# TeaCache4TangoFlux [TeaCache](https://github.com/LiewFeng/TeaCache) can speedup [TangoFlux](https://github.com/declare-lab/TangoFlux) 2x without much audio quality degradation, in a training-free manner. ## 📈 Inference Latency Comparisons on a Single A800 | TangoFlux | TeaCache (0.25) | TeaCache (0.4) | |:-------------------:|:----------------------------:|:--------------------:| | ~4.08 s | ~2.42 s | ~1.95 s | ## Installation ```shell pip install git+https://github.com/declare-lab/TangoFlux ``` ## Usage You can modify the thresh in line 266 to obtain your desired trade-off between latency and audio quality. For single-gpu inference, you can use the following command: ```bash python teacache_tango_flux.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 [TangoFlux](https://github.com/declare-lab/TangoFlux).