get_world_size from .modules.model import WanModel from .modules.t5 import T5EncoderModel from .modules.vae2_2 import Wan2_2_VAE from .utils.fm_solvers import ( FlowDPMSolverMultistepScheduler, get_sampling_sigmas, retrieve_timesteps, ) from .utils.fm_solvers_unipc import FlowUniPCMultistepScheduler from .utils.utils import best_output_size, masks_like class WanTI2V: def __init__( self, config, checkpoint_dir, device_id=0, rank=0, t5_fsdp=False, dit_fsdp=False, use_sp=False, t5_cpu=False, init_on_cpu=True, convert_model_dtype=False, ): r""" Initializes the Wan text-to-video generation model components. Args: config (EasyDict): Object containing model parameters initialized from config.py checkpoint_dir (`str`): Path to directory containing model checkpoints device_id (`int`, *optional*, defaults to 0): Id of target GPU device rank (`int`, *optional*, defaults to 0): Process rank for distributed training t5_fsdp (`bool`, *optional*, defaults to False): Enable FSDP sharding for T5 model dit_fsdp (`bool`, *optional*, defaults to False): Enable FSDP sharding for DiT model use_sp (`bool`, *optional*, defaults to False): Enable distribution strategy of sequence parallel. t5_cpu (`bool`, *optional*, defaults to False): Whether to place T5 model on CPU. Only works without t5_fsdp. init_on_cpu (`bool`, *optional*, defaults to True): Enable initializing Transformer Model on CPU. Only works without FSDP or USP. convert_model_dtype (`bool`, *optional*, defaults to False): Convert DiT model parameters dtype to 'config.param_dtype'. Only works without FSDP. """ self.device = torch.device(f"cuda:{device_id}") self.config = config self.rank = rank self.t5_cpu = t5_cpu self.init_on_cpu = init_on_cpu self.num_train_timesteps = config.num_train_timesteps self.param_dtype = config.param_dtype if t5_fsdp or dit_fsdp or use_sp: self.init_on_cpu = False shard_fn = partial(shard_model, device_id=device_id) self.text_encoder = T5EncoderModel( text_len=config.text_len, dtype=config.t5_dtype, device=torch.device('cpu'), checkpoint_path=os.path.join(checkpoint_dir, config.t5_checkpoint), tokenizer_path=os.path.join(checkpoint_dir, config.t5_tokenizer), shard_fn=shard_fn if t5_fsdp else None) self.vae_stride = config.vae_stride self.patch_size = config.patch_size self.vae = Wan2_2_VAE( vae_pth=os.path.join(checkpoint_dir, config.vae_checkpoint), device=self.device) logging.info(f"Creating WanModel from {checkpoint_dir}") self.model = WanModel.from_pretrained(checkpoint_dir) self.model = self._configure_model( model=self.model, use_sp=use_sp, dit_fsdp=dit_fsdp, shard_fn=shard_fn, convert_model_dtype=convert_model_dtype) if use_sp: self.sp_size = get_world_size() else: self.sp_size = 1 self.sample_neg_prompt = config.sample_neg_prompt def _configure_model(self, model, use_sp, dit_fsdp, shard_fn, convert_model_dtype): """ Configures a model object. This includes setting evaluation modes, applying distributed parallel strategy, and handling device placement. Args: model (torch.nn.Module): The model instance to configure. use_sp (`bool`): Enable distribution strategy of sequence parallel. dit_fsdp (`bool`): Enable FSDP sharding for DiT model. shard_fn (callable): The function to apply FSDP sharding. convert_model_dtype (`bool`): Convert DiT model parameters dtype to 'config.param_dtype'. Only works without FSDP. Returns: torch.nn.Module: The configured model. """ model.eval().requires_grad_(False) if use_sp: for block in model.blocks: block.self_attn.forward = types.MethodType( sp_attn_forward, block.self_attn) model.forward = types.MethodType(sp_dit_forward, model) if dist.is_initialized(): dist.barrier() if dit_fsdp: model = shard_fn(model) else: if convert_model_dtype: model.to(self.param_dtype) if not self.init_on_cpu: model.to(self.device) return model def generate(self, input_prompt, img=None, size=(1280, 704), max_area=704 * 1280, frame_num=81, shift=5.0, sample_solver='unipc', sampling_steps=50, guide_scale=5.0, n_prompt="", seed=-1, offload_model=True): r""" Generates video frames from text prompt using diffusion process. Args: input_prompt (`str`): Text prompt for content generation img (PIL.Image.Image): Input image tensor. Shape: [3, H, W] size (`tuple[int]`, *optional*, defaults to (1280,704)): Controls video resolution, (width,height). max_area (`int`, *optional*, defaults to 704*1280): Maximum pixel area for latent space calculation. Controls video resolution scaling frame_num (`int`, *optional*, defaults to 81): How many frames to sample from a video. The number should be 4n+1 shift (`float`, *optional*, defaults to 5.0): Noise schedule shift parameter. Affects temporal dynamics sample_solver (`str`, *optional*, defaults to 'unipc'): Solver used to sample the video. sampling_steps (`int`, *optional*, defaults to 50): Number of diffusion sampling steps. Higher values improve quality but slow generation guide_scale (`float`, *optional*, defaults 5.0): Classifier-free guidance scale. Controls prompt adherence vs. creativity. n_prompt (`str`, *optional*, defaults to ""): Negative prompt for content exclusion. If not given, use `config.sample_neg_prompt` seed (`int`, *optional*, defaults to -1): Random seed for noise generation. If -1, use random seed. offload_model (`bool`, *optional*, defaults to True): If True, offloads models to CPU during generation to save VRAM Returns: torch.Tensor: Generated video frames tensor. Dimensions: (C, N H, W) where: - C: Color channels (3 for RGB) - N: Number of frames (81) - H: Frame height (from size) - W: Frame width from size) """ # i2v if img is not None: return self.i2v( input_prompt=input_prompt, img=img, max_area=max_area, frame_num=frame_num, shift=shift, sample_solver=sample_solver, sampling_steps=sampling_steps, guide_scale=guide_scale, n_prompt=n_prompt, seed=seed, offload_model=offload_model) # t2v return self.t2v( input_prompt=input_prompt, size=size, frame_num=frame_num, shift=shift, sample_solver=sample_solver, sampling_steps=sampling_steps, guide_scale=guide_scale, n_prompt=n_prompt, seed=seed, offload_model=offload_model) def t2v(self, input_prompt, size=(1280, 704), frame_num=121, shift=5.0, sample_solver='unipc', sampling_steps=50, guide_scale=5.0, n_prompt="", seed=-1, offload_model=True): r""" Generates video frames from text prompt using diffusion process. Args: input_prompt (`str`): Text prompt for content generation size (`tuple[int]`, *optional*, defaults to (1280,704)): Controls video resolution, (width,height). frame_num (`int`, *optional*, defaults to 121): How many frames to sample from a video. The number should be 4n+1 shift (`float`, *optional*, defaults to 5.0): Noise schedule shift parameter. Affects temporal dynamics sample_solver (`str`, *optional*, defaults to 'unipc'): Solver used to sample the video. sampling_steps (`int`, *optional*, defaults to 50): Number of diffusion sampling steps. Higher values improve quality but slow generation guide_scale (`float`, *optional*, defaults 5.0): Classifier-free guidance scale. Controls prompt adherence vs. creativity. n_prompt (`str`, *optional*, defaults to ""): Negative prompt for content exclusion. If not given, use `config.sample_neg_prompt` seed (`int`, *optional*, defaults to -1): Random seed for noise generation. If -1, use random seed. offload_model (`bool`, *optional*, defaults to True): If True, offloads models to CPU during generation to save VRAM Returns: torch.Tensor: Generated video frames tensor. Dimensions: (C, N H, W) where: - C: Color channels (3 for RGB) - N: Number of frames (81) - H: Frame height (from size) - W: Frame width from size) """ # preprocess F = frame_num target_shape = (self.vae.model.z_dim, (F - 1) // self.vae_stride[0] + 1, size[1] // self.vae_stride[1], size[0] // self.vae_stride[2]) seq_len = math.ceil((target_shape[2] * target_shape[3]) / (self.patch_size[1] * self.patch_size[2]) * target_shape[1] / self.sp_size) * self.sp_size if n_prompt == "": n_prompt = self.sample_neg_prompt seed = seed if seed >= 0 else random.randint(0, sys.maxsize) seed_g = torch.Generator(device=self.device) seed_g.manual_seed(seed) if not self.t5_cpu: self.text_encoder.model.to(self.device) context = self.text_encoder([input_prompt], self.device) context_null = self.text_encoder([n_prompt], self.device) if offload_model: self.text_encoder.model.cpu() else: context = self.text_encoder([input_prompt], torch.device('cpu')) context_null = self.text_encoder([n_prompt], torch.device('cpu')) context = [t.to(self.device) for t in context] context_null = [t.to(self.device) for t in context_null] noise = [ torch.randn( target_shape[0], target_shape[1], target_shape[2], target_shape[3], dtype=torch.float32, device=self.device, generator=seed_g) ] @contextmanager def noop_no_sync(): yield no_sync = getattr(self.model, 'no_sync', noop_no_sync) # evaluation mode with ( torch.amp.autocast('cuda', dtype=self.param_dtype), torch.no_grad(), no_sync(), ): if sample_solver == 'unipc': sample_scheduler = FlowUniPCMultistepScheduler( num_train_timesteps=self.num_train_timesteps, shift=1, use_dynamic_shifting=False) sample_scheduler.set_timesteps( sampling_steps, device=self.device, shift=shift) timesteps = sample_scheduler.timesteps elif sample_solver == 'dpm++': sample_scheduler = FlowDPMSolverMultistepScheduler( num_train_timesteps=self.num_train_timesteps, shift=1, use_dynamic_shifting=False) sampling_sigmas = get_sampling_sigmas(sampling_steps, shift) timesteps, _ = retrieve_timesteps( sample_scheduler, device=self.device, sigmas=sampling_sigmas) else: raise NotImplementedError("Unsupported solver.") # sample videos latents = noise mask1, mask2 = masks_like(noise, zero=False) arg_c = {'context': context, 'seq_len': seq_len} arg_null = {'context': context_null, 'seq_len': seq_len} if offload_model or self.init_on_cpu: self.model.to(self.device) torch.cuda.empty_cache() for _, t in enumerate(tqdm(timesteps)): latent_model_input = latents timestep = [t] timestep = torch.stack(timestep) temp_ts = (mask2[0][0][:, ::2, ::2] * timestep).flatten() temp_ts = torch.cat([ temp_ts, temp_ts.new_ones(seq_len - temp_ts.size(0)) * timestep ]) timestep = temp_ts.unsqueeze(0) noise_pred_cond = self.model( latent_model_input, t=timestep, **arg_c)[0] noise_pred_uncond = self.model( latent_model_input, t=timestep, **arg_null)[0] noise_pred = noise_pred_uncond + guide_scale * ( noise_pred_cond - noise_pred_uncond) temp_x0 = sample_scheduler.step( noise_pred.unsqueeze(0), t, latents[0].unsqueeze(0), return_dict=False, generator=seed_g)[0] latents = [temp_x0.squeeze(0)] x0 = latents if offload_model: self.model.cpu() torch.cuda.synchronize() torch.cuda.empty_cache() if self.rank == 0: videos = self.vae.decode(x0) del noise, latents del sample_scheduler if offload_model: gc.collect() torch.cuda.synchronize() if dist.is_initialized(): dist.barrier() return videos[0] if self.rank == 0 else None def i2v(self, input_prompt, img, max_area=704 * 1280, frame_num=121, shift=5.0, sample_solver='unipc', sampling_steps=40, guide_scale=5.0, n_prompt="", seed=-1, offload_model=True): r""" Generates video frames from input image and text prompt using diffusion process. Args: input_prompt (`str`): Text prompt for content generation. img (PIL.Image.Image): Input image tensor. Shape: [3, H, W] max_area (`int`, *optional*, defaults to 704*1280): Maximum pixel area for latent space calculation. Controls video resolution scaling frame_num (`int`, *optional*, defaults to 121): How many frames to sample from a video. The number should be 4n+1 shift (`float`, *optional*, defaults to 5.0): Noise schedule shift parameter. Affects temporal dynamics [NOTE]: If you want to generate a 480p video, it is recommended to set the shift value to 3.0. sample_solver (`str`, *optional*, defaults to 'unipc'): Solver used to sample the video. sampling_steps (`int`, *optional*, defaults to 40): Number of diffusion sampling steps. Higher values improve quality but slow generation guide_scale (`float`, *optional*, defaults 5.0): Classifier-free guidance scale. Controls prompt adherence vs. creativity. n_prompt (`str`, *optional*, defaults to ""): Negative prompt for content exclusion. If not given, use `config.sample_neg_prompt` seed (`int`, *optional*, defaults to -1): Random seed for noise generation. If -1, use random seed offload_model (`bool`, *optional*, defaults to True): If True, offloads models to CPU during generation to save VRAM Returns: torch.Tensor: Generated video frames tensor. Dimensions: (C, N H, W) where: - C: Color channels (3 for RGB) - N: Number of frames (121) - H: Frame height (from max_area) - W: Frame width (from max_area) """ # preprocess ih, iw = img.height, img.width dh, dw = self.patch_size[1] * self.vae_stride[1], self.patch_size[ 2] * self.vae_stride[2] ow, oh = best_output_size(iw, ih, dw, dh, max_area) scale = max(ow / iw, oh / ih) img = img.resize((round(iw * scale), round(ih * scale)), Image.LANCZOS) # center-crop x1 = (img.width - ow) // 2 y1 = (img.height - oh) // 2 img = img.crop((x1, y1, x1 + ow, y1 + oh)) assert img.width == ow and img.height == oh # to tensor img = TF.to_tensor(img).sub_(0.5).div_(0.5).to(self.device).unsqueeze(1) F = frame_num seq_len = ((F - 1) // self.vae_stride[0] + 1) * ( oh // self.vae_stride[1]) * (ow // self.vae_stride[2]) // ( self.patch_size[1] * self.patch_size[2]) seq_len = int(math.ceil(seq_len / self.sp_size)) * self.sp_size seed = seed if seed >= 0 else random.randint(0, sys.maxsize) seed_g = torch.Generator(device=self.device) seed_g.manual_seed(seed) noise = torch.randn( self.vae.model.z_dim, (F - 1) // self.vae_stride[0] + 1, oh // self.vae_stride[1], ow // self.vae_stride[2], dtype=torch.float32, generator=seed_g, device=self.device) if n_prompt == "": n_prompt = self.sample_neg_prompt # preprocess if not self.t5_cpu: self.text_encoder.model.to(self.device) context = self.text_encoder([input_prompt], self.device) context_null = self.text_encoder([n_prompt], self.device) if offload_model: self.text_encoder.model.cpu() else: context = self.text_encoder([input_prompt], torch.device('cpu')) context_null = self.text_encoder([n_prompt], torch.device('cpu')) context = [t.to(self.device) for t in context] context_null = [t.to(self.device) for t in context_null] z = self.vae.encode([img]) @contextmanager def noop_no_sync(): yield no_sync = getattr(self.model, 'no_sync', noop_no_sync) # evaluation mode with ( torch.amp.autocast('cuda', dtype=self.param_dtype), torch.no_grad(), no_sync(), ): if sample_solver == 'unipc': sample_scheduler = FlowUniPCMultistepScheduler( num_train_timesteps=self.num_train_timesteps, shift=1, use_dynamic_shifting=False) sample_scheduler.set_timesteps( sampling_steps, device=self.device, shift=shift) timesteps = sample_scheduler.timesteps elif sample_solver == 'dpm++': sample_scheduler = FlowDPMSolverMultistepScheduler( num_train_timesteps=self.num_train_timesteps, shift=1, use_dynamic_shifting=False) sampling_sigmas = get_sampling_sigmas(sampling_steps, shift) timesteps, _ = retrieve_timesteps( sample_scheduler, device=self.device, sigmas=sampling_sigmas) else: raise NotImplementedError("Unsupported solver.") # sample videos latent = noise mask1, mask2 = masks_like([noise], zero=True) latent = (1. - mask2[0]) * z[0] + mask2[0] * latent arg_c = { 'context': [context[0]], 'seq_len': seq_len, } arg_null = { 'context': context_null, 'seq_len': seq_len, } if offload_model or self.init_on_cpu: self.model.to(self.device) torch.cuda.empty_cache() for _, t in enumerate(tqdm(timesteps)): latent_model_input = [latent.to(self.device)] timestep = [t] timestep = torch.stack(timestep).to(self.device) temp_ts = (mask2[0][0][:, ::2, ::2] * timestep).flatten() temp_ts = torch.cat([ temp_ts, temp_ts.new_ones(seq_len - temp_ts.size(0)) * timestep ]) timestep = temp_ts.unsqueeze(0) noise_pred_cond = self.model( latent_model_input, t=timestep, **arg_c)[0] if offload_model: torch.cuda.empty_cache() noise_pred_uncond = self.model( latent_model_input, t=timestep, **arg_null)[0] if offload_model: torch.cuda.empty_cache() noise_pred = noise_pred_uncond + guide_scale * ( noise_pred_cond - noise_pred_uncond) temp_x0 = sample_scheduler.step( noise_pred.unsqueeze(0), t, latent.unsqueeze(0), return_dict=False, generator=seed_g)[0] latent = temp_x0.squeeze(0) latent = (1. - mask2[0]) * z[0] + mask2[0] * latent x0 = [latent] del latent_model_input, timestep if offload_model: self.model.cpu() torch.cuda.synchronize() torch.cuda.empty_cache() if self.rank == 0: videos = self.vae.decode(x0) del noise, latent, x0 del sample_scheduler if offload_model: gc.collect() torch.cuda.synchronize() if dist.is_initialized(): dist.barrier() return videos[0] if self.rank == 0 else None ================================================ FILE: wan/utils/__init__.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. from .fm_solvers import ( FlowDPMSolverMultistepScheduler, get_sampling_sigmas, retrieve_timesteps, ) from .fm_solvers_unipc import FlowUniPCMultistepScheduler __all__ = [ 'HuggingfaceTokenizer', 'get_sampling_sigmas', 'retrieve_timesteps', 'FlowDPMSolverMultistepScheduler', 'FlowUniPCMultistepScheduler' ] ================================================ FILE: wan/utils/fm_solvers.py ================================================ # Copied from https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py # Convert dpm solver for flow matching # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import inspect import math 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 ( KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput, ) from diffusers.utils import deprecate, is_scipy_available from diffusers.utils.torch_utils import randn_tensor if is_scipy_available(): pass def get_sampling_sigmas(sampling_steps, shift): sigma = np.linspace(1, 0, sampling_steps + 1)[:sampling_steps] sigma = (shift * sigma / (1 + (shift - 1) * sigma)) return sigma def retrieve_timesteps( scheduler, num_inference_steps=None, device=None, timesteps=None, sigmas=None, **kwargs, ): 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 FlowDPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin): """ `FlowDPMSolverMultistepScheduler` is a fast dedicated high-order solver for diffusion ODEs. 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. This determines the resolution of the diffusion process. solver_order (`int`, defaults to 2): The DPMSolver order which can be `1`, `2`, or `3`. It is recommended to use `solver_order=2` for guided sampling, and `solver_order=3` for unconditional sampling. This affects the number of model outputs stored and used in multistep updates. prediction_type (`str`, defaults to "flow_prediction"): Prediction type of the scheduler function; must be `flow_prediction` for this scheduler, which predicts the flow of the diffusion process. shift (`float`, *optional*, defaults to 1.0): A factor used to adjust the sigmas in the noise schedule. It modifies the step sizes during the sampling process. use_dynamic_shifting (`bool`, defaults to `False`): Whether to apply dynamic shifting to the timesteps based on image resolution. If `True`, the shifting is applied on the fly. thresholding (`bool`, defaults to `False`): Whether to use the "dynamic thresholding" method. This method adjusts the predicted sample to prevent saturation and improve photorealism. dynamic_thresholding_ratio (`float`, defaults to 0.995): The ratio for the dynamic thresholding method. Valid only when `thresholding=True`. sample_max_value (`float`, defaults to 1.0): The threshold value for dynamic thresholding. Valid only when `thresholding=True` and `algorithm_type="dpmsolver++"`. algorithm_type (`str`, defaults to `dpmsolver++`): Algorithm type for the solver; can be `dpmsolver`, `dpmsolver++`, `sde-dpmsolver` or `sde-dpmsolver++`. The `dpmsolver` type implements the algorithms in the [DPMSolver](https://huggingface.co/papers/2206.00927) paper, and the `dpmsolver++` type implements the algorithms in the [DPMSolver++](https://huggingface.co/papers/2211.01095) paper. It is recommended to use `dpmsolver++` or `sde-dpmsolver++` with `solver_order=2` for guided sampling like in Stable Diffusion. solver_type (`str`, defaults to `midpoint`): Solver type for the second-order solver; can be `midpoint` or `heun`. The solver type slightly affects the sample quality, especially for a small number of steps. It is recommended to use `midpoint` solvers. lower_order_final (`bool`, defaults to `True`): Whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. This can stabilize the sampling of DPMSolver for steps < 15, especially for steps <= 10. euler_at_final (`bool`, defaults to `False`): Whether to use Euler's method in the final step. It is a trade-off between numerical stability and detail richness. This can stabilize the sampling of the SDE variant of DPMSolver for small number of inference steps, but sometimes may result in blurring. final_sigmas_type (`str`, *optional*, defaults to "zero"): The final `sigma` value for the noise schedule during the sampling process. If `"sigma_min"`, the final sigma is the same as the last sigma in the training schedule. If `zero`, the final sigma is set to 0. lambda_min_clipped (`float`, defaults to `-inf`): Clipping threshold for the minimum value of `lambda(t)` for numerical stability. This is critical for the cosine (`squaredcos_cap_v2`) noise schedule. variance_type (`str`, *optional*): Set to "learned" or "learned_range" for diffusion models that predict variance. If set, the model's output contains the predicted Gaussian variance. """ _compatibles = [e.name for e in KarrasDiffusionSchedulers] order = 1 @register_to_config def __init__( self, num_train_timesteps: int = 1000, solver_order: int = 2, prediction_type: str = "flow_prediction", shift: Optional[float] = 1.0, use_dynamic_shifting=False, thresholding: bool = False, dynamic_thresholding_ratio: float = 0.995, sample_max_value: float = 1.0, algorithm_type: str = "dpmsolver++", solver_type: str = "midpoint", lower_order_final: bool = True, euler_at_final: bool = False, final_sigmas_type: Optional[str] = "zero", # "zero", "sigma_min" lambda_min_clipped: float = -float("inf"), variance_type: Optional[str] = None, invert_sigmas: bool = False, ): if algorithm_type in ["dpmsolver", "sde-dpmsolver"]: deprecation_message = f"algorithm_type {algorithm_type} is deprecated and will be removed in a future version. Choose from `dpmsolver++` or `sde-dpmsolver++` instead" deprecate("algorithm_types dpmsolver and sde-dpmsolver", "1.0.0", deprecation_message) # settings for DPM-Solver if algorithm_type not in [ "dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++" ]: if algorithm_type == "deis": self.register_to_config(algorithm_type="dpmsolver++") else: raise NotImplementedError( f"{algorithm_type} is not implemented for {self.__class__}") if solver_type not in ["midpoint", "heun"]: if solver_type in ["logrho", "bh1", "bh2"]: self.register_to_config(solver_type="midpoint") else: raise NotImplementedError( f"{solver_type} is not implemented for {self.__class__}") if algorithm_type not in ["dpmsolver++", "sde-dpmsolver++" ] and final_sigmas_type == "zero": raise ValueError( f"`final_sigmas_type` {final_sigmas_type} is not supported for `algorithm_type` {algorithm_type}. Please choose `sigma_min` instead." ) # setable values self.num_inference_steps = None alphas = np.linspace(1, 1 / num_train_timesteps, num_train_timesteps)[::-1].copy() sigmas = 1.0 - alphas sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32) 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 = shift * sigmas / (1 + (shift - 1) * sigmas) # pyright: ignore self.sigmas = sigmas self.timesteps = sigmas * num_train_timesteps self.model_outputs = [None] * solver_order self.lower_order_nums = 0 self._step_index = None self._begin_index = None # self.sigmas = self.sigmas.to( # "cpu") # to avoid too much CPU/GPU communication self.sigma_min = self.sigmas[-1].item() self.sigma_max = self.sigmas[0].item() @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 # Modified from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler.set_timesteps def set_timesteps( self, num_inference_steps: Union[int, None] = None, device: Union[str, torch.device] = None, sigmas: Optional[List[float]] = None, mu: Optional[Union[float, None]] = None, shift: Optional[Union[float, None]] = None, ): """ Sets the discrete timesteps used for the diffusion chain (to be run before inference). Args: num_inference_steps (`int`): Total number of the spacing of the time steps. 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: sigmas = np.linspace(self.sigma_max, self.sigma_min, num_inference_steps + 1).copy()[:-1] # pyright: ignore if self.config.use_dynamic_shifting: sigmas = self.time_shift(mu, 1.0, sigmas) # pyright: ignore else: if shift is None: shift = self.config.shift sigmas = shift * sigmas / (1 + (shift - 1) * sigmas) # pyright: ignore if self.config.final_sigmas_type == "sigma_min": sigma_last = ((1 - self.alphas_cumprod[0]) / self.alphas_cumprod[0])**0.5 elif self.config.final_sigmas_type == "zero": sigma_last = 0 else: raise ValueError( f"`final_sigmas_type` must be one of 'zero', or 'sigma_min', but got {self.config.final_sigmas_type}" ) timesteps = sigmas * self.config.num_train_timesteps sigmas = np.concatenate([sigmas, [sigma_last] ]).astype(np.float32) # pyright: ignore self.sigmas = torch.from_numpy(sigmas) self.timesteps = torch.from_numpy(timesteps).to( device=device, dtype=torch.int64) self.num_inference_steps = len(timesteps) self.model_outputs = [ None, ] * self.config.solver_order self.lower_order_nums = 0 self._step_index = None self._begin_index = None # self.sigmas = self.sigmas.to( # "cpu") # to avoid too much CPU/GPU communication # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor: """ "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing pixels from saturation at each step. We find that dynamic thresholding results in significantly better photorealism as well as better image-text alignment, especially when using very large guidance weights." https://arxiv.org/abs/2205.11487 """ dtype = sample.dtype batch_size, channels, *remaining_dims = sample.shape if dtype not in (torch.float32, torch.float64): sample = sample.float( ) # upcast for quantile calculation, and clamp not implemented for cpu half # Flatten sample for doing quantile calculation along each image sample = sample.reshape(batch_size, channels * np.prod(remaining_dims)) abs_sample = sample.abs() # "a certain percentile absolute pixel value" s = torch.quantile( abs_sample, self.config.dynamic_thresholding_ratio, dim=1) s = torch.clamp( s, min=1, max=self.config.sample_max_value ) # When clamped to min=1, equivalent to standard clipping to [-1, 1] s = s.unsqueeze( 1) # (batch_size, 1) because clamp will broadcast along dim=0 sample = torch.clamp( sample, -s, s ) / s # "we threshold xt0 to the range [-s, s] and then divide by s" sample = sample.reshape(batch_size, channels, *remaining_dims) sample = sample.to(dtype) return sample # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler._sigma_to_t def _sigma_to_t(self, sigma): return sigma * self.config.num_train_timesteps def _sigma_to_alpha_sigma_t(self, sigma): return 1 - sigma, sigma # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.set_timesteps def time_shift(self, mu: float, sigma: float, t: torch.Tensor): return math.exp(mu) / (math.exp(mu) + (1 / t - 1)**sigma) # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.convert_model_output def convert_model_output( self, model_output: torch.Tensor, *args, sample: torch.Tensor = None, **kwargs, ) -> torch.Tensor: """ Convert the model output to the corresponding type the DPMSolver/DPMSolver++ algorithm needs. DPM-Solver is designed to discretize an integral of the noise prediction model, and DPM-Solver++ is designed to discretize an integral of the data prediction model. The algorithm and model type are decoupled. You can use either DPMSolver or DPMSolver++ for both noise prediction and data prediction models. Args: model_output (`torch.Tensor`): The direct output from the learned diffusion model. sample (`torch.Tensor`): A current instance of a sample created by the diffusion process. Returns: `torch.Tensor`: The converted model output. """ timestep = args[0] if len(args) > 0 else kwargs.pop("timestep", None) if sample is None: if len(args) > 1: sample = args[1] else: raise ValueError( "missing `sample` as a required keyward argument") if timestep is not None: deprecate( "timesteps", "1.0.0", "Passing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", ) # DPM-Solver++ needs to solve an integral of the data prediction model. if self.config.algorithm_type in ["dpmsolver++", "sde-dpmsolver++"]: if self.config.prediction_type == "flow_prediction": sigma_t = self.sigmas[self.step_index] x0_pred = sample - sigma_t * model_output else: raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`," " `v_prediction`, or `flow_prediction` for the FlowDPMSolverMultistepScheduler." ) if self.config.thresholding: x0_pred = self._threshold_sample(x0_pred) return x0_pred # DPM-Solver needs to solve an integral of the noise prediction model. elif self.config.algorithm_type in ["dpmsolver", "sde-dpmsolver"]: if self.config.prediction_type == "flow_prediction": sigma_t = self.sigmas[self.step_index] epsilon = sample - (1 - sigma_t) * model_output else: raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`," " `v_prediction` or `flow_prediction` for the FlowDPMSolverMultistepScheduler." ) if self.config.thresholding: sigma_t = self.sigmas[self.step_index] x0_pred = sample - sigma_t * model_output x0_pred = self._threshold_sample(x0_pred) epsilon = model_output + x0_pred return epsilon # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.dpm_solver_first_order_update def dpm_solver_first_order_update( self, model_output: torch.Tensor, *args, sample: torch.Tensor = None, noise: Optional[torch.Tensor] = None, **kwargs, ) -> torch.Tensor: """ One step for the first-order DPMSolver (equivalent to DDIM). Args: model_output (`torch.Tensor`): The direct output from the learned diffusion model. sample (`torch.Tensor`): A current instance of a sample created by the diffusion process. Returns: `torch.Tensor`: The sample tensor at the previous timestep. """ timestep = args[0] if len(args) > 0 else kwargs.pop("timestep", None) prev_timestep = args[1] if len(args) > 1 else kwargs.pop( "prev_timestep", None) if sample is None: if len(args) > 2: sample = args[2] else: raise ValueError( " missing `sample` as a required keyward argument") if timestep is not None: deprecate( "timesteps", "1.0.0", "Passing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", ) if prev_timestep is not None: deprecate( "prev_timestep", "1.0.0", "Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", ) sigma_t, sigma_s = self.sigmas[self.step_index + 1], self.sigmas[ self.step_index] # pyright: ignore alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t) alpha_s, sigma_s = self._sigma_to_alpha_sigma_t(sigma_s) lambda_t = torch.log(alpha_t) - torch.log(sigma_t) lambda_s = torch.log(alpha_s) - torch.log(sigma_s) h = lambda_t - lambda_s if self.config.algorithm_type == "dpmsolver++": x_t = (sigma_t / sigma_s) * sample - (alpha_t * (torch.exp(-h) - 1.0)) * model_output elif self.config.algorithm_type == "dpmsolver": x_t = (alpha_t / alpha_s) * sample - (sigma_t * (torch.exp(h) - 1.0)) * model_output elif self.config.algorithm_type == "sde-dpmsolver++": assert noise is not None x_t = ((sigma_t / sigma_s * torch.exp(-h)) * sample + (alpha_t * (1 - torch.exp(-2.0 * h))) * model_output + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise) elif self.config.algorithm_type == "sde-dpmsolver": assert noise is not None x_t = ((alpha_t / alpha_s) * sample - 2.0 * (sigma_t * (torch.exp(h) - 1.0)) * model_output + sigma_t * torch.sqrt(torch.exp(2 * h) - 1.0) * noise) return x_t # pyright: ignore # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.multistep_dpm_solver_second_order_update def multistep_dpm_solver_second_order_update( self, model_output_list: List[torch.Tensor], *args, sample: torch.Tensor = None, noise: Optional[torch.Tensor] = None, **kwargs, ) -> torch.Tensor: """ One step for the second-order multistep DPMSolver. Args: model_output_list (`List[torch.Tensor]`): The direct outputs from learned diffusion model at current and latter timesteps. sample (`torch.Tensor`): A current instance of a sample created by the diffusion process. Returns: `torch.Tensor`: The sample tensor at the previous timestep. """ timestep_list = args[0] if len(args) > 0 else kwargs.pop( "timestep_list", None) prev_timestep = args[1] if len(args) > 1 else kwargs.pop( "prev_timestep", None) if sample is None: if len(args) > 2: sample = args[2] else: raise ValueError( " missing `sample` as a required keyward argument") if timestep_list is not None: deprecate( "timestep_list", "1.0.0", "Passing `timestep_list` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", ) if prev_timestep is not None: deprecate( "prev_timestep", "1.0.0", "Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", ) sigma_t, sigma_s0, sigma_s1 = ( self.sigmas[self.step_index + 1], # pyright: ignore self.sigmas[self.step_index], self.sigmas[self.step_index - 1], # pyright: ignore ) alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t) alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0) alpha_s1, sigma_s1 = self._sigma_to_alpha_sigma_t(sigma_s1) lambda_t = torch.log(alpha_t) - torch.log(sigma_t) lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0) lambda_s1 = torch.log(alpha_s1) - torch.log(sigma_s1) m0, m1 = model_output_list[-1], model_output_list[-2] h, h_0 = lambda_t - lambda_s0, lambda_s0 - lambda_s1 r0 = h_0 / h D0, D1 = m0, (1.0 / r0) * (m0 - m1) if self.config.algorithm_type == "dpmsolver++": # See https://arxiv.org/abs/2211.01095 for detailed derivations if self.config.solver_type == "midpoint": x_t = ((sigma_t / sigma_s0) * sample - (alpha_t * (torch.exp(-h) - 1.0)) * D0 - 0.5 * (alpha_t * (torch.exp(-h) - 1.0)) * D1) elif self.config.solver_type == "heun": x_t = ((sigma_t / sigma_s0) * sample - (alpha_t * (torch.exp(-h) - 1.0)) * D0 + (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1) elif self.config.algorithm_type == "dpmsolver": # See https://arxiv.org/abs/2206.00927 for detailed derivations if self.config.solver_type == "midpoint": x_t = ((alpha_t / alpha_s0) * sample - (sigma_t * (torch.exp(h) - 1.0)) * D0 - 0.5 * (sigma_t * (torch.exp(h) - 1.0)) * D1) elif self.config.solver_type == "heun": x_t = ((alpha_t / alpha_s0) * sample - (sigma_t * (torch.exp(h) - 1.0)) * D0 - (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1) elif self.config.algorithm_type == "sde-dpmsolver++": assert noise is not None if self.config.solver_type == "midpoint": x_t = ((sigma_t / sigma_s0 * torch.exp(-h)) * sample + (alpha_t * (1 - torch.exp(-2.0 * h))) * D0 + 0.5 * (alpha_t * (1 - torch.exp(-2.0 * h))) * D1 + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise) elif self.config.solver_type == "heun": x_t = ((sigma_t / sigma_s0 * torch.exp(-h)) * sample + (alpha_t * (1 - torch.exp(-2.0 * h))) * D0 + (alpha_t * ((1.0 - torch.exp(-2.0 * h)) / (-2.0 * h) + 1.0)) * D1 + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise) elif self.config.algorithm_type == "sde-dpmsolver": assert noise is not None if self.config.solver_type == "midpoint": x_t = ((alpha_t / alpha_s0) * sample - 2.0 * (sigma_t * (torch.exp(h) - 1.0)) * D0 - (sigma_t * (torch.exp(h) - 1.0)) * D1 + sigma_t * torch.sqrt(torch.exp(2 * h) - 1.0) * noise) elif self.config.solver_type == "heun": x_t = ((alpha_t / alpha_s0) * sample - 2.0 * (sigma_t * (torch.exp(h) - 1.0)) * D0 - 2.0 * (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1 + sigma_t * torch.sqrt(torch.exp(2 * h) - 1.0) * noise) return x_t # pyright: ignore # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.multistep_dpm_solver_third_order_update def multistep_dpm_solver_third_order_update( self, model_output_list: List[torch.Tensor], *args, sample: torch.Tensor = None, **kwargs, ) -> torch.Tensor: """ One step for the third-order multistep DPMSolver. Args: model_output_list (`List[torch.Tensor]`): The direct outputs from learned diffusion model at current and latter timesteps. sample (`torch.Tensor`): A current instance of a sample created by diffusion process. Returns: `torch.Tensor`: The sample tensor at the previous timestep. """ timestep_list = args[0] if len(args) > 0 else kwargs.pop( "timestep_list", None) prev_timestep = args[1] if len(args) > 1 else kwargs.pop( "prev_timestep", None) if sample is None: if len(args) > 2: sample = args[2] else: raise ValueError( " missing`sample` as a required keyward argument") if timestep_list is not None: deprecate( "timestep_list", "1.0.0", "Passing `timestep_list` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", ) if prev_timestep is not None: deprecate( "prev_timestep", "1.0.0", "Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", ) sigma_t, sigma_s0, sigma_s1, sigma_s2 = ( self.sigmas[self.step_index + 1], # pyright: ignore self.sigmas[self.step_index], self.sigmas[self.step_index - 1], # pyright: ignore self.sigmas[self.step_index - 2], # pyright: ignore ) alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t) alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0) alpha_s1, sigma_s1 = self._sigma_to_alpha_sigma_t(sigma_s1) alpha_s2, sigma_s2 = self._sigma_to_alpha_sigma_t(sigma_s2) lambda_t = torch.log(alpha_t) - torch.log(sigma_t) lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0) lambda_s1 = torch.log(alpha_s1) - torch.log(sigma_s1) lambda_s2 = torch.log(alpha_s2) - torch.log(sigma_s2) m0, m1, m2 = model_output_list[-1], model_output_list[ -2], model_output_list[-3] h, h_0, h_1 = lambda_t - lambda_s0, lambda_s0 - lambda_s1, lambda_s1 - lambda_s2 r0, r1 = h_0 / h, h_1 / h D0 = m0 D1_0, D1_1 = (1.0 / r0) * (m0 - m1), (1.0 / r1) * (m1 - m2) D1 = D1_0 + (r0 / (r0 + r1)) * (D1_0 - D1_1) D2 = (1.0 / (r0 + r1)) * (D1_0 - D1_1) if self.config.algorithm_type == "dpmsolver++": # See https://arxiv.org/abs/2206.00927 for detailed derivations x_t = ((sigma_t / sigma_s0) * sample - (alpha_t * (torch.exp(-h) - 1.0)) * D0 + (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1 - (alpha_t * ((torch.exp(-h) - 1.0 + h) / h**2 - 0.5)) * D2) elif self.config.algorithm_type == "dpmsolver": # See https://arxiv.org/abs/2206.00927 for detailed derivations x_t = ((alpha_t / alpha_s0) * sample - (sigma_t * (torch.exp(h) - 1.0)) * D0 - (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1 - (sigma_t * ((torch.exp(h) - 1.0 - h) / h**2 - 0.5)) * D2) return x_t # pyright: ignore 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): """ Initialize the step_index counter for the scheduler. """ 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 # Modified from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.step def step( self, model_output: torch.Tensor, timestep: Union[int, torch.Tensor], sample: torch.Tensor, generator=None, variance_noise: Optional[torch.Tensor] = None, return_dict: bool = True, ) -> Union[SchedulerOutput, Tuple]: """ Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with the multistep DPMSolver. Args: model_output (`torch.Tensor`): The direct output from learned diffusion model. timestep (`int`): The current discrete timestep in the diffusion chain. sample (`torch.Tensor`): A current instance of a sample created by the diffusion process. generator (`torch.Generator`, *optional*): A random number generator. variance_noise (`torch.Tensor`): Alternative to generating noise with `generator` by directly providing the noise for the variance itself. Useful for methods such as [`LEdits++`]. return_dict (`bool`): Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`. Returns: [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`: If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a tuple is returned where the first element is the sample tensor. """ if self.num_inference_steps is None: raise ValueError( "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" ) if self.step_index is None: self._init_step_index(timestep) # Improve numerical stability for small number of steps lower_order_final = (self.step_index == len(self.timesteps) - 1) and ( self.config.euler_at_final or (self.config.lower_order_final and len(self.timesteps) < 15) or self.config.final_sigmas_type == "zero") lower_order_second = ((self.step_index == len(self.timesteps) - 