AnomalyEditor: Few-Shot Mask-Free Image Editing for High-Fidelity Anomaly Synthesis

Realistic anomaly image synthesis can alleviate the scarcity of labeled anomaly data in industrial visual inspection. Existing reference-based methods require explicit anomaly masks, while global synthesis methods can compromise normal-region fidelity. Mask-free image editing offers a promising alternative, but its supervised adaptation requires spatially aligned normal–anomaly pairs, which are rarely available. We observe that such pairs can be constructed more readily in reverse. Adding an anomaly (forward: normal$\to$anomaly) requires learning anomalous appearance from scarce examples, whereas removing an anomaly (reverse: anomaly$\to$normal) can be formulated as inpainting guided by a normal prior learned from abundant normal images. Based on this observation, we propose AnomalyEditor, a few-shot reverse-to-forward framework for mask-free anomaly editing. Specifically, we first remove annotated anomalies from real images with an inpainter fine-tuned on normal images, then use the resulting aligned pairs to fine-tune a pretrained editor to add them back, guided by text instructions. As a result, AnomalyEditor learns from real anomalies how to add a specified anomaly type to an unseen normal image. During inference, AnomalyEditor generates a realistic anomaly without an input mask while leaving normal regions intact. Additionally, we introduce a spatial anomaly-attentive loss to strengthen supervision on small anomalies and severe-diverse anomaly generation to generate diverse anomalies of varying severity. Experiments on MVTec-AD, VisA, and MVTec-3D demonstrate improved Kernel Inception Distance and higher downstream anomaly classification accuracy than the evaluated synthesis baselines, alongside competitive detection and localization performance.