| license: apache-2.0 | |
| pipeline_tag: text-to-image | |
| library_name: transformers | |
| # RealGen: Photorealistic Text-to-Image Generation via Detector-Guided Rewards | |
| The model presented in the paper [RealGen: Photorealistic Text-to-Image Generation via Detector-Guided Rewards](https://huggingface.co/papers/2512.00473) proposes a photorealistic text-to-image framework. RealGen integrates an LLM component for prompt optimization and a diffusion model for realistic image generation. It introduces a "Detector Reward" mechanism, which quantifies artifacts and assesses realism using both semantic-level and feature-level synthetic image detectors. This reward signal is leveraged with the GRPO algorithm to optimize the entire generation pipeline, significantly enhancing image realism and detail. | |
| Project Page: https://yejy53.github.io/RealGen/ | |
| Code: https://github.com/yejy53/RealGen | |
| ## Detection Models | |
| RealGen utilizes specialized detection models to guide its generation process: | |
| - **Semantic Detector**: Forensic-Chat, a generalizable and interpretable detector optimized from Qwen2.5-VL-7B. It assesses authenticity by analyzing image content (e.g., smooth greasy skin, artifacts in faces/hands, unnatural background blur). | |
| - **Feature Detector**: OmniAID achieves stable and accurate detection by being pre-trained on large-scale real and synthetic datasets. Feature-level artifacts are primarily associated with frequency artifacts and abnormal noise patterns. | |
| ## Citation | |
| If you find our work helpful or inspiring, please feel free to cite it. | |
| ```bib | |
| @article{ye2025realgen, | |
| title={RealGen: Photorealistic Text-to-Image Generation via Detector-Guided Rewards}, | |
| author={Ye, Junyan and Zhu, Leqi and Guo, Yuncheng and Jiang, Dongzhi and Huang, Zilong and Zhang, Yifan and Yan, Zhiyuan and Fu, Haohuan and He, Conghui and Li, Weijia}, | |
| journal={arXiv preprint arXiv:2512.00473}, | |
| year={2025} | |
| } | |
| ``` |