2) and self.config.lower_order_final and len(self.timesteps) < 15) model_output = self.convert_model_output(model_output, sample=sample) for i in range(self.config.solver_order - 1): self.model_outputs[i] = self.model_outputs[i + 1] self.model_outputs[-1] = model_output # Upcast to avoid precision issues when computing prev_sample sample = sample.to(torch.float32) if self.config.algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++" ] and variance_noise is None: noise = randn_tensor( model_output.shape, generator=generator, device=model_output.device, dtype=torch.float32) elif self.config.algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]: noise = variance_noise.to( device=model_output.device, dtype=torch.float32) # pyright: ignore else: noise = None if self.config.solver_order == 1 or self.lower_order_nums < 1 or lower_order_final: prev_sample = self.dpm_solver_first_order_update( model_output, sample=sample, noise=noise) elif self.config.solver_order == 2 or self.lower_order_nums < 2 or lower_order_second: prev_sample = self.multistep_dpm_solver_second_order_update( self.model_outputs, sample=sample, noise=noise) else: prev_sample = self.multistep_dpm_solver_third_order_update( self.model_outputs, sample=sample) if self.lower_order_nums < self.config.solver_order: self.lower_order_nums += 1 # Cast sample back to expected dtype prev_sample = prev_sample.to(model_output.dtype) # upon completion increase step index by one self._step_index += 1 # pyright: ignore if not return_dict: return (prev_sample,) return SchedulerOutput(prev_sample=prev_sample) # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.scale_model_input def scale_model_input(self, sample: torch.Tensor, *args, **kwargs) -> torch.Tensor: """ Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Args: sample (`torch.Tensor`): The input sample. Returns: `torch.Tensor`: A scaled input sample. """ return sample # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.scale_model_input def add_noise( self, original_samples: torch.Tensor, noise: torch.Tensor, timesteps: torch.IntTensor, ) -> torch.Tensor: # Make sure sigmas and timesteps have the same device and dtype as original_samples sigmas = self.sigmas.to( device=original_samples.device, dtype=original_samples.dtype) if original_samples.device.type == "mps" and torch.is_floating_point( timesteps): # mps does not support float64 schedule_timesteps = self.timesteps.to( original_samples.device, dtype=torch.float32) timesteps = timesteps.to( original_samples.device, dtype=torch.float32) else: schedule_timesteps = self.timesteps.to(original_samples.device) timesteps = timesteps.to(original_samples.device) # begin_index is None when the scheduler is used for training or pipeline does not implement set_begin_index if self.begin_index is None: step_indices = [ self.index_for_timestep(t, schedule_timesteps) for t in timesteps ] elif self.step_index is not None: # add_noise is called after first denoising step (for inpainting) step_indices = [self.step_index] * timesteps.shape[0] else: # add noise is called before first denoising step to create initial latent(img2img) step_indices = [self.begin_index] * timesteps.shape[0] sigma = sigmas[step_indices].flatten() while len(sigma.shape) < len(original_samples.shape): sigma = sigma.unsqueeze(-1) alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma) noisy_samples = alpha_t * original_samples + sigma_t * noise return noisy_samples def __len__(self): return self.config.num_train_timesteps ================================================ FILE: wan/utils/fm_solvers_unipc.py ================================================ # Copied from https://github.com/huggingface/diffusers/blob/v0.31.0/src/diffusers/schedulers/scheduling_unipc_multistep.py # Convert unipc for flow matching # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import math 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 ( KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput, ) from diffusers.utils import deprecate, is_scipy_available if is_scipy_available(): import scipy.stats class FlowUniPCMultistepScheduler(SchedulerMixin, ConfigMixin): """ `UniPCMultistepScheduler` is a training-free framework designed for the fast sampling of diffusion models. 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. solver_order (`int`, default `2`): The UniPC order which can be any positive integer. The effective order of accuracy is `solver_order + 1` due to the UniC. It is recommended to use `solver_order=2` for guided sampling, and `solver_order=3` for unconditional sampling. prediction_type (`str`, defaults to "flow_prediction"): Prediction type of the scheduler function; must be `flow_prediction` for this scheduler, which predicts the flow of the diffusion process. thresholding (`bool`, defaults to `False`): Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such as Stable Diffusion. dynamic_thresholding_ratio (`float`, defaults to 0.995): The ratio for the dynamic thresholding method. Valid only when `thresholding=True`. sample_max_value (`float`, defaults to 1.0): The threshold value for dynamic thresholding. Valid only when `thresholding=True` and `predict_x0=True`. predict_x0 (`bool`, defaults to `True`): Whether to use the updating algorithm on the predicted x0. solver_type (`str`, default `bh2`): Solver type for UniPC. It is recommended to use `bh1` for unconditional sampling when steps < 10, and `bh2` otherwise. lower_order_final (`bool`, default `True`): Whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. This can stabilize the sampling of DPMSolver for steps < 15, especially for steps <= 10. disable_corrector (`list`, default `[]`): Decides which step to disable the corrector to mitigate the misalignment between `epsilon_theta(x_t, c)` and `epsilon_theta(x_t^c, c)` which can influence convergence for a large guidance scale. Corrector is usually disabled during the first few steps. solver_p (`SchedulerMixin`, default `None`): Any other scheduler that if specified, the algorithm becomes `solver_p + UniC`. use_karras_sigmas (`bool`, *optional*, defaults to `False`): Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`, the sigmas are determined according to a sequence of noise levels {σi}. use_exponential_sigmas (`bool`, *optional*, defaults to `False`): Whether to use exponential sigmas for step sizes in the noise schedule during the sampling process. 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. steps_offset (`int`, defaults to 0): An offset added to the inference steps, as required by some model families. final_sigmas_type (`str`, defaults to `"zero"`): The final `sigma` value for the noise schedule during the sampling process. If `"sigma_min"`, the final sigma is the same as the last sigma in the training schedule. If `zero`, the final sigma is set to 0. """ _compatibles = [e.name for e in KarrasDiffusionSchedulers] order = 1 @register_to_config def __init__( self, num_train_timesteps: int = 1000, solver_order: int = 2, prediction_type: str = "flow_prediction", shift: Optional[float] = 1.0, use_dynamic_shifting=False, thresholding: bool = False, dynamic_thresholding_ratio: float = 0.995, sample_max_value: float = 1.0, predict_x0: bool = True, solver_type: str = "bh2", lower_order_final: bool = True, disable_corrector: List[int] = [], solver_p: SchedulerMixin = None, timestep_spacing: str = "linspace", steps_offset: int = 0, final_sigmas_type: Optional[str] = "zero", # "zero", "sigma_min" ): if solver_type not in ["bh1", "bh2"]: if solver_type in ["midpoint", "heun", "logrho"]: self.register_to_config(solver_type="bh2") else: raise NotImplementedError( f"{solver_type} is not implemented for {self.__class__}") self.predict_x0 = predict_x0 # setable values self.num_inference_steps = None alphas = np.linspace(1, 1 / num_train_timesteps, num_train_timesteps)[::-1].copy() sigmas = 1.0 - alphas sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32) 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 = shift * sigmas / (1 + (shift - 1) * sigmas) # pyright: ignore self.sigmas = sigmas self.timesteps = sigmas * num_train_timesteps self.model_outputs = [None] * solver_order self.timestep_list = [None] * solver_order self.lower_order_nums = 0 self.disable_corrector = disable_corrector self.solver_p = solver_p self.last_sample = None self._step_index = None self._begin_index = None self.sigmas = self.sigmas.to( "cpu") # to avoid too much CPU/GPU communication self.sigma_min = self.sigmas[-1].item() self.sigma_max = self.sigmas[0].item() @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 # Modified from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler.set_timesteps def set_timesteps( self, num_inference_steps: Union[int, None] = None, device: Union[str, torch.device] = None, sigmas: Optional[List[float]] = None, mu: Optional[Union[float, None]] = None, shift: Optional[Union[float, None]] = None, ): """ Sets the discrete timesteps used for the diffusion chain (to be run before inference). Args: num_inference_steps (`int`): Total number of the spacing of the time steps. 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: sigmas = np.linspace(self.sigma_max, self.sigma_min, num_inference_steps + 1).copy()[:-1] # pyright: ignore if self.config.use_dynamic_shifting: sigmas = self.time_shift(mu, 1.0, sigmas) # pyright: ignore else: if shift is None: shift = self.config.shift sigmas = shift * sigmas / (1 + (shift - 1) * sigmas) # pyright: ignore if self.config.final_sigmas_type == "sigma_min": sigma_last = ((1 - self.alphas_cumprod[0]) / self.alphas_cumprod[0])**0.5 elif self.config.final_sigmas_type == "zero": sigma_last = 0 else: raise ValueError( f"`final_sigmas_type` must be one of 'zero', or 'sigma_min', but got {self.config.final_sigmas_type}" ) timesteps = sigmas * self.config.num_train_timesteps sigmas = np.concatenate([sigmas, [sigma_last] ]).astype(np.float32) # pyright: ignore self.sigmas = torch.from_numpy(sigmas) self.timesteps = torch.from_numpy(timesteps).to( device=device, dtype=torch.int64) self.num_inference_steps = len(timesteps) self.model_outputs = [ None, ] * self.config.solver_order self.lower_order_nums = 0 self.last_sample = None if self.solver_p: self.solver_p.set_timesteps(self.num_inference_steps, device=device) # add an index counter for schedulers that allow duplicated timesteps self._step_index = None self._begin_index = None self.sigmas = self.sigmas.to( "cpu") # to avoid too much CPU/GPU communication # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor: """ "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing pixels from saturation at each step. We find that dynamic thresholding results in significantly better photorealism as well as better image-text alignment, especially when using very large guidance weights." https://arxiv.org/abs/2205.11487 """ dtype = sample.dtype batch_size, channels, *remaining_dims = sample.shape if dtype not in (torch.float32, torch.float64): sample = sample.float( ) # upcast for quantile calculation, and clamp not implemented for cpu half # Flatten sample for doing quantile calculation along each image sample = sample.reshape(batch_size, channels * np.prod(remaining_dims)) abs_sample = sample.abs() # "a certain percentile absolute pixel value" s = torch.quantile( abs_sample, self.config.dynamic_thresholding_ratio, dim=1) s = torch.clamp( s, min=1, max=self.config.sample_max_value ) # When clamped to min=1, equivalent to standard clipping to [-1, 1] s = s.unsqueeze( 1) # (batch_size, 1) because clamp will broadcast along dim=0 sample = torch.clamp( sample, -s, s ) / s # "we threshold xt0 to the range [-s, s] and then divide by s" sample = sample.reshape(batch_size, channels, *remaining_dims) sample = sample.to(dtype) return sample # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler._sigma_to_t def _sigma_to_t(self, sigma): return sigma * self.config.num_train_timesteps def _sigma_to_alpha_sigma_t(self, sigma): return 1 - sigma, sigma # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.set_timesteps def time_shift(self, mu: float, sigma: float, t: torch.Tensor): return math.exp(mu) / (math.exp(mu) + (1 / t - 1)**sigma) def convert_model_output( self, model_output: torch.Tensor, *args, sample: torch.Tensor = None, **kwargs, ) -> torch.Tensor: r""" Convert the model output to the corresponding type the UniPC algorithm needs. Args: model_output (`torch.Tensor`): The direct output from the learned diffusion model. timestep (`int`): The current discrete timestep in the diffusion chain. sample (`torch.Tensor`): A current instance of a sample created by the diffusion process. Returns: `torch.Tensor`: The converted model output. """ timestep = args[0] if len(args) > 0 else kwargs.pop("timestep", None) if sample is None: if len(args) > 1: sample = args[1] else: raise ValueError( "missing `sample` as a required keyward argument") if timestep is not None: deprecate( "timesteps", "1.0.0", "Passing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", ) sigma = self.sigmas[self.step_index] alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma) if self.predict_x0: if self.config.prediction_type == "flow_prediction": sigma_t = self.sigmas[self.step_index] x0_pred = sample - sigma_t * model_output else: raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`," " `v_prediction` or `flow_prediction` for the UniPCMultistepScheduler." ) if self.config.thresholding: x0_pred = self._threshold_sample(x0_pred) return x0_pred else: if