Zengtudor/Wan2.1
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README.md
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@ -28,6 +28,7 @@ In this repository, we present **Wan2.1**, a comprehensive and open suite of vid
## 🔥 Latest News!! ## 🔥 Latest News!!
* Feb 25, 2025: 👋 We've released the inference code and weights of Wan2.1. * Feb 25, 2025: 👋 We've released the inference code and weights of Wan2.1.
* Feb 27, 2025: 👋 Wan2.1 has been integrated into [ComfyUI](https://comfyanonymous.github.io/ComfyUI_examples/wan/). Enjoy!
## 📑 Todo List ## 📑 Todo List
@ -35,14 +36,15 @@ In this repository, we present **Wan2.1**, a comprehensive and open suite of vid
- [x] Multi-GPU Inference code of the 14B and 1.3B models - [x] Multi-GPU Inference code of the 14B and 1.3B models
- [x] Checkpoints of the 14B and 1.3B models - [x] Checkpoints of the 14B and 1.3B models
- [x] Gradio demo - [x] Gradio demo
- [x] ComfyUI integration
- [ ] Diffusers integration - [ ] Diffusers integration
- [ ] ComfyUI integration
- Wan2.1 Image-to-Video - Wan2.1 Image-to-Video
- [x] Multi-GPU Inference code of the 14B model - [x] Multi-GPU Inference code of the 14B model
- [x] Checkpoints of the 14B model - [x] Checkpoints of the 14B model
- [x] Gradio demo - [x] Gradio demo
- [X] ComfyUI integration
- [ ] Diffusers integration - [ ] Diffusers integration
- [ ] ComfyUI integration
## Quickstart ## Quickstart
@ -79,6 +81,11 @@ pip install "huggingface_hub[cli]"
huggingface-cli download Wan-AI/Wan2.1-T2V-14B --local-dir ./Wan2.1-T2V-14B huggingface-cli download Wan-AI/Wan2.1-T2V-14B --local-dir ./Wan2.1-T2V-14B
``` ```
Download models using modelscope-cli:
```
pip install modelscope
modelscope download Wan-AI/Wan2.1-T2V-14B --local_dir ./Wan2.1-T2V-14B
```
#### Run Text-to-Video Generation #### 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: 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:
@ -324,7 +331,7 @@ We test the computational efficiency of different **Wan2.1** models on different
## Community Contributions ## 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). - [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) provides more support for **Wan2.1**, 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).
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