Summary of Measuring Style Similarity in Diffusion Models, by Gowthami Somepalli et al.
Measuring Style Similarity in Diffusion Models
by Gowthami Somepalli, Anubhav Gupta, Kamal Gupta, Shramay Palta, Micah Goldblum, Jonas Geiping, Abhinav Shrivastava, Tom Goldstein
First submitted to arxiv on: 1 Apr 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 This paper presents a framework for understanding and extracting style descriptors from images, which is crucial in generative models used by graphic designers and artists. The authors propose a new dataset curated using the insight that style is a subjective property of an image, capturing complex interactions of factors like colors, textures, and shapes. They also introduce a method to extract style descriptors that can be used to attribute the style of a generated image to the training images of a text-to-image model. The authors showcase promising results in various style retrieval tasks and quantitatively analyze style attribution and matching in the Stable Diffusion model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Generative models are used by graphic designers and artists, but they can remember and replicate content from their training data during generation. Before using a generated image for professional purposes, it’s important to know if its properties come from specific training data. Existing tools focus on similar semantic content, but artists are concerned with style replication in text-to-image models. This paper presents a way to understand and extract style descriptors from images, which can help attribute the style of a generated image to its training data. The authors show promising results and analyze the Stable Diffusion model. |
Keywords
» Artificial intelligence » Diffusion model