self.config.prediction_type == "flow_prediction": sigma_t = self.sigmas[self.step_index] epsilon = sample - (1 - sigma_t) * model_output else: raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`," " `v_prediction` or `flow_prediction` for the UniPCMultistepScheduler." ) if self.config.thresholding: sigma_t = self.sigmas[self.step_index] x0_pred = sample - sigma_t * model_output x0_pred = self._threshold_sample(x0_pred) epsilon = model_output + x0_pred return epsilon def multistep_uni_p_bh_update( self, model_output: torch.Tensor, *args, sample: torch.Tensor = None, order: int = None, # pyright: ignore **kwargs, ) -> torch.Tensor: """ One step for the UniP (B(h) version). Alternatively, `self.solver_p` is used if is specified. Args: model_output (`torch.Tensor`): The direct output from the learned diffusion model at the current timestep. prev_timestep (`int`): The previous discrete timestep in the diffusion chain. sample (`torch.Tensor`): A current instance of a sample created by the diffusion process. order (`int`): The order of UniP at this timestep (corresponds to the *p* in UniPC-p). Returns: `torch.Tensor`: The sample tensor at the previous timestep. """ prev_timestep = args[0] if len(args) > 0 else kwargs.pop( "prev_timestep", None) if sample is None: if len(args) > 1: sample = args[1] else: raise ValueError( " missing `sample` as a required keyward argument") if order is None: if len(args) > 2: order = args[2] else: raise ValueError( " missing `order` as a required keyward argument") if prev_timestep is not None: deprecate( "prev_timestep", "1.0.0", "Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", ) model_output_list = self.model_outputs s0 = self.timestep_list[-1] m0 = model_output_list[-1] x = sample if self.solver_p: x_t = self.solver_p.step(model_output, s0, x).prev_sample return x_t sigma_t, sigma_s0 = self.sigmas[self.step_index + 1], self.sigmas[ self.step_index] # pyright: ignore alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t) alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0) lambda_t = torch.log(alpha_t) - torch.log(sigma_t) lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0) h = lambda_t - lambda_s0 device = sample.device rks = [] D1s = [] for i in range(1, order): si = self.step_index - i # pyright: ignore mi = model_output_list[-(i + 1)] alpha_si, sigma_si = self._sigma_to_alpha_sigma_t(self.sigmas[si]) lambda_si = torch.log(alpha_si) - torch.log(sigma_si) rk = (lambda_si - lambda_s0) / h rks.append(rk) D1s.append((mi - m0) / rk) # pyright: ignore rks.append(1.0) rks = torch.tensor(rks, device=device) R = [] b = [] hh = -h if self.predict_x0 else h h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1 h_phi_k = h_phi_1 / hh - 1 factorial_i = 1 if self.config.solver_type == "bh1": B_h = hh elif self.config.solver_type == "bh2": B_h = torch.expm1(hh) else: raise NotImplementedError() for i in range(1, order + 1): R.append(torch.pow(rks, i - 1)) b.append(h_phi_k * factorial_i / B_h) factorial_i *= i + 1 h_phi_k = h_phi_k / hh - 1 / factorial_i R = torch.stack(R) b = torch.tensor(b, device=device) if len(D1s) > 0: D1s = torch.stack(D1s, dim=1) # (B, K) # for order 2, we use a simplified version if order == 2: rhos_p = torch.tensor([0.5], dtype=x.dtype, device=device) else: rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1]).to(device).to(x.dtype) else: D1s = None if self.predict_x0: x_t_ = sigma_t / sigma_s0 * x - alpha_t * h_phi_1 * m0 if D1s is not None: pred_res = torch.einsum("k,bkc...->bc...", rhos_p, D1s) # pyright: ignore else: pred_res = 0 x_t = x_t_ - alpha_t * B_h * pred_res else: x_t_ = alpha_t / alpha_s0 * x - sigma_t * h_phi_1 * m0 if D1s is not None: pred_res = torch.einsum("k,bkc...->bc...", rhos_p, D1s) # pyright: ignore else: pred_res = 0 x_t = x_t_ - sigma_t * B_h * pred_res x_t = x_t.to(x.dtype) return x_t def multistep_uni_c_bh_update( self, this_model_output: torch.Tensor, *args, last_sample: torch.Tensor = None, this_sample: torch.Tensor = None, order: int = None, # pyright: ignore **kwargs, ) -> torch.Tensor: """ One step for the UniC (B(h) version). Args: this_model_output (`torch.Tensor`): The model outputs at `x_t`. this_timestep (`int`): The current timestep `t`. last_sample (`torch.Tensor`): The generated sample before the last predictor `x_{t-1}`. this_sample (`torch.Tensor`): The generated sample after the last predictor `x_{t}`. order (`int`): The `p` of UniC-p at this step. The effective order of accuracy should be `order + 1`. Returns: `torch.Tensor`: The corrected sample tensor at the current timestep. """ this_timestep = args[0] if len(args) > 0 else kwargs.pop( "this_timestep", None) if last_sample is None: if len(args) > 1: last_sample = args[1] else: raise ValueError( " missing`last_sample` as a required keyward argument") if this_sample is None: if len(args) > 2: this_sample = args[2] else: raise ValueError( " missing`this_sample` as a required keyward argument") if order is None: if len(args) > 3: order = args[3] else: raise ValueError( " missing`order` as a required keyward argument") if this_timestep is not None: deprecate( "this_timestep", "1.0.0", "Passing `this_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", ) model_output_list = self.model_outputs m0 = model_output_list[-1] x = last_sample x_t = this_sample model_t = this_model_output sigma_t, sigma_s0 = self.sigmas[self.step_index], self.sigmas[ self.step_index - 1] # pyright: ignore alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t) alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0) lambda_t = torch.log(alpha_t) - torch.log(sigma_t) lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0) h = lambda_t - lambda_s0 device = this_sample.device rks = [] D1s = [] for i in range(1, order): si = self.step_index - (i + 1) # pyright: ignore mi = model_output_list[-(i + 1)] alpha_si, sigma_si = self._sigma_to_alpha_sigma_t(self.sigmas[si]) lambda_si = torch.log(alpha_si) - torch.log(sigma_si) rk = (lambda_si - lambda_s0) / h rks.append(rk) D1s.append((mi - m0) / rk) # pyright: ignore rks.append(1.0) rks = torch.tensor(rks, device=device) R = [] b = [] hh = -h if self.predict_x0 else h h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1 h_phi_k = h_phi_1 / hh - 1 factorial_i = 1 if self.config.solver_type == "bh1": B_h = hh elif self.config.solver_type == "bh2": B_h = torch.expm1(hh) else: raise NotImplementedError() for i in range(1, order + 1): R.append(torch.pow(rks, i - 1)) b.append(h_phi_k * factorial_i / B_h) factorial_i *= i + 1 h_phi_k = h_phi_k / hh - 1 / factorial_i R = torch.stack(R) b = torch.tensor(b, device=device) if len(D1s) > 0: D1s = torch.stack(D1s, dim=1) else: D1s = None # for order 1, we use a simplified version if order == 1: rhos_c = torch.tensor([0.5], dtype=x.dtype, device=device) else: rhos_c = torch.linalg.solve(R, b).to(device).to(x.dtype) if self.predict_x0: x_t_ = sigma_t / sigma_s0 * x - alpha_t * h_phi_1 * m0 if D1s is not None: corr_res = torch.einsum("k,bkc...->bc...", rhos_c[:-1], D1s) else: corr_res = 0 D1_t = model_t - m0 x_t = x_t_ - alpha_t * B_h * (corr_res + rhos_c[-1] * D1_t) else: x_t_ = alpha_t / alpha_s0 * x - sigma_t * h_phi_1 * m0 if D1s is not None: corr_res = torch.einsum("k,bkc...->bc...", rhos_c[:-1], D1s) else: corr_res = 0 D1_t = model_t - m0 x_t = x_t_ - sigma_t * B_h * (corr_res + rhos_c[-1] * D1_t) x_t = x_t.to(x.dtype) return x_t 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() # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler._init_step_index def _init_step_index(self, timestep): """ Initialize the step_index counter for the scheduler. """ 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.Tensor, timestep: Union[int, torch.Tensor], sample: torch.Tensor, return_dict: bool = True, generator=None) -> Union[SchedulerOutput, Tuple]: """ Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with the multistep UniPC. Args: model_output (`torch.Tensor`): The direct output from learned diffusion model. timestep (`int`): The current discrete timestep in the diffusion chain. sample (`torch.Tensor`): A current instance of a sample created by the diffusion process. return_dict (`bool`): Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`. Returns: [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`: If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a tuple is returned where the first element is the sample tensor. """ if self.num_inference_steps is None: raise ValueError( "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" ) if self.step_index is None: self._init_step_index(timestep) use_corrector = ( self.step_index > 0 and self.step_index - 1 not in self.disable_corrector and self.last_sample is not None # pyright: ignore ) model_output_convert = self.convert_model_output( model_output, sample=sample) if use_corrector: sample = self.multistep_uni_c_bh_update( this_model_output=model_output_convert, last_sample=self.last_sample, this_sample=sample, order=self.this_order, ) for i in range(self.config.solver_order - 1): self.model_outputs[i] = self.model_outputs[i + 1] self.timestep_list[i] = self.timestep_list[i + 1] self.model_outputs[-1] = model_output_convert self.timestep_list[-1] = timestep # pyright: ignore if self.config.lower_order_final: this_order = min(self.config.solver_order, len(self.timesteps) - self.step_index) # pyright: ignore else: this_order = self.config.solver_order self.this_order = min(this_order, self.lower_order_nums + 1) # warmup for multistep assert self.this_order > 0 self.last_sample = sample prev_sample = self.multistep_uni_p_bh_update( model_output=model_output, # pass the original non-converted model output, in case solver-p is used sample=sample, order=self.this_order, ) if self.lower_order_nums < self.config.solver_order: self.lower_order_nums += 1 # upon completion increase step index by one self._step_index += 1 # pyright: ignore if not return_dict: return (prev_sample,) return SchedulerOutput(prev_sample=prev_sample) def scale_model_input(self, sample: torch.Tensor, *args, **kwargs) -> torch.Tensor: """ Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Args: sample (`torch.Tensor`): The input sample. Returns: `torch.Tensor`: A scaled input sample. """ return sample # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.add_noise def add_noise( self, original_samples: torch.Tensor, noise: torch.Tensor, timesteps: torch.IntTensor, ) -> torch.Tensor: # Make sure sigmas and timesteps have the same device and dtype as original_samples sigmas = self.sigmas.to( device=original_samples.device, dtype=original_samples.dtype) if original_samples.device.type == "mps" and torch.is_floating_point( timesteps): # mps does not support float64 schedule_timesteps = self.timesteps.to( original_samples.device, dtype=torch.float32) timesteps = timesteps.to( original_samples.device, dtype=torch.float32) else: schedule_timesteps = self.timesteps.to(original_samples.device) timesteps = timesteps.to(original_samples.device) # begin_index is None when the scheduler is used for training or pipeline does not implement set_begin_index if self.begin_index is None: step_indices = [ self.index_for_timestep(t, schedule_timesteps) for t in timesteps ] elif self.step_index is not None: # add_noise is called after first denoising step (for inpainting) step_indices = [self.step_index] * timesteps.shape[0] else: # add noise is called before first denoising step to create initial latent(img2img) step_indices = [self.begin_index] * timesteps.shape[0] sigma = sigmas[step_indices].flatten() while len(sigma.shape) < len(original_samples.shape): sigma = sigma.unsqueeze(-1) alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma) noisy_samples = alpha_t * original_samples + sigma_t * noise return noisy_samples def __len__(self): return self.config.num_train_timesteps ================================================ FILE: wan/utils/prompt_extend.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import json import logging import math import os import random import sys import tempfile from dataclasses import dataclass from http import HTTPStatus from typing import Optional, Union import dashscope import torch from PIL import Image try: from flash_attn import flash_attn_varlen_func FLASH_VER = 2 except ModuleNotFoundError: flash_attn_varlen_func = None # in compatible with CPU machines FLASH_VER = None from .system_prompt import * DEFAULT_SYS_PROMPTS = { "t2v-A14B": { "zh": T2V_A14B_ZH_SYS_PROMPT, "en": T2V_A14B_EN_SYS_PROMPT, }, "i2v-A14B": { "zh": I2V_A14B_ZH_SYS_PROMPT, "en": I2V_A14B_EN_SYS_PROMPT, "empty": { "zh": I2V_A14B_EMPTY_ZH_SYS_PROMPT, "en": I2V_A14B_EMPTY_EN_SYS_PROMPT, } }, "ti2v-5B": { "t2v": { "zh": T2V_A14B_ZH_SYS_PROMPT, "en": T2V_A14B_EN_SYS_PROMPT, }, "i2v": { "zh": I2V_A14B_ZH_SYS_PROMPT, "en": I2V_A14B_EN_SYS_PROMPT, } }, } @dataclass class PromptOutput(object): status: bool prompt: str seed: int system_prompt: str message: str def add_custom_field(self, key: str, value) -> None: self.