DuMo: Dual Encoder Modulation Network for Precise Concept Erasure

TL;DR

DuMo is a novel network that precisely erases inappropriate concepts from text-to-image models by targeting high-frequency image details while preserving non-target concepts, using a frozen backbone and adaptive modulation.

Contribution

The paper introduces DuMo, which employs a novel EPR module and TLMO process to achieve precise concept erasure with minimal impact on non-target concepts, outperforming existing methods.

Findings

Achieves state-of-the-art results in content erasure tasks.

Effectively preserves non-target concepts during erasure.

Demonstrates adaptive erasure across different image layers and timesteps.

Abstract

The exceptional generative capability of text-to-image models has raised substantial safety concerns regarding the generation of Not-Safe-For-Work (NSFW) content and potential copyright infringement. To address these concerns, previous methods safeguard the models by eliminating inappropriate concepts. Nonetheless, these models alter the parameters of the backbone network and exert considerable influences on the structural (low-frequency) components of the image, which undermines the model's ability to retain non-target concepts. In this work, we propose our Dual encoder Modulation network (DuMo), which achieves precise erasure of inappropriate target concepts with minimum impairment to non-target concepts. In contrast to previous methods, DuMo employs the Eraser with PRior Knowledge (EPR) module which modifies the skip connection features of the U-NET and primarily achieves concept erasure on details (high-frequency) components of the image. To minimize the damage to non-target concepts during erasure, the parameters of the backbone U-NET are frozen and the prior knowledge from the original skip connection features is introduced to the erasure process. Meanwhile, the phenomenon is observed that distinct erasing preferences for the image structure and details are demonstrated by the EPR at different timesteps and layers. Therefore, we adopt a novel Time-Layer MOdulation process (TLMO) that adjusts the erasure scale of EPR module's outputs across different layers and timesteps, automatically balancing the erasure effects and model's generative ability. Our method achieves state-of-the-art performance on Explicit Content Erasure, Cartoon Concept Removal and Artistic Style Erasure, clearly outperforming alternative methods. Code is available at https://github.com/Maplebb/DuMo