  Diffusion models have found valuable applications in anomaly detection by
capturing the nominal data distribution and identifying anomalies via
reconstruction. Despite their merits, they struggle to localize anomalies of
varying scales, especially larger anomalies such as entire missing components.
Addressing this, we present a novel framework that enhances the capability of
diffusion models, by extending the previous introduced implicit conditioning
approach Meng et al. (2022) in three significant ways. First, we incorporate a
dynamic step size computation that allows for variable noising steps in the
forward process guided by an initial anomaly prediction. Second, we demonstrate
that denoising an only scaled input, without any added noise, outperforms
conventional denoising process. Third, we project images in a latent space to
abstract away from fine details that interfere with reconstruction of large
missing components. Additionally, we propose a fine-tuning mechanism that
facilitates the model to effectively grasp the nuances of the target domain.
Our method undergoes rigorous evaluation on prominent anomaly detection
datasets VisA, BTAD and MVTec yielding strong performance. Importantly, our
framework effectively localizes anomalies regardless of their scale, marking a
pivotal advancement in diffusion-based anomaly detection.
