Despite generating increasingly photorealistic images, text-to-image (T2I) models still exhibit localized, subtle, and structurally complex failures. Diagnosing these failures requires instance-level feedback that answers where a defect occurs, what type it is, why it is defective, and its importance to overall image quality. While recent dense-feedback methods move beyond scalar supervision, their heatmap-centric representations still formulate diagnosis as pixel-field regression, making it difficult to localize variable-cardinality defects and bind semantic reasons to individual failures. To address this representation bottleneck, we propose Structured Defect Grounding (SDG), which casts T2I diagnosis as structured set prediction by modeling each defect as a (location, type, reason, importance) tuple. To make this formulation trainable and measurable, we introduce SDG-30K, a 30K-image dataset with box-grounded annotations across four modern T2I generators, together with a dedicated evaluation protocol, SDG-Eval. Building on this structured representation, we further present a diagnosis-to-alignment framework in which a Vision-Language Model (VLM) serves as the SDG detector, and BoxFlow-GRPO converts predicted defect sets into box-derived, importance-weighted spatial rewards for diffusion model alignment. Extensive experiments show that our SDG detector outperforms leading proprietary VLMs on structured defect grounding, while SDG-guided rewards consistently improve T2I alignment and support localized image refinement. These results establish SDG as a unified, instance-level interface for diagnosing, evaluating, and enhancing modern generative models.

翻译：摘要：尽管文本到图像（T2I）模型生成的图像日益逼真，但仍存在局部、细微且结构复杂的缺陷。诊断这些缺陷需要实例级反馈，以回答缺陷发生的位置、类型、成因及其对整体图像质量的重要性。近期密集反馈方法虽已超越标量监督，但其以热力图为核心的表示形式仍将诊断过程视为像素场回归，难以定位可变数量的缺陷，也无法将语义原因与单个缺陷绑定。为解决这一表示瓶颈，我们提出结构化缺陷定位（SDG），将每个缺陷建模为（位置、类型、原因、重要性）元组，从而将T2I诊断转化为结构化集合预测。为使该方案可训练且可度量，我们构建了SDG-30K数据集——包含跨越四种现代T2I生成器的3万幅图像，每幅图像均带有边界框标注，并配套专用评估协议SDG-Eval。基于此结构化表示，我们进一步提出诊断到对齐框架：以视觉语言模型（VLM）作为SDG检测器，通过BoxFlow-GRPO将预测缺陷集转化为基于边界框推导的重要性加权空间奖励，用于扩散模型对齐。大量实验表明，我们的SDG检测器在结构化缺陷定位任务上优于领先的专有VLM；SDG指导的奖励可持续改善T2I对齐效果，并支持局部图像精修。这些结果确立了SDG作为诊断、评估与增强现代生成模型的统一实例级接口。