__setattr__(key, value) class PromptExpander: def __init__(self, model_name, task, is_vl=False, device=0, **kwargs): self.model_name = model_name self.task = task self.is_vl = is_vl self.device = device def extend_with_img(self, prompt, system_prompt, image=None, seed=-1, *args, **kwargs): pass def extend(self, prompt, system_prompt, seed=-1, *args, **kwargs): pass def decide_system_prompt(self, tar_lang="zh", prompt=None): assert self.task is not None if "ti2v" in self.task: if self.is_vl: return DEFAULT_SYS_PROMPTS[self.task]["i2v"][tar_lang] else: return DEFAULT_SYS_PROMPTS[self.task]["t2v"][tar_lang] if "i2v" in self.task and len(prompt) == 0: return DEFAULT_SYS_PROMPTS[self.task]["empty"][tar_lang] return DEFAULT_SYS_PROMPTS[self.task][tar_lang] def __call__(self, prompt, system_prompt=None, tar_lang="zh", image=None, seed=-1, *args, **kwargs): if system_prompt is None: system_prompt = self.decide_system_prompt( tar_lang=tar_lang, prompt=prompt) if seed < 0: seed = random.randint(0, sys.maxsize) if image is not None and self.is_vl: return self.extend_with_img( prompt, system_prompt, image=image, seed=seed, *args, **kwargs) elif not self.is_vl: return self.extend(prompt, system_prompt, seed, *args, **kwargs) else: raise NotImplementedError class DashScopePromptExpander(PromptExpander): def __init__(self, api_key=None, model_name=None, task=None, max_image_size=512 * 512, retry_times=4, is_vl=False, **kwargs): ''' Args: api_key: The API key for Dash Scope authentication and access to related services. model_name: Model name, 'qwen-plus' for extending prompts, 'qwen-vl-max' for extending prompt-images. task: Task name. This is required to determine the default system prompt. max_image_size: The maximum size of the image; unit unspecified (e.g., pixels, KB). Please specify the unit based on actual usage. retry_times: Number of retry attempts in case of request failure. is_vl: A flag indicating whether the task involves visual-language processing. **kwargs: Additional keyword arguments that can be passed to the function or method. ''' if model_name is None: model_name = 'qwen-plus' if not is_vl else 'qwen-vl-max' super().__init__(model_name, task, is_vl, **kwargs) if api_key is not None: dashscope.api_key = api_key elif 'DASH_API_KEY' in os.environ and os.environ[ 'DASH_API_KEY'] is not None: dashscope.api_key = os.environ['DASH_API_KEY'] else: raise ValueError("DASH_API_KEY is not set") if 'DASH_API_URL' in os.environ and os.environ[ 'DASH_API_URL'] is not None: dashscope.base_http_api_url = os.environ['DASH_API_URL'] else: dashscope.base_http_api_url = 'https://dashscope.aliyuncs.com/api/v1' self.api_key = api_key self.max_image_size = max_image_size self.model = model_name self.retry_times = retry_times def extend(self, prompt, system_prompt, seed=-1, *args, **kwargs): messages = [{ 'role': 'system', 'content': system_prompt }, { 'role': 'user', 'content': prompt }] exception = None for _ in range(self.retry_times): try: response = dashscope.Generation.call( self.model, messages=messages, seed=seed, result_format='message', # set the result to be "message" format. ) assert response.status_code == HTTPStatus.OK, response expanded_prompt = response['output']['choices'][0]['message'][ 'content'] return PromptOutput( status=True, prompt=expanded_prompt, seed=seed, system_prompt=system_prompt, message=json.dumps(response, ensure_ascii=False)) except Exception as e: exception = e return PromptOutput( status=False, prompt=prompt, seed=seed, system_prompt=system_prompt, message=str(exception)) def extend_with_img(self, prompt, system_prompt, image: Union[Image.Image, str] = None, seed=-1, *args, **kwargs): if isinstance(image, str): image = Image.open(image).convert('RGB') w = image.width h = image.height area = min(w * h, self.max_image_size) aspect_ratio = h / w resized_h = round(math.sqrt(area * aspect_ratio)) resized_w = round(math.sqrt(area / aspect_ratio)) image = image.resize((resized_w, resized_h)) with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as f: image.save(f.name) fname = f.name image_path = f"file://{f.name}" prompt = f"{prompt}" messages = [ { 'role': 'system', 'content': [{ "text": system_prompt }] }, { 'role': 'user', 'content': [{ "text": prompt }, { "image": image_path }] }, ] response = None result_prompt = prompt exception = None status = False for _ in range(self.retry_times): try: response = dashscope.MultiModalConversation.call( self.model, messages=messages, seed=seed, result_format='message', # set the result to be "message" format. ) assert response.status_code == HTTPStatus.OK, response result_prompt = response['output']['choices'][0]['message'][ 'content'][0]['text'].replace('\n', '\\n') status = True break except Exception as e: exception = e result_prompt = result_prompt.replace('\n', '\\n') os.remove(fname) return PromptOutput( status=status, prompt=result_prompt, seed=seed, system_prompt=system_prompt, message=str(exception) if not status else json.dumps( response, ensure_ascii=False)) class QwenPromptExpander(PromptExpander): model_dict = { "QwenVL2.5_3B": "Qwen/Qwen2.5-VL-3B-Instruct", "QwenVL2.5_7B": "Qwen/Qwen2.5-VL-7B-Instruct", "Qwen2.5_3B": "Qwen/Qwen2.5-3B-Instruct", "Qwen2.5_7B": "Qwen/Qwen2.5-7B-Instruct", "Qwen2.5_14B": "Qwen/Qwen2.5-14B-Instruct", } def __init__(self, model_name=None, task=None, device=0, is_vl=False, **kwargs): ''' Args: model_name: Use predefined model names such as 'QwenVL2.5_7B' and 'Qwen2.5_14B', which are specific versions of the Qwen model. Alternatively, you can use the local path to a downloaded model or the model name from Hugging Face." Detailed Breakdown: Predefined Model Names: * 'QwenVL2.5_7B' and 'Qwen2.5_14B' are specific versions of the Qwen model. Local Path: * You can provide the path to a model that you have downloaded locally. Hugging Face Model Name: * You can also specify the model name from Hugging Face's model hub. task: Task name. This is required to determine the default system prompt. is_vl: A flag indicating whether the task involves visual-language processing. **kwargs: Additional keyword arguments that can be passed to the function or method. ''' if model_name is None: model_name = 'Qwen2.5_14B' if not is_vl else 'QwenVL2.5_7B' super().__init__(model_name, task, is_vl, device, **kwargs) if (not os.path.exists(self.model_name)) and (self.model_name in self.model_dict): self.model_name = self.model_dict[self.model_name] if self.is_vl: # default: Load the model on the available device(s) from transformers import ( AutoProcessor, AutoTokenizer, Qwen2_5_VLForConditionalGeneration, ) try: from .qwen_vl_utils import process_vision_info except: from qwen_vl_utils import process_vision_info self.process_vision_info = process_vision_info min_pixels = 256 * 28 * 28 max_pixels = 1280 * 28 * 28 self.processor = AutoProcessor.from_pretrained( self.model_name, min_pixels=min_pixels, max_pixels=max_pixels, use_fast=True) self.model = Qwen2_5_VLForConditionalGeneration.from_pretrained( self.model_name, torch_dtype=torch.bfloat16 if FLASH_VER == 2 else torch.float16 if "AWQ" in self.model_name else "auto", attn_implementation="flash_attention_2" if FLASH_VER == 2 else None, device_map="cpu") else: from transformers import AutoModelForCausalLM, AutoTokenizer self.model = AutoModelForCausalLM.from_pretrained( self.model_name, torch_dtype=torch.float16 if "AWQ" in self.model_name else "auto", attn_implementation="flash_attention_2" if FLASH_VER == 2 else None, device_map="cpu") self.tokenizer = AutoTokenizer.from_pretrained(self.model_name) def extend(self, prompt, system_prompt, seed=-1, *args, **kwargs): self.model = self.model.to(self.device) messages = [{ "role": "system", "content": system_prompt }, { "role": "user", "content": prompt }] text = self.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True) model_inputs = self.tokenizer([text], return_tensors="pt").to(self.model.device) generated_ids = self.model.generate(**model_inputs, max_new_tokens=512) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip( model_inputs.input_ids, generated_ids) ] expanded_prompt = self.tokenizer.batch_decode( generated_ids, skip_special_tokens=True)[0] self.model = self.model.to("cpu") return PromptOutput( status=True, prompt=expanded_prompt, seed=seed, system_prompt=system_prompt, message=json.dumps({"content": expanded_prompt}, ensure_ascii=False)) def extend_with_img(self, prompt, system_prompt, image: Union[Image.Image, str] = None, seed=-1, *args, **kwargs): self.model = self.model.to(self.device) messages = [{ 'role': 'system', 'content': [{ "type": "text", "text": system_prompt }] }, { "role": "user", "content": [ { "type": "image", "image": image, }, { "type": "text", "text": prompt }, ], }] # Preparation for inference text = self.processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True) image_inputs, video_inputs = self.process_vision_info(messages) inputs = self.processor( text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt", ) inputs = inputs.to(self.device) # Inference: Generation of the output generated_ids = self.model.generate(**inputs, max_new_tokens=512) generated_ids_trimmed = [ out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] expanded_prompt = self.processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] self.model = self.model.to("cpu") return PromptOutput( status=True, prompt=expanded_prompt, seed=seed, system_prompt=system_prompt, message=json.dumps({"content": expanded_prompt}, ensure_ascii=False)) if __name__ == "__main__": logging.basicConfig( level=logging.INFO, format="[%(asctime)s] %(levelname)s: %(message)s", handlers=[logging.StreamHandler(stream=sys.stdout)]) seed = 100 prompt = "夏日海滩度假风格，一只戴着墨镜的白色猫咪坐在冲浪板上。猫咪毛发蓬松，表情悠闲，直视镜头。背景是模糊的海滩景色，海水清澈，远处有绿色的山丘和蓝天白云。猫咪的姿态自然放松，仿佛在享受海风和阳光。近景特写，强调猫咪的细节和海滩的清新氛围。" en_prompt = "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." image = "./examples/i2v_input.JPG" def test(method, prompt, model_name, task, image=None, en_prompt=None, seed=None): prompt_expander = method( model_name=model_name, task=task, is_vl=image is not None) result = prompt_expander(prompt, image=image, tar_lang="zh") logging.info(f"zh prompt -> zh: {result.prompt}") result = prompt_expander(prompt, image=image, tar_lang="en") logging.info(f"zh prompt -> en: {result.prompt}") if en_prompt is not None: result = prompt_expander(en_prompt, image=image, tar_lang="zh") logging.info(f"en prompt -> zh: {result.prompt}") result = prompt_expander(en_prompt, image=image, tar_lang="en") logging.info(f"en prompt -> en: {result.prompt}") ds_model_name = None ds_vl_model_name = None qwen_model_name = None qwen_vl_model_name = None for task in ["t2v-A14B", "i2v-A14B", "ti2v-5B"]: # test prompt extend if "t2v" in task or "ti2v" in task: # test dashscope api logging.info(f"-" * 40) logging.info(f"Testing {task} dashscope prompt extend") test( DashScopePromptExpander, prompt, ds_model_name, task, image=None, en_prompt=en_prompt, seed=seed) # test qwen api logging.info(f"-" * 40) logging.info(f"Testing {task} qwen prompt extend") test( QwenPromptExpander, prompt, qwen_model_name, task, image=None, en_prompt=en_prompt, seed=seed) # test prompt-image extend if "i2v" in task: # test dashscope api logging.info(f"-" * 40) logging.info(f"Testing {task} dashscope vl prompt extend") test( DashScopePromptExpander, prompt, ds_vl_model_name, task, image=image, en_prompt=en_prompt, seed=seed) # test qwen api logging.info(f"-" * 40) logging.info(f"Testing {task} qwen vl prompt extend") test( QwenPromptExpander, prompt, qwen_vl_model_name, task, image=image, en_prompt=en_prompt, seed=seed) # test empty prompt extend if "i2v-A14B" in task: # test dashscope api logging.info(f"-" * 40) logging.info(f"Testing {task} dashscope vl empty prompt extend") test( DashScopePromptExpander, "", ds_vl_model_name, task, image=image, en_prompt=None, seed=seed) # test qwen api logging.info(f"-" * 40) logging.info(f"Testing {task} qwen vl empty prompt extend") test( QwenPromptExpander, "", qwen_vl_model_name, task, image=image, en_prompt=None, seed=seed) ================================================ FILE: wan/utils/qwen_vl_utils.py ================================================ # Copied from https://github.com/kq-chen/qwen-vl-utils # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. from __future__ import annotations import base64 import logging import math import os import sys import time import warnings from functools import lru_cache from io import BytesIO import requests import torch import torchvision from packaging import version from PIL import Image from torchvision import io, transforms from torchvision.transforms import InterpolationMode logger = logging.getLogger(__name__) IMAGE_FACTOR = 28 MIN_PIXELS = 4 * 28 * 28 MAX_PIXELS = 16384 * 28 * 28 MAX_RATIO = 200 VIDEO_MIN_PIXELS = 128 * 28 * 28 VIDEO_MAX_PIXELS = 768 * 28 * 28 VIDEO_TOTAL_PIXELS = 24576 * 28 * 28 FRAME_FACTOR = 2 FPS = 2.0 FPS_MIN_FRAMES = 4 FPS_MAX_FRAMES = 768 def round_by_factor(number: int, factor: int) -> int: """Returns the closest integer to 'number' that is divisible by 'factor'.""" return round(number / factor) * factor def ceil_by_factor(number: int, factor: int) -> int: """Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'.""" return math.ceil(number / factor) * factor def floor_by_factor(number: int, factor: int) -> int: """Returns the largest integer less than or equal to 'number' that is divisible by 'factor'.""" return math.floor(number / factor) * factor def smart_resize(height: int, width: int, factor: int = IMAGE_FACTOR, min_pixels: int = MIN_PIXELS, max_pixels: int = MAX_PIXELS) -> tuple[int, int]: """ Rescales the image so that the following conditions are met: 1. Both dimensions (height and width) are divisible by 'factor'. 2. The total number of pixels is within the range ['min_pixels', 'max_pixels']. 