Summary of Eliminating Oversaturation and Artifacts Of High Guidance Scales in Diffusion Models, by Seyedmorteza Sadat et al.
Eliminating Oversaturation and Artifacts of High Guidance Scales in Diffusion Models
by Seyedmorteza Sadat, Otmar Hilliges, Romann M. Weber
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 introduces adaptive projected guidance (APG), a modified classifier-free guidance (CFG) update rule that addresses oversaturation issues in diffusion models. APG decomposes the CFG update term into parallel and orthogonal components, finding that the parallel component causes oversaturation while the orthogonal component enhances image quality. By down-weighting the parallel component, APG achieves high-quality generations without oversaturation. The method is easy to implement and introduces minimal computational overhead. Experimental results show that APG outperforms standard CFG in terms of FID, recall, and saturation scores, making it a superior plug-and-play alternative. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better images using computer models. Right now, these models can get too good at making things that didn’t exist before, which is called oversaturation. The researchers looked at how the model makes changes to improve its pictures and found that some parts of the process are causing the problems. They came up with a new way to make the changes that doesn’t cause oversaturation and keeps the good qualities. This new method is easy to use and works well, making it a better choice for people who want to make good images. |
Keywords
» Artificial intelligence » Diffusion » Recall
