CRET: Target-Selective Subspace Learning for Concept Erasure in Text-to-Image Diffusion Models

Text-to-image diffusion models have demonstrated remarkable capabilities in visual generation, but their widespread use also raises concerns about the generation of unsafe content. Concept erasure aims to suppress designated undesirable concepts while preserving non-target semantics. Projection-based methods provide interpretable intervention by removing target-related representation components. Their projection subspaces are commonly constructed directly from selected embeddings. However, for complex concepts, target-related and benign representations can share certain components, making it difficult for such subspaces to separate the information to be edited from that to be preserved, resulting in incomplete target-concept erasure or suppression of benign information. To overcome this limitation, we propose CRET, a representation-level concept erasure framework that learns an editable subspace rather than directly constructing it from selected embeddings. This subspace is parameterized by a rank-constrained orthogonal projector. In particular, CRET optimizes the projector to selectively capture target-related information from controlled risky-safe prompt pairs while limiting the projections of safe and preservation representations onto the editable subspace. The projector is learned in a latent feature space of text representations, and the induced change is transferred back to the original representation without updating the pretrained diffusion model weights. Experiments on multiple adversarial benchmarks demonstrate that CRET achieves more effective target-concept erasure than competing methods while maintaining competitive generation quality on benign prompts.