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：有效解决工业异常检测（AD）的挑战需要充足的缺陷样本供应，而工业场景中缺陷样本的稀缺性往往制约了这一需求。本文提出一种新型算法，用于扩充缺陷样本以提升AD性能。该方法针对缺陷样本生成任务定制了混合潜在扩散模型，通过扩散模型在潜在空间中生成缺陷样本。由"trimap"掩码和文本提示控制的特征编辑过程对生成样本进行优化。图像生成推理流程分为三个阶段：自由扩散阶段、编辑扩散阶段和在线解码器自适应阶段。这种精细的推理策略能够生成具有多样化模式变化的高质量合成缺陷样本，基于扩充后的训练集显著提升AD精度。具体而言，在广泛认可的MVTec AD数据集上，所提方法将基于增强数据的AD最新性能（SOTA）在AP、IAP和IAP90指标上分别提升了1.5%、1.9%和3.1%。本工作实现代码已开源至GitHub仓库：https://github.com/GrandpaXun242/AdaBLDM.git