RASteer erases concepts in diffusion models via retain-aware activation steering, matching or outperforming evaluated activation-steering and weight-editing baselines across backbones and benchmarks
The work proposes Retain-aware Activation Steering (RASteer), a training-free method that first builds a retain subspace from the concepts to preserve, then uses Retain-Orthogonal Steering (ROS) to remove components aligned with this subspace from the erasure direction, and further introduces Overlap-Adaptive Calibration (OAC) to control, at each layer and denoising step, how much of each shared component is removed based on the overlap between the erasure direction and the retain subspace, thereby better balancing target erasure and concept preservation; experiments on unsafe-content, instance, and artistic-style erasure across multiple backbones and benchmarks show RASteer matches or outperforms the activation-steering and weight-editing baselines evaluated.