# TeaCache4HunyuanVideo [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. ## 📈 Inference Latency Comparisons on a Single A800 GPU | Resolution | HunyuanVideo | TeaCache (0.1) | TeaCache (0.15) | |:---------------------:|:-------------------------:|:--------------------:|:----------------------:| | 540p | ~18 min | ~11 min | ~8 min | | 720p | ~50 min | ~30 min | ~23 min | ## Usage 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 thresh in line 220 to obtain your desired trade-off between latency and visul quality. For single-gpu inference, you can use the following command: ```bash cd HunyuanVideo python3 teacache_sample_video.py \ --video-size 720 1280 \ --video-length 129 \ --infer-steps 50 \ --prompt "A cat walks on the grass, realistic style." \ --flow-reverse \ --use-cpu-offload \ --save-path ./teacache_results ``` To generate a video with 8 GPUs, you can use the following command: ```bash cd HunyuanVideo torchrun --nproc_per_node=8 teacache_sample_video.py \ --video-size 1280 720 \ --video-length 129 \ --infer-steps 50 \ --prompt "A cat walks on the grass, realistic style." \ --flow-reverse \ --seed 42 \ --ulysses-degree 8 \ --ring-degree 1 \ --save-path ./teacache_results ``` ## Acknowledgements We would like to thank the contributors to the [HunyuanVideo](https://github.com/Tencent/HunyuanVideo).