wan2.1

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README.md

+# Wan2.1
+
+## 论文
+
+`Tech Report`
+
+* https://wanxai.com/
+
+## 模型结构
+
+模型采用主流`Latent Diffusion`架构，包含用于数据压缩/恢复的`3D VAE`，去噪模块`DiT`，文本使用`T5`编码器处理。
+
+![alt text](readme_imgs/arch.png)
+
+## 算法原理
+
+采用Flow matching算法。
+
+![alt text](readme_imgs/alg.png)
+
+## 环境配置
+
+### Docker（方法一）
+    
+    docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.3.0-ubuntu22.04-dtk24.04.3-py3.10
+
+    docker run --shm-size 100g --network=host --name=wan --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home/ -v /opt/hyhal:/opt/hyhal:ro -it <your IMAGE ID> bash
+
+    pip install -r requirements.txt
+
+    pip install "xfuser>=0.4" --no-deps torch
+
+    bash modified/fix.sh
+
+### Dockerfile（方法二）
+
+    docker build -t <IMAGE_NAME>:<TAG> .
+
+    docker run --shm-size 100g --network=host --name=wan --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home/ -v /opt/hyhal:/opt/hyhal:ro -it <your IMAGE ID> bash
+    
+    pip install -r requirements.txt
+
+    pip install "xfuser>=0.4" --no-deps torch
+
+    bash modified/fix.sh
+
+### Anaconda（方法三）
+
+1、关于本项目DCU显卡所需的特殊深度学习库可从光合开发者社区下载安装： https://developer.hpccube.com/tool/
+
+```
+DTK驱动:dtk24.04.3
+python:python3.10
+torch:2.3.0
+torchvision:0.18.1
+torchaudio:2.1.2
+triton:2.1.0
+vllm:0.6.2
+flash-attn:2.6.1
+deepspeed:0.14.2
+apex:1.3.0
+xformers:0.0.25
+transformers:4.48.0
+```
+
+2、其他非特殊库直接按照requirements.txt安装
+
+```
+pip install -r requirements.txt
+
+pip install "xfuser>=0.4" --no-deps torch
+
+# 需要参考 modified/fix.sh中的命令修改相应位置的代码
+```
+
+## 数据集
+
+无
+
+## 训练
+
+无
+
+## 推理
+
+### 文字-视频生成
+
+1、单卡
+
+<!-- # # 14B模型支持480/720P
+# python generate.py  --task t2v-14B --size 832*480 --ckpt_dir models/Wan2.1-T2V-14B --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage." -->
+
+```bash
+# 1.3B模型支持480P
+python generate.py  --task t2v-1.3B --size 832*480 --ckpt_dir models/Wan2.1-T2V-1.3B --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
+```
+
+注意：若遇到显存不够的问题，可以尝试`--offload_model True`，`--t5_cpu`
+
+2、多卡
+
+```bash
+# 1.3B
+torchrun --nproc_per_node=4 generate.py --task t2v-1.3B --size 832*480 --ckpt_dir models/Wan2.1-T2V-1.3B --dit_fsdp --t5_fsdp --ulysses_size 4 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
+
+# 14B
+torchrun --nproc_per_node=4 generate.py --task t2v-14B --size 1280*720 --ckpt_dir models/Wan2.1-T2V-14B --dit_fsdp --t5_fsdp --ulysses_size 4 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
+```
+
+启用提示增强
+
+```bash
+<命令> --use_prompt_extend --prompt_extend_method 'local_qwen' --prompt_extend_target_lang 'ch' --prompt_extend_model models/Qwen2.5-7B-Instruct
+
+# example
+python generate.py  --task t2v-1.3B --size 832*480 --ckpt_dir models/Wan2.1-T2V-1.3B --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 'ch' --prompt_extend_model models/Qwen2.5-7B-Instruct
+```
+
+### 图像-视频生成
+
+<!-- 1、单卡
+
+```bash
+python generate.py --task i2v-14B --size 1280*720 --ckpt_dir models/Wan2.1-I2V-14B-720P --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."
+```
+
+注意：若遇到显存不够的问题，可以尝试`--offload_model True`，`--t5_cpu` -->
+
+
+```bash
+torchrun --nproc_per_node=4 generate.py --task i2v-14B --size 1280*720 --ckpt_dir models/Wan2.1-I2V-14B-720P --image examples/i2v_input.JPG --dit_fsdp --t5_fsdp --ulysses_size 4 --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."
+```
+
+启用提示增强
+
+```bash
+<命令> --use_prompt_extend --prompt_extend_model Qwen/Qwen2.5-VL-7B-Instruct
+
+# example
+torchrun --nproc_per_node=4 generate.py --task i2v-14B --size 1280*720 --ckpt_dir models/Wan2.1-I2V-14B-720P --image examples/i2v_input.JPG --dit_fsdp --t5_fsdp --ulysses_size 4 --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." --use_prompt_extend --prompt_extend_model Qwen/Qwen2.5-VL-7B-Instruct
+```
+
+### 文本-图像生成
+
+<!-- 1、单卡
+
+```bash
+python generate.py --task t2i-14B --size 1024*1024 --ckpt_dir models/Wan2.1-T2V-14B  --prompt '一个朴素端庄的美人'
+```
+
+注意：若遇到显存不够的问题，可以尝试`--offload_model True`，`--t5_cpu` -->
+
+```bash
+torchrun --nproc_per_node=4 generate.py --dit_fsdp --t5_fsdp --ulysses_size 4 --base_seed 0 --frame_num 1 --task t2i-14B  --size 1024*1024 --prompt '一个朴素端庄的美人' --ckpt_dir models/Wan2.1-T2V-14B
+```
+
+启用提示增强
+
+```bash
+<命令> --use_prompt_extend --prompt_extend_method 'local_qwen' --prompt_extend_target_lang 'ch' --prompt_extend_model models/Qwen2.5-7B-Instruct
+
+# example
+torchrun --nproc_per_node=4 generate.py --dit_fsdp --t5_fsdp --ulysses_size 4 --base_seed 0 --frame_num 1 --task t2i-14B  --size 1024*1024 --prompt '一个朴素端庄的美人' --ckpt_dir models/Wan2.1-T2V-14B --use_prompt_extend --prompt_extend_method 'local_qwen' --prompt_extend_target_lang 'ch' --prompt_extend_model models/Qwen2.5-7B-Instruct
+```
+
+### webui
+
+```bash
+python gradio/t2v_1.3B_singleGPU.py --ckpt_dir models/Wan2.1-T2V-1.3B --prompt_extend_method 'local_qwen' --prompt_extend_model models/Qwen2.5-7B-Instruct
+```
+
+## result
+
+
+|model/task|t2v|i2v|t2i|
+|:---:|:---:|:---:|:---:|
+|T2V-14B|![](readme_imgs/t2v-14B.gif)||![](readme_imgs/t2i-14B.png)|
+|T2V-1.3B|![](readme_imgs/t2v-1.3B.gif)||||
+|I2V-14B-720P||![](readme_imgs/i2v-14B_720.gif)||
+|I2V-14B-480P||![](readme_imgs/i2v-14B_480.gif)||
+
+
+### 精度
+
+无
+
+## 应用场景
+
+### 算法类别
+
+`视频生成`
+
+### 热点应用行业
+
+`电商,教育,广媒`
+
+## 预训练权重
+
+下载后的模型放在 `models` 目录（自行创建）
+
+
+|Models|下载链接|
+|:---:|:---:|
+|T2V-14B|[modelscope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B) \| [SCNet高速下载通道](http://113.200.138.88:18080/aimodels/Wan-AI/Wan2.1-T2V-14B) |
+|I2V-14B-720P|[modelscope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P)  \| [SCNet高速下载通道](http://113.200.138.88:18080/aimodels/Wan-AI/Wan2.1-I2V-14B-720P) |
+|I2V-14B-480P|[modelscope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P) \| [SCNet高速下载通道](http://113.200.138.88:18080/aimodels/Wan-AI/Wan2.1-I2V-14B-480P) |
+|T2V-1.3B|[modelscope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B) \| [SCNet高速下载通道](http://113.200.138.88:18080/aimodels/Wan-AI/Wan2.1-T2V-1.3B) |
+|Qwen2.5-7B-Instruct|[modelscope](https://www.modelscope.cn/models/Qwen/Qwen2.5-7B-Instruct) \| [SCNet高速下载通道](http://113.200.138.88:18080/aimodels/qwen/Qwen2.5-7B-Instruct) |
+|Qwen2.5-VL-7B-Instruct|[modelscope](https://www.modelscope.cn/models/Qwen/Qwen2.5-VL-7B-Instruct) \| [SCNet高速下载通道](http://113.200.138.88:18080/aimodels/qwen/qwen2.5-vl-7b-instruct) |
+
+
+## 源码仓库及问题反馈
+
+* https://developer.sourcefind.cn/codes/modelzoo/wan2.1_pytorch
+
+## 参考资料
+
+* https://github.com/Wan-Video/Wan2.1

