Update README.md

README.md

@@ -1,199 +1,185 @@
 ---
 library_name: transformers
-tags: []
+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
 
-<!-- Provide a quick summary of what the model is/does. -->
+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](xxx)".
 
+Website: https://aka.ms/reasongen
 
+Code: https://github.com/Franklin-Zhang0/Image-RL
 
-## Model Details
 
-### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+<div align="center">
+<h1>🚀 ReasonGen-R1: Cot for Autoregressive Image generation models through SFT and RL</h1>
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+</div>
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+<div align="center">
 
-### Model Sources [optional]
+  <a href="https://www.deepseek.com/" target="_blank">
+    <img alt="Homepage" src="https://img.shields.io/badge/HomePage-blue" />
+  </a>
+  </a>
+  <a href="https://huggingface.co/collections/Franklin0/reasongen-r1-6836ed61fc4f6db543c0d368" target="_blank">
+    <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-ReasonGen%20R1-ffc107?color=ffc107&logoColor=white" />
+  </a>
 
-<!-- Provide the basic links for the model. -->
+</div>
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 
-## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-### Direct Use
+<p align="center">
+  <a href="#2-model-download"><b>📥 Model Download</b></a> |
+  <a href="#3-quick-start"><b>⚡ Quick Start</b></a> |
+  <a href="#4-license"><b>📜 License</b></a> |
+  <a href="#5-citation"><b>📖 Citation</b></a> <br>
+  📄 <a href="xxxxxx"><b>Paper Link</b></a>
+  <!-- 🤗 Online Demo (<a href="https://huggingface.co/spaces/deepseek-ai/Janus-Pro-7B"><b>Janus-Pro-7B</b></a>, <a href="https://huggingface.co/spaces/deepseek-ai/Janus-1.3B"><b>Janus</b></a>, <a href="https://huggingface.co/spaces/deepseek-ai/JanusFlow-1.3B"><b>JanusFlow</b></a>) -->
+</p>
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+## 1. Introduction
 
-[More Information Needed]
+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.
 
-### Downstream Use [optional]
+<div align="center">
+<img alt="image" src="images/model_structure_white_bg.png" style="width:90%;">
+<br>
+<img alt="image" src="images/benchmark_and_comparison_white_bg.png" style="width:90%; margin-top: 10px;">
+</div>
+ 
+
+## 2. Model Download
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+We release ReasonGen-R1 to the public to support a broader and more diverse range of research within both academic and commercial communities.
+Please note that the use of this model is subject to the terms outlined in [License section](#5-license). Commercial usage is
+permitted under these terms.
+
+### Huggingface
 
-[More Information Needed]
+| Model                 | Download                                                                    |
+|-----------------------|-----------------------------------------------------------------------------|
+| ReasonGen-R1 | [🤗 Hugging Face](https://huggingface.co/Franklin0/ReasonGen-R1) |
+| ReasonGen-R1-SFT-Only | [🤗 Hugging Face](https://huggingface.co/Franklin0/ReasonGen-R1-SFT) |
 
-### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-[More Information Needed]
+## 3. Quick Start
 
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+### Installation
+
+You can install the necessary dependencies by running the following command:
+
+```shell
+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
+```
+
+<details>
+<summary><h3>Evaluation Environment Installation (Optional)</h3></summary>
+If you want to run the evaluation code, you can install the evaluation environment by running the following commands:
+
+```shell
+# 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"
+```
+
+```shell
+# 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:
+```shell
+bash -i benchmark/geneval.sh
+bash -i benchmark/dpg_eval.sh
+```
+</details>
+
+### Inference
+To inference with the ReasonGen-R1 model, you can use the following command:
+```shell
+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:
+```shell
+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:
+```shell
+bash Image-RL/Janus/janus_rl.py
+```
+
+
+## 5. Acknowledgements
+We would like to thank <a href="https://github.com/volcengine/verl">Verl</a>, upon which our repo is built.
+
+## 4. Citation
+
+```bibtex
+@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}
+}
+```