Unified Anomaly Understanding and Generation through Causal Diffusion Inference

Existing anomaly understanding and anomaly generation methods are usually treated as isolated tasks and rely on separate models, which limits knowledge sharing and scalability. In this paper, we investigate how to unify anomaly understanding and anomaly generation in a single latent diffusion model. Motivated by causal effects, we propose a novel factual-counterfactual generation framework, which enables us to capture the pure anomaly-related response as the direct causal effect of the defect reference on the generation process and suppress background interference by subtracting the counterfactual normal-reference effect from the total attention response. Extensive experiments validate our method's superiority in both anomaly generation and downstream detection tasks. Compared with specialized models, it slashes the FID generation score by roughly 50 points and boosts downstream detection performance by around 20% in PRO and 10% in max-F1. The codes will be public after acceptance.