GitHub - ali-vilab/TeaCache: Timestep Embedding Tells: It's Time to Cache for Video Diffusion Model (original) (raw)

[CVPR 2025 Highlight] Timestep Embedding Tells: It's Time to Cache for Video Diffusion Model

1University of Chinese Academy of Sciences, 2Alibaba Group
3Institute of Automation, Chinese Academy of Sciences
4Fudan University, 5Nanyang Technological University

(* Work was done during internship at Alibaba Group. † Project Leader. ‡ CorresCorresponding author.)

visualization

🫖 Introduction

We introduce Timestep Embedding Aware Cache (TeaCache), a training-free caching approach that estimates and leverages the fluctuating differences among model outputs across timesteps, thereby accelerating the inference. TeaCache works well for Video Diffusion Models, Image Diffusion models and Audio Diffusion Models. For more details and results, please visit our project page.

🔥 Latest News

🧩 Community Contributions

If you develop/use TeaCache in your projects and you would like more people to see it, please inform us.(liufeng20@mails.ucas.ac.cn)

Model

ComfyUI

Parallelism

Engine

🎉 Supported Models

Text to Video

Image to Video

Text to Image

Text to Audio

🤖 Instructions for Supporting Other Models

💐 Acknowledgement

This repository is built based on VideoSys, Diffusers, Open-Sora, Open-Sora-Plan, Latte, CogVideoX, HunyuanVideo, ConsisID, FLUX, Mochi, LTX-Video, Lumina-T2X, TangoFlux, Cosmos, Wan2.1, HiDream-I1 and Lumina-Image-2.0. Thanks for their contributions!

🔒 License

📖 Citation

If you find TeaCache is useful in your research or applications, please consider giving us a star ⭐ and citing it by the following BibTeX entry.

@article{liu2024timestep,
  title={Timestep Embedding Tells: It's Time to Cache for Video Diffusion Model},
  author={Liu, Feng and Zhang, Shiwei and Wang, Xiaofeng and Wei, Yujie and Qiu, Haonan and Zhao, Yuzhong and Zhang, Yingya and Ye, Qixiang and Wan, Fang},
  journal={arXiv preprint arXiv:2411.19108},
  year={2024}
}