T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoT
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909669553864704 |
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| author | Jiang, Dongzhi Guo, Ziyu Zhang, Renrui Zong, Zhuofan Li, Hao Zhuo, Le Yan, Shilin Heng, Pheng-Ann Li, Hongsheng |
| author_facet | Jiang, Dongzhi Guo, Ziyu Zhang, Renrui Zong, Zhuofan Li, Hao Zhuo, Le Yan, Shilin Heng, Pheng-Ann Li, Hongsheng |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_00703 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoT Jiang, Dongzhi Guo, Ziyu Zhang, Renrui Zong, Zhuofan Li, Hao Zhuo, Le Yan, Shilin Heng, Pheng-Ann Li, Hongsheng Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Machine Learning 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 |
| title | T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoT |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2505.00703 |