Authors: Ziyu Guo, Renrui Zhang, Chengzhuo Tong, Zhizheng Zhao, Peng Gao, Hongsheng Li, Pheng-Ann Heng
Abstract: Chain-of-Thought (CoT) reasoning has been extensively explored in large
models to tackle complex understanding tasks. However, it still remains an open
question whether such strategies can be applied to verifying and reinforcing
image generation scenarios. In this paper, we provide the first comprehensive
investigation of the potential of CoT reasoning to enhance autoregressive image
generation. We focus on three techniques: scaling test-time computation for
verification, aligning model preferences with Direct Preference Optimization
(DPO), and integrating these techniques for complementary effects. Our results
demonstrate that these approaches can be effectively adapted and combined to
significantly improve image generation performance. Furthermore, given the
pivotal role of reward models in our findings, we propose the Potential
Assessment Reward Model (PARM) and PARM++, specialized for autoregressive image
generation. PARM adaptively assesses each generation step through a potential
assessment approach, merging the strengths of existing reward models, and
PARM++ further introduces a reflection mechanism to self-correct the generated
unsatisfactory image. Using our investigated reasoning strategies, we enhance a
baseline model, Show-o, to achieve superior results, with a significant +24%
improvement on the GenEval benchmark, surpassing Stable Diffusion 3 by +15%. We
hope our study provides unique insights and paves a new path for integrating
CoT reasoning with autoregressive image generation. Code and models are
released at https://github.com/ZiyuGuo99/Image-Generation-CoT
Source: http://arxiv.org/abs/2501.13926v1
