UniDG: Unified Defect Generation via Defect-Context Editing

UniDG introduces a unified defect generation framework that addresses the scarcity of abnormal samples in anomaly detection. By creating a large dataset of 300K normal‑abnormal‑mask‑caption quadruplets and formulating defect synthesis as Defect‑Context Editing, the model extracts reference defect context, organizes inputs in a diptych format, and fuses multimodal conditions via MM‑DiT attention. A two‑stage training strategy—Diversity‑SFT for transferable defect priors and optional Consistency‑RFT for reference adherence—enables UniDG to outperform prior few‑shot anomaly generation and editing baselines in both synthesis quality and downstream anomaly detection tasks without per‑category fine‑tuning.

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• fine-tuning
• multimodal