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
