Visual anomaly detection (AD) presents significant challenges due to the scarcity of anomalous data samples. While numerous works have been proposed to synthesize anomalous samples, these synthetic anomalies often lack authenticity or require extensive training data, limiting their applicability in real-world scenarios. In this work, we propose Anomaly Anything (AnomalyAny), a novel framework that leverages Stable Diffusion (SD)'s image generation capabilities to generate diverse and realistic unseen anomalies. By conditioning on a single normal sample during test time, AnomalyAny is able to generate unseen anomalies for arbitrary object types with text descriptions. Within AnomalyAny, we propose attention-guided anomaly optimization to direct SD attention on generating hard anomaly concepts. Additionally, we introduce prompt-guided anomaly refinement, incorporating detailed descriptions to further improve the generation quality. Extensive experiments on MVTec AD and VisA datasets demonstrate AnomalyAny's ability in generating high-quality unseen anomalies and its effectiveness in enhancing downstream AD performance.

翻译：视觉异常检测（AD）由于异常数据样本稀缺而面临重大挑战。尽管已有大量工作提出合成异常样本，但这些合成异常往往缺乏真实性或需要大量训练数据，限制了其在真实场景中的适用性。本文提出Anomaly Anything（AnomalyAny）框架，该框架利用Stable Diffusion（SD）的图像生成能力，生成多样且真实的未见异常。通过在测试时以单个正常样本为条件，AnomalyAny能够结合文本描述为任意对象类型生成未见异常。在AnomalyAny中，我们提出注意力引导的异常优化方法，引导SD注意力集中于生成困难异常概念。此外，我们引入提示引导的异常细化机制，通过融入详细描述进一步提升生成质量。在MVTec AD和VisA数据集上的大量实验表明，AnomalyAny能够生成高质量的未见异常，并能有效提升下游AD性能。