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(* Work was done during internship at Alibaba Group. † Corresponding author.)
</div>
<div class="is-size-5 publication-authors", align="center">
<a href="https://arxiv.org/abs/2411.19108">Paper</a> |
<a href="https://liewfeng.github.io/TeaCache/">Project Page</a>
</div>
<h5 align="center"> If you like our project, please give us a star ⭐ on GitHub for the latest update. </h2>
<h5 align="center">
[![hf_paper](https://img.shields.io/badge/🤗-Paper%20In%20HF-red.svg)](tps://huggingface.co/papers/2411.19108)
[![arXiv](https://img.shields.io/badge/Arxiv-2411.19108-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2411.19108)
[![Home Page](https://img.shields.io/badge/Project-<Website>-blue.svg)](https://liewfeng.github.io/TeaCache/)
[![License](https://img.shields.io/badge/License-Apache%202.0-yellow)](./LICENSE)
[![github](https://img.shields.io/github/stars/LiewFeng/TeaCache.svg?style=social)](https://github.com/LiewFeng/TeaCache/)
</h5>
![visualization](./assets/tisser.png)
## Latest News 🔥
- [2024/12/26] 🔥 Support [ConsisID](https://github.com/PKU-YuanGroup/ConsisID).
- [2024/12/26] 🔥 Support [ConsisID](https://github.com/PKU-YuanGroup/ConsisID). Thanks [@SHYuanBest](https://github.com/SHYuanBest).
- [2024/12/24] 🔥 Support [HunyuanVideo](https://github.com/Tencent/HunyuanVideo).
- [2024/12/19] 🔥 Support [CogVideoX](https://github.com/THUDM/CogVideo).
- [2024/12/06] 🎉 Release the [code](https://github.com/LiewFeng/TeaCache) of TeaCache. Support [Open-Sora](https://github.com/hpcaitech/Open-Sora), [Open-Sora-Plan](https://github.com/PKU-YuanGroup/Open-Sora-Plan) and [Latte](https://github.com/Vchitect/Latte).
- [2024/11/28] 🎉 Release the [paper](https://arxiv.org/abs/2411.19108) of TeaCache.
## 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. For more details and visual results, please visit our [project page](https://github.com/LiewFeng/TeaCache).
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. For more details and visual results, please visit our [project page](https://github.com/LiewFeng/TeaCache).
## TeaCache for HunyuanVideo
Please refer to [TeaCache4HunyuanVideo](./TeaCache4HunyuanVideo/README.md).
@ -125,6 +134,15 @@ python vbench/cal_vbench.py --score_dir bbb
# generated video is our methods's results
python common_metrics/eval.py --gt_video_dir aa --generated_video_dir bb
```
## Acknowledgement
This repository is built based on [VideoSys](https://github.com/NUS-HPC-AI-Lab/VideoSys), [Open-Sora](https://github.com/hpcaitech/Open-Sora), [Open-Sora-Plan](https://github.com/PKU-YuanGroup/Open-Sora-Plan), [Latte](https://github.com/Vchitect/Latte), [CogVideoX](https://github.com/THUDM/CogVideo), [HunyuanVideo](https://github.com/Tencent/HunyuanVideo) and [ConsisID](https://github.com/PKU-YuanGroup/ConsisID). Thanks for their contributions!
## License
* The majority of this project is released under the Apache 2.0 license as found in the [LICENSE](./LICENSE) file.
* For [VideoSys](https://github.com/NUS-HPC-AI-Lab/VideoSys), [Open-Sora](https://github.com/hpcaitech/Open-Sora), [Open-Sora-Plan](https://github.com/PKU-YuanGroup/Open-Sora-Plan), [Latte](https://github.com/Vchitect/Latte), [CogVideoX](https://github.com/THUDM/CogVideo), [HunyuanVideo](https://github.com/Tencent/HunyuanVideo) and [ConsisID](https://github.com/PKU-YuanGroup/ConsisID), please follow thier LICENSE.
* The service is a research preview. Please contact us if you find any potential violations. (liufeng20@mails.ucas.ac.cn)
## 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.
@ -138,6 +156,4 @@ If you find TeaCache is useful in your research or applications, please consider
}
```
## Acknowledgement
This repository is built based on [VideoSys](https://github.com/NUS-HPC-AI-Lab/VideoSys), [Open-Sora](https://github.com/hpcaitech/Open-Sora), [Open-Sora-Plan](https://github.com/PKU-YuanGroup/Open-Sora-Plan), [Latte](https://github.com/Vchitect/Latte), [CogVideoX](https://github.com/THUDM/CogVideo), [HunyuanVideo](https://github.com/Tencent/HunyuanVideo) and [ConsisID](https://github.com/PKU-YuanGroup/ConsisID). Thanks for their contributions!