【第158期】图像生成CoT是什么样的

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今天的主题是：

Can We Generate Images with CoT? Let's Verify and Reinforce Image Generation Step by Step

Summary

This research explores enhancing autoregressive image generation using Chain-of-Thought (CoT) reasoning strategies commonly applied to language models. The study adapts techniques like test-time verification and preference alignment to improve image quality and text alignment. The authors introduce a Potential Assessment Reward Model (PARM) and PARM++ to better evaluate and refine image generation steps. PARM adaptively assesses potential during generation while PARM++ incorporates a reflection mechanism for self-correction. Experiments show significant improvements over existing methods, including Stable Diffusion, highlighting the potential of CoT reasoning in image generation. The authors provide insights into adapting these strategies and show the effectiveness of tailored reward models.

本研究探讨了如何利用 Chain-of-Thought (CoT) 思维链推理策略来增强自回归图像生成，这些策略通常应用于语言模型。研究采用 测试时验证 和 偏好对齐 等技术，以提高图像质量和文本对齐度。作者提出了 潜在性评估奖励模型（PARM） 及其增强版本 PARM++，用于优化图像生成过程。PARM 在生成过程中自适应地评估潜在质量，而 PARM++ 进一步引入反思机制，实现自我修正。实验结果表明，该方法相较于现有技术（包括 Stable Diffusion）具有显著优势，验证了 CoT 推理 在图像生成中的潜力。研究还深入探讨了如何调整这些策略，并展示了定制化奖励模型的有效性。

原文链接：https://arxiv.org/abs/2501.13926