Wan-Dancer-14B

💜 Wan-Dancer    ｜    🖥️ GitHub    |   🤗 Hugging Face   |   🤖 ModelScope   |    📑 Paper   

Wan-Dancer: A Hierarchical Framework for Minute-scale Coherent Music-to-Dance Generation

🔥 Latest News!!

• July 13, 2026: 💃 We introduce Wan-Dancer, a method can generate long-duration, high-quality, rhythmic dance videos from music with global structure and temporal continuity. We released the model weights and inference code. And now you can try it on ModelScope Studio or HuggingFace Space!

📑 Todo List

• Wan-Dancer Music-to-Dance
  • Inference code of Wan-Dancer
  • Checkpoints of Wan-Dancer
  • ComfyUI integration

Run Wan-Dancer

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

🔧 Important Parameters

Parameter	Description
seed	Random seed for reproducibility.
image_path	Path to reference image. Example: gen_video/ref_image/1001.jpg
prompt_path	Path to prompt file (defines dance style).
Available styles: Chinese Classic Dance: gen_video/prompt/古典舞_global.txt K-Pop Dance: gen_video/prompt/kpop_global.txt Street Dance: gen_video/prompt/街舞_global.txt Tap Dance: gen_video/prompt/踢踏舞_global.txt Latin Dance: gen_video/prompt/拉丁舞_global.txt
music_path	Path to input music file. Example: gen_video/music/ChineseClassicDance.WAV
output_folder	Output directory for generated video.
timestamp	Timestamp identifier for output files.
num_inference_steps	Number of diffusion inference steps (e.g., 48).

🌰 Examples

Dance Genres	Parameter	Generated Global Video
Chinese Classical Dance	seed=0
image_path='gen_video/ref_image/1001.jpg'
prompt_path='gen_video/prompt/古典舞_global.txt'
music_path='gen_video/music/ChineseClassicDance.WAV'
num_inference_steps=48
cfg_scale=5
Street Dance	seed=0
image_path='gen_video/ref_image/2001.jpg'
prompt_path='gen_video/prompt/街舞_global.txt'
music_path='gen_video/music/StreetDance.WAV'
num_inference_steps=48
cfg_scale=5
K-Pop Dance	seed=0
image_path='gen_video/ref_image/3001.jpg'
prompt_path='gen_video/prompt/kpop_global.txt'
music_path='gen_video/music_suno/3001.WAV'
num_inference_steps=48
cfg_scale=5
Latin Dance	seed=0
image_path='gen_video/ref_image/4001.jpg'
prompt_path='gen_video/prompt/拉丁舞_global.txt'
music_path='gen_video/music/LatinDance.WAV'
num_inference_steps=48
cfg_scale=5
Tap Dance	seed=0
image_path='gen_video/ref_image/5001.jpg'
prompt_path='gen_video/prompt/踢踏舞_global.txt'
music_path='gen_video/music/TapDance.wav'
num_inference_steps=48
cfg_scale=5

2. 🎥 Generate Final High-Resolution Video

Run the local refinement stage:

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.

🌰 Examples

Dance Genres	Parameter	Generated Final Video
Chinese Classical Dance	seed=0
image_path='gen_video/ref_image/1001.jpg'
prompt_path='gen_video/prompt/古典舞_local.txt'
music_path='gen_video/music/ChineseClassicDance.WAV'
num_inference_steps=24
cfg_scale=5
global_video_path='outputs/global_video/1001_ChineseClassicDance_seed0.mp4'
Street Dance	seed=0
image_path='gen_video/ref_image/2001.jpg'
prompt_path='gen_video/prompt/街舞_local.txt'
music_path='gen_video/music/StreetDance.WAV'
num_inference_steps=24
cfg_scale=5
global_video_path='outputs/global_video/2001_StreetDance_seed0.mp4'
K-Pop Dance	seed=100
image_path='gen_video/ref_image/3001.jpg'
prompt_path='gen_video/prompt/kpop_local.txt'
music_path='gen_video/music_suno/3001.WAV'
num_inference_steps=24
cfg_scale=5
global_video_path='outputs/global_video/3001_KPopDance_seed0.mp4'
Latin Dance	seed=0
image_path='gen_video/ref_image/4001.jpg'
prompt_path='gen_video/prompt/拉丁舞_local.txt'
music_path='gen_video/music/LatinDance.WAV'
num_inference_steps=24
cfg_scale=5
global_video_path='outputs/global_video/4001_LatinDance_seed0.mp4'
Tap Dance	seed=0
image_path='gen_video/ref_image/5001.jpg'
prompt_path='gen_video/prompt/踢踏舞_local.txt'
music_path='gen_video/music/TapDance.wav'
num_inference_steps=24
cfg_scale=5
global_video_path='outputs/global_video/5001_TapDance_seed0.mp4'

Note: The num_inference_steps should be set to a larger value (e.g., 48) for longer time videos.

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Citation

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

@article{wan-dancer-2026,
  title={Wan-Dancer: A Hierarchical Framework for Minute-scale Coherent Music-to-Dance Generation},
  author={Mingyang Huang, Peng Zhang, Li Hu, Guangyuan Wang, Bang Zhang},
  website={https://humanaigc.github.io/wan-dancer/},
  url={https://arxiv.org/abs/2607.09581},
  year={2026}
}

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:

1. DiffSynth-Studio
2. Wan2.1