Journal of East China Normal University(Natural Science) >

2025 , Vol. 2025 >Issue 1: 138 - 150

DOI: https://doi.org/10.3969/j.issn.1000-5641.2025.01.011

Computer Science

Purging diffusion models through CLIP based fine-tuning

  • Ping WU ,
  • Xin LIN
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  • School of Computer Science and Technology, East China Normal University, Shanghai　200062, China

Received date: 2024-01-11

  Online published: 2025-01-20

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, 2025, Copyright reserved © 2025.
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Abstract

Diffusion models have revolutionized text-to-image synthesis, enabling users to generate high-quality and imaginative artworks from simple natural-language text prompts. Unfortunately, due to the large and unfiltered training dataset, inappropriate content such as nudity and violence can be generated from them. To deploy such models at a higher level of safety, we propose a novel method, directional contrastive language-image pre-training (CLIP) loss-based fine-tuning, dubbed as CLIF. This method utilizes directional CLIP loss to suppress the model’s inappropriate generation ability. CLIF is lightweight and immune to circumvention. To demonstrate the effectiveness of CLIF, we proposed a benchmark called categorized toxic prompts (CTP) to evaluate the ability to generate inappropriate content for text-to-image diffusion models. As shown by our experiments on CTP and common objects in context (COCO) datasets, CLIF is capable of significantly suppressing inappropriate generation while preserving the model’s ability to produce general content.

Key words： text-to-image generative models; security; datasets; diffusion models

Cite this article

Ping WU , Xin LIN . Purging diffusion models through CLIP based fine-tuning[J]. Journal of East China Normal University(Natural Science), 2025 , 2025(1) : 138 -150 . DOI: 10.3969/j.issn.1000-5641.2025.01.011

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