Summary of Unveiling Concept Attribution in Diffusion Models, by Quang H. Nguyen et al.
Unveiling Concept Attribution in Diffusion Models
by Quang H. Nguyen, Hoang Phan, Khoa D. Doan
First submitted to arxiv on: 3 Dec 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 explores the interpretability of diffusion models, specifically diffusion-based image generation from text prompts. The authors pose a question: “How do model components work jointly to demonstrate knowledge?” To answer this, they propose Component Attribution for Diffusion Models (CAD), a framework that decomposes diffusion models and reveals the importance of each component in generating a concept. CAD uncovers not only positive components that contribute to generating a concept but also negative components that hinder it. The authors introduce two inference-time model editing algorithms: CAD-Erase, which erases generated concepts, and CAD-Amplify, which amplifies them while retaining knowledge of other concepts. Experimental results validate the significance of both positive and negative components, highlighting the potential for providing a complete view of interpreting generative models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you can ask a computer to create an image from words, like “a sunny day with a beach”. Computers are getting better at this task, but we don’t really know how they do it. Researchers wanted to understand what parts of the computer program make this magic happen. They created a new way to see which parts of the program help or hurt the image generation process. This new approach showed that some parts of the program are super important for creating certain concepts, like objects or styles. The researchers also developed ways to edit these generated images in real-time, making them more realistic or erasing unwanted details. |
Keywords
» Artificial intelligence » Diffusion » Image generation » Inference
