Summary of Smoothcache: a Universal Inference Acceleration Technique For Diffusion Transformers, by Joseph Liu et al.
SmoothCache: A Universal Inference Acceleration Technique for Diffusion Transformers
by Joseph Liu, Joshua Geddes, Ziyu Guo, Haomiao Jiang, Mahesh Kumar Nandwana
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Diffusion Transformers (DiT) have been shown to be effective in various tasks like image, video, and speech synthesis. However, their inference process is computationally expensive due to repeated evaluations of attention and feed-forward modules. To address this issue, the authors introduce SmoothCache, a model-agnostic inference acceleration technique for DiT architectures. SmoothCache leverages the high similarity between layer outputs across adjacent diffusion timesteps by analyzing representation errors from a calibration set. This allows it to cache and reuse key features during inference, resulting in speedups of 8% to 71% while maintaining or improving generation quality. The authors demonstrate its effectiveness on DiT-XL for image generation, Open-Sora for text-to-video, and Stable Audio Open for text-to-audio, highlighting its potential to enable real-time applications and broaden the accessibility of powerful DiT models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a new way to make computers generate things like images and videos faster. Right now, these computers need to do a lot of extra work when they’re making these things, which makes them take longer. The researchers came up with a solution called SmoothCache that can help speed up this process. They found that the computer’s outputs are very similar from one step to another, so they can reuse some of that information instead of having to do it all over again. This made their computers 8% to 71% faster without sacrificing quality. The researchers tested this on different tasks like making images and videos from text, and showed that it can make these processes much faster. |
Keywords
» Artificial intelligence » Attention » Diffusion » Image generation » Inference