README_official.md

+# Wan2.1
+
+<p align="center">
+    <img src="assets/logo.png" width="400"/>
+<p>
+
+<p align="center">
+    💜 <a href=""><b>Wan</b></a> &nbsp&nbsp ｜ &nbsp&nbsp 🖥️ <a href="https://github.com/Wan-Video/Wan2.1">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="">Paper (Coming soon)</a> &nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://wanxai.com">Blog</a> &nbsp&nbsp | &nbsp&nbsp💬 <a href="https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg">WeChat Group</a>&nbsp&nbsp | &nbsp&nbsp 📖 <a href="https://discord.gg/p5XbdQV7">Discord</a>&nbsp&nbsp
+<br>
+
+-----
+
+[**Wan: Open and Advanced Large-Scale Video Generative Models**]("") <be>
+
+In this repository, we present **Wan2.1**, a comprehensive and open suite of video foundation models that pushes the boundaries of video generation. **Wan2.1** offers these key features:
+- 👍 **SOTA Performance**: **Wan2.1** consistently outperforms existing open-source models and state-of-the-art commercial solutions across multiple benchmarks.
+- 👍 **Supports Consumer-grade GPUs**: The T2V-1.3B model requires only 8.19 GB VRAM, making it compatible with almost all consumer-grade GPUs. It can generate a 5-second 480P video on an RTX 4090 in about 4 minutes (without optimization techniques like quantization). Its performance is even comparable to some closed-source models.
+- 👍 **Multiple Tasks**: **Wan2.1** excels in Text-to-Video, Image-to-Video, Video Editing, Text-to-Image, and Video-to-Audio, advancing the field of video generation.
+- 👍 **Visual Text Generation**: **Wan2.1** is the first video model capable of generating both Chinese and English text, featuring robust text generation that enhances its practical applications.
+- 👍 **Powerful Video VAE**: **Wan-VAE** delivers exceptional efficiency and performance, encoding and decoding 1080P videos of any length while preserving temporal information, making it an ideal foundation for video and image generation.
+
+## Video Demos
+
+<div align="center">
+  <video src="https://github.com/user-attachments/assets/4aca6063-60bf-4953-bfb7-e265053f49ef" width="70%" poster=""> </video>
+</div>
+
+## 🔥 Latest News!!
+
+* Feb 25, 2025: 👋 We've released the inference code and weights of Wan2.1.
+
+
+## 📑 Todo List
+- Wan2.1 Text-to-Video
+    - [x] Multi-GPU Inference code of the 14B and 1.3B models
+    - [x] Checkpoints of the 14B and 1.3B models
+    - [x] Gradio demo
+    - [ ] Diffusers integration
+    - [ ] ComfyUI integration
+- Wan2.1 Image-to-Video
+    - [x] Multi-GPU Inference code of the 14B model
+    - [x] Checkpoints of the 14B model
+    - [x] Gradio demo
+    - [ ] Diffusers integration
+    - [ ] ComfyUI integration
+
+
+## Quickstart
+
+#### Installation
+Clone the repo:
+```
+git clone https://github.com/Wan-Video/Wan2.1.git
+cd Wan2.1
+```
+
+Install dependencies:
+```
+# Ensure torch >= 2.4.0
+pip install -r requirements.txt
+```
+
+
+#### Model Download
+
+| Models        |                       Download Link                                           |    Notes                      |
+| --------------|-------------------------------------------------------------------------------|-------------------------------|
+| T2V-14B       |      🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-T2V-14B)      🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B)          | Supports both 480P and 720P
+| I2V-14B-720P  |      🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P)    🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P)     | Supports 720P
+| I2V-14B-480P  |      🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-480P)    🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P)      | Supports 480P
+| T2V-1.3B      |      🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B)     🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B)         | Supports 480P
+
+> 💡Note: The 1.3B model is capable of generating videos at 720P resolution. However, due to limited training at this resolution, the results are generally less stable compared to 480P. For optimal performance, we recommend using 480P resolution.
+
+
+Download models using huggingface-cli:
+```
+pip install "huggingface_hub[cli]"
+huggingface-cli download Wan-AI/Wan2.1-T2V-14B --local-dir ./Wan2.1-T2V-14B
+```
+
+#### Run Text-to-Video Generation
+
+This repository supports two Text-to-Video models (1.3B and 14B) and two resolutions (480P and 720P). The parameters and configurations for these models are as follows:
+
+<table>
+    <thead>
+        <tr>
+            <th rowspan="2">Task</th>
+            <th colspan="2">Resolution</th>
+            <th rowspan="2">Model</th>
+        </tr>
+        <tr>
+            <th>480P</th>
+            <th>720P</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td>t2v-14B</td>
+            <td style="color: green;">✔️</td>
+            <td style="color: green;">✔️</td>
+            <td>Wan2.1-T2V-14B</td>
+        </tr>
+        <tr>
+            <td>t2v-1.3B</td>
+            <td style="color: green;">✔️</td>
+            <td style="color: red;">❌</td>
+            <td>Wan2.1-T2V-1.3B</td>
+        </tr>
+    </tbody>
+</table>
+
+
+##### (1) Without Prompt Extention
+
+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
+
+```
+python generate.py  --task t2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-T2V-14B --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
+```
+
+If you encounter OOM (Out-of-Memory) issues, you can use the `--offload_model True` and `--t5_cpu` options to reduce GPU memory usage. For example, on an RTX 4090 GPU:
+
+```
+python generate.py  --task t2v-1.3B --size 832*480 --ckpt_dir ./Wan2.1-T2V-1.3B --offload_model True --t5_cpu --sample_shift 8 --sample_guide_scale 6 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
+```
+
+> 💡Note: If you are using the `T2V-1.3B` model, we recommend setting the parameter `--sample_guide_scale 6`. The `--sample_shift parameter` can be adjusted within the range of 8 to 12 based on the performance.
+
+
+- Multi-GPU inference using FSDP + xDiT USP
+
+```
+pip install "xfuser>=0.4.1"
+torchrun --nproc_per_node=8 generate.py --task t2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-T2V-14B --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 Extention
+
+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:
+```
+DASH_API_KEY=your_key python generate.py  --task t2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-T2V-14B --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 'ch'
+```
+
+- 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:
+
+```
+python generate.py  --task t2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-T2V-14B --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 'ch'
+```
+
+##### (3) Runing local gradio
+
+```
+cd gradio
+# if one uses dashscope’s API for prompt extension
+DASH_API_KEY=your_key python t2v_14B_singleGPU.py --prompt_extend_method 'dashscope' --ckpt_dir ./Wan2.1-T2V-14B
+
+# if one uses a local model for prompt extension
+python t2v_14B_singleGPU.py --prompt_extend_method 'local_qwen' --ckpt_dir ./Wan2.1-T2V-14B
+```
+
+
+#### Run Image-to-Video Generation
+
+Similar to Text-to-Video, Image-to-Video is also divided into processes with and without the prompt extension step. The specific parameters and their corresponding settings are as follows:
+<table>
+    <thead>
+        <tr>
+            <th rowspan="2">Task</th>
+            <th colspan="2">Resolution</th>
+            <th rowspan="2">Model</th>
+        </tr>
+        <tr>
+            <th>480P</th>
+            <th>720P</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td>i2v-14B</td>
+            <td style="color: green;">❌</td>
+            <td style="color: green;">✔️</td>
+            <td>Wan2.1-I2V-14B-720P</td>
+        </tr>
+        <tr>
+            <td>i2v-14B</td>
+            <td style="color: green;">✔️</td>
+            <td style="color: red;">❌</td>
+            <td>Wan2.1-T2V-14B-480P</td>
+        </tr>
+    </tbody>
+</table>
+
+
+##### (1) Without Prompt Extention
+
+- Single-GPU inference
+```
+python generate.py --task i2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-I2V-14B-720P --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."
+```
+
+> 💡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 + xDiT USP
+
+```
+pip install "xfuser>=0.4.1"
