Add pipeline tag, library name, and abstract to model card (#1)
Browse files- Add pipeline tag, library name, and abstract to model card (a41f54c6973df24e2b153f8bd74a151cb4197afe)
Co-authored-by: Niels Rogge <[email protected]>
README.md
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license: apache-2.0
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base_model:
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- deepseek-ai/Janus-Pro-7B
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---
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# 🌟🔥 T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoT
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Official checkpoint for the paper "[T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoT](https://arxiv.org/pdf/2505.00703)".
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Code: https://github.com/CaraJ7/T2I-R1
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cd t2i-r1/src/infer
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python reason_inference.py \
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--model_path YOUR_MODEL_CKPT \
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--data_path test_data.txt
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```
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---
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base_model:
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- deepseek-ai/Janus-Pro-7B
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license: apache-2.0
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pipeline_tag: text-to-image
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library_name: transformers
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---
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# 🌟🔥 T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoT
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Official checkpoint for the paper "[T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoT](https://arxiv.org/pdf/2505.00703)".
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## Abstract
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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
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Code: https://github.com/CaraJ7/T2I-R1
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cd t2i-r1/src/infer
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python reason_inference.py \
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--model_path YOUR_MODEL_CKPT \
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--data_path test_data.txt
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```
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