Diffusion Models are Secretly One-step Anomaly Detectors

Diffusion-based anomaly detection (AD) commonly relies on iterative denoising to obtain high-quality reconstructions, following the intuition that better reconstruction leads to better AD. In this work, we revisit this intuition and ask whether iterative refinement is truly beneficial for AD. Through a large-scale empirical study across diverse AD datasets and diffusion designs, we uncover a striking phenomenon that we term *one-step optimality*: diffusion models trained solely on normal samples often achieve their best detection performance with just a single denoising step. To understand why, we theoretically analyze the optimal velocity field and show that iterative denoising can increasingly reconstruct anomalous information, leading to greater *anomaly leakage* and reduced separation between normal and anomaly scores. Under our analysis setting, one-step denoising minimizes such leakage and achieves optimal detection performance. This understanding naturally leads to the *One-step Diffusion anomaly Detector (ODD)*, which retains standard diffusion training but performs reconstruction with a single denoising step. Extensive experiments show that **ODD** achieves strong detection performance with substantially improved inference efficiency across diverse datasets and modalities, while also serving as a plug-and-play replacement for iterative denoising in existing diffusion-based AD methods. Code is available at: [https://anonymous.4open.science/r/odd](https://anonymous.4open.science/r/odd).