Core Viewpoint - The research introduces a new model called DualAnoDiff for generating anomalous images and masks, which utilizes a parallel dual-branch diffusion mechanism to ensure high alignment and realism of generated anomalous images [20][21][22]. Summary by Sections Industrial Anomaly Detection - The industrial sector often faces challenges in detecting product anomalies due to a lack of real defective product data for training detection models [2][7]. - Traditional methods involve generating realistic "defective images" and annotating the specific defects [2]. DualAnoDiff Model - Researchers from Fudan University and Tencent Youtu Lab have developed the DualAnoDiff model, which is based on diffusion models for few-shot anomaly image generation [3][4]. - The model has achieved state-of-the-art (SOTA) results compared to previous methods [4]. Generation Mechanism - DualAnoDiff employs a dual-branch parallel generation mechanism that synchronously generates anomalous images and their corresponding anomalous regions [10][12]. - The main branch focuses on generating complete images with anomalies, while the sub-branch emphasizes the authenticity of local anomalous areas [11][12]. Background Compensation Module - A Background Compensation Module (BCM) is introduced to enhance the model's ability to fit complex backgrounds by separating key and value features from normal images [14][21]. Experimental Results - The model has demonstrated superior performance in generating high-quality and diverse image data compared to existing anomaly generation methods [16][22]. - Quantitative metrics indicate that the generated data significantly improves downstream anomaly detection tasks [19][22]. Future Implications - The research is expected to advance the field of anomalous image generation, providing better tools for industrial anomaly detection [23].