TeaCache4LuminaT2X

TeaCache can speedup Lumina-Image-2.0 without much visual quality degradation, in a training-free manner. The following image shows the results generated by TeaCache-Lumina-Image-2.0 with various rel_l1_thresh values: 0 (original), 0.1 (1.05x speedup), 0.2 (1.15x speedup), 0.3 (1.25x speedup).

📈 Inference Latency Comparisons on a 4070 laptop(size 1024 x 1536)

Lumina-Image-2.0	TeaCache (0.1)	TeaCache (0.2)	TeaCache (0.3)
~97.74s	~93.19s	~84.72s	~78.43s

Installation

pip install --upgrade diffusers[torch] transformers protobuf tokenizers sentencepiece
pip install flash-attn --no-build-isolation

Usage

You can modify the thresh in line 154 to obtain your desired trade-off between latency and visul quality. For single-gpu inference, you can use the following command:

python teacache_lumina2.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 Lumina-Image-2.0 and Diffusers.

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