Now You See It, Now You Don't - Instant Concept Erasure for Safe Text-to-Image and Video Generation

TL;DR

The paper introduces Instant Concept Erasure (ICE), a training-free, modality-agnostic method for precise and persistent concept removal in text-to-image and video models, enhancing safety with minimal performance impact.

Contribution

ICE presents a novel, analytical approach for one-shot concept erasure that works across T2I and T2V models without retraining or inference overhead.

Findings

Achieves targeted concept removal with minimal collateral damage.

Demonstrates robustness against adversarial red-teaming.

Maintains core generative capabilities post-erasure.

Abstract

Robust concept removal for text-to-image (T2I) and text-to-video (T2V) models is essential for their safe deployment. Existing methods, however, suffer from costly retraining, inference overhead, or vulnerability to adversarial attacks. Crucially, they rarely model the latent semantic overlap between the target erase concept and surrounding content -- causing collateral damage post-erasure -- and even fewer methods work reliably across both T2I and T2V domains. We introduce Instant Concept Erasure (ICE), a training-free, modality-agnostic, one-shot weight modification approach that achieves precise, persistent unlearning with zero overhead. ICE defines erase and preserve subspaces using anisotropic energy-weighted scaling, then explicitly regularises against their intersection using a unique, closed-form overlap projector. We pose a convex and Lipschitz-bounded Spectral Unlearning Objective, balancing erasure fidelity and intersection preservation, that admits a stable and unique analytical solution. This solution defines a dissociation operator that is translated to the model's text-conditioning layers, making the edit permanent and runtime-free. Across targeted removals of artistic styles, objects, identities, and explicit content, ICE efficiently achieves strong erasure with improved robustness to red-teaming, all while causing only minimal degradation of original generative abilities in both T2I and T2V models.