Summary of Customizing Text-to-image Models with a Single Image Pair, by Maxwell Jones et al.
Customizing Text-to-Image Models with a Single Image Pair
by Maxwell Jones, Sheng-Yu Wang, Nupur Kumari, David Bau, Jun-Yan Zhu
First submitted to arxiv on: 2 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Graphics (cs.GR); 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 This paper explores the idea of using paired artworks to customize generative models, enabling them to capture distinct artistic styles. The proposed method, Pair Customization, learns the stylistic difference between paired images and applies it to the generation process, allowing for stylized changes without overfitting to specific image content. This is achieved through a joint optimization approach that separates style and content into distinct weight spaces, which are then used to guide the diffusion process during inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artists can now use paired artworks to customize generative models in new ways! The researchers came up with an innovative method called Pair Customization that learns the difference between two artistic styles from just one pair of images. This helps the model create new artwork with a similar style, without getting stuck on specific details from the original image. By using this method, artists can make their own unique creations by teaching the AI to recognize and mimic different styles. |
Keywords
» Artificial intelligence » Diffusion » Inference » Optimization » Overfitting
