RealGen: Photorealistic Text-to-Image Generation via Detector-Guided Rewards

Rapid advancements in image generation have led models likeGPT-Image and Qwen-Image to excel in text-to-image consistency andworld knowledge. However, they still struggle with photorealism, oftenproducing "fake" images laden with obvious AI artifacts, such as overlysmooth skin or unnatural oily sheens. To recapture the original goal of"indistinguishable-from-reality" generation, we propose RealGen, a photo-realistic text-to-image framework. RealGen integrates an LLM componentfor prompt optimization and a diffusion model for realistic image genera-tion. Inspired by adversarial generation, RealGen introduces a "DetectorReward" mechanism, which quantifies artifacts and assesses realism usingboth semantic and feature-level synthetic image detectors. We leveragethis reward with the GRPO algorithm to optimize the entire generationpipeline, significantly enhancing image realism and detail. Furthermore,we propose RealBench, an automated evaluation benchmark employingDetector-Scoring and Arena-Scoring. It enables human-free photorealismassessment, yielding results that are more accurate and aligned with realuser experience. Experiments show that RealGen produces photorealisticimages with enhanced realism, detail, and aesthetics.