Anomaly inspection plays an important role in industrial manufacture.
Existing anomaly inspection methods are limited in their performance due to
insufficient anomaly data. Although anomaly generation methods have been
proposed to augment the anomaly data, they either suffer from poor generation
authenticity or inaccurate alignment between the generated anomalies and masks.
To address the above problems, we propose AnomalyDiffusion, a novel
diffusion-based few-shot anomaly generation model, which utilizes the strong
prior information of latent diffusion model learned from large-scale dataset to
enhance the generation authenticity under few-shot training data. Firstly, we
propose Spatial Anomaly Embedding, which consists of a learnable anomaly
embedding and a spatial embedding encoded from an anomaly mask, disentangling
the anomaly information into anomaly appearance and location information.
Moreover, to improve the alignment between the generated anomalies and the
anomaly masks, we introduce a novel Adaptive Attention Re-weighting Mechanism.
Based on the disparities between the generated anomaly image and normal sample,
it dynamically guides the model to focus more on the areas with less noticeable
generated anomalies, enabling generation of accurately-matched anomalous
image-mask pairs. Extensive experiments demonstrate that our model
significantly outperforms the state-of-the-art methods in generation
authenticity and diversity, and effectively improves the performance of
downstream anomaly inspection tasks. The code and data are available in
this https URL

提出了一种基于扩散的少样本异常生成模型，利用大规模数据集中学习到的潜在扩散模型的强优先信息，增强生成的真实性，并通过创新的自适应注意力重新加权机制提高生成异常图像和异常遮罩之间的对齐，实现了准确匹配的异常图像 - 遮罩对，从而显著提高异常检测任务的性能。