To Erase, or Not to Erase: Robust Training-Free Concept Erasure with Preservation aware Adaptive Ranked Subspace Expansion

Concept erasure techniques (CETs) edit text-to-image diffusion models to erase undesired targets (e.g., NSFW content or copyrighted styles) while preserving model utility on image generation for benign concepts. Current CETs often face a trade-off between erasure robustness and model utility: stronger edits erase target more reliably, but degrade model utility on non-target concepts and vice-versa. A key source of this trade-off is how existing methods define what to erase and what to preserve. Many CETs rely on static concept banks, where erase or retain concepts are manually specified, generated by Large Language Models (LLMs), or selected using CLIP-based image–text similarity. However, such static banks do not explicitly model how prompts steer the diffusion model during denoising, leaving edited models vulnerable to triggers that can reintroduce the target while unintentionally suppressing nearby benign concepts. We present Preservation aware Adaptive Ranked Subspace Expansion (PARSE), a training-free framework for robust concept erasure in latent diffusion models. Given a target, PARSE queries the diffusion model using classifier-free guidance to dynamically discover target-inducing erase concepts and nearby retain concepts from the model vocabulary. PARSE then edits the cross-attention value space with a preservation-aware projection that removes target directions while leaving retain directions intact. To address triggers beyond the vocabulary-indexed search space, PARSE iteratively searches for re-emergence triggers using textual inversion and adaptively expands the erased subspace only when a new trigger direction does not conflict with retain semantics. We further introduce Balanced Erasure Utility Score (BEUS), a balanced erasure utility score that combines robustness (ASR under multiple attacks) and utility preservation (FID) via bounded monotone transforms and harmonic mean aggregation. Extensive experiments across NSFW, artistic style, and object erasure, together with a large-scale robustness utility analysis over many CET baselines, show that PARSE achieves robust erasure across multiple concepts without sacrificing post-edit model utility. Project page: https: //shaswati1.github.io/parse/