mirror of
https://git.datalinker.icu/ali-vilab/TeaCache
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43 lines
2.0 KiB
Markdown
43 lines
2.0 KiB
Markdown
<!-- ## **TeaCache4HiDream-I1** -->
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# TeaCache4HiDream-I1
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[TeaCache](https://github.com/LiewFeng/TeaCache) can speedup [HiDream-I1](https://github.com/HiDream-ai/HiDream-I1) 2x without much visual quality degradation, in a training-free manner. The following image shows the results generated by TeaCache-HiDream-I1-Full with various `rel_l1_thresh` values: 0 (original), 0.17 (1.5x speedup), 0.25 (1.7x speedup), 0.3 (2.0x speedup), and 0.45 (2.6x speedup).
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## 📈 Inference Latency Comparisons on a Single A100
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| HiDream-I1-Full | TeaCache (0.17) | TeaCache (0.25) | TeaCache (0.3) | TeaCache (0.45) |
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|:-----------------------:|:----------------------------:|:--------------------:|:---------------------:|:--------------------:|
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| ~50 s | ~34 s | ~29 s | ~25 s | ~19 s |
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## Installation
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```shell
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pip install git+https://github.com/huggingface/diffusers
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pip install --upgrade transformers protobuf tiktoken tokenizers sentencepiece
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```
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## Usage
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You can modify the `rel_l1_thresh` in line 297 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_hidream_i1.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 [HiDream-I1](https://github.com/HiDream-ai/HiDream-I1) and [Diffusers](https://github.com/huggingface/diffusers). |