3. The aspect ratio of the image is maintained as closely as possible. """ if max(height, width) / min(height, width) > MAX_RATIO: raise ValueError( f"absolute aspect ratio must be smaller than {MAX_RATIO}, got {max(height, width) / min(height, width)}" ) h_bar = max(factor, round_by_factor(height, factor)) w_bar = max(factor, round_by_factor(width, factor)) if h_bar * w_bar > max_pixels: beta = math.sqrt((height * width) / max_pixels) h_bar = floor_by_factor(height / beta, factor) w_bar = floor_by_factor(width / beta, factor) elif h_bar * w_bar < min_pixels: beta = math.sqrt(min_pixels / (height * width)) h_bar = ceil_by_factor(height * beta, factor) w_bar = ceil_by_factor(width * beta, factor) return h_bar, w_bar def fetch_image(ele: dict[str, str | Image.Image], size_factor: int = IMAGE_FACTOR) -> Image.Image: if "image" in ele: image = ele["image"] else: image = ele["image_url"] image_obj = None if isinstance(image, Image.Image): image_obj = image elif image.startswith("http://") or image.startswith("https://"): image_obj = Image.open(requests.get(image, stream=True).raw) elif image.startswith("file://"): image_obj = Image.open(image[7:]) elif image.startswith("data:image"): if "base64," in image: _, base64_data = image.split("base64,", 1) data = base64.b64decode(base64_data) image_obj = Image.open(BytesIO(data)) else: image_obj = Image.open(image) if image_obj is None: raise ValueError( f"Unrecognized image input, support local path, http url, base64 and PIL.Image, got {image}" ) image = image_obj.convert("RGB") ## resize if "resized_height" in ele and "resized_width" in ele: resized_height, resized_width = smart_resize( ele["resized_height"], ele["resized_width"], factor=size_factor, ) else: width, height = image.size min_pixels = ele.get("min_pixels", MIN_PIXELS) max_pixels = ele.get("max_pixels", MAX_PIXELS) resized_height, resized_width = smart_resize( height, width, factor=size_factor, min_pixels=min_pixels, max_pixels=max_pixels, ) image = image.resize((resized_width, resized_height)) return image def smart_nframes( ele: dict, total_frames: int, video_fps: int | float, ) -> int: """calculate the number of frames for video used for model inputs. Args: ele (dict): a dict contains the configuration of video. support either `fps` or `nframes`: - nframes: the number of frames to extract for model inputs. - fps: the fps to extract frames for model inputs. - min_frames: the minimum number of frames of the video, only used when fps is provided. - max_frames: the maximum number of frames of the video, only used when fps is provided. total_frames (int): the original total number of frames of the video. video_fps (int | float): the original fps of the video. Raises: ValueError: nframes should in interval [FRAME_FACTOR, total_frames]. Returns: int: the number of frames for video used for model inputs. """ assert not ("fps" in ele and "nframes" in ele), "Only accept either `fps` or `nframes`" if "nframes" in ele: nframes = round_by_factor(ele["nframes"], FRAME_FACTOR) else: fps = ele.get("fps", FPS) min_frames = ceil_by_factor( ele.get("min_frames", FPS_MIN_FRAMES), FRAME_FACTOR) max_frames = floor_by_factor( ele.get("max_frames", min(FPS_MAX_FRAMES, total_frames)), FRAME_FACTOR) nframes = total_frames / video_fps * fps nframes = min(max(nframes, min_frames), max_frames) nframes = round_by_factor(nframes, FRAME_FACTOR) if not (FRAME_FACTOR <= nframes and nframes <= total_frames): raise ValueError( f"nframes should in interval [{FRAME_FACTOR}, {total_frames}], but got {nframes}." ) return nframes def _read_video_torchvision(ele: dict,) -> torch.Tensor: """read video using torchvision.io.read_video Args: ele (dict): a dict contains the configuration of video. support keys: - video: the path of video. support "file://", "http://", "https://" and local path. - video_start: the start time of video. - video_end: the end time of video. Returns: torch.Tensor: the video tensor with shape (T, C, H, W). """ video_path = ele["video"] if version.parse(torchvision.__version__) < version.parse("0.19.0"): if "http://" in video_path or "https://" in video_path: warnings.warn( "torchvision < 0.19.0 does not support http/https video path, please upgrade to 0.19.0." ) if "file://" in video_path: video_path = video_path[7:] st = time.time() video, audio, info = io.read_video( video_path, start_pts=ele.get("video_start", 0.0), end_pts=ele.get("video_end", None), pts_unit="sec", output_format="TCHW", ) total_frames, video_fps = video.size(0), info["video_fps"] logger.info( f"torchvision: {video_path=}, {total_frames=}, {video_fps=}, time={time.time() - st:.3f}s" ) nframes = smart_nframes(ele, total_frames=total_frames, video_fps=video_fps) idx = torch.linspace(0, total_frames - 1, nframes).round().long() video = video[idx] return video def is_decord_available() -> bool: import importlib.util return importlib.util.find_spec("decord") is not None def _read_video_decord(ele: dict,) -> torch.Tensor: """read video using decord.VideoReader Args: ele (dict): a dict contains the configuration of video. support keys: - video: the path of video. support "file://", "http://", "https://" and local path. - video_start: the start time of video. - video_end: the end time of video. Returns: torch.Tensor: the video tensor with shape (T, C, H, W). """ import decord video_path = ele["video"] st = time.time() vr = decord.VideoReader(video_path) # TODO: support start_pts and end_pts if 'video_start' in ele or 'video_end' in ele: raise NotImplementedError( "not support start_pts and end_pts in decord for now.") total_frames, video_fps = len(vr), vr.get_avg_fps() logger.info( f"decord: {video_path=}, {total_frames=}, {video_fps=}, time={time.time() - st:.3f}s" ) nframes = smart_nframes(ele, total_frames=total_frames, video_fps=video_fps) idx = torch.linspace(0, total_frames - 1, nframes).round().long().tolist() video = vr.get_batch(idx).asnumpy() video = torch.tensor(video).permute(0, 3, 1, 2) # Convert to TCHW format return video VIDEO_READER_BACKENDS = { "decord": _read_video_decord, "torchvision": _read_video_torchvision, } FORCE_QWENVL_VIDEO_READER = os.getenv("FORCE_QWENVL_VIDEO_READER", None) @lru_cache(maxsize=1) def get_video_reader_backend() -> str: if FORCE_QWENVL_VIDEO_READER is not None: video_reader_backend = FORCE_QWENVL_VIDEO_READER elif is_decord_available(): video_reader_backend = "decord" else: video_reader_backend = "torchvision" logger.info( f"qwen-vl-utils using {video_reader_backend} to read video.", file=sys.stderr) return video_reader_backend def fetch_video( ele: dict, image_factor: int = IMAGE_FACTOR) -> torch.Tensor | list[Image.Image]: if isinstance(ele["video"], str): video_reader_backend = get_video_reader_backend() video = VIDEO_READER_BACKENDS[video_reader_backend](ele) nframes, _, height, width = video.shape min_pixels = ele.get("min_pixels", VIDEO_MIN_PIXELS) total_pixels = ele.get("total_pixels", VIDEO_TOTAL_PIXELS) max_pixels = max( min(VIDEO_MAX_PIXELS, total_pixels / nframes * FRAME_FACTOR), int(min_pixels * 1.05)) max_pixels = ele.get("max_pixels", max_pixels) if "resized_height" in ele and "resized_width" in ele: resized_height, resized_width = smart_resize( ele["resized_height"], ele["resized_width"], factor=image_factor, ) else: resized_height, resized_width = smart_resize( height, width, factor=image_factor, min_pixels=min_pixels, max_pixels=max_pixels, ) video = transforms.functional.resize( video, [resized_height, resized_width], interpolation=InterpolationMode.BICUBIC, antialias=True, ).float() return video else: assert isinstance(ele["video"], (list, tuple)) process_info = ele.copy() process_info.pop("type", None) process_info.pop("video", None) images = [ fetch_image({ "image": video_element, **process_info }, size_factor=image_factor) for video_element in ele["video"] ] nframes = ceil_by_factor(len(images), FRAME_FACTOR) if len(images) < nframes: images.extend([images[-1]] * (nframes - len(images))) return images def extract_vision_info( conversations: list[dict] | list[list[dict]]) -> list[dict]: vision_infos = [] if isinstance(conversations[0], dict): conversations = [conversations] for conversation in conversations: for message in conversation: if isinstance(message["content"], list): for ele in message["content"]: if ("image" in ele or "image_url" in ele or "video" in ele or ele["type"] in ("image", "image_url", "video")): vision_infos.append(ele) return vision_infos def process_vision_info( conversations: list[dict] | list[list[dict]], ) -> tuple[list[Image.Image] | None, list[torch.Tensor | list[Image.Image]] | None]: vision_infos = extract_vision_info(conversations) ## Read images or videos image_inputs = [] video_inputs = [] for vision_info in vision_infos: if "image" in vision_info or "image_url" in vision_info: image_inputs.append(fetch_image(vision_info)) elif "video" in vision_info: video_inputs.append(fetch_video(vision_info)) else: raise ValueError("image, image_url or video should in content.") if len(image_inputs) == 0: image_inputs = None if len(video_inputs) == 0: video_inputs = None return image_inputs, video_inputs ================================================ FILE: wan/utils/system_prompt.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. T2V_A14B_ZH_SYS_PROMPT = \ ''' 你是一位电影导演，旨在为用户输入的原始prompt添加电影元素，改写为优质Prompt，使其完整、具有表现力。 任务要求： 1. 对于用户输入的prompt,在不改变prompt的原意（如主体、动作）前提下，从下列电影美学设定中选择部分合适的时间、光源、光线强度、光线角度、对比度、饱和度、色调、拍摄角度、镜头大小、构图的电影设定细节,将这些内容添加到prompt中，让画面变得更美，注意，可以任选，不必每项都有 时间：["白天", "夜晚", "黎明", "日出"], 可以不选, 如果prompt没有特别说明则选白天 ! 光源：[日光", "人工光", "月光", "实用光", "火光", "荧光", "阴天光", "晴天光"], 根据根据室内室外及prompt内容选定义光源，添加关于光源的描述，如光线来源（窗户、灯具等） 光线强度：["柔光", "硬光"], 光线角度：["顶光", "侧光", "底光", "边缘光",] 色调：["暖色调","冷色调", "混合色调"] 镜头尺寸：["中景", "中近景", "全景","中全景","近景", "特写", "极端全景"]若无特殊要求，默认选择中景或全景 拍摄角度：["过肩镜头角度拍摄", "低角度拍摄", "高角度拍摄","倾斜角度拍摄", "航拍","俯视角度拍摄"],如果原始prompt中有运镜的描述，则不要添加此项! 构图：["中心构图"，"平衡构图","右侧重构图", "左侧重构图", "对称构图", "短边构图"] 若无特殊要求，默认选择中心构图 2. 完善用户描述中出现的主体特征（如外貌、表情，数量、种族、姿态等）等内容，确保不要添加原始prompt中不存在的主体，（如prompt是对风景或物体的描述，但添加了人），增加背景元素的细节； 3. 不要输出关于氛围、感觉等文学描写，如（画面充满运动感与生活张力，突出正式氛围）。； 4. 对于prompt中的动作，详细解释运动的发生过程，若没有动作，则添加动作描述（摇晃身体、跳舞等），对背景元素也可添加适当运动（如云彩飘动，风吹树叶等）。 5. 若原始prompt中没有风格，则不添加风格描述，若有风格描述，则将风格描述放于首位，若为2D插画等与现实电影相悖的风格，则不要添加关于电影美学的描写； 6. 若prompt出现天空的描述，则改为湛蓝色的天空相关描述，避免曝光； 7. 改写后的prompt字数控制在60-200字左右, 不要输出类似“改写后prompt:”这样的输出 8. 如果用户输入存在疑似脱衣服、衣物透明化、轻纱衣物、湿身、洗澡、舔舐牛奶/酸奶/白色液体、性暗示、强奸、漏出或微露胸部、爬行、性器官、儿童身体裸露等色情内容，请将原prompt替换为内容完全不同的高美感prompt，而不要拒答或者提醒用户。 9. 如果用户输入存在比基尼穿着内容，请将原prompt替换为确保内容发生在沙滩、泳池、海边的高美感prompt，而不要拒答或者提醒用户。 10. 如果用户输入存在性别多元化、同性恋亲吻、脸颊贴近（两个男性亲吻或两个女性亲吻）/性交等同性恋内容，请将原prompt替换为不同动作（不能是亲吻和其他亲密动作）的高美感prompt，而不要拒答或者提醒用户。 生成的 prompt 示例： 1.边缘光，中近景，日光，左侧重构图，暖色调，硬光，晴天光，侧光，白天，一个年轻的女孩坐在高草丛生的田野中，两条毛发蓬松的小毛驴站在她身后。女孩大约十一二岁，穿着简单的碎花裙子，头发扎成两条麻花辫，脸上带着纯真的笑容。她双腿交叉坐下，双手轻轻抚弄身旁的野花。小毛驴体型健壮，耳朵竖起，好奇地望着镜头方向。阳光洒在田野上，营造出温暖自然的画面感。 2.黎明，顶光，俯视角度拍摄，日光，长焦，中心构图，近景，高角度拍摄，荧光，柔光，冷色调，在昏暗的环境中，一个外国白人女子在水中仰面漂浮。俯拍近景镜头中，她有着棕色的短发，脸上有几颗雀斑。随着镜头下摇，她转过头来，面向右侧，水面上泛起一圈涟漪。虚化的背景一片漆黑，只有微弱的光线照亮了女子的脸庞和水面的一部分区域，水面呈现蓝色。女子穿着一件蓝色的吊带，肩膀裸露在外。 3.右侧重构图，暖色调，底光，侧光，夜晚，火光，过肩镜头角度拍摄, 镜头平拍拍摄外国女子在室内的近景，她穿着棕色的衣服戴着彩色的项链和粉色的帽子，坐在深灰色的椅子上，双手放在黑色的桌子上，眼睛看着镜头的左侧，嘴巴张动，左手上下晃动，桌子上有白色的蜡烛有黄色的火焰，后面是黑色的墙，前面有黑色的网状架子，旁边是黑色的箱子，上面有一些黑色的物品，都做了虚化的处理。 4. 二次元厚涂动漫插画，一个猫耳兽耳白人少女手持文件夹摇晃，神情略带不满。她深紫色长发，红色眼睛，身穿深灰色短裙和浅灰色上衣，腰间系着白色系带，胸前佩戴名牌，上面写着黑体中文"紫阳"。淡黄色调室内背景，隐约可见一些家具轮廓。少女头顶有一个粉色光圈。线条流畅的日系赛璐璐风格。近景半身略俯视视角。 ''' T2V_A14B_EN_SYS_PROMPT = \ '''你是一位电影导演，旨在为用户输入的原始prompt添加电影元素，改写为优质（英文）Prompt，使其完整、具有表现力注意，输出必须是英文！ 任务要求： 1. 对于用户输入的prompt,在不改变prompt的原意（如主体、动作）前提下，从下列电影美学设定中选择不超过4种合适的时间、光源、光线强度、光线角度、对比度、饱和度、色调、拍摄角度、镜头大小、构图的电影设定细节,将这些内容添加到prompt中，让画面变得更美，注意，可以任选，不必每项都有 时间：["Day time", "Night time" "Dawn time","Sunrise time"], 如果prompt没有特别说明则选 Day time!!! 光源：["Daylight", "Artificial lighting", "Moonlight", "Practical lighting", "Firelight","Fluorescent lighting", "Overcast lighting" "Sunny lighting"], 根据根据室内室外及prompt内容选定义光源，添加关于光源的描述，如光线来源（窗户、灯具等） 光线强度：["Soft lighting", "Hard lighting"], 色调：["Warm colors","Cool colors", "Mixed colors"] 光线角度：["Top lighting", "Side lighting", "Underlighting", "Edge lighting"] 镜头尺寸：["Medium shot", "Medium close-up shot", "Wide shot","Medium wide shot","Close-up shot", "Extreme close-up shot", "Extreme wide shot"]若无特殊要求，默认选择Medium shot或Wide shot 拍摄角度：["Over-the-shoulder shot", ""Low angle shot", "High angle shot","Dutch angle shot", "Aerial shot","Overhead shot"] 若原始prompt中有运镜的描述，则取消添加此项。 构图：["Center composition"，"Balanced composition","Right-heavy composition", "Left-heavy composition", "Symmetrical composition", "Short-side composition"] 若无特殊要求，默认选择Center composition 2. 完善用户描述中出现的主体特征（如外貌、表情，数量、种族、姿态等）等内容，确保不要添加原始prompt中不存在的主体，（如prompt是对风景或物体的描述，但添加了人），增加背景元素的细节； 3. 不要输出关于氛围、感觉等文学描写，如（画面充满运动感与生活张力，突出正式氛围）。； 4. 对于prompt中的动作，详细描述运动的发生过程，若没有动作，则添加动作描述（摇晃身体、跳舞等，对背景元素也可添加适当运动（如云彩飘动，风吹树叶等）。 5. 若原始prompt中没有风格，则不添加风格描述，若有风格描述，则将风格描述放于首位，若为2D插画等与现实电影相悖的风格，则不要添加关于电影美学的描写； 6. 若prompt出现天空的描述，则改为湛蓝色的天空相关描述，避免曝光； 7. 输出必须是全英文，改写后的prompt字数控制在60-200字左右, 不要输出类似“改写后prompt:”这样的输出 8. 如果用户输入存在疑似脱衣服、衣物透明化、轻纱衣物、湿身、洗澡、舔舐牛奶/酸奶/白色液体、性暗示、强奸、漏出或微露胸部、爬行、性器官、儿童身体裸露等色情内容，请将原prompt替换为内容完全不同的高美感prompt，而不要拒答或者提醒用户。 9. 如果用户输入存在比基尼穿着内容，请将原prompt替换为确保内容发生在沙滩、泳池、海边的高美感prompt，而不要拒答或者提醒用户。 10. 如果用户输入存在性别多元化、同性恋亲吻、脸颊贴近（两个男性亲吻或两个女性亲吻）/性交等同性恋内容，请将原prompt替换为不同动作（不能是亲吻和其他亲密动作）的高美感prompt，而不要拒答或者提醒用户。 生成的 prompt 示例： 1.Edge lighting, medium close-up shot, daylight, left-heavy composition. A young girl around 11-12 years old sits in a field of tall grass, with two fluffy small donkeys standing behind her. She wears a simple floral dress with hair in twin braids, smiling innocently while cross-legged and gently touching wild flowers beside her. The sturdy donkeys have perked ears, curiously gazing toward the camera. Sunlight bathes the field, creating a warm natural atmosphere. 