MapRoute: Precise-Concept Erasing Mappers via Semantic Routing

Contemporary commercial and open-source diffusion models have demonstrated remarkable performance in text-to-image generation, enabling widespread applications in creative design and content creation. However, legitimate requirements—such as copyright protection, privacy compliance, or personalized customization—often necessitate the removal of specific semantic concepts from pretrained models. Existing concept erasure methods suffer from two critical limitations: (1) **Incomplete suppression**, where the model still occasionally generates images containing the target concept; (2) **Poor semantic selectivity**, which degrades the generation quality of unrelated concepts and compromises overall model utility.To address these challenges, we propose **`MapRoute`**, a lightweight, semantics-aware concept erasure framework based on dynamic routing. Our approach introduces a set of modular components—termed *Mappers*—placed after a frozen pretrained text encoder. Each Mapper learns a linear mapping from a target concept to a surrogate concept. During inference, the system dynamically activates the top-$K$ Mappers most relevant to the input prompt, based on cosine similarity between the text embedding and all the target concept embeddings, and applies their transformations sequentially. This input-driven, modular intervention enables precise, on-demand erasure while avoiding unnecessary interference with irrelevant semantics.Extensive experiments demonstrate that **`MapRoute`** effectively suppresses specified concepts while significantly reducing collateral damage to unrelated concept. By operating without full-model fine-tuning, our method entirely avoids parameter drift and concept erosion. Moreover, **`MapRoute`** outperforms state-of-the-art baselines in terms of generation fidelity, semantic consistency, and scalability to multi-concept erasure scenarios.