SevAnoDiff: Severity-Aware Diffusion Adaptation for Zero-Shot Industrial Anomaly Generation

Industrial anomaly detection relies on diverse anomaly images with pixel-level annotations, yet real industrial anomalies are rare and costly to collect. Existing anomaly generation methods mainly synthesize isolated samples while not modeling how the anomaly changes across various severity stages. First, we construct SevADSet, an industrial anomaly dataset with four-stage ordinal trajectories from normal images to increasingly visible anomaly stages. Second, based on SevADSet, we propose SevAnoDiff, a diffusion-based method for zero-shot severity-aware anomaly generation. An Appearance Prior Adapter first learns transferable anomaly appearance from observed anomalies. A Severity Progression Module then models adjacent-stage changes using stage tokens, mask trajectories, and previous-stage visual references. Without using anomalous images or masks from the target domain, SevAnoDiff generates realistic anomaly image-mask pairs with clear severity progression on target domain normal images. Extensive experiments on MVTec-AD and VisA demonstrate the effectiveness of SevAnoDiff in anomaly generation and its utility for anomaly detection and localization.