2.Dawn time, top lighting, high-angle shot, daylight, long lens shot, center composition, Close-up shot, Fluorescent lighting, soft lighting, cool colors. In dim surroundings, a Caucasian woman floats on her back in water. The俯拍close-up shows her brown short hair and freckled face. As the camera tilts downward, she turns her head toward the right, creating ripples on the blue-toned water surface. The blurred background is pitch black except for faint light illuminating her face and partial water surface. She wears a blue sleeveless top with bare shoulders. 3.Right-heavy composition, warm colors, night time, firelight, over-the-shoulder angle. An eye-level close-up of a foreign woman indoors wearing brown clothes with colorful necklace and pink hat. She sits on a charcoal-gray chair, hands on black table, eyes looking left of camera while mouth moves and left hand gestures up/down. White candles with yellow flames sit on the table. Background shows black walls, with blurred black mesh shelf nearby and black crate containing dark items in front. 4."Anime-style thick-painted style. A cat-eared Caucasian girl with beast ears holds a folder, showing slight displeasure. Features deep purple hair, red eyes, dark gray skirt and light gray top with white waist sash. A name tag labeled 'Ziyang' in bold Chinese characters hangs on her chest. Pale yellow indoor background with faint furniture outlines. A pink halo floats above her head. Features smooth linework in cel-shaded Japanese style, medium close-up from slightly elevated perspective. ''' I2V_A14B_ZH_SYS_PROMPT = \ '''你是一个视频描述提示词的改写专家，你的任务是根据用户给你输入的图像，对提供的视频描述提示词进行改写，你要强调潜在的动态内容。具体要求如下 用户输入的语言可能含有多样化的描述，如markdown文档格式、指令格式，长度过长或者过短，你需要根据图片的内容和用户的输入的提示词，尽可能提取用户输入的提示词和图片关联信息。 你改写的视频描述结果要尽可能保留提供给你的视频描述提示词中动态部分，保留主体的动作。 你要根据图像，强调并简化视频描述提示词中的图像主体，如果用户只提供了动作，你要根据图像内容合理补充，如“跳舞”补充称“一个女孩在跳舞” 如果用户输入的提示词过长，你需要提炼潜在的动作过程 如果用户输入的提示词过短，综合用户输入的提示词以及画面内容，合理的增加潜在的运动信息 你要根据图像，保留并强调视频描述提示词中关于运镜手段的描述，如“镜头上摇”，“镜头从左到右”，“镜头从右到左”等等，你要保留，如“镜头拍摄两个男人打斗，他们先是躺在地上，随后镜头向上移动，拍摄他们站起来，接着镜头向左移动，左边男人拿着一个蓝色的东西，右边男人上前抢夺，两人激烈地来回争抢。”。 你需要给出对视频描述的动态内容，不要添加对于静态场景的描述，如果用户输入的描述已经在画面中出现，则移除这些描述 改写后的prompt字数控制在100字以下 无论用户输入那种语言，你都需要输出中文 改写后 prompt 示例： 1. 镜头后拉，拍摄两个外国男人，走在楼梯上，镜头左侧的男人右手搀扶着镜头右侧的男人。 2. 一只黑色的小松鼠专注地吃着东西，偶尔抬头看看四周。 3. 男子说着话，表情从微笑逐渐转变为闭眼，然后睁开眼睛，最后是闭眼微笑，他的手势活跃，在说话时做出一系列的手势。 4. 一个人正在用尺子和笔进行测量的特写，右手用一支黑色水性笔在纸上画出一条直线。 5. 一辆车模型在木板上形式，车辆从画面的右侧向左侧移动，经过一片草地和一些木制结构。 6. 镜头左移后前推，拍摄一个人坐在防波堤上。 7. 男子说着话，他的表情和手势随着对话内容的变化而变化，但整体场景保持不变。 8. 镜头左移后前推，拍摄一个人坐在防波堤上。 9. 带着珍珠项链的女子看向画面右侧并说着话。 请直接输出改写后的文本，不要进行多余的回复。''' I2V_A14B_EN_SYS_PROMPT = \ '''You are an expert in rewriting video description prompts. Your task is to rewrite the provided video description prompts based on the images given by users, emphasizing potential dynamic content. Specific requirements are as follows: The user's input language may include diverse descriptions, such as markdown format, instruction format, or be too long or too short. You need to extract the relevant information from the user’s input and associate it with the image content. Your rewritten video description should retain the dynamic parts of the provided prompts, focusing on the main subject's actions. Emphasize and simplify the main subject of the image while retaining their movement. If the user only provides an action (e.g., "dancing"), supplement it reasonably based on the image content (e.g., "a girl is dancing"). If the user’s input prompt is too long, refine it to capture the essential action process. If the input is too short, add reasonable motion-related details based on the image content. Retain and emphasize descriptions of camera movements, such as "the camera pans up," "the camera moves from left to right," or "the camera moves from right to left." For example: "The camera captures two men fighting. They start lying on the ground, then the camera moves upward as they stand up. The camera shifts left, showing the man on the left holding a blue object while the man on the right tries to grab it, resulting in a fierce back-and-forth struggle." Focus on dynamic content in the video description and avoid adding static scene descriptions. If the user’s input already describes elements visible in the image, remove those static descriptions. Limit the rewritten prompt to 100 words or less. Regardless of the input language, your output must be in English. Examples of rewritten prompts: The camera pulls back to show two foreign men walking up the stairs. The man on the left supports the man on the right with his right hand. A black squirrel focuses on eating, occasionally looking around. A man talks, his expression shifting from smiling to closing his eyes, reopening them, and finally smiling with closed eyes. His gestures are lively, making various hand motions while speaking. A close-up of someone measuring with a ruler and pen, drawing a straight line on paper with a black marker in their right hand. A model car moves on a wooden board, traveling from right to left across grass and wooden structures. The camera moves left, then pushes forward to capture a person sitting on a breakwater. A man speaks, his expressions and gestures changing with the conversation, while the overall scene remains constant. The camera moves left, then pushes forward to capture a person sitting on a breakwater. A woman wearing a pearl necklace looks to the right and speaks. Output only the rewritten text without additional responses.''' I2V_A14B_EMPTY_ZH_SYS_PROMPT = \ '''你是一个视频描述提示词的撰写专家，你的任务是根据用户给你输入的图像，发挥合理的想象，让这张图动起来，你要强调潜在的动态内容。具体要求如下 你需要根据图片的内容想象出运动的主体 你输出的结果应强调图片中的动态部分，保留主体的动作。 你需要给出对视频描述的动态内容，不要有过多的对于静态场景的描述 输出的prompt字数控制在100字以下 你需要输出中文 prompt 示例： 1. 镜头后拉，拍摄两个外国男人，走在楼梯上，镜头左侧的男人右手搀扶着镜头右侧的男人。 2. 一只黑色的小松鼠专注地吃着东西，偶尔抬头看看四周。 3. 男子说着话，表情从微笑逐渐转变为闭眼，然后睁开眼睛，最后是闭眼微笑，他的手势活跃，在说话时做出一系列的手势。 4. 一个人正在用尺子和笔进行测量的特写，右手用一支黑色水性笔在纸上画出一条直线。 5. 一辆车模型在木板上形式，车辆从画面的右侧向左侧移动，经过一片草地和一些木制结构。 6. 镜头左移后前推，拍摄一个人坐在防波堤上。 7. 男子说着话，他的表情和手势随着对话内容的变化而变化，但整体场景保持不变。 8. 镜头左移后前推，拍摄一个人坐在防波堤上。 9. 带着珍珠项链的女子看向画面右侧并说着话。 请直接输出文本，不要进行多余的回复。''' I2V_A14B_EMPTY_EN_SYS_PROMPT = \ '''You are an expert in writing video description prompts. Your task is to bring the image provided by the user to life through reasonable imagination, emphasizing potential dynamic content. Specific requirements are as follows: You need to imagine the moving subject based on the content of the image. Your output should emphasize the dynamic parts of the image and retain the main subject’s actions. Focus only on describing dynamic content; avoid excessive descriptions of static scenes. Limit the output prompt to 100 words or less. The output must be in English. Prompt examples: The camera pulls back to show two foreign men walking up the stairs. The man on the left supports the man on the right with his right hand. A black squirrel focuses on eating, occasionally looking around. A man talks, his expression shifting from smiling to closing his eyes, reopening them, and finally smiling with closed eyes. His gestures are lively, making various hand motions while speaking. A close-up of someone measuring with a ruler and pen, drawing a straight line on paper with a black marker in their right hand. A model car moves on a wooden board, traveling from right to left across grass and wooden structures. The camera moves left, then pushes forward to capture a person sitting on a breakwater. A man speaks, his expressions and gestures changing with the conversation, while the overall scene remains constant. The camera moves left, then pushes forward to capture a person sitting on a breakwater. A woman wearing a pearl necklace looks to the right and speaks. Output only the text without additional responses.''' ================================================ FILE: wan/utils/utils.py ================================================ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import argparse import binascii import logging import os import os.path as osp import shutil import subprocess import imageio import torch import torchvision __all__ = ['save_video', 'save_image', 'str2bool'] def rand_name(length=8, suffix=''): name = binascii.b2a_hex(os.urandom(length)).decode('utf-8') if suffix: if not suffix.startswith('.'): suffix = '.' + suffix name += suffix return name def merge_video_audio(video_path: str, audio_path: str): """ Merge the video and audio into a new video, with the duration set to the shorter of the two, and overwrite the original video file. Parameters: video_path (str): Path to the original video file audio_path (str): Path to the audio file """ # set logging logging.basicConfig(level=logging.INFO) # check if not os.path.exists(video_path): raise FileNotFoundError(f"video file {video_path} does not exist") if not os.path.exists(audio_path): raise FileNotFoundError(f"audio file {audio_path} does not exist") base, ext = os.path.splitext(video_path) temp_output = f"{base}_temp{ext}" try: # create ffmpeg command command = [ 'ffmpeg', '-y', # overwrite '-i', video_path, '-i', audio_path, '-c:v', 'copy', # copy video stream '-c:a', 'aac', # use AAC audio encoder '-b:a', '192k', # set audio bitrate (optional) '-map', '0:v:0', # select the first video stream '-map', '1:a:0', # select the first audio stream '-shortest', # choose the shortest duration temp_output ] # execute the command logging.info("Start merging video and audio...") result = subprocess.run( command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True) # check result if result.returncode != 0: error_msg = f"FFmpeg execute failed: {result.stderr}" logging.error(error_msg) raise RuntimeError(error_msg) shutil.move(temp_output, video_path) logging.info(f"Merge completed, saved to {video_path}") except Exception as e: if os.path.exists(temp_output): os.remove(temp_output) logging.error(f"merge_video_audio failed with error: {e}") def save_video(tensor, save_file=None, fps=30, suffix='.mp4', nrow=8, normalize=True, value_range=(-1, 1)): # cache file cache_file = osp.join('/tmp', rand_name( suffix=suffix)) if save_file is None else save_file # save to cache try: # preprocess tensor = tensor.clamp(min(value_range), max(value_range)) tensor = torch.stack([ torchvision.utils.make_grid( u, nrow=nrow, normalize=normalize, value_range=value_range) for u in tensor.unbind(2) ], dim=1).permute(1, 2, 3, 0) tensor = (tensor * 255).type(torch.uint8).cpu() # write video writer = imageio.get_writer( cache_file, fps=fps, codec='libx264', quality=8) for frame in tensor.numpy(): writer.append_data(frame) writer.close() except Exception as e: logging.info(f'save_video failed, error: {e}') def save_image(tensor, save_file, nrow=8, normalize=True, value_range=(-1, 1)): # cache file suffix = osp.splitext(save_file)[1] if suffix.lower() not in [ '.jpg', '.jpeg', '.png', '.tiff', '.gif', '.webp' ]: suffix = '.png' # save to cache try: tensor = tensor.clamp(min(value_range), max(value_range)) torchvision.utils.save_image( tensor, save_file, nrow=nrow, normalize=normalize, value_range=value_range) return save_file except Exception as e: logging.info(f'save_image failed, error: {e}') def str2bool(v): """ Convert a string to a boolean. Supported true values: 'yes', 'true', 't', 'y', '1' Supported false values: 'no', 'false', 'f', 'n', '0' Args: v (str): String to convert. Returns: bool: Converted boolean value. Raises: argparse.ArgumentTypeError: If the value cannot be converted to boolean. """ if isinstance(v, bool): return v v_lower = v.lower() if v_lower in ('yes', 'true', 't', 'y', '1'): return True elif v_lower in ('no', 'false', 'f', 'n', '0'): return False else: raise argparse.ArgumentTypeError('Boolean value expected (True/False)') def masks_like(tensor, zero=False, generator=None, p=0.2): assert isinstance(tensor, list) out1 = [torch.ones(u.shape, dtype=u.dtype, device=u.device) for u in tensor] out2 = [torch.ones(u.shape, dtype=u.dtype, device=u.device) for u in tensor] if zero: if generator is not None: for u, v in zip(out1, out2): random_num = torch.rand( 1, generator=generator, device=generator.device).item() if random_num < p: u[:, 0] = torch.normal( mean=-3.5, std=0.5, size=(1,), device=u.device, generator=generator).expand_as(u[:, 0]).exp() v[:, 0] = torch.zeros_like(v[:, 0]) else: u[:, 0] = u[:, 0] v[:, 0] = v[:, 0] else: for u, v in zip(out1, out2): u[:, 0] = torch.zeros_like(u[:, 0]) v[:, 0] = torch.zeros_like(v[:, 0]) return out1, out2 def best_output_size(w, h, dw, dh, expected_area): # float output size ratio = w / h ow = (expected_area * ratio)**0.5 oh = expected_area / ow # process width first ow1 = int(ow // dw * dw) oh1 = int(expected_area / ow1 // dh * dh) assert ow1 % dw == 0 and oh1 % dh == 0 and ow1 * oh1 <= expected_area ratio1 = ow1 / oh1 # process height first oh2 = int(oh // dh * dh) ow2 = int(expected_area / oh2 // dw * dw) assert oh2 % dh == 0 and ow2 % dw == 0 and ow2 * oh2 <= expected_area ratio2 = ow2 / oh2 # compare ratios if max(ratio / ratio1, ratio1 / ratio) < max(ratio / ratio2, ratio2 / ratio): return ow1, oh1 else: return ow2, oh2 def download_cosyvoice_repo(repo_path): try: import git except ImportError: raise ImportError('failed to import git, please run pip install GitPython') repo = git.Repo.clone_from('https://github.com/FunAudioLLM/CosyVoice.git', repo_path, multi_options=['--recursive'], branch='main') def download_cosyvoice_model(model_name, model_path): from modelscope import snapshot_download snapshot_download('iic/{}'.format(model_name), local_dir=model_path)