用大模型重写提示词，让文生图更准更美。
Improving Text-to-Image Generation with Input-Side Inference-Time Scaling
- 用大模型自动优化用户输入提示词，提升图文一致性。
- 不依赖标注数据，通过强化学习训练重写器，效果优于基线。
- 重写器可跨模型通用，且越大模型效果越好。
近期文本到图像（T2I）生成取得显著进展，但现有模型在简单或模糊提示下仍表现不佳，导致图文对齐、视觉质量与美感不足。本文提出一种基于大语言模型（LLM）的提示词重写框架，在输入端对用户提示进行优化后再送入T2I主干模型。该方法设计了精细奖励机制，并采用迭代直接偏好优化（DPO）训练流程，使重写器无需监督微调数据即可提升提示质量。我们在多种T2I模型和基准上评估该方法，结果表明其能持续改善图文对齐、视觉质量和美学表现，优于强基线。此外，我们验证了重写器的强泛化能力：在一种T2I主干上训练的重写器可有效迁移到其他模型，无需重新训练。系统性研究还揭示了性能随重写所用大模型容量增长而提升的规律。这些发现表明，提示词重写是一种高效、可扩展、与模型无关的T2I优化策略。代码与训练好的重写器即将开源。
原文摘要 · Abstract (English)
Recent advances in text-to-image (T2I) generation have achieved impressive results, yet existing models often struggle with simple or underspecified prompts, leading to suboptimal image-text alignment, aesthetics, and quality. We propose a prompt rewriting framework that leverages large language models (LLMs) to refine user inputs before feeding them into T2I backbones. Our approach introduces a carefully designed reward system and an iterative direct preference optimization (DPO) training pipeline, enabling the rewriter to enhance prompts without requiring supervised fine-tuning data. We evaluate our method across diverse T2I models and benchmarks. Results 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 prompt rewriter trained on one T2I backbone generalizes effectively to others without needing to be retrained. We also systematically study scalability, evaluating how performance gains scale with the capacity of the large LLM used as the rewriter. These findings highlight that prompt rewriting is an effective, scalable, and practical model-agnostic strategy for improving T2I systems. We plan to release the code and trained prompt rewriters soon.
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