Installation
Clone the repo:
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