wan2.2

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 .
+```

LICENSE.txt

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Makefile

+.PHONY: format
+
+format:
+	isort generate.py wan
+	yapf -i -r *.py generate.py wan

README.md

+# Wan2.2-T2V-A14B
+## 论文
+[Wan](https://arxiv.org/abs/2503.20314)
+
+## 模型简介
+Wan2.2是一个开放且先进的大规模视频生成模型，在Wan2.2中，重点引入了以下创新：
+-👍 有效的MoE架构：Wan2.2在视频扩散模型中引入了混合专家（MoE）架构。通过使用专门的强专家模型来分离跨时间步的去噪过程，这扩大了整个模型的容量，同时保持了相同的计算成本。
+-👍 电影级美学：Wan2.2包含精心策划的美学数据，附带详细的照明、构图、对比度、色调等标签。这使得电影风格的生成更加精确和可控，便于创建具有自定义美学偏好的视频。
+-👍 复杂的运动生成：与Wan2.1相比，Wan2.2在显著更多的数据上进行训练，图像数量增加了+65.6%，视频数量增加了+83.2%。这一扩展显著增强了模型在多个维度上的泛化能力，如运动、语义和美学，在所有开源和闭源模型中达到顶级性能。
+-👍 高效的高清晰度混合TI2V：Wan2.2 开源了一个基于我们先进的Wan2.2-VAE构建的5B模型，实现了16×16×4的压缩比。该模型支持以720P分辨率24fps的速度生成文本到视频和图像到视频，并且可以在消费级显卡如4090上运行。它是目前可用的最快的720P@24fps模型之一，能够同时服务于工业和学术领域。
+该模型采用混合专家（MoE）架构构建，提供了出色的视频生成质量。在新基准Wan-Bench2.0上，该模型在大多数关键评估维度上超越了领先的商业模型。模型架构如下：
+<div align=center>
+    <img src="./doc/arch.png"/>
+</div>
+
+## 环境依赖
+- 列举基础环境需求，根据实际情况填写
+
+| 软件 | 版本 |
+| :------: | :------: |
+| DTK | 25.04.2 |
+| python | 3.10 |
+| transformers | 4.57.1 |
+| pytorch | 2.7.1+das.opt1.dtk25042 |
+| torchaudio | 2.5.1a0+d178b24 |
+| torchvision | 0.22.0+das.opt1.dtk25042 |
+
+推荐使用镜像:
+- 挂载地址`-v`，`{docker_name}`和 `{docker_image_name}`根据实际模型情况修改
+
+```bash
+docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.7.1-ubuntu22.04-dtk25.04.2-py3.10-alpha
+docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash
+pip install http://10.16.4.1:8000/debug/torchaudio/dtk25.04.2-beta-bug-fix/torch251-audio/torch251-audio-fastpt/torchaudio-2.5.1a0%2Bd178b24-cp310-cp310-manylinux_2_28_x86_64.whl
+pip install http://10.16.4.1:8000/debug/flash_attn/dtk25.04.2-rc1/dtk25.04-llvm0106/flash_attn-2.6.1%2Bdas.opt1.dtk2504-cp310-cp310-manylinux_2_28_x86_64.whl
+cd /your_code_path/wan2.2_pytorch
+pip install -r requirements.txt
+
+```
+更多镜像可前往[光源](https://sourcefind.cn/#/service-list)下载使用。
+
+关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.sourcefind.cn/tool/)开发者社区下载安装，其它包参照requirements.txt安装：
+```
+pip install -r requirements.txt
+```
+
+## 数据集
+
+暂无
+
+## 训练
+暂无
+
+## 推理
+
+### transformers
+#### 单机推理
+1、单卡
+```bash
+python generate.py  --task t2v-A14B --size 832*480 --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."
+```
+2.多卡
+```bash
+torchrun --nproc_per_node=8 generate.py --task t2v-A14B --size 832*480 --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."
+```
+
+## 效果展示
+
+
+<div align=center>
+    <video width="600" controls>
+  <source src="./doc/t2v-A14B_832*480_1_Two_anthropomorphic_cats_in_comfy_boxing_gear_and__20251110_164740.mp4">
+  
+</video>
+</div>
+
+### 精度
+DCU与GPU精度一致，推理框架：pytorch。
+
+## 预训练权重
+| 模型名称  | 权重大小  | DCU型号  | 最低卡数需求 |下载地址|
+|:-----:|:----------:|:----------:|:---------------------:|:----------:|
+| Wan2.2-T2V-A14B | 14B | K100AI,BW1000 | 1 | [下载地址](https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B) |
+
+## 源码仓库及问题反馈
+- https://developer.sourcefind.cn/codes/modelzoo/wan2.2_pytorch
+
+## 参考资料
+- https://github.com/Wan-Video/Wan2.2
\ No newline at end of file

README_origin.md

+# Wan2.2
+
+<p align="center">
+    <img src="assets/logo.png" width="400"/>
+<p>
+
+<p align="center">
+    💜 <a href="https://wan.video"><b>Wan</b></a> &nbsp&nbsp ｜ &nbsp&nbsp 🖥️ <a href="https://github.com/Wan-Video/Wan2.2">GitHub</a> &nbsp&nbsp  | &nbsp&nbsp🤗 <a href="https://huggingface.co/Wan-AI/">Hugging Face</a>&nbsp&nbsp | &nbsp&nbsp🤖 <a href="https://modelscope.cn/organization/Wan-AI">ModelScope</a>&nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://arxiv.org/abs/2503.20314">Paper</a> &nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://wan.video/welcome?spm=a2ty_o02.30011076.0.0.6c9ee41eCcluqg">Blog</a> &nbsp&nbsp |  &nbsp&nbsp 💬  <a href="https://discord.gg/AKNgpMK4Yj">Discord</a>&nbsp&nbsp
+    <br>
+    📕 <a href="https://alidocs.dingtalk.com/i/nodes/jb9Y4gmKWrx9eo4dCql9LlbYJGXn6lpz">使用指南(中文)</a>&nbsp&nbsp | &nbsp&nbsp 📘 <a href="https://alidocs.dingtalk.com/i/nodes/EpGBa2Lm8aZxe5myC99MelA2WgN7R35y">User Guide(English)</a>&nbsp&nbsp | &nbsp&nbsp💬 <a href="https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg">WeChat(微信)</a>&nbsp&nbsp
+<br>
+
+-----
+
+[**Wan: Open and Advanced Large-Scale Video Generative Models**](https://arxiv.org/abs/2503.20314) <be>
+
+
+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
+
+<div align="center">
+  <video src="https://github.com/user-attachments/assets/b63bfa58-d5d7-4de6-a1a2-98970b06d9a7" width="70%" poster=""> </video>
+</div>
+
+## 🔥 Latest News!!
+
+* 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-with-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.
+
+- [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
+    - [ ] 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
+```
+
+##### (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
+```
+
+> 💡 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)**.
+
+
+<div align="center">
+    <img src="assets/comp_effic.png" alt="" style="width: 80%;" />
+</div>
+
+> 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.
+
+<div align="center">
+    <img src="assets/moe_arch.png" alt="" style="width: 90%;" />
+</div>
+
+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 $t<{t}_{moe}$.
+
+<div align="center">
+    <img src="assets/moe_2.png" alt="" style="width: 90%;" />
+</div>
+
+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.
+
+
+<div align="center">
+    <img src="assets/vae.png" alt="" style="width: 80%;" />
+</div>
+
+
+
+##### 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.
+
+
+<div align="center">
+    <img src="assets/performance.png" alt="" style="width: 90%;" />
+</div>
+
+## 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)!
+