PromptEnhancer：通过细粒度奖励优化重写器以提升文本到图像生成
PromptEnhancer: Taming Your Rewriter for Text-to-Image Generation via Fine-Grained Reward
提出可插拔的提示词重写框架，通过监督微调与基于细粒度奖励模型的强化学习优化重写器，可提升任意预训练T2I模型对复杂提示词的遵循能力，为改善文生图对齐提供通用后处理方案。
原文依据 we propose PromptEnhancer, a universal prompt rewriting framework that improves prompt interpretability for any pre-trained T2I model.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论近期文本到图像（T2I）扩散模型在生成高保真图像方面取得了显著进展，但它们往往难以忠实遵循复杂的用户提示词，尤其是在属性绑定、否定和组合推理方面。为解决这一局限，我们提出PromptEnhancer，一个通用的提示词重写框架，可提升任何预训练T2I模型的提示词可解释性。PromptEnhancer通过多阶段流程训练。我们首先在思维链风格的重写数据上进行监督微调，赋予重写器结构化的提示词分析与重写能力。然后，我们引入AlignEvaluator，一个任务特定的奖励模型，基于常见T2I失败模式的分类体系提供明确且细粒度的反馈，并进一步用强化学习优化重写器。这一设计使重写器能够生成更精确、语义完整且更易于T2I模型遵循的提示词。为支持评估，我们还构建了一个全面的、与人类对齐的基准，涵盖多样的语义与组合挑战。大量实验表明，PromptEnhancer在多个T2I模型上持续提升图像-文本对齐，并在挑战性案例上显著增强提示词保真度。
英文原文摘要 1310 字符
Recent text-to-image (T2I) diffusion models have achieved impressive progress in generating high-fidelity images, yet they often fail to faithfully follow complex user prompts, especially in attribute binding, negation, and compositional reasoning. To address this limitation, we propose PromptEnhancer, a universal prompt rewriting framework that improves prompt interpretability for any pre-trained T2I model. PromptEnhancer is trained through a multi-stage pipeline. We first perform supervised fine-tuning on chain-of-thought-style rewriting data to endow the rewriter with structured prompt analysis and rewriting capabilities. We then introduce AlignEvaluator, a task-specific reward model that provides explicit and fine-grained feedback based on a taxonomy of common T2I failure modes, and further optimize the rewriter with reinforcement learning. This design enables the rewriter to produce prompts that are more precise, semantically complete, and easier for T2I models to follow. To support evaluation, we also construct a comprehensive human-aligned benchmark covering diverse semantic and compositional challenges. Extensive experiments show that PromptEnhancer consistently improves image-text alignment across multiple T2I models and significantly enhances prompt fidelity on challenging cases.