README.md

TeaCache4TangoFlux

TeaCache can speedup 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

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:

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.