Summary of Diff-instruct++: Training One-step Text-to-image Generator Model to Align with Human Preferences, by Weijian Luo
Diff-Instruct++: Training One-step Text-to-image Generator Model to Align with Human Preferences
by Weijian Luo
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 addresses the problem of aligning one-step text-to-image generator models with human preferences. By formulating the alignment problem as maximizing expected human reward functions while adding an Integral Kullback-Leibler divergence term, the authors introduce Diff-Instruct++ (DI++), a novel method for one-step text-to-image generators that achieves fast convergence and does not require image data. The paper also provides theoretical insights on the connection between CFG for diffusion distillation and RLHF with DI++. Experimental results show that the DiT-based one-step text-to-image model achieves strong aesthetic scores, image rewards, and human preference scores on the COCO validation prompt dataset, outperforming other open-sourced models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary One-step text-to-image generator models are getting better at creating images from text. But how do we make sure they create images that people like? This paper tries to answer this question by coming up with a new way to align these generators with what humans prefer. The method, called Diff-Instruct++, is fast and doesn’t need any image data. It’s also connected to another idea in AI research that helps explain how it works. When the authors tested their method, they found that it did very well on creating images that people like. |
Keywords
» Artificial intelligence » Alignment » Diffusion » Distillation » Prompt » Rlhf
