---
datasets:
- CSU-JPG/VisPrompt5M
- CSU-JPG/VPBench
language:
- en
license: apache-2.0
pipeline_tag: image-to-image
tags:
- flow-matching
- image-generation
- image-editing
- vision-centric
---

<div align="center">
  <h2 align="center" style="margin-top: 0; margin-bottom: 15px;">
    <span style="color:#0052CC">F</span><span style="color:#135FD0">l</span><span style="color:#266CD4">o</span><span style="color:#3979D7">w</span><span style="color:#4C86DB">I</span><span style="color:#6093DF">n</span><span style="color:#73A0E3">O</span><span style="color:#86ADE7">n</span><span style="color:#99BAEB\">e</span>: Unifying Multimodal Generation as 
    <span style="color:#0052CC">I</span><span style="color:#0958CE">m</span><span style="color:#125ED0">a</span><span style="color:#1B64D2">g</span><span style="color:#246AD4">e</span><span style="color:#2D70D6">-</span><span style="color:#3676D8">i</span><span style="color:#3F7CDA\">n</span><span style="color:#4882DC">,</span>&nbsp;<span style="color:#5188DE">I</span><span style="color:#5A8EE0\">m</span><span style="color:#6394E2\">a</span><span style="color:#6C9AE4\">g</span><span style="color:#75A0E6\">e</span><span style="color:#7EA6E8">-</span><span style="color:#87ACEA\">o</span><span style="color:#90B2EC\">u</span><span style="color:#99B8EE\">t</span> Flow Matching
  </h2>
  <p align="center" style="font-size: 15px;">
    <span style="color:#E74C3C; font-weight: bold;">TL;DR:</span> <strong>The first vision-centric image-in, image-out image generation model.</strong>
  </p>
  <p align="center" style="font-size: 16px;">
    <a href="https://csu-jpg.github.io/FlowInOne.github.io/" style="text-decoration: none;">🌐 Homepage</a> | 
    <a href="https://github.com/CSU-JPG/FlowInOne" style="text-decoration: none;">💻 Code</a> | 
    <a href="https://huggingface.co/papers/2604.06757" style="text-decoration: none;">📄 Paper</a> | 
    <a href="https://huggingface.co/datasets/CSU-JPG/VisPrompt5M" style="text-decoration: none;">📁 Dataset</a> | 
    <a href="https://huggingface.co/datasets/CSU-JPG/VPBench" style="text-decoration: none;">🌏 Benchmark</a> | 
    <a href="https://huggingface.co/CSU-JPG/FlowInOne" style="text-decoration: none;">🤗 Model</a>
</p>
</div>

## Authors
Junchao Yi, Rui Zhao, Jiahao Tang, Weixian Lei, Linjie Li, Qisheng Su, Zhengyuan Yang, Lijuan Wang, Xiaofeng Zhu, Alex Jinpeng Wang.

## About
FlowInOne is a framework that reformulates multimodal generation as a **purely visual flow**, converting all inputs into visual prompts and enabling a clean **image-in, image-out** pipeline governed by a single flow matching model. 

This vision-centric formulation naturally eliminates cross-modal alignment bottlenecks, noise scheduling, and task-specific architectural branches, **unifying text-to-image generation, layout-guided editing, and visual instruction following under one coherent paradigm**.

## 🚀 Setup

```bash
# Create conda environment
conda create -n flowinone python=3.10 -y
conda activate flowinone

# Install required packages
git clone https://github.com/CSU-JPG/FlowInOne.git
cd FlowInOne/scripts
sh setup.sh
```

## ✨ Usage

### 1. Download Weights
You can download the model weights and model preparation files using the following commands:
```bash
# model weights
wget -O checkpoints/flowinone_256px.pth https://huggingface.co/CSU-JPG/FlowInOne/resolve/main/flowinone_256px.pth

# model preparation
wget https://huggingface.co/CSU-JPG/FlowInOne/resolve/main/preparation.tar.gz
tar -xzvf "preparation.tar.gz"
```

### 2. Inference
Run inference with the provided script in the repository:
```bash
sh scripts/inference.sh
```

Our training and inference scripts are fully available on [GitHub](https://github.com/CSU-JPG/FlowInOne).

## Citation

If you found our work useful, please consider citing:
```bibtex
@article{yi2026flowinoneunifyingmultimodalgenerationimagein,
      title={FlowInOne:Unifying Multimodal Generation as Image-in, Image-out Flow Matching}, 
      author={Junchao Yi and Rui Zhao and Jiahao Tang and Weixian Lei and Linjie Li and Qisheng Su and Zhengyuan Yang and Lijuan Wang and Xiaofeng Zhu and Alex Jinpeng Wang},    
      journal={arXiv preprint arXiv:2604.06757},
      year={2026}
}
```