UniScale: Arbitrary-Scale Anomaly Generation

Industrial anomaly inspection faces a major challenge dueto the lack of real-world anomaly samples. While generative models areused to create anomaly data, existing methods still struggle when han-dling small-scale anomalies. This failure occurs because extreme down-sampling in diffusion models causes the information of small anomaliesto be lost in the latent space. To address this, we introduce UniScale,a unified training and inference framework for high-fidelity industrialanomaly generation across arbitrary scales. During training, we intro-duce an Error-Suppressed Multi-Scale Training (EMT) strategy, whichenables the model to learn the rich location-aware textures of anomalies,while suppressing upsampling-induced interpolation errors in texture ac-quisition, ensuring the model is capable of learning small-scale anomalies,while remaining effective for regular scale anomalies. For inference, wepropose Generation-then-Fusion Denoising. It decouples anomaly genera-tion from background integration, preventing small anomalies from beingoverwhelmed. Extensive experiments demonstrate that our method out-performs state-of-the-art competitors in both anomaly generation qualityand downstream detection performance. It achieves a relative IS(a) im-provement of 45.86% (from 1.81 to 2.64) on VisA and 37.70% (from 1.22to 1.68) on MVTec AD 2, while also improving the downstream pixel-level IoU by 4.22% on VisA and AUROC by 6.55% on MVTec AD 2.Code is available at https://github.com/HUST-SLOW/UniScale.