Summary of Simpler Diffusion (sid2): 1.5 Fid on Imagenet512 with Pixel-space Diffusion, by Emiel Hoogeboom et al.
Simpler Diffusion (SiD2): 1.5 FID on ImageNet512 with pixel-space diffusion
by Emiel Hoogeboom, Thomas Mensink, Jonathan Heek, Kay Lamerigts, Ruiqi Gao, Tim Salimans
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Machine Learning (stat.ML)
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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 The paper challenges the notion that latent diffusion models are superior for high-resolution image synthesis. Instead, it shows that pixel-space models can be competitive in terms of quality and efficiency, achieving state-of-the-art (SOTA) results on several datasets. The authors demonstrate that these models can produce images with a 1.5 FID score on ImageNet512, outperforming latent approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper compares different types of diffusion models for high-resolution image synthesis. It shows that pixel-space models are just as good as latent models in terms of quality and efficiency. The authors achieve state-of-the-art results on several datasets, including ImageNet128 and ImageNet256. This means that pixel-space models can be used to create realistic images at high resolution. |
Keywords
» Artificial intelligence » Diffusion » Image synthesis
