Wan2.2-TI2V-5B-Turbo

<p align="center"> <h1 align="center">Wan2.2-TI2V-5B-Turbo</h1> <a href="https://github.com/quanhaol/Wan2.2-TI2V-5B-Turbo"><img src="https://img.shields.io/badge/GitHub-Repository-0066cc.svg" alt="GitHub"></a> <a href="https://huggingface.co/quanhaol/Wan2.2-TI2V-5B-Turbo"><img src="https://img.shields.io/badge/🤗_HuggingFace-Model-ffbd45.svg" alt="HuggingFace"></a> <a href="https://huggingface.co/datasets/quanhaol/MagicData"><img src="https://img.shields.io/badge/🤗_HuggingFace-Dataset-ffbd45.svg" alt="HuggingFace"></a>

Wan2.2-TI2V-5B-Turbo is designed for efficient step distillation and CFG distillation based on <a href="https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B"><b>Wan2.2-TI2V-5B</b></a>.

Leveraging the Self-Forcing framework, it enables 4-step TI2V-5B model training. Our model can generate 121-frame videos at 24 FPS with a resolution of 1280×704 in just 4 steps, eliminating the need for the CFG trick.

To the best of our knowledge, Wan2.2-TI2V-5B-Turbo is the first open-source repository of the distilled I2V version of Wan2.2-TI2V-5B.

🔥Video Demos

The videos below can be reproduced using examples/example.csv.

<table border="0" style="width: 100%; text-align: left; margin-top: 20px;"> <tr> <td> <video src="https://github.com/user-attachments/assets/dae5045c-c7a0-4e99-aa1c-e07d2300ea1c" width="100%" controls loop></video> </td> <td> <video src="https://github.com/user-attachments/assets/f6d66a0e-eb8b-4b69-a29f-9d9607c02dda" width="100%" controls loop></video> </td> </tr> <tr> <td> <video src="https://github.com/user-attachments/assets/0adc81ad-0389-4a06-b362-1078e5b4b564" width="100%" controls loop></video> </td> <td> <video src="https://github.com/user-attachments/assets/dcf860c4-1da7-469c-bd88-0ab8641d400a" width="100%" controls loop></video> </td> </tr> <tr> <td> <video src="https://github.com/user-attachments/assets/c5478230-2093-4443-8d76-f845675a4331" width="100%" controls loop></video> </td> <td> <video src="https://github.com/user-attachments/assets/661daf97-aff3-4c5d-8912-44696a86a24e" width="100%" controls loop></video> </td> </tr> </table>

📣 Updates

• 2025/08/06 🔥Wan2.2-TI2V-5B-Turbo has been released here.

🐍 Installation

Create a conda environment and install dependencies:

conda create -n wanturbo python=3.10 -y
conda activate wanturbo
pip install -r requirements.txt
pip install flash-attn --no-build-isolation
python setup.py develop

🚀Quick Start

Checkpoint Download

pip install "huggingface_hub[hf_transfer]"
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download Wan-AI/Wan2.2-TI2V-5B --local-dir wan_models/Wan2.2-TI2V-5B
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download quanhaol/Wan2.2-TI2V-5B-Turbo --local-dir wan_models/Wan2.2-TI2V-5B-Turbo

DMD Training

bash running_scripts/train/Wan2.2/dmd.sh

Our training run uses 4000 iterations and completes in under 2 days using 16 A100 GPUs.

Fewstep Inference

bash running_scripts/inference/Wan2.2/i2v_fewstep.sh

🤝 Acknowledgements

We would like to express our gratitude to the following open-source projects that have been instrumental in the development of our project:

• CausVid
• Self-Forcing
• Self-Forcing-Plus
• Wan2.1
• Wan2.2

Special thanks to the contributors of these libraries for their hard work and dedication!

📚 Contact

If you have any suggestions or find our work helpful, feel free to contact us

Email: [email protected] or [email protected] or [email protected]

If you find our work useful, <b>please consider giving a star to this github repository and citing it</b>:

@article{li2025magicmotion,
  title={MagicMotion: Controllable Video Generation with Dense-to-Sparse Trajectory Guidance},
  author={Li, Quanhao and Xing, Zhen and Wang, Rui and Zhang, Hui and Dai, Qi and Wu, Zuxuan},
  journal={arXiv preprint arXiv:2503.16421},
  year={2025}
}