ConceptPrune: Concept Editing in Diffusion Models via Skilled Neuron Pruning

• URL: http://arxiv.org/abs/2405.19237v1
• Date: Wed, 29 May 2024 16:19:37 GMT
• Title: ConceptPrune: Concept Editing in Diffusion Models via Skilled Neuron Pruning
• Authors: Ruchika Chavhan, Da Li, Timothy Hospedales,
• Abstract summary: Large-scale text-to-image diffusion models have demonstrated impressive image-generation capabilities. We present ConceptPrune, wherein we first identify critical regions within pre-trained models responsible for generating undesirable concepts. Experiments across a range of concepts including artistic styles, nudity, object erasure, and gender debiasing demonstrate that target concepts can be efficiently erased by pruning a tiny fraction.
• Score: 10.201633236997104
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While large-scale text-to-image diffusion models have demonstrated impressive image-generation capabilities, there are significant concerns about their potential misuse for generating unsafe content, violating copyright, and perpetuating societal biases. Recently, the text-to-image generation community has begun addressing these concerns by editing or unlearning undesired concepts from pre-trained models. However, these methods often involve data-intensive and inefficient fine-tuning or utilize various forms of token remapping, rendering them susceptible to adversarial jailbreaks. In this paper, we present a simple and effective training-free approach, ConceptPrune, wherein we first identify critical regions within pre-trained models responsible for generating undesirable concepts, thereby facilitating straightforward concept unlearning via weight pruning. Experiments across a range of concepts including artistic styles, nudity, object erasure, and gender debiasing demonstrate that target concepts can be efficiently erased by pruning a tiny fraction, approximately 0.12% of total weights, enabling multi-concept erasure and robustness against various white-box and black-box adversarial attacks.

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