Summary of Cfg++: Manifold-constrained Classifier Free Guidance For Diffusion Models, by Hyungjin Chung et al.
CFG++: Manifold-constrained Classifier Free Guidance for Diffusion Models
by Hyungjin Chung, Jeongsol Kim, Geon Yeong Park, Hyelin Nam, Jong Chul Ye
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 In this paper, the authors investigate the limitations of classifier-free guidance (CFG) in modern diffusion models for text-guided generation. Specifically, they reveal that the drawbacks of CFG, such as lack of invertibility and mode collapse, stem from an off-manifold phenomenon rather than inherent limitations of the diffusion models themselves. The authors propose a novel approach, CFG++, which tackles these challenges by reformulating text-guidance as an inverse problem with a text-conditioned score matching loss. This approach enables significant improvements in sample quality, invertibility, and guidance scales, while also enabling seamless interpolation between unconditional and conditional sampling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CFG is a fundamental tool for text-guided generation, but it has some notable drawbacks. For example, DDIM with CFG lacks invertibility, making image editing more complicated. Additionally, high guidance scales are necessary for good outputs, but these often result in mode collapse. The authors show that these issues don’t come from the diffusion models themselves, but rather from an off-manifold phenomenon associated with CFG. They propose a new approach called CFG++, which fixes this problem and offers better sample quality, invertibility, and guidance scales. |
Keywords
» Artificial intelligence » Diffusion