+torchrun --nproc_per_node=8 generate.py --task i2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-I2V-14B-720P --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."
+```
+
+##### (2) Using Prompt Extention
+
+
+The process of prompt extension can be referenced [here](#2-using-prompt-extention).
+
+Run with local prompt extention using `Qwen/Qwen2.5-VL-7B-Instruct`:
+```
+python generate.py --task i2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-I2V-14B-720P --image examples/i2v_input.JPG --use_prompt_extend --prompt_extend_model Qwen/Qwen2.5-VL-7B-Instruct --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."
+```
+
+Run with remote prompt extention using `dashscope`:
+```
+DASH_API_KEY=your_key python generate.py --task i2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-I2V-14B-720P --image examples/i2v_input.JPG --use_prompt_extend --prompt_extend_method 'dashscope' --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."
+```
+
+##### (3) Runing local gradio
+
+```
+cd gradio
+# if one only uses 480P model in gradio
+DASH_API_KEY=your_key python i2v_14B_singleGPU.py --prompt_extend_method 'dashscope' --ckpt_dir_480p ./Wan2.1-I2V-14B-480P
+
+# if one only uses 720P model in gradio
+DASH_API_KEY=your_key python i2v_14B_singleGPU.py --prompt_extend_method 'dashscope' --ckpt_dir_720p ./Wan2.1-I2V-14B-720P
+
+# if one uses both 480P and 720P models in gradio
+DASH_API_KEY=your_key python i2v_14B_singleGPU.py --prompt_extend_method 'dashscope' --ckpt_dir_480p ./Wan2.1-I2V-14B-480P --ckpt_dir_720p ./Wan2.1-I2V-14B-720P
+```
+
+
+#### Run Text-to-Image Generation
+
+Wan2.1 is a unified model for both image and video generation. Since it was trained on both types of data, it can also generate images. The command for generating images is similar to video generation, as follows:
+
+##### (1) Without Prompt Extention
+
+- Single-GPU inference
+```
+python generate.py --task t2i-14B --size 1024*1024 --ckpt_dir ./Wan2.1-T2V-14B  --prompt '一个朴素端庄的美人'
+```
+
+- Multi-GPU inference using FSDP + xDiT USP
+
+```
+torchrun --nproc_per_node=8 generate.py --dit_fsdp --t5_fsdp --ulysses_size 8 --base_seed 0 --frame_num 1 --task t2i-14B  --size 1024*1024 --prompt '一个朴素端庄的美人' --ckpt_dir ./Wan2.1-T2V-14B
+```
+
+##### (2) With Prompt Extention
+
+- Single-GPU inference
+```
+python generate.py --task t2i-14B --size 1024*1024 --ckpt_dir ./Wan2.1-T2V-14B  --prompt '一个朴素端庄的美人' --use_prompt_extend
+```
+
+- Multi-GPU inference using FSDP + xDiT USP
+```
+torchrun --nproc_per_node=8 generate.py --dit_fsdp --t5_fsdp --ulysses_size 8 --base_seed 0 --frame_num 1 --task t2i-14B  --size 1024*1024 --ckpt_dir ./Wan2.1-T2V-14B --prompt '一个朴素端庄的美人' --use_prompt_extend
+```
+
+
+## Manual Evaluation
+
+##### (1) Text-to-Video Evaluation
+
+Through manual evaluation, the results generated after prompt extension are superior to those from both closed-source and open-source models.
+
+<div align="center">
+    <img src="assets/t2v_res.jpg" alt="" style="width: 80%;" />
+</div>
+
+
+##### (2) Image-to-Video Evaluation
+
+We also conducted extensive manual evaluations to evaluate the performance of the Image-to-Video model, and the results are presented in the table below. The results clearly indicate that **Wan2.1** outperforms both closed-source and open-source models.
+
+<div align="center">
+    <img src="assets/i2v_res.png" alt="" style="width: 80%;" />
+</div>
+
+
+## Computational Efficiency on Different GPUs
+
+We test the computational efficiency of different **Wan2.1** 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) For the 1.3B model on 8 GPUs, set `--ring_size 8` and `--ulysses_size 1`;
+> (2) For the 14B model on 1 GPU, use `--offload_model True`;
+> (3) For the 1.3B model on a single 4090 GPU, set `--offload_model True --t5_cpu`;
+> (4) For all testings, no prompt extension was applied, meaning `--use_prompt_extend` was not enabled.
+
+> 💡Note: T2V-14B is slower than I2V-14B because the former samples 50 steps while the latter uses 40 steps.
+
+
+## Community Contributions
+- [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) provides more support for Wan, including video-to-video, FP8 quantization, VRAM optimization, LoRA training, and more. Please refer to [their examples](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/wanvideo).
+
+-------
+
+## Introduction of Wan2.1
+
+**Wan2.1**  is designed on the mainstream diffusion transformer paradigm, achieving significant advancements in generative capabilities through a series of innovations. These include our novel spatio-temporal variational autoencoder (VAE), scalable training strategies, large-scale data construction, and automated evaluation metrics. Collectively, these contributions enhance the model’s performance and versatility.
+
+
+##### (1) 3D Variational Autoencoders
+We propose a novel 3D causal VAE architecture, termed **Wan-VAE** specifically designed for video generation. By combining multiple strategies, we improve spatio-temporal compression, reduce memory usage, and ensure temporal causality. **Wan-VAE** demonstrates significant advantages in performance efficiency compared to other open-source VAEs. Furthermore, our **Wan-VAE** can encode and decode unlimited-length 1080P videos without losing historical temporal information, making it particularly well-suited for video generation tasks.
+
+
+<div align="center">
+    <img src="assets/video_vae_res.jpg" alt="" style="width: 80%;" />
+</div>
+
+
+##### (2) Video Diffusion DiT
+
+**Wan2.1** is designed using the Flow Matching framework within the paradigm of mainstream Diffusion Transformers. Our model's architecture uses the T5 Encoder to encode multilingual text input, with cross-attention in each transformer block embedding the text into the model structure. Additionally, we employ an MLP with a Linear layer and a SiLU layer to process the input time embeddings and predict six modulation parameters individually. This MLP is shared across all transformer blocks, with each block learning a distinct set of biases. Our experimental findings reveal a significant performance improvement with this approach at the same parameter scale.
+
+<div align="center">
+    <img src="assets/video_dit_arch.jpg" alt="" style="width: 80%;" />
+</div>
+
+
+| Model  | Dimension | Input Dimension | Output Dimension | Feedforward Dimension | Frequency Dimension | Number of Heads | Number of Layers |
+|--------|-----------|-----------------|------------------|-----------------------|---------------------|-----------------|------------------|
+| 1.3B   | 1536      | 16              | 16               | 8960                  | 256                 | 12              | 30               |
+| 14B   | 5120       | 16              | 16               | 13824                 | 256                 | 40              | 40               |
+
+
+
+##### Data
+
+We curated and deduplicated a candidate dataset comprising a vast amount of image and video data. During the data curation process, we designed a four-step data cleaning process, focusing on fundamental dimensions, visual quality and motion quality. Through the robust data processing pipeline, we can easily obtain high-quality, diverse, and large-scale training sets of images and videos.
+
+![figure1](assets/data_for_diff_stage.jpg "figure1")
+
+
+##### Comparisons to SOTA
+We compared **Wan2.1** with leading open-source and closed-source models to evaluate the performace. Using our carefully designed set of 1,035 internal prompts, we tested across 14 major dimensions and 26 sub-dimensions. We then compute the total score by performing a weighted calculation on the scores of each dimension, utilizing weights derived from human preferences in the matching process. The detailed results are shown in the table below. These results demonstrate our model's superior performance compared to both open-source and closed-source models.
+
+![figure1](assets/vben_vs_sota.png "figure1")
+
+
+## Citation
+If you find our work helpful, please cite us.
+
+```
+@article{wan2.1,
+    title   = {Wan: Open and Advanced Large-Scale Video Generative Models},
+    author  = {Wan Team},
+    journal = {},
+    year    = {2025}
+}
+```
+
+## License Agreement
+The models in this repository are licensed under the Apache 2.0 License. We claim no rights over the your generate 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/p5XbdQV7) or [WeChat groups](https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg)!

