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

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arxiv 2501.13926 v2 pith:BAXQJPSA submitted 2025-01-23 cs.CV cs.AIcs.CL

Ziyu Guo , Renrui Zhang , Chengzhuo Tong , Zhizheng Zhao , Rui Huang , Haoquan Zhang , Manyuan Zhang , Jiaming Liu
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Shanghang Zhang Peng Gao Hongsheng Li Pheng-Ann Heng
This is my paper
classification cs.CVcs.AIcs.CL
keywords generationimageautoregressivemodelsparmreasoningmodelpotential
verification ladder T0 review T1 audit T2 compute T3 formal
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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, which is the first to incorporate reflection in autoregressive image generation. 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

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Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    Early Generation Intent and Visual Fidelity signals let AdaViG abort low-utility intermediate image generations in UMM math CoT, improving accuracy up to 5.7% and cutting visual FLOPs 25–91%.

  4. Reconstruction Alignment Improves Unified Multimodal Models

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    RECA, a self-supervised post-training objective that conditions unified multimodal models on their own visual understanding embeddings to reconstruct input images, improves text-to-image and editing benchmarks across ...

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