<p>Automated visual inspection is critical for quality control in industrial manufacturing, yet training robust defect detection models is often hindered by the scarcity of defective samples. To address this challenge, we introduce a diffusion-based data augmentation framework capable of generating diverse and realistic synthetic defect images. Our method personalizes a text-to-image Latent Diffusion Model using DreamBooth, introducing a spatially weighted loss that forces the model to prioritize learning specific defect characteristics without attempting to reconstruct the non-defective background. To synthesize new data, we utilize Differential Diffusion to generate the learned defects onto defect-free images, eliminating boundary artifacts. We systematically evaluate the efficacy of our generated data on downstream supervised defect detection and localization tasks in two practical scenarios: as a complete substitute for real defect data and as a complement to it. Extensive experiments on the MVTec-AD dataset and a real-world industrial dataset demonstrate that our synthetic defects significantly boost downstream task performance. Furthermore, ablation studies confirm that these performance gains are robust and architecture-agnostic across various detectors.</p>