ReVersion: Diffusion-Based Relation Inversion from Images

This paper proposes a novel method for inverting diffusion models from exemplar images to generate customized images with new objects, backgrounds, and styles. The proposed method, called ReVersion, first learns a relation prompt from a frozen pre-trained text-to-image diffusion model. The learned relation prompt can then be applied to generate relation-specific images with new objects, backgrounds, and styles. The key insight of the proposed method is the "preposition prior" - real-world relation prompts can be sparsely activated upon a set of basis prepositional words.

Key insights and lessons learned from the paper:

• The proposed method is able to generate customized images with new objects, backgrounds, and styles.
• The proposed method is able to capture object relations from exemplar images.
• The proposed method is able to generate images that are faithful to the input text and that preserve the identity of the input images.

Questions for the authors:

• How does the proposed method compare to other methods for relation inversion?
• What are the limitations of the proposed method?
• How can the proposed method be improved?
• What are the potential applications of the proposed method?

Related topics or future research directions:

• Other methods for relation inversion
• Limitations of the proposed method
• Ways to improve the proposed method
• Potential applications of the proposed method

References:

• [1] "Diffusion Models for Image Generation"
• [2] "Text-to-Image Generation with Diffusion Models"
• [3] "Relation Inversion from Images"