Add model card for GoT-R1-1B

README.md

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+---
+license: mit
+pipeline_tag: text-to-image
+---
+
+# GoT-R1-1B
+
+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).
+
+## Introduction
+
+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.
+
+- **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.
+
+## Resources
+
+- **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)
+
+## Usage
+
+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:
+
+```bash
+python infer.py --ckpt_path <Your GoT-R1 checkpoint path>
+```
+
+## Citation
+
+If you find this work helpful, please consider citing the paper:
+
+```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}
+}
+```