generate.py

+# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
+import argparse
+from datetime import datetime
+import logging
+import os
+import sys
+import warnings
+
+warnings.filterwarnings('ignore')
+
+import torch, random
+import torch.distributed as dist
+from PIL import Image
+
+import wan
+from wan.configs import WAN_CONFIGS, SIZE_CONFIGS, MAX_AREA_CONFIGS, SUPPORTED_SIZES
+from wan.utils.prompt_extend import DashScopePromptExpander, QwenPromptExpander
+from wan.utils.utils import cache_video, cache_image, str2bool
+
+EXAMPLE_PROMPT = {
+    "t2v-1.3B": {
+        "prompt": "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage.",
+    },
+    "t2v-14B": {
+        "prompt": "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage.",
+    },
+    "t2i-14B": {
+        "prompt": "一个朴素端庄的美人",
+    },
+    "i2v-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",
+    },
+}
+
+
+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}"
+
+    # The default sampling steps are 40 for image-to-video tasks and 50 for text-to-video tasks.
+    if args.sample_steps is None:
+        args.sample_steps = 40 if "i2v" in args.task else 50
+
+    if args.sample_shift is None:
+        args.sample_shift = 5.0
+        if "i2v" in args.task and args.size in ["832*480", "480*832"]:
+            args.sample_shift = 3.0
+
+    # The default number of frames are 1 for text-to-image tasks and 81 for other tasks.
+    if args.frame_num is None:
+        args.frame_num = 1 if "t2i" in args.task else 81
+
+    # T2I frame_num check
+    if "t2i" in args.task:
+        assert args.frame_num == 1, f"Unsupport frame_num {args.frame_num} for task {args.task}"
+
+    args.base_seed = args.base_seed if args.base_seed >= 0 else random.randint(
+        0, sys.maxsize)
+    # Size check
+    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-14B",
+        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 to sample from a image or video. 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(
+        "--ring_size",
+        type=int,
+        default=1,
+        help="The size of the ring attention 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 image or video to.")
+    parser.add_argument(
+        "--prompt",
+        type=str,
+        default=None,
+        help="The prompt to generate the image or 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="ch",
+        choices=["ch", "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 image or 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=5.0,
+        help="Classifier free guidance scale.")
+
+    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 or args.ring_size > 1
+        ), f"context parallel are not supported in non-distributed environments."
+
+    if args.ulysses_size > 1 or args.ring_size > 1:
+        assert args.ulysses_size * args.ring_size == world_size, f"The number of ulysses_size and ring_size should be equal to the world size."
+        from xfuser.core.distributed import (initialize_model_parallel,
+                                             init_distributed_environment)
+        init_distributed_environment(
+            rank=dist.get_rank(), world_size=dist.get_world_size())
+
+        initialize_model_parallel(
+            sequence_parallel_degree=dist.get_world_size(),
+            ring_degree=args.ring_size,
+            ulysses_degree=args.ulysses_size,
+        )
+
+    if args.use_prompt_extend:
+        if args.prompt_extend_method == "dashscope":
+            prompt_expander = DashScopePromptExpander(
+                model_name=args.prompt_extend_model, is_vl="i2v" in args.task)
+        elif args.prompt_extend_method == "local_qwen":
+            prompt_expander = QwenPromptExpander(
+                model_name=args.prompt_extend_model,
+                is_vl="i2v" in args.task,
+                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"`num_heads` must be divisible by `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]
+
+    if "t2v" in args.task or "t2i" in args.task:
+        if args.prompt is None:
+            args.prompt = EXAMPLE_PROMPT[args.task]["prompt"]
+        logging.info(f"Input prompt: {args.prompt}")
+        if args.use_prompt_extend:
+            logging.info("Extending prompt ...")
+            if rank == 0:
+                prompt_output = prompt_expander(
+                    args.prompt,
+                    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}")
+
+        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_usp=(args.ulysses_size > 1 or args.ring_size > 1),
+            t5_cpu=args.t5_cpu,
+        )
+
+        logging.info(
+            f"Generating {'image' if 't2i' in args.task else '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)
+
+    else:
+        if args.prompt is None:
+            args.prompt = EXAMPLE_PROMPT[args.task]["prompt"]
+        if args.image is None:
+            args.image = EXAMPLE_PROMPT[args.task]["image"]
+        logging.info(f"Input prompt: {args.prompt}")
+        logging.info(f"Input image: {args.image}")
+
+        img = Image.open(args.image).convert("RGB")
+        if args.use_prompt_extend:
+            logging.info("Extending prompt ...")
+            if rank == 0:
+                prompt_output = prompt_expander(
+                    args.prompt,
+                    tar_lang=args.prompt_extend_target_lang,
+                    image=img,
+                    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}")
+
+        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_usp=(args.ulysses_size > 1 or args.ring_size > 1),
+            t5_cpu=args.t5_cpu,
+        )
+
+        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 = '.png' if "t2i" in args.task else '.mp4'
+            args.save_file = f"{args.task}_{args.size}_{args.ulysses_size}_{args.ring_size}_{formatted_prompt}_{formatted_time}" + suffix
+
+        if "t2i" in args.task:
+            logging.info(f"Saving generated image to {args.save_file}")
+            cache_image(
+                tensor=video.squeeze(1)[None],
+                save_file=args.save_file,
+                nrow=1,
+                normalize=True,
+                value_range=(-1, 1))
+        else:
+            logging.info(f"Saving generated video to {args.save_file}")
+            cache_video(
+                tensor=video[None],
+                save_file=args.save_file,
+                fps=cfg.sample_fps,
+                nrow=1,
+                normalize=True,
+                value_range=(-1, 1))
+    logging.info("Finished.")
+
+
+if __name__ == "__main__":
+    args = _parse_args()
+    generate(args)

