  In the context of flexible manufacturing systems that are required to produce
different types and quantities of products with minimal reconfiguration, this
paper addresses the problem of unsupervised multi-class anomaly detection:
develop a unified model to detect anomalies from objects belonging to multiple
classes when only normal data is accessible. We first explore the
generative-based approach and investigate latent diffusion models for
reconstruction to mitigate the notorious ``identity shortcut'' issue in
auto-encoder based methods. We then introduce a feature editing strategy that
modifies the input feature space of the diffusion model to further alleviate
``identity shortcuts'' and meanwhile improve the reconstruction quality of
normal regions, leading to fewer false positive predictions. Moreover, we are
the first who pose the problem of hyperparameter selection in unsupervised
anomaly detection, and propose a solution of synthesizing anomaly data for a
pseudo validation set to address this problem. Extensive experiments on
benchmark datasets MVTec-AD and MPDD show that the proposed LafitE, \ie, Latent
Diffusion Model with Feature Editing, outperforms state-of-art methods by a
significant margin in terms of average AUROC. The hyperparamters selected via
our pseudo validation set are well-matched to the real test set.
