title: Can We Generate Images with CoT? Let's Verify and Reinforce Image
    Generation Step by Step
summary: 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
paper_authors._id: 6793040ec67af4a116a25d05
paper_authors.name: Ziyu Guo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-24T10:07:58.258Z
paper_authors._id: 647d9ab61a1fcad2fdbf2d3d
paper_authors.avatarUrl: /avatars/48c8aeae8979d2c87df8bde922437d62.svg
paper_authors.fullname: Ziyu Guo
paper_authors.type: user
paper_authors.user: ZiyuG
paper_authors._id: 6793040ec67af4a116a25d06
paper_authors.name: Renrui Zhang
paper_authors._id: 6793040ec67af4a116a25d07
paper_authors.name: Chengzhuo Tong
paper_authors._id: 6793040ec67af4a116a25d08
paper_authors.name: Zhizheng Zhao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-24T10:08:20.272Z
paper_authors._id: 6713a71e7dfe714b425cccfb
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/95YYcbv_f6J8yWTunwn4z.png
paper_authors.fullname: zhizhengzhao
paper_authors.type: user
paper_authors.user: zhizhengzhao
paper_authors._id: 6793040ec67af4a116a25d09
paper_authors.name: Peng Gao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-24T10:08:26.816Z
paper_authors._id: 6759af3eccbc8817f9169179
paper_authors.avatarUrl: /avatars/49e64c7ccf71b8f25c52783b6ae93620.svg
paper_authors.fullname: Peng Gao
paper_authors.type: user
paper_authors.user: gaopenghigh
paper_authors._id: 6793040ec67af4a116a25d0a
paper_authors.name: Hongsheng Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-24T10:08:33.312Z
paper_authors._id: 65c04e9c27a5fdca81abcbd9
paper_authors.avatarUrl: /avatars/12a155683c824fa23da4a9e2bed4f64e.svg
paper_authors.fullname: Hongsheng LI
paper_authors.type: user
paper_authors.user: hsli-cuhk
paper_authors._id: 6793040ec67af4a116a25d0b
paper_authors.name: Pheng-Ann Heng
paper_title: Can We Generate Images with CoT? Let's Verify and Reinforce Image
    Generation Step by Step
paper_summary: 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
paper_ai_summary: Chain-of-Thought reasoning techniques enhance autoregressive image generation by improving verification, aligning model preferences, and using specialized reward models like PARM and PARM++.
paper_ai_keywords: Chain-of-Thought reasoning autoregressive image generation scaling test-time computation Direct Preference Optimization potential assessment reflection mechanism reward models PARM PARM++ Show-o GenEval benchmark Stable Diffusion 3