Yanran Zhang, Wenzhao Zheng^†, Yifei Li, Bingyao Yu, Yu Zheng, Lei Chen, Jie Zhou^*, Jiwen Lu
Department of Automation, Tsinghua University, China
^*Corresponding author ^†Project leader

UniGenDet is a unified co-evolutionary framework that jointly optimizes image generation and generated-image detection in a single loop. By bridging generation and authenticity understanding through symbiotic multimodal self-attention, UniGenDet turns the traditional "generator vs. detector" arms race into a closed-loop collaboration.

This repository hosts the fine-tuned model weights for UniGenDet.

🔗 Links

GitHub Repository (Code & Detailed Instructions): Zhangyr2022/UniGenDet
Paper (arXiv): 2604.21904
Project Website: UniGenDet Project Page

🚀 Getting Started

The UniGenDet model supports two main tasks:

Text-to-Image Generation (t2i)
AI-Generated Image Detection and Explanation (detection)

To use these weights for generation, detection, or further fine-tuning, please refer to the official GitHub repository. The repository provides a comprehensive demo.py script for interactive inference.

Quick Inference Example Setup:

Clone the GitHub repository: git clone https://github.com/Zhangyr2022/UniGenDet.git
Install dependencies as outlined in the repo's README.md.
Download the base BAGEL pretrained assets.
Run demo.py pointing to this Hugging Face model directory.

For complete installation, data preparation, training (GDUF/DIGA), and evaluation instructions, please consult the main GitHub repository.

Citation

bibtex

@article{zhang2026unigendet,
  title   = {UniGenDet: A Unified Generative-Discriminative Framework for Co-Evolutionary Image Generation and Generated Image Detection},
  author  = {Zhang, Yanran and Zheng, Wenzhao and Li, Yifei and Yu, Bingyao and Zheng, Yu and Chen, Lei and Zhou, Jie and Lu, Jiwen},
  journal = {CoRR},
  volume  = {abs/2604.21904},
  year    = {2026},
  url     = {[https://arxiv.org/abs/2604.21904](https://arxiv.org/abs/2604.21904)},
}

Yanran21/UniGenDet

[CVPR 2026] UniGenDet: A Unified Generative-Discriminative Framework

🔗 Links

🚀 Getting Started

Citation

Yanran21/
UniGenDet