OSD-IRF: One-Step Diffusion Anomaly Detection with Inverse Residual Fields

Diffusion models have demonstrated strong performance in unsupervised industrial anomaly detection (uIAD) by learning the distribution of normal data. These methods typically assume that off-manifold anomalies are more difficult to reconstruct or generate, leading to larger reconstruction errors in the data space or lower probability densities in a tractable latent space. However, their iterative denoising process incurs substantial inference latency. To overcome this limitation, we propose OSD-IRF, a novel one-step diffusion framework based on inverse residual fields (IRFs). Our key observation is that anomalies become distinguishable in the IRF space, enabling efficient detection in a single diffusion step without iterative denoising or model distillation. Specifically, we first train an unconditional denoising diffusion probabilistic model (DDPM) exclusively on normal data. At inference time, we use the score function learned by the DDPM to estimate the IRF of each test sample. Anomaly detection is then performed by evaluating the IRF likelihood under a Gaussian distribution and comparing it against a predefined threshold. We further leverage the aggregate signal-to-noise ratio (SNR) statistics of the entire training set to automatically determine the probing timestep used during inference, thereby eliminating empirical timestep selection in IRF estimation. Extensive experiments on widely used uIAD benchmarks show that OSD-IRF achieves competitive performance in anomaly detection and localization while substantially reducing the inference cost of diffusion-based methods.