| base_model: | |
| - deepseek-ai/Janus-Pro-7B | |
| license: apache-2.0 | |
| pipeline_tag: text-to-image | |
| library_name: transformers | |
| # 🌟🔥 T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoT | |
| Official checkpoint for the paper "[T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoT](https://arxiv.org/pdf/2505.00703)". | |
| ## Abstract | |
| 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: this https URL | |
| Code: https://github.com/CaraJ7/T2I-R1 | |
| # Usage | |
| Please refer to the instructions in GitHub to set up the repository first then run the inference script below. | |
| ``` | |
| cd t2i-r1/src/infer | |
| python reason_inference.py \ | |
| --model_path YOUR_MODEL_CKPT \ | |
| --data_path test_data.txt | |
| ``` |