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35 lines
1.2 KiB
Markdown
35 lines
1.2 KiB
Markdown
# TeaCache
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TeaCache ([Timestep Embedding Aware Cache](https://github.com/ali-vilab/TeaCache)) is a training-free caching approach that estimates and leverages the fluctuating differences among model outputs across timesteps, thereby accelerating the inference.
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## Examples
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### FLUX
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Script: [./flux_teacache.py](./flux_teacache.py)
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Model: FLUX.1-dev
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Steps: 50
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GPU: A100
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|TeaCache is disabled|tea_cache_l1_thresh=0.2|tea_cache_l1_thresh=0.8|
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|23s|13s|5s|
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### Hunyuan Video
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Script: [./hunyuanvideo_teacache.py](./hunyuanvideo_teacache.py)
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Model: Hunyuan Video
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Steps: 30
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GPU: A100
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The following video was generated using TeaCache. It is nearly identical to [the video without TeaCache enabled](https://github.com/user-attachments/assets/48dd24bb-0cc6-40d2-88c3-10feed3267e9), but with double the speed.
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https://github.com/user-attachments/assets/cd9801c5-88ce-4efc-b055-2c7737166f34
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