T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level
  and Token-level CoT
Recent advancements in large language models have demonstrated how
chain-of-thought (CoT) and reinforcement learning (RL) can improve performance.
However, applying such reasoning strategies to the visual generation domain
remains largely unexplored. In this paper, we present T2I-R1, a novel
reasoning-enhanced text-to-image generation model, powered by RL with a
bi-level CoT reasoning process. Specifically, we identify two levels of CoT
that can be utilized to enhance different stages of generation: (1) the
semantic-level CoT for high-level planning of the prompt and (2) the
token-level CoT for low-level pixel processing during patch-by-patch
generation. To better coordinate these two levels of CoT, we introduce
BiCoT-GRPO with an ensemble of generation rewards, which seamlessly optimizes
both generation CoTs within the same training step. By applying our reasoning
strategies to the baseline model, Janus-Pro, we achieve superior performance
with 13% improvement on T2I-CompBench and 19% improvement on the WISE
benchmark, even surpassing the state-of-the-art model FLUX.1. Code is available
at: https://github.com/CaraJ7/T2I-R1
681428debcdf962d03da2797
Dongzhi Jiang
claimed_verified
2025-05-04T10:05:18.482Z
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/avatars/cfd24caac9a87efb528d0f4c375932bc.svg
Dongzhi Jiang
user
CaraJ
681428debcdf962d03da2798
Ziyu Guo
admin_assigned
2025-05-05T07:44:41.980Z
647d9ab61a1fcad2fdbf2d3d
/avatars/48c8aeae8979d2c87df8bde922437d62.svg
Ziyu Guo
user
ZiyuG
681428debcdf962d03da2799
Renrui Zhang
681428debcdf962d03da279a
Zhuofan Zong
admin_assigned
2025-05-05T07:44:57.694Z
64db05f23725f8d9a9f566c4
/avatars/345b6d2dfadb91ac1f02be023dd27c8f.svg
Zhuofan Zong
user
zongzhuofan
681428debcdf962d03da279b
Hao Li
681428debcdf962d03da279c
Le Zhuo
admin_assigned
2025-05-05T07:45:04.806Z
6358a167f56b03ec9147074d
/avatars/e54ea7bf0c240cf76d538296efb3976c.svg
Le Zhuo
user
JackyZhuo
681428debcdf962d03da279d
Shilin Yan
admin_assigned
2025-05-05T07:45:11.106Z
65273fea0ef49cfb783fa5c1
/avatars/0c9e204bc2151c8cc533311900d05a36.svg
shilinyan
user
shilinyan
681428debcdf962d03da279e
Pheng-Ann Heng
681428debcdf962d03da279f
Hongsheng Li
admin_assigned
2025-05-05T07:45:27.964Z
65c04e9c27a5fdca81abcbd9
/avatars/12a155683c824fa23da4a9e2bed4f64e.svg
Hongsheng LI
user
hsli-cuhk
T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level
  and Token-level CoT
Recent advancements in large language models have demonstrated how
chain-of-thought (CoT) and reinforcement learning (RL) can improve performance.
However, applying such reasoning strategies to the visual generation domain
remains largely unexplored. In this paper, we present T2I-R1, a novel
reasoning-enhanced text-to-image generation model, powered by RL with a
bi-level CoT reasoning process. Specifically, we identify two levels of CoT
that can be utilized to enhance different stages of generation: (1) the
semantic-level CoT for high-level planning of the prompt and (2) the
token-level CoT for low-level pixel processing during patch-by-patch
generation. To better coordinate these two levels of CoT, we introduce
BiCoT-GRPO with an ensemble of generation rewards, which seamlessly optimizes
both generation CoTs within the same training step. By applying our reasoning
strategies to the baseline model, Janus-Pro, we achieve superior performance
with 13% improvement on T2I-CompBench and 19% improvement on the WISE
benchmark, even surpassing the state-of-the-art model FLUX.1. Code is available
at: https://github.com/CaraJ7/T2I-R1
T2I-R1, a reasoning-enhanced text-to-image generator using RL and bi-level chain-of-thought reasoning, improves performance by 13% on T2I-CompBench and 19% on WISE compared to Janus-Pro.
chain-of-thought reinforcement learning text-to-image generation semantic-level CoT token-level CoT BiCoT-GRPO generation rewards T2I-CompBench WISE FLUX