Summary of Inference-time Alignment Of Diffusion Models with Direct Noise Optimization, by Zhiwei Tang et al.
Inference-Time Alignment of Diffusion Models with Direct Noise Optimization
by Zhiwei Tang, Jiangweizhi Peng, Jiasheng Tang, Mingyi Hong, Fan Wang, Tsung-Hui Chang
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 addresses the alignment problem in diffusion models, where the goal is to adjust the distribution learned by these models such that generated samples maximize a target continuous reward function. The authors propose Direct Noise Optimization (DNO), an online and prompt-agnostic method that optimizes injected noise during sampling. DNO operates at inference-time, making it tuning-free, and can handle non-differentiable reward functions. However, naive implementation may suffer from the out-of-distribution reward hacking problem. To address this, the authors leverage high-dimensional statistics to develop a probability regularization technique. They demonstrate state-of-the-art results on several important reward functions within a reasonable time budget. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem in making images or videos that meet certain goals, like being darker or more beautiful. The goal is to make the computer-generated images match what we want them to look like. The authors create a new way called Direct Noise Optimization (DNO) that adjusts the noise during image generation to fit our desired outcome. This method works well and can even handle tricky reward functions that aren’t easy to understand. However, there’s a potential problem where the generated images might not be what we want anymore. To fix this, the authors use some clever math to keep the generated images in line with our goals. |
Keywords
» Artificial intelligence » Alignment » Image generation » Inference » Optimization » Probability » Prompt » Regularization
