WAN-Dancer-14b-FP8

Installation

Clone the repo:

git clone https://github.com/Wan-Video/Wan-Dancer.git cd Wan-Dancer

Install dependencies:

python -m venv venv_wan_dancer source venv_wan_dancer/bin/activate

Install package in editable mode

pip install -e .

Install additional and specific versions dependencies

pip install moviepy loguru librosa pip install https://mirrors.aliyun.com/pytorch-wheels/cu124/torch-2.6.0+cu124-cp310-cp310-linux_x86_64.whl pip install torchvision==0.21.0 pip install diffusers==0.34.0 pip install yunchang==0.5.0 pip install flash_attn==2.6.3 pip install xfuser==0.4.0 pip install transformers==4.46.2

Model Download Models Download Links Description Wan-Dancer-14B 🤗 Huggingface 🤖 ModelScope Music-to-Dance Download models using huggingface-cli:

pip install "huggingface_hub[cli]" huggingface-cli download Wan-AI/Wan-Dancer-14B --local-dir ./Wan-Dancer-14B

Download models using modelscope-cli:

pip install modelscope modelscope download Wan-AI/Wan-Dancer-14B --local_dir ./Wan-Dancer-14B

Run Wan-Dancer Wan-Dancer can generate long-duration, high-quality, rhythmic dance videos from music with global structure and temporal continuity. Our method decouples the process into global keyframe planning and local temporal refinement, leveraging full-track musical context to ensure long-range coherence.

1. 🎬 Generate Global Keyframe Video

Run the global stage script:

cd Wan-Dancer ./gen_video_global.sh

cd Wan-Dancer ./gen_video_local.sh

🔧 Additional Required Parameters

Parameter Description global_video_path Path to the global video generated in Step 1. Required for local refinement. prompt_path Path to prompt file (defines dance style). Available styles: Chinese Classic Dance: gen_video/prompt/古典舞_local.txt K-Pop Dance: gen_video/prompt/kpop_local.txt Street Dance: gen_video/prompt/街舞_local.txt Tap Dance: gen_video/prompt/踢踏舞_local.txt Latin Dance: gen_video/prompt/拉丁舞_local.txt ✅ All other parameters (seed, image_path, etc.) are identical to Step 1.

Citation

If you use this code or framework in your research, please cite:

Huang, Mingyang and Zhang, Peng and Hu, Li and Wang, Guangyuan and Zhang, Ruoshi and Lu, Yi and Cheng, Gang and Zhang

License Agreement

This project is licensed under the Apache 2.0 License — see the LICENSE file for details.

Acknowledgements

This work builds upon and integrates components from the following open-source projects:

DiffSynth-Studio

Wan2.1