gradio/i2v_14B_singleGPU.py

+# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
+import argparse
+import gc
+import os.path as osp
+import sys
+import warnings
+
+import gradio as gr
+
+warnings.filterwarnings('ignore')
+
+# Model
+sys.path.insert(0, '/'.join(osp.realpath(__file__).split('/')[:-2]))
+import wan
+from wan.configs import MAX_AREA_CONFIGS, WAN_CONFIGS
+from wan.utils.prompt_extend import DashScopePromptExpander, QwenPromptExpander
+from wan.utils.utils import cache_video
+
+# Global Var
+prompt_expander = None
+wan_i2v_480P = None
+wan_i2v_720P = None
+
+
+# Button Func
+def load_model(value):
+    global wan_i2v_480P, wan_i2v_720P
+
+    if value == '------':
+        print("No model loaded")
+        return '------'
+
+    if value == '720P':
+        if args.ckpt_dir_720p is None:
+            print("Please specify the checkpoint directory for 720P model")
+            return '------'
+        if wan_i2v_720P is not None:
+            pass
+        else:
+            del wan_i2v_480P
+            gc.collect()
+            wan_i2v_480P = None
+
+            print("load 14B-720P i2v model...", end='', flush=True)
+            cfg = WAN_CONFIGS['i2v-14B']
+            wan_i2v_720P = wan.WanI2V(
+                config=cfg,
+                checkpoint_dir=args.ckpt_dir_720p,
+                device_id=0,
+                rank=0,
+                t5_fsdp=False,
+                dit_fsdp=False,
+                use_usp=False,
+            )
+            print("done", flush=True)
+            return '720P'
+
+    if value == '480P':
+        if args.ckpt_dir_480p is None:
+            print("Please specify the checkpoint directory for 480P model")
+            return '------'
+        if wan_i2v_480P is not None:
+            pass
+        else:
+            del wan_i2v_720P
+            gc.collect()
+            wan_i2v_720P = None
+
+            print("load 14B-480P i2v model...", end='', flush=True)
+            cfg = WAN_CONFIGS['i2v-14B']
+            wan_i2v_480P = wan.WanI2V(
+                config=cfg,
+                checkpoint_dir=args.ckpt_dir_480p,
+                device_id=0,
+                rank=0,
+                t5_fsdp=False,
+                dit_fsdp=False,
+                use_usp=False,
+            )
+            print("done", flush=True)
+            return '480P'
+
+
+def prompt_enc(prompt, img, tar_lang):
+    print('prompt extend...')
+    if img is None:
+        print('Please upload an image')
+        return prompt
+    global prompt_expander
+    prompt_output = prompt_expander(
+        prompt, image=img, tar_lang=tar_lang.lower())
+    if prompt_output.status == False:
+        return prompt
+    else:
+        return prompt_output.prompt
+
+
+def i2v_generation(img2vid_prompt, img2vid_image, resolution, sd_steps,
+                   guide_scale, shift_scale, seed, n_prompt):
+    # print(f"{img2vid_prompt},{resolution},{sd_steps},{guide_scale},{shift_scale},{seed},{n_prompt}")
+
+    if resolution == '------':
+        print(
+            'Please specify at least one resolution ckpt dir or specify the resolution'
+        )
+        return None
+
+    else:
+        if resolution == '720P':
+            global wan_i2v_720P
+            video = wan_i2v_720P.generate(
+                img2vid_prompt,
+                img2vid_image,
+                max_area=MAX_AREA_CONFIGS['720*1280'],
+                shift=shift_scale,
+                sampling_steps=sd_steps,
+                guide_scale=guide_scale,
+                n_prompt=n_prompt,
+                seed=seed,
+                offload_model=True)
+        else:
+            global wan_i2v_480P
+            video = wan_i2v_480P.generate(
+                img2vid_prompt,
+                img2vid_image,
+                max_area=MAX_AREA_CONFIGS['480*832'],
+                shift=shift_scale,
+                sampling_steps=sd_steps,
+                guide_scale=guide_scale,
+                n_prompt=n_prompt,
+                seed=seed,
+                offload_model=True)
+
+        cache_video(
+            tensor=video[None],
+            save_file="example.mp4",
+            fps=16,
+            nrow=1,
+            normalize=True,
+            value_range=(-1, 1))
+
+        return "example.mp4"
+
+
+# Interface
+def gradio_interface():
+    with gr.Blocks() as demo:
+        gr.Markdown("""
+                    <div style="text-align: center; font-size: 32px; font-weight: bold; margin-bottom: 20px;">
+                        Wan2.1 (I2V-14B)
+                    </div>
+                    <div style="text-align: center; font-size: 16px; font-weight: normal; margin-bottom: 20px;">
+                        Wan: Open and Advanced Large-Scale Video Generative Models.
+                    </div>
+                    """)
+
+        with gr.Row():
+            with gr.Column():
+                resolution = gr.Dropdown(
+                    label='Resolution',
+                    choices=['------', '720P', '480P'],
+                    value='------')
+
+                img2vid_image = gr.Image(
+                    type="pil",
+                    label="Upload Input Image",
+                    elem_id="image_upload",
+                )
+                img2vid_prompt = gr.Textbox(
+                    label="Prompt",
+                    placeholder="Describe the video you want to generate",
+                )
+                tar_lang = gr.Radio(
+                    choices=["CH", "EN"],
+                    label="Target language of prompt enhance",
+                    value="CH")
+                run_p_button = gr.Button(value="Prompt Enhance")
+
+                with gr.Accordion("Advanced Options", open=True):
+                    with gr.Row():
+                        sd_steps = gr.Slider(
+                            label="Diffusion steps",
+                            minimum=1,
+                            maximum=1000,
+                            value=50,
+                            step=1)
+                        guide_scale = gr.Slider(
+                            label="Guide scale",
+                            minimum=0,
+                            maximum=20,
+                            value=5.0,
+                            step=1)
+                    with gr.Row():
+                        shift_scale = gr.Slider(
+                            label="Shift scale",
+                            minimum=0,
+                            maximum=10,
+                            value=5.0,
+                            step=1)
+                        seed = gr.Slider(
+                            label="Seed",
+                            minimum=-1,
+                            maximum=2147483647,
+                            step=1,
+                            value=-1)
+                    n_prompt = gr.Textbox(
+                        label="Negative Prompt",
+                        placeholder="Describe the negative prompt you want to add"
+                    )
+
+                run_i2v_button = gr.Button("Generate Video")
+
+            with gr.Column():
+                result_gallery = gr.Video(
+                    label='Generated Video', interactive=False, height=600)
+
+        resolution.input(
+            fn=load_model, inputs=[resolution], outputs=[resolution])
+
+        run_p_button.click(
+            fn=prompt_enc,
+            inputs=[img2vid_prompt, img2vid_image, tar_lang],
+            outputs=[img2vid_prompt])
+
+        run_i2v_button.click(
+            fn=i2v_generation,
+            inputs=[
+                img2vid_prompt, img2vid_image, resolution, sd_steps,
+                guide_scale, shift_scale, seed, n_prompt
+            ],
+            outputs=[result_gallery],
+        )
+
+    return demo
+
+
+# Main
+def _parse_args():
+    parser = argparse.ArgumentParser(
+        description="Generate a video from a text prompt or image using Gradio")
+    parser.add_argument(
+        "--ckpt_dir_720p",
+        type=str,
+        default=None,
+        help="The path to the checkpoint directory.")
+    parser.add_argument(
+        "--ckpt_dir_480p",
+        type=str,
+        default=None,
+        help="The path to the checkpoint directory.")
+    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.")
+
+    args = parser.parse_args()
+    assert args.ckpt_dir_720p is not None or args.ckpt_dir_480p is not None, "Please specify at least one checkpoint directory."
+
+    return args
+
+
+if __name__ == '__main__':
+    args = _parse_args()
+
+    print("Step1: Init prompt_expander...", end='', flush=True)
+    if args.prompt_extend_method == "dashscope":
+        prompt_expander = DashScopePromptExpander(
+            model_name=args.prompt_extend_model, is_vl=True)
+    elif args.prompt_extend_method == "local_qwen":
+        prompt_expander = QwenPromptExpander(
+            model_name=args.prompt_extend_model, is_vl=True, device=0)
+    else:
+        raise NotImplementedError(
+            f"Unsupport prompt_extend_method: {args.prompt_extend_method}")
+    print("done", flush=True)
+
+    demo = gradio_interface()
+    demo.launch(server_name="0.0.0.0", share=False, server_port=7860)

gradio/t2i_14B_singleGPU.py

+# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
+import argparse
+import os.path as osp
+import sys
+import warnings
+
+import gradio as gr
+
+warnings.filterwarnings('ignore')
+
+# Model
+sys.path.insert(0, '/'.join(osp.realpath(__file__).split('/')[:-2]))
+import wan
+from wan.configs import WAN_CONFIGS
+from wan.utils.prompt_extend import DashScopePromptExpander, QwenPromptExpander
+from wan.utils.utils import cache_image
+
+# Global Var
+prompt_expander = None
+wan_t2i = None
+
+
+# Button Func
+def prompt_enc(prompt, tar_lang):
+    global prompt_expander
+    prompt_output = prompt_expander(prompt, tar_lang=tar_lang.lower())
+    if prompt_output.status == False:
+        return prompt
+    else:
+        return prompt_output.prompt
+
+
+def t2i_generation(txt2img_prompt, resolution, sd_steps, guide_scale,
+                   shift_scale, seed, n_prompt):
+    global wan_t2i
+    # print(f"{txt2img_prompt},{resolution},{sd_steps},{guide_scale},{shift_scale},{seed},{n_prompt}")
+
+    W = int(resolution.split("*")[0])
+    H = int(resolution.split("*")[1])
+    video = wan_t2i.generate(
+        txt2img_prompt,
+        size=(W, H),
+        frame_num=1,
+        shift=shift_scale,
+        sampling_steps=sd_steps,
+        guide_scale=guide_scale,
+        n_prompt=n_prompt,
+        seed=seed,
+        offload_model=True)
+
+    cache_image(
+        tensor=video.squeeze(1)[None],
+        save_file="example.png",
+        nrow=1,
+        normalize=True,
+        value_range=(-1, 1))
+
+    return "example.png"
+
+
+# Interface
+def gradio_interface():
+    with gr.Blocks() as demo:
+        gr.Markdown("""
+                    <div style="text-align: center; font-size: 32px; font-weight: bold; margin-bottom: 20px;">
+                        Wan2.1 (T2I-14B)
+                    </div>
+                    <div style="text-align: center; font-size: 16px; font-weight: normal; margin-bottom: 20px;">
+                        Wan: Open and Advanced Large-Scale Video Generative Models.
+                    </div>
+                    """)
+
+        with gr.Row():
+            with gr.Column():
+                txt2img_prompt = gr.Textbox(
+                    label="Prompt",
+                    placeholder="Describe the image you want to generate",
+                )
+                tar_lang = gr.Radio(
+                    choices=["CH", "EN"],
+                    label="Target language of prompt enhance",
+                    value="CH")
+                run_p_button = gr.Button(value="Prompt Enhance")
+
+                with gr.Accordion("Advanced Options", open=True):
+                    resolution = gr.Dropdown(
+                        label='Resolution(Width*Height)',
+                        choices=[
+                            '720*1280', '1280*720', '960*960', '1088*832',
+                            '832*1088', '480*832', '832*480', '624*624',
+                            '704*544', '544*704'
+                        ],
+                        value='720*1280')
+
+                    with gr.Row():
+                        sd_steps = gr.Slider(
+                            label="Diffusion steps",
+                            minimum=1,
+                            maximum=1000,
+                            value=50,
+                            step=1)
+                        guide_scale = gr.Slider(
+                            label="Guide scale",
+                            minimum=0,
+                            maximum=20,
+                            value=5.0,
+                            step=1)
+                    with gr.Row():
+                        shift_scale = gr.Slider(
+                            label="Shift scale",
+                            minimum=0,
+                            maximum=10,
+                            value=5.0,
+                            step=1)
+                        seed = gr.Slider(
+                            label="Seed",
+                            minimum=-1,
+                            maximum=2147483647,
+                            step=1,
+                            value=-1)
+                    n_prompt = gr.Textbox(
+                        label="Negative Prompt",
+                        placeholder="Describe the negative prompt you want to add"
+                    )
+
+                run_t2i_button = gr.Button("Generate Image")
+
+            with gr.Column():
+                result_gallery = gr.Image(
+                    label='Generated Image', interactive=False, height=600)
+
+        run_p_button.click(
+            fn=prompt_enc,
+            inputs=[txt2img_prompt, tar_lang],
+            outputs=[txt2img_prompt])
+
+        run_t2i_button.click(
+            fn=t2i_generation,
+            inputs=[
+                txt2img_prompt, resolution, sd_steps, guide_scale, shift_scale,
+                seed, n_prompt
+            ],
+            outputs=[result_gallery],
+        )
+
+    return demo
+
+
+# Main
+def _parse_args():
+    parser = argparse.ArgumentParser(
+        description="Generate a image from a text prompt or image using Gradio")
+    parser.add_argument(
+        "--ckpt_dir",
+        type=str,
+        default="cache",
+        help="The path to the checkpoint directory.")
+    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.")
+
+    args = parser.parse_args()
+
+    return args
+
+
+if __name__ == '__main__':
+    args = _parse_args()
+
+    print("Step1: Init prompt_expander...", end='', flush=True)
+    if args.prompt_extend_method == "dashscope":
+        prompt_expander = DashScopePromptExpander(
+            model_name=args.prompt_extend_model, is_vl=False)
+    elif args.prompt_extend_method == "local_qwen":
+        prompt_expander = QwenPromptExpander(
+            model_name=args.prompt_extend_model, is_vl=False, device=0)
+    else:
+        raise NotImplementedError(
+            f"Unsupport prompt_extend_method: {args.prompt_extend_method}")
+    print("done", flush=True)
+
+    print("Step2: Init 14B t2i model...", end='', flush=True)
+    cfg = WAN_CONFIGS['t2i-14B']
+    wan_t2i = wan.WanT2V(
+        config=cfg,
+        checkpoint_dir=args.ckpt_dir,
+        device_id=0,
+        rank=0,
+        t5_fsdp=False,
+        dit_fsdp=False,
+        use_usp=False,
+    )
+    print("done", flush=True)
+
+    demo = gradio_interface()
+    demo.launch(server_name="0.0.0.0", share=False, server_port=7860)

