Removing NSFW Concepts from Vision-and-Language Models for Text-to-Image Retrieval and Generation

Vision-and-Language models such as CLIP have demonstrated remarkable effectiveness across a wide range of tasks. However, these models are typically trained on web-scale data, which can introduce inappropriate content and lead to the development of unsafe and biased behavior. This, in turn, hampers their applicability in sensitive and trustworthy contexts and could raise significant concern in their adoption. To overcome these limitations, we introduce a methodology to make Vision-and-Language models safer by removing their sensitivity to not-safe-for-work concepts. We show how this can be done by distilling from a large language model which converts between safe and unsafe sentences and which is fine-tuned starting from just 100 manually-curated pairs. We conduct extensive experiments on the resulting embedding space for both retrieval and text-to-image generation, where we show that our model can also be properly employed with pre-trained image generators. Our source code and trained models are available at: https://github.com/aimagelab/safe-clip.