TeaCache can speedup LTX-Video 2x without much visual quality degradation, in a training-free manner. The following video presents the videos generated by TeaCache-LTX-Video with various rel_l1_thresh values: 0 (original), 0.03 (1.6x speedup), 0.05 (2.1x speedup).

pip install --upgrade diffusers[torch] transformers protobuf tokenizers sentencepiece imageio

You can modify the thresh in line 187 to obtain your desired trade-off between latency and visul quality. For single-gpu inference, you can use the following command:

python teacache_ltx.py

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}
}

We would like to thank the contributors to the LTX-Video and Diffusers.