gradio/t2v_1.3B_singleGPU.py

+# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
+import argparse
+import os.path as osp
+import sys
+import warnings
+
+import gradio as gr
+
+warnings.filterwarnings('ignore')
+
+# Model
+sys.path.insert(0, '/'.join(osp.realpath(__file__).split('/')[:-2]))
+import wan
+from wan.configs import WAN_CONFIGS
+from wan.utils.prompt_extend import DashScopePromptExpander, QwenPromptExpander
+from wan.utils.utils import cache_video
+
+# Global Var
+prompt_expander = None
+wan_t2v = None
+
+
+# Button Func
+def prompt_enc(prompt, tar_lang):
+    global prompt_expander
+    prompt_output = prompt_expander(prompt, tar_lang=tar_lang.lower())
+    if prompt_output.status == False:
+        return prompt
+    else:
+        return prompt_output.prompt
+
+
+def t2v_generation(txt2vid_prompt, resolution, sd_steps, guide_scale,
+                   shift_scale, seed, n_prompt):
+    global wan_t2v
+    # print(f"{txt2vid_prompt},{resolution},{sd_steps},{guide_scale},{shift_scale},{seed},{n_prompt}")
+
+    W = int(resolution.split("*")[0])
+    H = int(resolution.split("*")[1])
+    video = wan_t2v.generate(
+        txt2vid_prompt,
+        size=(W, H),
+        shift=shift_scale,
+        sampling_steps=sd_steps,
+        guide_scale=guide_scale,
+        n_prompt=n_prompt,
+        seed=seed,
+        offload_model=False)
+
+    cache_video(
+        tensor=video[None],
+        save_file="example.mp4",
+        fps=16,
+        nrow=1,
+        normalize=True,
+        value_range=(-1, 1))
+
+    return "example.mp4"
+
+
+# Interface
+def gradio_interface():
+    with gr.Blocks() as demo:
+        gr.Markdown("""
+                    <div style="text-align: center; font-size: 32px; font-weight: bold; margin-bottom: 20px;">
+                        Wan2.1 (T2V-1.3B)
+                    </div>
+                    <div style="text-align: center; font-size: 16px; font-weight: normal; margin-bottom: 20px;">
+                        Wan: Open and Advanced Large-Scale Video Generative Models.
+                    </div>
+                    """)
+
+        with gr.Row():
+            with gr.Column():
+                txt2vid_prompt = gr.Textbox(
+                    label="Prompt",
+                    placeholder="Describe the video you want to generate",
+                )
+                tar_lang = gr.Radio(
+                    choices=["CH", "EN"],
+                    label="Target language of prompt enhance",
+                    value="CH")
+                run_p_button = gr.Button(value="Prompt Enhance")
+
+                with gr.Accordion("Advanced Options", open=True):
+                    resolution = gr.Dropdown(
+                        label='Resolution(Width*Height)',
+                        choices=[
+                            '480*832',
+                            '832*480',
+                            '624*624',
+                            '704*544',
+                            '544*704',
+                        ],
+                        value='480*832')
+
+                    with gr.Row():
+                        sd_steps = gr.Slider(
+                            label="Diffusion steps",
+                            minimum=1,
+                            maximum=1000,
+                            value=50,
+                            step=1)
+                        guide_scale = gr.Slider(
+                            label="Guide scale",
+                            minimum=0,
+                            maximum=20,
+                            value=6.0,
+                            step=1)
+                    with gr.Row():
+                        shift_scale = gr.Slider(
+                            label="Shift scale",
+                            minimum=0,
+                            maximum=20,
+                            value=8.0,
+                            step=1)
+                        seed = gr.Slider(
+                            label="Seed",
+                            minimum=-1,
+                            maximum=2147483647,
+                            step=1,
+                            value=-1)
+                    n_prompt = gr.Textbox(
+                        label="Negative Prompt",
+                        placeholder="Describe the negative prompt you want to add"
+                    )
+
+                run_t2v_button = gr.Button("Generate Video")
+
+            with gr.Column():
+                result_gallery = gr.Video(
+                    label='Generated Video', interactive=False, height=600)
+
+        run_p_button.click(
+            fn=prompt_enc,
+            inputs=[txt2vid_prompt, tar_lang],
+            outputs=[txt2vid_prompt])
+
+        run_t2v_button.click(
+            fn=t2v_generation,
+            inputs=[
+                txt2vid_prompt, resolution, sd_steps, guide_scale, shift_scale,
+                seed, n_prompt
+            ],
+            outputs=[result_gallery],
+        )
+
+    return demo
+
+
+# Main
+def _parse_args():
+    parser = argparse.ArgumentParser(
+        description="Generate a video from a text prompt or image using Gradio")
+    parser.add_argument(
+        "--ckpt_dir",
+        type=str,
+        default="cache",
+        help="The path to the checkpoint directory.")
+    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.")
+
+    args = parser.parse_args()
+
+    return args
+
+
+if __name__ == '__main__':
+    args = _parse_args()
+
+    print("Step1: Init prompt_expander...", end='', flush=True)
+    if args.prompt_extend_method == "dashscope":
+        prompt_expander = DashScopePromptExpander(
+            model_name=args.prompt_extend_model, is_vl=False)
+    elif args.prompt_extend_method == "local_qwen":
+        prompt_expander = QwenPromptExpander(
+            model_name=args.prompt_extend_model, is_vl=False, device=0)
+    else:
+        raise NotImplementedError(
+            f"Unsupport prompt_extend_method: {args.prompt_extend_method}")
+    print("done", flush=True)
+
+    print("Step2: Init 1.3B t2v model...", end='', flush=True)
+    cfg = WAN_CONFIGS['t2v-1.3B']
+    wan_t2v = wan.WanT2V(
+        config=cfg,
+        checkpoint_dir=args.ckpt_dir,
+        device_id=0,
+        rank=0,
+        t5_fsdp=False,
+        dit_fsdp=False,
+        use_usp=False,
+    )
+    print("done", flush=True)
+
+    demo = gradio_interface()
+    demo.launch(server_name="0.0.0.0", share=False, server_port=7860)

