DeltaDeno: Zero-Shot Anomaly Generation via Delta-Denoising Attribution

Anomaly generation is often framed as few-shot fine-tuningwith anomalous samples, which contradicts the scarcity that motivatesgeneration and tends to overfit category priors. We tackle the settingwhere no real anomaly samples or training are available. We proposeDelta-Denoising (DeltaDeno), a training-free zero-shot anomaly gener-ation method that localizes and edits defects by contrasting two diffusionbranches driven by a minimal prompt pair under a shared schedule. Byaccumulating per-step denoising deltas into an image-specific localizationmap, we obtain a mask to guide the latent inpainting during later diffu-sion steps and preserve the surrounding context while generating realis-tic local defects. To improve stability and control, DeltaDeno performstoken-level prompt refinement that aligns shared content and strength-ens anomaly tokens, and applies a spatial attention bias restricted toanomaly tokens in the predicted region. Experiments on public datasetsshow that DeltaDeno achieves great generation, realism and consistentgains in downstream detection performance. Code will be made publiclyavailable at https://github.com/CROVO1026/DeltaDeno.