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.

通过使用一个能够生成多样化和逼真异常样本的稳定扩散图像生成框架（CUT），我们提出了一种可控、通用且无需训练的视觉异常生成方法。通过在视觉 - 语言异常检测框架（VLAD）中使用 CUT 生成的异常样本进行训练，我们在多个基准异常检测任务上实现了最先进的性能，突显了合成数据所带来的显著改进。