Concept Unlearning via Cross-Attention Activation Projection for Diffusion Models

Existing closed-form methods for concept unlearning in text-to-image diffusion models typically derive editing directions from fixed text embeddings, which may not fully capture how concepts are expressed across latent states, timesteps, and layers. To capture this variation, we investigate cross-at