GenErase: Generalizable and Semantically-Aware Concept Erasure in Diffusion Models

Text-to-Image (T2I) diffusion models power modern creative tools, but their open-ended generative nature raises safety, ethical, and copyright concerns. Retraining or fine-tuning to remove every unsafe or copyrighted concept is impractical, motivating training-free interventions that suppress specific semantics while preserving general visual quality. Existing guard-railing methods face a core trade-off: they are either rigid, failing to generalize to paraphrased or context-shifted prompts, or coarse, distorting unrelated content and fidelity. We present GenErase (GENeralizable ERAsure with SEmantic Awareness), a training-free, geometry-grounded framework for robust concept removal in diffusion models. GenErase enforces semantic orthogonality in the cross-attention value space via an explicit \emph{erase-and-replace} operation, guided by a per-token preserve projector and a hard geometric gate. This design enables precise erasure, explicit protection of critical semantics, and stability across layers, paraphrases, and multi-concept cases. Extensive experiments on identity, object, and style erasure, together with a new GenBench-40 benchmark, show that GenErase achieves state-of-the-art erasure fidelity and superior paraphrase-level generalization, establishing it as a practical and principled guard-rail for safe, real-time diffusion deployment. Code will be released upon acceptance.