Summary of Preference Alignment For Diffusion Model Via Explicit Denoised Distribution Estimation, by Dingyuan Shi et al.
Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation
by Dingyuan Shi, Yong Wang, Hangyu Li, Xiangxiang Chu
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 The paper proposes Denoised Distribution Estimation (DDE) to optimize intermediate denoising steps in diffusion models for text-to-image generation. The approach connects intermediate steps to the terminal denoised distribution, enabling preference labels to be used for entire trajectory optimization. Two estimation strategies are designed: stepwise estimation and single-shot estimation. These methods derive a novel credit assignment scheme that prioritizes optimizing the middle part of the denoising trajectory. Experimental results demonstrate superior performance in both quantitative and qualitative evaluations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps make better images from text using computers. It’s like giving a recipe to a machine so it can create a picture based on what you wrote. The problem is that these machines don’t always follow the instructions correctly, so the paper finds a way to help them by connecting small steps together. This lets us use feedback to improve how well they do their job. The new method does better than before in making both good and bad images. |
Keywords
» Artificial intelligence » Diffusion » Image generation » Optimization
