Summary of All Seeds Are Not Equal: Enhancing Compositional Text-to-image Generation with Reliable Random Seeds, by Shuangqi Li et al.
All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds
by Shuangqi Li, Hieu Le, Jingyi Xu, Mathieu Salzmann
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 A novel study on text-to-image diffusion models reveals the crucial impact of initial noise patterns on the consistency of generated images, particularly for compositional prompts like “two dogs” or “a penguin on the right of a bowl”. Researchers identify that distinct initial random seeds influence the model’s placement of objects in specific image areas, adhering to patterns of camera angles and image composition. To enhance the models’ compositional abilities, they propose mining reliable cases and fine-tuning on generated images without manual annotation. The approach yields significant gains for numerical (29.3% and 19.5%) and spatial (60.7% and 21.1%) composition capabilities in Stable Diffusion and PixArt-α. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Text-to-image models can generate realistic images from text prompts, but they often have trouble with complex requests like “two dogs”. Scientists are trying to figure out why this happens and how to make the models better. They found that tiny differences in the starting point of the model’s random seed can cause it to place objects in different parts of the image. To fix this, they developed a new way to use these reliable cases to train the model, which makes it much better at creating complex images. |
Keywords
» Artificial intelligence » Diffusion » Fine tuning
