Effectively addressing the challenge of industrial Anomaly Detection (AD) necessitates an ample supply of defective samples, a constraint often hindered by their scarcity in industrial contexts. This paper introduces a novel algorithm designed to augment defective samples, thereby enhancing AD performance. The proposed method tailors the blended latent diffusion model for defect sample generation, employing a diffusion model to generate defective samples in the latent space. A feature editing process, controlled by a "trimap" mask and text prompts, refines the generated samples. The image generation inference process is structured into three stages: a free diffusion stage, an editing diffusion stage, and an online decoder adaptation stage. This sophisticated inference strategy yields high-quality synthetic defective samples with diverse pattern variations, leading to significantly improved AD accuracies based on the augmented training set. Specifically, on the widely recognized MVTec AD dataset, the proposed method elevates the state-of-the-art (SOTA) performance of AD with augmented data by 1.5%, 1.9%, and 3.1% for AD metrics AP, IAP, and IAP90, respectively. The implementation code of this work can be found at the GitHub repository https://github.com/GrandpaXun242/AdaBLDM.git

翻译：有效应对工业异常检测（AD）挑战需要充足的缺陷样本，而工业场景中缺陷样本的稀缺性常制约这一需求。本文提出一种新型算法以扩充缺陷样本，从而提升AD性能。该方法定制混合潜扩散模型用于缺陷样本生成，利用扩散模型在潜空间中生成缺陷样本。通过"三图"掩码与文本提示控制特征编辑过程，精炼生成的样本。图像生成推理过程分为三个阶段：自由扩散阶段、编辑扩散阶段及在线解码器自适应阶段。这一精密的推理策略能产生具有多样化模式变化的高质量合成缺陷样本，基于扩充训练集显著提升AD精度。具体而言，在广泛使用的MVTec AD数据集上，所提方法将基于增强数据的AD性能在AP、IAP与IAP90指标上分别提升了1.5%、1.9%与3.1%，达到当前最优水平（SOTA）。本工作实现代码见GitHub仓库 https://github.com/GrandpaXun242/AdaBLDM.git