| --- |
| license: mit |
| pipeline_tag: text-to-image |
| --- |
| |
| # GoT-R1-1B |
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| GoT-R1 is a framework that applies reinforcement learning to enhance semantic-spatial reasoning in visual generation, as presented in [GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation with Reinforcement Learning](https://huggingface.co/papers/2505.17022). |
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| ## Introduction |
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| Visual generation models often struggle with complex prompts specifying multiple objects with precise spatial relationships. **GoT-R1** addresses this by applying reinforcement learning to enhance semantic-spatial reasoning. Building upon the Generation Chain-of-Thought (GoT) approach, GoT-R1 enables models to autonomously discover effective reasoning strategies through a dual-stage multi-dimensional reward framework. |
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| - **Enhanced Semantic-Spatial Reasoning**: Uses RL to improve planning of complex scenes. |
| - **Autonomous Reasoning Chain Discovery**: Moves beyond fixed templates to allow the model to explore more effective reasoning paths. |
| - **Comprehensive MLLM-based Rewards**: Evaluates both the intermediate reasoning process and the final visual output. |
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| ## Resources |
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| - **GitHub Repository**: [https://github.com/gogoduan/GoT-R1](https://github.com/gogoduan/GoT-R1) |
| - **Paper**: [GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation with Reinforcement Learning](https://huggingface.co/papers/2505.17022) |
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| ## Usage |
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| For installation and setup, please refer to the [official GitHub repository](https://github.com/gogoduan/GoT-R1). To run inference using the provided script from the repository: |
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| ```bash |
| python infer.py --ckpt_path <Your GoT-R1 checkpoint path> |
| ``` |
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| ## Citation |
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| If you find this work helpful, please consider citing the paper: |
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| ```bibtex |
| @article{duan2025got, |
| title={GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation with Reinforcement Learning}, |
| author={Duan, Chengqi and Fang, General and Wang, Yuqing and Wang, Kun and Huang, Linjiang and Zeng, Xingyu and Li, Hongsheng and Liu, Xihui}, |
| journal={arXiv preprint arXiv:2505.17022}, |
| year={2025} |
| } |
| ``` |
