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STA accelerates attention by exploiting redundancy in 3D full attention. Another approach to speeding up video generation focuses on caching, leveraging redundancy across diffusion steps. We demonstrate that STA is **compatible** to [TeaCache](https://github.com/ali-vilab/TeaCache), a state-of-the-art diffusion acceleration technique based on caching. Together, our solution brings **3x** speedup, reducing DiT inference time from 945 seconds to **317** seconds with no quality loss.
-We evaluate our method on 200 randomly selected prompts from the MovieGen Bench. Below, we provide additional qualitative comparisons between the original Hunyuan model and our 3× speedup solution. The embedded webpage below is scrollable.
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+We evaluate our method on 200 randomly selected prompts from the MovieGen Bench. Below, we provide additional **uncherry-picked** qualitative comparisons between the original Hunyuan model and our 3× speedup solution.
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+More results can be found [here](https://fast-video.github.io/).
## Training with STA Unlocks Greater Speedup