TeaCache4Lumina2
TeaCache can speedup Lumina-Image-2.0 without much visual quality degradation, in a training-free manner. The following image shows the experimental results of Lumina-Image-2.0 and TeaCache with different versions: v1(0 (original), 0.2 (1.25x speedup), 0.3 (1.5625x speedup), 0.4 (2.0833x speedup), 0.5 (2.5x speedup).) and v2(Lumina-Image-2.0 (~25 s), TeaCache (0.2) (~16.7 s, 1.5x speedup), TeaCache (0.3) (~15.6 s, 1.6x speedup), TeaCache (0.5) (~13.79 s, 1.8x speedup), TeaCache (1.1) (~11.9 s, 2.1x speedup)).
The v1 coefficients
[393.76566581,−603.50993606,209.10239044,−23.00726601,0.86377344]
exhibit poor quality at low L1 values but perform better with higher L1 settings, though at a slower speed. The v2 coefficients
[225.7042019806413,−608.8453716535591,304.1869942338369,124.21267720116742,−1.4089066892956552]
, however, offer faster computation and better quality at low L1 levels but incur significant feature loss at high L1 values.
v1
v2
📈 Inference Latency Comparisons on a single 4090 (step 50)
v1
| Lumina-Image-2.0 | TeaCache (0.2) | TeaCache (0.3) | TeaCache (0.4) | TeaCache (0.5) |
|---|---|---|---|---|
| ~25 s | ~20 s | ~16 s | ~12 s | ~10 s |
v2
| Lumina-Image-2.0 | TeaCache (0.2) | TeaCache (0.3) | TeaCache (0.5) | TeaCache (1.1) |
|---|---|---|---|---|
| ~25 s | ~16.7 s | ~15.6 s | ~13.79 s | ~11.9 s |
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.