gradio/t2v_14B_singleGPU.py

+# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
+import argparse
+import os.path as osp
+import sys
+import warnings
+
+import gradio as gr
+
+warnings.filterwarnings('ignore')
+
+# Model
+sys.path.insert(0, '/'.join(osp.realpath(__file__).split('/')[:-2]))
+import wan
+from wan.configs import WAN_CONFIGS
+from wan.utils.prompt_extend import DashScopePromptExpander, QwenPromptExpander
+from wan.utils.utils import cache_video
+
+# Global Var
+prompt_expander = None
+wan_t2v = None
+
+
+# Button Func
+def prompt_enc(prompt, tar_lang):
+    global prompt_expander
+    prompt_output = prompt_expander(prompt, tar_lang=tar_lang.lower())
+    if prompt_output.status == False:
+        return prompt
+    else:
+        return prompt_output.prompt
+
+
+def t2v_generation(txt2vid_prompt, resolution, sd_steps, guide_scale,
+                   shift_scale, seed, n_prompt):
+    global wan_t2v
+    # print(f"{txt2vid_prompt},{resolution},{sd_steps},{guide_scale},{shift_scale},{seed},{n_prompt}")
+
+    W = int(resolution.split("*")[0])
+    H = int(resolution.split("*")[1])
+    video = wan_t2v.generate(
+        txt2vid_prompt,
+        size=(W, H),
+        shift=shift_scale,
+        sampling_steps=sd_steps,
+        guide_scale=guide_scale,
+        n_prompt=n_prompt,
+        seed=seed,
+        offload_model=True)
+
+    cache_video(
+        tensor=video[None],
+        save_file="example.mp4",
+        fps=16,
+        nrow=1,
+        normalize=True,
+        value_range=(-1, 1))
+
+    return "example.mp4"
+
+
+# Interface
+def gradio_interface():
+    with gr.Blocks() as demo:
+        gr.Markdown("""
+                    <div style="text-align: center; font-size: 32px; font-weight: bold; margin-bottom: 20px;">
+                        Wan2.1 (T2V-14B)
+                    </div>
+                    <div style="text-align: center; font-size: 16px; font-weight: normal; margin-bottom: 20px;">
+                        Wan: Open and Advanced Large-Scale Video Generative Models.
+                    </div>
+                    """)
+
+        with gr.Row():
+            with gr.Column():
+                txt2vid_prompt = gr.Textbox(
+                    label="Prompt",
+                    placeholder="Describe the video you want to generate",
+                )
+                tar_lang = gr.Radio(
+                    choices=["CH", "EN"],
+                    label="Target language of prompt enhance",
+                    value="CH")
+                run_p_button = gr.Button(value="Prompt Enhance")
+
+                with gr.Accordion("Advanced Options", open=True):
+                    resolution = gr.Dropdown(
+                        label='Resolution(Width*Height)',
+                        choices=[
+                            '720*1280', '1280*720', '960*960', '1088*832',
+                            '832*1088', '480*832', '832*480', '624*624',
+                            '704*544', '544*704'
+                        ],
+                        value='720*1280')
+
+                    with gr.Row():
+                        sd_steps = gr.Slider(
+                            label="Diffusion steps",
+                            minimum=1,
+                            maximum=1000,
+                            value=50,
+                            step=1)
+                        guide_scale = gr.Slider(
+                            label="Guide scale",
+                            minimum=0,
+                            maximum=20,
+                            value=5.0,
+                            step=1)
+                    with gr.Row():
+                        shift_scale = gr.Slider(
+                            label="Shift scale",
+                            minimum=0,
+                            maximum=10,
+                            value=5.0,
+                            step=1)
+                        seed = gr.Slider(
+                            label="Seed",
+                            minimum=-1,
+                            maximum=2147483647,
+                            step=1,
+                            value=-1)
+                    n_prompt = gr.Textbox(
+                        label="Negative Prompt",
+                        placeholder="Describe the negative prompt you want to add"
+                    )
+
+                run_t2v_button = gr.Button("Generate Video")
+
+            with gr.Column():
+                result_gallery = gr.Video(
+                    label='Generated Video', interactive=False, height=600)
+
+        run_p_button.click(
+            fn=prompt_enc,
+            inputs=[txt2vid_prompt, tar_lang],
+            outputs=[txt2vid_prompt])
+
+        run_t2v_button.click(
+            fn=t2v_generation,
+            inputs=[
+                txt2vid_prompt, resolution, sd_steps, guide_scale, shift_scale,
+                seed, n_prompt
+            ],
+            outputs=[result_gallery],
+        )
+
+    return demo
+
+
+# Main
+def _parse_args():
+    parser = argparse.ArgumentParser(
+        description="Generate a video from a text prompt or image using Gradio")
+    parser.add_argument(
+        "--ckpt_dir",
+        type=str,
+        default="cache",
+        help="The path to the checkpoint directory.")
+    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.")
+
+    args = parser.parse_args()
+
+    return args
+
+
+if __name__ == '__main__':
+    args = _parse_args()
+
+    print("Step1: Init prompt_expander...", end='', flush=True)
+    if args.prompt_extend_method == "dashscope":
+        prompt_expander = DashScopePromptExpander(
+            model_name=args.prompt_extend_model, is_vl=False)
+    elif args.prompt_extend_method == "local_qwen":
+        prompt_expander = QwenPromptExpander(
+            model_name=args.prompt_extend_model, is_vl=False, device=0)
+    else:
+        raise NotImplementedError(
+            f"Unsupport prompt_extend_method: {args.prompt_extend_method}")
+    print("done", flush=True)
+
+    print("Step2: Init 14B t2v model...", end='', flush=True)
+    cfg = WAN_CONFIGS['t2v-14B']
+    wan_t2v = wan.WanT2V(
+        config=cfg,
+        checkpoint_dir=args.ckpt_dir,
+        device_id=0,
+        rank=0,
+        t5_fsdp=False,
+        dit_fsdp=False,
+        use_usp=False,
+    )
+    print("done", flush=True)
+
+    demo = gradio_interface()
+    demo.launch(server_name="0.0.0.0", share=False, server_port=7860)

model.properties

+# 模型编码
+modelCode=1437
+# 模型名称
+modelName=Wan2.1_pytorch
+# 模型描述
+modelDescription=阿里开源的超高质量视频生成模型
+# 应用场景
+appScenario=推理,视频生成,电商,教育,广媒
+# 框架类型
+frameType=pytorch

