Fine-Tuning-Free Reference-Guided Diffusion for Structure-Preserving Elongated Fabric Defect Generation

In industrial anomaly detection, anomaly generation helps reduce reliance on scarce real defect samples. However, for elongated fabric defects where morphology is highly coupled with background texture, maintaining the realism of the defects remains a challenge. To address this issue, we propose the Shape-Aware Implicitly Anchored Industrial Defect Generation (SLAID) framework, a reference-image-guided synthesis framework for elongated fabric defects that is fine-tuning-free. Within this framework, we introduce Shape-Sensitive Sliding-Window Reference Modeling (SSRM) to preserve the geometric structure of long-span defects. Additionally, we incorporate Adaptive High-Frequency Guidance (AHFG) to enhance fine edges and textural features. Finally, we propose Target-Domain Latent Anchoring (TLA) to constrain target-background consistency in non-edited regions. SLAID generates structurally continuous defects while preserving the target fabric background, without additional fine-tuning. In a data augmentation experiment on a dataset comprising five categories of elongated textile defects, SLAID improved the mAP@0.5 of YOLOv8 from 0.616 to 0.817. Furthermore, SLAID achieved a mean MMD² of 0.0435 for defect-distribution consistency, a mean LPIPS of 0.0739 for defect-level perceptual similarity, and a full-image SSIM of 0.9754 for target-background preservation. Cross-dataset evaluation on the MHPSD steel-surface defect dataset provides preliminary evidence of the applicability of the proposed inference-stage generation strategy to the evaluated steel-surface defect categories.