Summary of Diff-instruct*: Towards Human-preferred One-step Text-to-image Generative Models, by Weijian Luo and Colin Zhang and Debing Zhang and Zhengyang Geng
Diff-Instruct*: Towards Human-Preferred One-step Text-to-image Generative Models
by Weijian Luo, Colin Zhang, Debing Zhang, Zhengyang Geng
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM)
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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 introduces Diff-Instruct* (DI), a novel approach for building one-step text-to-image generative models that align with human preference and generate highly realistic images. The method frames human preference alignment as online reinforcement learning using human feedback, which is regularized by a score-based divergence regularization to maintain the generator distribution close to a reference diffusion process. The paper demonstrates the effectiveness of DI in generating high-quality images with only one generation step, outperforming existing models on multiple benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way for computers to create realistic pictures from text descriptions. It uses a special kind of learning called reinforcement learning that helps the computer learn what makes human-preferred images. The method is tested and shown to be much better than other approaches, generating high-quality images with just one step. This could have many applications in areas like art, entertainment, and education. |
Keywords
» Artificial intelligence » Alignment » Diffusion » Regularization » Reinforcement learning
