---
license: mit
pipeline_tag: text-to-image
---

# GoT-R1-7B

GoT-R1-7B is a multimodal large language model (MLLM) designed for high-quality text-to-image generation with advanced semantic-spatial reasoning, as introduced in the paper [GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation with Reinforcement Learning](https://huggingface.co/papers/2505.17022).

- **Paper:** [GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation with Reinforcement Learning](https://huggingface.co/papers/2505.17022)
- **Repository:** [https://github.com/gogoduan/GoT-R1](https://github.com/gogoduan/GoT-R1)

## Overview

Visual generation models often struggle with complex prompts that specify multiple objects with precise spatial relationships and attributes. 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. The model uses a unified MLLM architecture (based on Janus-Pro) that autoregressively generates a textual reasoning chain followed by image tokens.

## Usage

To use this model, please follow the installation instructions in the [official GitHub repository](https://github.com/gogoduan/GoT-R1). Inference can be performed using the provided script:

```bash
python infer.py --ckpt_path <path-to-GoT-R1-7B-weights>
```

## Citation

```bibtex
@article{duan2025got,
  title={GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation with Reinforcement Learning},
  author={Duan, Chengqi and Fang, Rongyao 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}
}
```