Add pipeline tag, library name, and abstract to model card

README.md

@@ -1,13 +1,17 @@
 ---
-license: apache-2.0
 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)".
 
-Paper: 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
 
@@ -18,5 +22,5 @@ Please refer to the instructions in GitHub to set up the repository first then r
 cd t2i-r1/src/infer
 python reason_inference.py \
 --model_path YOUR_MODEL_CKPT \
---data_path test_data.txt 
+--data_path test_data.txt
 ```