Can We Generate Images with CoT? Let's Verify and Reinforce Image
  Generation Step by Step
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
6793040ec67af4a116a25d05
Ziyu Guo
admin_assigned
2025-01-24T10:07:58.258Z
647d9ab61a1fcad2fdbf2d3d
/avatars/48c8aeae8979d2c87df8bde922437d62.svg
Ziyu Guo
user
ZiyuG
6793040ec67af4a116a25d06
Renrui Zhang
6793040ec67af4a116a25d07
Chengzhuo Tong
6793040ec67af4a116a25d08
Zhizheng Zhao
admin_assigned
2025-01-24T10:08:20.272Z
6713a71e7dfe714b425cccfb
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/95YYcbv_f6J8yWTunwn4z.png
zhizhengzhao
user
zhizhengzhao
6793040ec67af4a116a25d09
Peng Gao
admin_assigned
2025-01-24T10:08:26.816Z
6759af3eccbc8817f9169179
/avatars/49e64c7ccf71b8f25c52783b6ae93620.svg
Peng Gao
user
gaopenghigh
6793040ec67af4a116a25d0a
Hongsheng Li
admin_assigned
2025-01-24T10:08:33.312Z
65c04e9c27a5fdca81abcbd9
/avatars/12a155683c824fa23da4a9e2bed4f64e.svg
Hongsheng LI
user
hsli-cuhk
6793040ec67af4a116a25d0b
Pheng-Ann Heng
Can We Generate Images with CoT? Let's Verify and Reinforce Image
  Generation Step by Step
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
Chain-of-Thought reasoning techniques enhance autoregressive image generation by improving verification, aligning model preferences, and using specialized reward models like PARM and PARM++.
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