RealGen: Photorealistic Text-to-Image Generation via Detector-Guided Rewards
With the continuous advancement of image generation technology, advanced models such as GPT-Image-1 and Qwen-Image have achieved remarkable text-to-image consistency and world knowledge However, these models still fall short in photorealistic image generation. Even on simple T2I tasks, they tend to produce " fake" images with distinct AI artifacts, often characterized by "overly smooth skin" and "oily facial sheens". To recapture the original goal of "indistinguishable-from-reality" generation, we propose RealGen, a photorealistic text-to-image framework. RealGen integrates an LLM component for prompt optimization and a diffusion model for realistic image generation. Inspired by adversarial generation, RealGen introduces a "Detector Reward" mechanism, which quantifies artifacts and assesses realism using both semantic-level and feature-level synthetic image detectors. We leverage this reward signal with the GRPO algorithm to optimize the entire generation pipeline, significantly enhancing image realism and detail. Furthermore, we propose RealBench, an automated evaluation benchmark employing Detector-Scoring and Arena-Scoring. It enables human-free photorealism assessment, yielding results that are more accurate and aligned with real user experience. Experiments demonstrate that RealGen significantly outperforms general models like GPT-Image-1 and Qwen-Image, as well as specialized photorealistic models like FLUX-Krea, in terms of realism, detail, and aesthetics. The code is available at https://github.com/yejy53/RealGen.
693655753962c926cf6807eb
Junyan Ye
693655753962c926cf6807ec
Leiqi Zhu
693655753962c926cf6807ed
Yuncheng Guo
claimed_verified
2025-12-10T09:10:10.016Z
6877917cb2d7c0a48afb5487
/avatars/ef3ea56c925537a6ab745cea1a8b8067.svg
Yuncheng Guo
Yunncheng
user
Yunncheng
693655753962c926cf6807ee
Dongzhi Jiang
claimed_verified
2026-03-05T09:58:31.995Z
6349214f8146350b3a4c5cdf
/avatars/cfd24caac9a87efb528d0f4c375932bc.svg
Dongzhi Jiang
CaraJ
user
CaraJ
693655753962c926cf6807ef
Zilong Huang
693655753962c926cf6807f0
Yifan Zhang
693655753962c926cf6807f1
Zhiyuan Yan
693655753962c926cf6807f2
Haohuan Fu
693655753962c926cf6807f3
Conghui He
693655753962c926cf6807f4
Weijia Li
RealGen: Photorealistic Text-to-Image Generation via Detector-Guided Rewards
With the continuous advancement of image generation technology, advanced models such as GPT-Image-1 and Qwen-Image have achieved remarkable text-to-image consistency and world knowledge However, these models still fall short in photorealistic image generation. Even on simple T2I tasks, they tend to produce " fake" images with distinct AI artifacts, often characterized by "overly smooth skin" and "oily facial sheens". To recapture the original goal of "indistinguishable-from-reality" generation, we propose RealGen, a photorealistic text-to-image framework. RealGen integrates an LLM component for prompt optimization and a diffusion model for realistic image generation. Inspired by adversarial generation, RealGen introduces a "Detector Reward" mechanism, which quantifies artifacts and assesses realism using both semantic-level and feature-level synthetic image detectors. We leverage this reward signal with the GRPO algorithm to optimize the entire generation pipeline, significantly enhancing image realism and detail. Furthermore, we propose RealBench, an automated evaluation benchmark employing Detector-Scoring and Arena-Scoring. It enables human-free photorealism assessment, yielding results that are more accurate and aligned with real user experience. Experiments demonstrate that RealGen significantly outperforms general models like GPT-Image-1 and Qwen-Image, as well as specialized photorealistic models like FLUX-Krea, in terms of realism, detail, and aesthetics. The code is available at https://github.com/yejy53/RealGen.
RealGen is a photorealistic text-to-image framework that uses an LLM for prompt optimization and a diffusion model for image generation, enhanced by a Detector Reward mechanism and RealBench for automated evaluation.
LLM diffusion model Detector Reward semantic-level detectors feature-level detectors GRPO algorithm RealBench Detector-Scoring Arena-Scoring