Con2Diff: Controllable Condition Diffusion Model for Unsupervised Anomaly Detection
Zhipeng Wang, Yonghong Song · 2025
Recent advances in reconstruction-based anomaly detection have demonstrated strong performance, with diffusion models increasingly applied due to their superior image reconstruction capabilities. However, these methods often suffer from a lack of guidance, resulting in uncontrollable reconstructions and reduced quality, which impacts anomaly detection and localization. To address these issues, we propose a Controllable Condition Diffusion Model (Con2Diff). First, we introduce a Controllable Condition Guidance mechanism (CCG) that incorporates target image guidance during denoising, enhancing controllability and improving reconstruction quality while preserving original image details. Second, we propose a Category Domain Enhancement strategy (CDE) to fine-tune the feature extractor, reducing the gap between pre-trained and industrial image features and improving feature comparison accuracy. Finally, we design a Dual-Comparison Paradigm (DCP) that integrates pixel-level and feature-level anomaly scores, addressing the limitations of single-method approaches and boosting anomaly score precision. Experiments on the MVTec AD and VisA datasets achieve state-of-the-art performance, with image AUROC scores of 99.8% and 99.0%, respectively.