# TeaCache4Mochi [TeaCache](https://github.com/LiewFeng/TeaCache) can speedup [Mochi](https://github.com/genmoai/mochi) 2x without much visual quality degradation, in a training-free manner. The following video shows the results generated by TeaCache-Mochi with various rel_l1_thresh values: 0 (original), 0.06 (1.5x speedup), 0.09 (2.1x speedup). https://github.com/user-attachments/assets/29a81380-46b3-414f-a96b-6e3acc71b6c4 ## 📈 Inference Latency Comparisons on a Single A800 | mochi-1-preview | TeaCache (0.06) | TeaCache (0.09) | |:--------------------------:|:----------------------------:|:--------------------:| | ~30 min | ~20 min | ~14 min | ## Installation ```shell pip install --upgrade diffusers[torch] transformers protobuf tokenizers sentencepiece imageio ``` ## Usage You can modify the thresh in line 174 to obtain your desired trade-off between latency and visul quality. For single-gpu inference, you can use the following command: ```bash python teacache_mochi.py ``` ## 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} } ``` ## Acknowledgements We would like to thank the contributors to the [Mochi](https://github.com/genmoai/mochi) and [Diffusers](https://github.com/huggingface/diffusers).