Summary of Sparse Repellency For Shielded Generation in Text-to-image Diffusion Models, by Michael Kirchhof et al.
Sparse Repellency for Shielded Generation in Text-to-image Diffusion Models
by Michael Kirchhof, James Thornton, Pierre Ablin, Louis Béthune, Eugene Ndiaye, Marco Cuturi
First submitted to arxiv on: 8 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 proposed method, called SPELL (Sparse Repellency), addresses concerns about the reliability of diffusion models in text-to-image generation. By adding repellency terms to the model’s SDE, SPELL coaxes sampled trajectories to land on novel images rather than recreating training set examples or failing to generate diverse results due to a lack of prompting diversity. This method is applicable with either static or dynamic reference sets and demonstrates improved diversity while maintaining comparable FID scores compared to other recent methods. Additionally, SPELL can shield the generation process from a large protected image set like ImageNet. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SPELL is a new approach that helps diffusion models generate more diverse images by avoiding previously seen pictures. This method adds special “repellent” forces to the model’s calculations to push its results away from known images and encourage it to create new ones. The results show that SPELL can improve diversity while keeping the generated images similar in quality to those produced by other methods. |
Keywords
» Artificial intelligence » Diffusion » Image generation » Prompting
