library_name: transformers
license: apache-2.0
datasets:
- Franklin0/ReasonGen-R1-RL-Geneval-12k
- Franklin0/ReasonGen-R1-RL-DPG-5k
- Franklin0/ReasonGen-R1-RL-T2I-11k
base_model:
- deepseek-ai/Janus-Pro-7B
Model Card for Model ID
An autoregressive image generation with text-based chain-of-thought. Official checkpoint for the paper "ReasonGen-R1: Cot for Autoregressive Image generation models through SFT and RL".
Website: https://aka.ms/reasongen
Code: https://github.com/Franklin-Zhang0/Image-RL
🚀 ReasonGen-R1:
Cot for Autoregressive Image generation models through SFT and RL
📥 Model Download |
⚡ Quick Start |
📜 Acknowledgement |
📖 Citation
📄 Paper Link
1. Introduction
Although chain-of-thought (CoT) reasoning and reinforcement learning (RL) have driven breakthroughs in NLP, their integration into generative vision models remains underexplored. We introduce ReasonGen-R1, a two-stage framework that first imbues an autoregressive image generator with explicit text-based “thinking” skills via supervised fine-tuning (SFT) on a newly generated reasoning dataset of written rationales, and then refines its outputs using Generation-Reward Proximal Optimization (GRPO). Text-based CoT reasoning dataset for image synthesis. We automatically generate and release a corpus of step-by-step, model-crafted rationales paired with visual prompts, enabling controlled planning of object layouts, styles, and scene compositions. RL refinement with GRPO. Our GRPO algorithm uses reward signals from a pretrained vision–language model to assess overall visual quality, optimizing the policy in each update. Evaluations on Geneval, DPG, and the T2I benchmark demonstrate that ReasonGen-R1 consistently outperforms strong baselines and prior state-of-the-art models. We will open-source our generated reasoning dataset and training code to accelerate further advances in text-based reasoning–driven image generation.
2. Model Download
Huggingface
| Model | Download |
|---|---|
| ReasonGen-R1 | 🤗 Hugging Face |
| ReasonGen-R1-SFT-Only | 🤗 Hugging Face |
3. Quick Start
Installation
You can install the necessary dependencies by running the following command:
cd ~
mkdir project
cd project
conda create -n image_rl python==3.12 -y
conda activate image_rl
pip3 install torch==2.6.0 torchvision --index-url https://download.pytorch.org/whl/cu124
pip3 install flash-attn --no-build-isolation
git clone https://github.com/Franklin-Zhang0/Image-RL.git
cd Image-RL
pip install -r requirements.txt
pip install -e .
pip install -e ./Janus
Evaluation Environment Installation (Optional)
If you want to run the evaluation code, you can install the evaluation environment by running the following commands:
# Geneval
cd ~
mkdir project
cd project
git clone https://github.com/djghosh13/geneval.git
cd geneval
conda deactivate
conda create -n geneval python=3.9 -y
conda activate geneval
pip install torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1
pip install mmcv-full==1.7.0 -f https://download.openmmlab.com/mmcv/dist/cu117/torch1.13/index.html
pip install mmengine==0.7.3
pip install pandas
pip install numpy==1.23.1
pip install open-clip-torch
pip install clip-benchmark
git clone https://github.com/open-mmlab/mmdetection.git
cd mmdetection; git checkout 2.x
pip install -v -e .
cd ../
bash ./evaluation/download_models.sh "./models"
# DPG
cd ~
cd project
git clone https://github.com/TencentQQGYLab/ELLA.git
cd ELLA
cp ~/project/ReasonGen-R1/requirements-for-dpg_bench.txt .
conda deactivate
conda create -n dpg_test python=3.9 -y
conda activate dpg_test
conda install conda-forge::fairseq -y
pip install -r requirements-for-dpg_bench.txt
Once the eval environment is setup, you can use the following commands to run the evaluation:
bash -i benchmark/geneval.sh
bash -i benchmark/dpg_eval.sh
Inference
To inference with the ReasonGen-R1 model, you can use the following command:
python Image-RL/Janus/cot_generate_inference.py
SFT Training
To train the SFT model from Janus-Pro-7B model on the ReasonGen-R1-SFT-200k dataset, you can use the following command:
bash Image-RL/examples/janus_sft.sh
RL Training
To train the RL model from the ReasonGen-R1-SFT model, you can use the following command:
bash Image-RL/Janus/janus_rl.py
4. Acknowledgements
We would like to thank Verl, upon which our repo is built.
5. Citation
@article{yu2025reasongen,
title={ReasonGen-R1: Cot for Autoregressive Image generation models through SFT and RL},
author={Yu Zhang, Yunqi Li, Yifan Yang, Rui Wang， Yuqin Yang, Qi Dai, Jianming Bao, Dongdong Chen, Chong Luo, Lili Qiu},
year={2025}
}