  Visual anomaly detection (AD) inherently faces significant challenges due to
the scarcity of anomalous data. Although numerous works have been proposed to
synthesize anomalous samples, the generated samples often lack authenticity or
can only reflect the distribution of the available training data samples. In
this work, we propose CUT: a Controllable, Universal and Training-free visual
anomaly generation framework, which leverages the capability of Stable
Diffusion (SD) in image generation to generate diverse and realistic anomalies.
With CUT, we achieve controllable and realistic anomaly generation universally
across both unseen data and novel anomaly types, using a single model without
acquiring additional training effort. To demonstrate the effectiveness of our
approach, we propose a Vision-Language-based Anomaly Detection framework
(VLAD). By training the VLAD model with our generated anomalous samples, we
achieve state-of-the-art performance on several benchmark anomaly detection
tasks, highlighting the significant improvements enabled by our synthetic data.
