POET: Preference Optimization for Enhanced Text-to-Image Generation

Recent advances in text-to-image (T2I) generation have achieved impressive results, yet existing models often struggle with simple or underspecified user prompts due to a distributional gap with their descriptive training captions. This frequently leads to suboptimal imagetext alignment, aesthetics, and overall visual quality. To bridge this gap, we propose POET (Preference Optimization for Enhanced Text-to-Image generation), an automated prompt rewriting framework that leverages large language models (LLMs) to refine user inputs before feeding them into frozen T2I backbones. POET introduces a carefully designed composite reward system and an iterative Direct Preference Optimization (DPO) training pipeline, enabling the rewriter to learn model-preferred prompt structures directly from multimodal feedback without requiring costly high-quality supervised fine-tuning (SFT) data. Extensive evaluations across diverse T2I models and benchmarks show that our prompt rewriter consistently improves image-text alignment, visual quality, and aesthetics, outperforming strong baselines. Furthermore, we demonstrate strong transferability by showing that a rewriter trained on one T2I backbone generalizes effectively to others without needing to be retrained. These findings highlight that POET is an effective, robust, and practical model-agnostic strategy for improving T2I systems.