Substation meters play a critical role in monitoring and ensuring the stable operation of power grids, yet their detection of cracks and other physical defects is often hampered by a severe scarcity of annotated samples. To address this few-shot generation challenge, we propose a novel framework that integrates Knowledge Embedding and Hypernetwork-Guided Conditional Control into a Stable Diffusion pipeline, enabling realistic and controllable synthesis of defect images from limited data. First, we bridge the substantial domain gap between natural-image pre-trained models and industrial equipment by fine-tuning a Stable Diffusion backbone using DreamBooth-style knowledge embedding. This process encodes the unique structural and textural priors of substation meters, ensuring generated images retain authentic meter characteristics. Second, we introduce a geometric crack modeling module that parameterizes defect attributes--such as location, length, curvature, and branching pattern--to produce spatially constrained control maps. These maps provide precise, pixel-level guidance during generation. Third, we design a lightweight hypernetwork that dynamically modulates the denoising process of the diffusion model in response to the control maps and high-level defect descriptors, achieving a flexible balance between generation fidelity and controllability. Extensive experiments on a real-world substation meter dataset demonstrate that our method substantially outperforms existing augmentation and generation baselines. It reduces Frechet Inception Distance (FID) by 32.7%, increases diversity metrics, and--most importantly--boosts the mAP of a downstream defect detector by 15.3% when trained on augmented data. The framework offers a practical, high-quality data synthesis solution for industrial inspection systems where defect samples are rare.

翻译：变电站仪表在监测和保障电网稳定运行中起着关键作用，然而对其裂纹等物理缺陷的检测常因标注样本严重稀缺而受阻。为应对这一少样本生成挑战，本文提出一种新颖框架，将知识嵌入与超网络引导的条件控制集成至 Stable Diffusion 流程中，实现了基于有限数据生成真实且可控的缺陷图像。首先，我们通过采用 DreamBooth 式知识嵌入对 Stable Diffusion 主干网络进行微调，弥合了自然图像预训练模型与工业设备间显著的领域差距。该过程编码了变电站仪表独特的结构与纹理先验，确保生成图像保留真实的仪表特征。其次，我们引入几何裂纹建模模块，将缺陷属性（如位置、长度、曲率及分支模式）参数化，以生成空间约束的控制图。这些控制图在生成过程中提供精确的像素级引导。第三，我们设计了一个轻量级超网络，能够根据控制图与高层缺陷描述符动态调制扩散模型的去噪过程，从而在生成保真度与可控性之间实现灵活平衡。在真实变电站仪表数据集上的大量实验表明，本方法显著优于现有的数据增强与生成基线方法：其将弗雷歇起始距离（FID）降低了 32.7%，提升了多样性指标，且最重要的是，当使用增强数据训练下游缺陷检测器时，其平均精度均值（mAP）提升了 15.3%。该框架为缺陷样本稀少的工业检测系统提供了一种实用、高质量的数据合成解决方案。