AdaNMD: Nested Diffusion with Adaptive Resolution Decision for Efficient Industrial Anomaly Localization

Abstract

Accurate anomalous localization is a core challenge in industrial visual quality inspection. Current diffusion-based methods typically rely on single-scale reconstruction at a fixed resolution, often exhibiting limited performance on subtle or low-contrast anomalies due to the lack of hierarchical feature collaboration. We propose AdaNMD, a nested adaptive multi-resolution diffusion model that jointly optimizes accuracy and efficiency. AdaNMD constructs a three-branch nested decoding architecture. It utilizes Adaptive Group Normalization (AdaGN) to embed diffusion time steps and a top-down feature fusion module to generate semantically rich pyramid representations. Crucially, a resolution decision module dynamically evaluates image complexity, activating only the most suitable branch during inference to reduce redundant computation. To bridge the capability gap between branches, we introduce a cross-scale self-distillation mechanism where the high-resolution branch acts as a teacher for lighter branches. A unified multi-task loss function further guides the model toward robust inference policies. Experiments on the VisA and MVTec AD benchmarks demonstrate that AdaNMD achieves Area Under the Per-Region Overlap Curve (AUPRO) scores of 95.0% and 94.4%, respectively. Furthermore, compared to a baseline always using the high-resolution branch, our method improves inference speed by approximately 21%, confirming the architecture’s advantage in achieving high-precision anomaly localization and efficient inference simultaneously.