  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
https://github.com/sjtuplayer/anomalydiffusion.
