Summary of Multistep Distillation Of Diffusion Models Via Moment Matching, by Tim Salimans and Thomas Mensink and Jonathan Heek and Emiel Hoogeboom
Multistep Distillation of Diffusion Models via Moment Matching
by Tim Salimans, Thomas Mensink, Jonathan Heek, Emiel Hoogeboom
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Neural and Evolutionary Computing (cs.NE)
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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 Medium Difficulty summary: Our paper introduces a novel method for accelerating diffusion models by distilling many-step models into few-step ones. This is achieved by matching conditional expectations between clean and noisy data along the sampling trajectory. Building upon one-step methods, our approach provides a new perspective on moment matching. By utilizing up to 8 sampling steps, we demonstrate that distilled models outperform their one-step counterparts and original teacher models on Imagenet, setting new state-of-the-art results. Additionally, we achieve fast generation of high-resolution images directly in image space for large text-to-image models, without requiring autoencoders or upsamplers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: We’ve developed a way to make diffusion models work faster. Our method takes many-step models and simplifies them into fewer steps by matching expectations between clean and noisy data. This approach is better than previous methods because it can handle multiple steps, not just one. By using this technique, we were able to create models that are even more efficient and effective on large datasets like Imagenet. We also showed that our method can generate high-quality images quickly, without needing extra tools. |
Keywords
» Artificial intelligence » Diffusion
