Training-Free Industrial Defect Generation with Diffusion Models

Anomaly generation has become essential in addressing the scarcity of defective samples in industrial anomaly inspection. However, existing training-based methods fail to handle complex anomalies and multiple defects simultaneously, especially when only a single anomaly sample is available per defect type. To address this issue, we propose TF-IDG, a novel training-free defect generation framework capable of generating diverse anomaly samples in a oneshot setting. We propose a Feature Alignment strategy that provides fine-grained appearance guidance by minimizing the distributional gap between generated and real defects with high complexity. Additionally, we introduce an Adaptive Anomaly Mask mechanism to mitigate the issue of defects with small regions being ignored during the generation process, enhancing consistency between synthetic defects and their corresponding masks. Finally, we incorporate a Texture Preservation module that extracts background information from anomaly-free images, ensuring that the visual properties of synthetic defects are seamlessly integrated into the image. Extensive experiments demonstrate the effectiveness of our method in generating accurate and diverse anomalies, further leading to superior performance in downstream anomaly inspection tasks. Our code is available at https://github.com/rubymiaomiao/TF-IDG.