modified/config.py

+import os
+import torch
+import torch.distributed as dist
+from packaging import version
+from dataclasses import dataclass, fields
+
+from torch import distributed as dist
+
+from xfuser.logger import init_logger
+import xfuser.envs as envs
+# from xfuser.envs import CUDA_VERSION, TORCH_VERSION, PACKAGES_CHECKER
+from xfuser.envs import TORCH_VERSION, PACKAGES_CHECKER
+
+logger = init_logger(__name__)
+
+from typing import Union, Optional, List
+
+env_info = PACKAGES_CHECKER.get_packages_info()
+HAS_LONG_CTX_ATTN = env_info["has_long_ctx_attn"]
+HAS_FLASH_ATTN = env_info["has_flash_attn"]
+
+
+def check_packages():
+    import diffusers
+
+    if not version.parse(diffusers.__version__) > version.parse("0.30.2"):
+        raise RuntimeError(
+            "This project requires diffusers version > 0.30.2. Currently, you can not install a correct version of diffusers by pip install."
+            "Please install it from source code!"
+        )
+
+
+def check_env():
+    # https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/usage/cudagraph.html
+    #if CUDA_VERSION < version.parse("11.3"):
+    #    raise RuntimeError("NCCL CUDA Graph support requires CUDA 11.3 or above")
+    if TORCH_VERSION < version.parse("2.2.0"):
+        # https://pytorch.org/blog/accelerating-pytorch-with-cuda-graphs/
+        raise RuntimeError(
+            "CUDAGraph with NCCL support requires PyTorch 2.2.0 or above. "
+            "If it is not released yet, please install nightly built PyTorch "
+            "with `pip3 install --pre torch torchvision torchaudio --index-url "
+            "https://download.pytorch.org/whl/nightly/cu121`"
+        )
+
+
+@dataclass
+class ModelConfig:
+    model: str
+    download_dir: Optional[str] = None
+    trust_remote_code: bool = False
+
+
+@dataclass
+class RuntimeConfig:
+    warmup_steps: int = 1
+    dtype: torch.dtype = torch.float16
+    use_cuda_graph: bool = False
+    use_parallel_vae: bool = False
+    use_profiler: bool = False
+    use_torch_compile: bool = False
+    use_onediff: bool = False
+    use_fp8_t5_encoder: bool = False
+
+    def __post_init__(self):
+        check_packages()
+        if self.use_cuda_graph:
+            check_env()
+
+
+@dataclass
+class FastAttnConfig:
+    use_fast_attn: bool = False
+    n_step: int = 20
+    n_calib: int = 8
+    threshold: float = 0.5
+    window_size: int = 64
+    coco_path: Optional[str] = None
+    use_cache: bool = False
+
+    def __post_init__(self):
+        assert self.n_calib > 0, "n_calib must be greater than 0"
+        assert self.threshold > 0.0, "threshold must be greater than 0"
+
+
+@dataclass
+class DataParallelConfig:
+    dp_degree: int = 1
+    use_cfg_parallel: bool = False
+    world_size: int = 1
+
+    def __post_init__(self):
+        assert self.dp_degree >= 1, "dp_degree must greater than or equal to 1"
+
+        # set classifier_free_guidance_degree parallel for split batch
+        if self.use_cfg_parallel:
+            self.cfg_degree = 2
+        else:
+            self.cfg_degree = 1
+        assert self.dp_degree * self.cfg_degree <= self.world_size, (
+            "dp_degree * cfg_degree must be less than or equal to "
+            "world_size because of classifier free guidance"
+        )
+        assert (
+            self.world_size % (self.dp_degree * self.cfg_degree) == 0
+        ), "world_size must be divisible by dp_degree * cfg_degree"
+
+
+@dataclass
+class SequenceParallelConfig:
+    ulysses_degree: Optional[int] = None
+    ring_degree: Optional[int] = None
+    world_size: int = 1
+
+    def __post_init__(self):
+        if self.ulysses_degree is None:
+            self.ulysses_degree = 1
+            logger.info(
+                f"Ulysses degree not set, " f"using default value {self.ulysses_degree}"
+            )
+        if self.ring_degree is None:
+            self.ring_degree = 1
+            logger.info(
+                f"Ring degree not set, " f"using default value {self.ring_degree}"
+            )
+        self.sp_degree = self.ulysses_degree * self.ring_degree
+
+        if not HAS_LONG_CTX_ATTN and self.sp_degree > 1:
+            raise ImportError(
+                f"Sequence Parallel kit 'yunchang' not found but "
+                f"sp_degree is {self.sp_degree}, please set it "
+                f"to 1 or install 'yunchang' to use it"
+            )
+
+
+@dataclass
+class TensorParallelConfig:
+    tp_degree: int = 1
+    split_scheme: Optional[str] = "row"
+    world_size: int = 1
+
+    def __post_init__(self):
+        assert self.tp_degree >= 1, "tp_degree must greater than 1"
+        assert (
+            self.tp_degree <= self.world_size
+        ), "tp_degree must be less than or equal to world_size"
+
+
+@dataclass
+class PipeFusionParallelConfig:
+    pp_degree: int = 1
+    num_pipeline_patch: Optional[int] = None
+    attn_layer_num_for_pp: Optional[List[int]] = (None,)
+    world_size: int = 1
+
+    def __post_init__(self):
+        assert (
+            self.pp_degree is not None and self.pp_degree >= 1
+        ), "pipefusion_degree must be set and greater than 1 to use pipefusion"
+        assert (
+            self.pp_degree <= self.world_size
+        ), "pipefusion_degree must be less than or equal to world_size"
+        if self.num_pipeline_patch is None:
+            self.num_pipeline_patch = self.pp_degree
+            logger.info(
+                f"Pipeline patch number not set, "
+                f"using default value {self.pp_degree}"
+            )
+        if self.attn_layer_num_for_pp is not None:
+            logger.info(
+                f"attn_layer_num_for_pp set, splitting attention layers"
+                f"to {self.attn_layer_num_for_pp}"
+            )
+            assert len(self.attn_layer_num_for_pp) == self.pp_degree, (
+                "attn_layer_num_for_pp must have the same "
+                "length as pp_degree if not None"
+            )
+        if self.pp_degree == 1 and self.num_pipeline_patch > 1:
+            logger.warning(
+                f"Pipefusion degree is 1, pipeline will not be used,"
+                f"num_pipeline_patch will be ignored"
+            )
+            self.num_pipeline_patch = 1
+
+
+@dataclass
+class ParallelConfig:
+    dp_config: DataParallelConfig
+    sp_config: SequenceParallelConfig
+    pp_config: PipeFusionParallelConfig
+    tp_config: TensorParallelConfig
+    world_size: int = 1 # FIXME: remove this
+    worker_cls: str = "xfuser.ray.worker.worker.Worker"
+
+    def __post_init__(self):
+        assert self.tp_config is not None, "tp_config must be set"
+        assert self.dp_config is not None, "dp_config must be set"
+        assert self.sp_config is not None, "sp_config must be set"
+        assert self.pp_config is not None, "pp_config must be set"
+        parallel_world_size = (
+            self.dp_config.dp_degree
+            * self.dp_config.cfg_degree
+            * self.sp_config.sp_degree
+            * self.tp_config.tp_degree
+            * self.pp_config.pp_degree
+        )
+        world_size = self.world_size
+        assert parallel_world_size == world_size, (
+            f"parallel_world_size {parallel_world_size} "
+            f"must be equal to world_size {self.world_size}"
+        )
+        assert (
+            world_size % (self.dp_config.dp_degree * self.dp_config.cfg_degree) == 0
+        ), "world_size must be divisible by dp_degree * cfg_degree"
+        assert (
+            world_size % self.pp_config.pp_degree == 0
+        ), "world_size must be divisible by pp_degree"
+        assert (
+            world_size % self.sp_config.sp_degree == 0
+        ), "world_size must be divisible by sp_degree"
+        assert (
+            world_size % self.tp_config.tp_degree == 0
+        ), "world_size must be divisible by tp_degree"
+        self.dp_degree = self.dp_config.dp_degree
+        self.cfg_degree = self.dp_config.cfg_degree
+        self.sp_degree = self.sp_config.sp_degree
+        self.pp_degree = self.pp_config.pp_degree
+        self.tp_degree = self.tp_config.tp_degree
+
+        self.ulysses_degree = self.sp_config.ulysses_degree
+        self.ring_degree = self.sp_config.ring_degree
+
+
+@dataclass(frozen=True)
+class EngineConfig:
+    model_config: ModelConfig
+    runtime_config: RuntimeConfig
+    parallel_config: ParallelConfig
+    fast_attn_config: FastAttnConfig
+
+    def __post_init__(self):
+        world_size = self.parallel_config.world_size
+        if self.fast_attn_config.use_fast_attn:
+            assert self.parallel_config.dp_degree == world_size, f"world_size must be equal to dp_degree when using DiTFastAttn"
+
+    def to_dict(self):
+        """Return the configs as a dictionary, for use in **kwargs."""
+        return dict((field.name, getattr(self, field.name)) for field in fields(self))
+
+
+@dataclass
+class InputConfig:
+    height: int = 1024
+    width: int = 1024
+    num_frames: int = 49
+    use_resolution_binning: bool = (True,)
+    batch_size: Optional[int] = None
+    img_file_path: Optional[str] = None
+    prompt: Union[str, List[str]] = ""
+    negative_prompt: Union[str, List[str]] = ""
+    num_inference_steps: int = 20
+    max_sequence_length: int = 256
+    seed: int = 42
+    output_type: str = "pil"
+
+    def __post_init__(self):
+        if isinstance(self.prompt, list):
+            assert (
+                len(self.prompt) == len(self.negative_prompt)
+                or len(self.negative_prompt) == 0
+            ), "prompts and negative_prompts must have the same quantities"
+            self.batch_size = self.batch_size or len(self.prompt)
+        else:
+            self.batch_size = self.batch_size or 1
+        assert self.output_type in [
+            "pil",
+            "latent",
+            "pt",
+        ], "output_pil must be either 'pil' or 'latent'"