mirror of
https://git.datalinker.icu/ali-vilab/TeaCache
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44 lines
2.0 KiB
Markdown
44 lines
2.0 KiB
Markdown
<!-- ## **TeaCache4LuminaT2X** -->
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# TeaCache4LuminaT2X
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[TeaCache](https://github.com/LiewFeng/TeaCache) can speedup [Lumina-T2X](https://github.com/Alpha-VLLM/Lumina-T2X) 2x without much visual quality degradation, in a training-free manner. The following image shows the results generated by TeaCache-Lumina-Next with various rel_l1_thresh values: 0 (original), 0.2 (1.5x speedup), 0.3 (1.9x speedup), 0.4 (2.4x speedup), and 0.5 (2.8x speedup).
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## 📈 Inference Latency Comparisons on a Single A800
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| Lumina-Next-SFT | TeaCache (0.2) | TeaCache (0.3) | TeaCache (0.4) | TeaCache (0.5) |
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|:-------------------------:|:---------------------------:|:--------------------:|:---------------------:|:---------------------:|
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| ~17 s | ~11 s | ~9 s | ~7 s | ~6 s |
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## Installation
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```shell
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pip install --upgrade diffusers[torch] transformers protobuf tokenizers sentencepiece
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pip install flash-attn --no-build-isolation
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```
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## Usage
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You can modify the thresh in line 113 to obtain your desired trade-off between latency and visul quality. For single-gpu inference, you can use the following command:
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```bash
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python teacache_lumina_next.py
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```
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## Citation
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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.
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```
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@article{liu2024timestep,
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title={Timestep Embedding Tells: It's Time to Cache for Video Diffusion Model},
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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},
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journal={arXiv preprint arXiv:2411.19108},
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year={2024}
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}
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```
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## Acknowledgements
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We would like to thank the contributors to the [Lumina-T2X](https://github.com/Alpha-VLLM/Lumina-T2X) and [Diffusers](https://github.com/huggingface/diffusers). |