mirror of
https://git.datalinker.icu/ali-vilab/TeaCache
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92 lines
3.2 KiB
Markdown
92 lines
3.2 KiB
Markdown
<!-- ## **TeaCache4HunyuanVideo** -->
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# TeaCache4HunyuanVideo
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[TeaCache](https://github.com/LiewFeng/TeaCache) can speedup [HunyuanVideo](https://github.com/Tencent/HunyuanVideo) 2x without much visual quality degradation, in a training-free manner. The following video shows the results generated by TeaCache-HunyuanVideo with various `rel_l1_thresh` values: 0 (original), 0.1 (1.6x speedup), 0.15 (2.1x speedup).
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https://github.com/user-attachments/assets/34b5dab0-5b0f-48a0-968d-88af18b84803
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## 📈 Inference Latency Comparisons on a Single A800 GPU
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| Resolution | HunyuanVideo | TeaCache (0.1) | TeaCache (0.15) |
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|:---------------------:|:-------------------------:|:--------------------:|:----------------------:|
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| 540p | ~18 min | ~11 min | ~8 min |
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| 720p | ~50 min | ~30 min | ~23 min |
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## Usage
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Follow [HunyuanVideo](https://github.com/Tencent/HunyuanVideo) to clone the repo and finish the installation, then copy 'teacache_sample_video.py' in this repo to the HunyuanVideo repo. You can modify the '`rel_l1_thresh`' in line 220 to obtain your desired trade-off between latency and visul quality.
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For single-gpu inference, you can use the following command:
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```bash
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cd HunyuanVideo
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python3 teacache_sample_video.py \
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--video-size 720 1280 \
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--video-length 129 \
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--infer-steps 50 \
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--prompt "A cat walks on the grass, realistic style." \
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--flow-reverse \
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--use-cpu-offload \
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--save-path ./teacache_results
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```
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To generate a video with 8 GPUs, you can use the following command:
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```bash
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cd HunyuanVideo
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torchrun --nproc_per_node=8 teacache_sample_video.py \
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--video-size 1280 720 \
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--video-length 129 \
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--infer-steps 50 \
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--prompt "A cat walks on the grass, realistic style." \
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--flow-reverse \
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--seed 42 \
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--ulysses-degree 8 \
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--ring-degree 1 \
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--save-path ./teacache_results
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```
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For FP8 inference, you must explicitly specify the FP8 weight path. For example, to generate a video with fp8 weights, you can use the following command:
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```bash
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cd HunyuanVideo
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DIT_CKPT_PATH={PATH_TO_FP8_WEIGHTS}/{WEIGHT_NAME}_fp8.pt
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python3 teacache_sample_video.py \
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--dit-weight ${DIT_CKPT_PATH} \
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--video-size 1280 720 \
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--video-length 129 \
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--infer-steps 50 \
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--prompt "A cat walks on the grass, realistic style." \
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--seed 42 \
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--embedded-cfg-scale 6.0 \
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--flow-shift 7.0 \
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--flow-reverse \
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--use-cpu-offload \
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--use-fp8 \
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--save-path ./teacache_fp8_results
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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 [HunyuanVideo](https://github.com/Tencent/HunyuanVideo).
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