Fine-Grained Erasure in Text-to-Image Diffusion-based Foundation Models

• URL: http://arxiv.org/abs/2503.19783v1
• Date: Tue, 25 Mar 2025 15:49:48 GMT
• Title: Fine-Grained Erasure in Text-to-Image Diffusion-based Foundation Models
• Authors: Kartik Thakral, Tamar Glaser, Tal Hassner, Mayank Vatsa, Richa Singh,
• Abstract summary: FADE (Fine grained Attenuation for Diffusion Erasure) is an adjacency-aware unlearning algorithm for text-to-image generative models.<n>It removes target concepts with minimal impact on correlated concepts, achieving a 12% improvement in retention performance over state-of-the-art methods.
• Score: 56.35484513848296
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing unlearning algorithms in text-to-image generative models often fail to preserve the knowledge of semantically related concepts when removing specific target concepts: a challenge known as adjacency. To address this, we propose FADE (Fine grained Attenuation for Diffusion Erasure), introducing adjacency aware unlearning in diffusion models. FADE comprises two components: (1) the Concept Neighborhood, which identifies an adjacency set of related concepts, and (2) Mesh Modules, employing a structured combination of Expungement, Adjacency, and Guidance loss components. These enable precise erasure of target concepts while preserving fidelity across related and unrelated concepts. Evaluated on datasets like Stanford Dogs, Oxford Flowers, CUB, I2P, Imagenette, and ImageNet1k, FADE effectively removes target concepts with minimal impact on correlated concepts, achieving atleast a 12% improvement in retention performance over state-of-the-art methods.

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