Discovering and Mitigating Biases in CLIP-based Image Editing

In recent years, the use of CLIP (Contrastive Language-Image Pre-Training) has become increasingly popular in a wide range of downstream applications, including zero-shot image classification and text-to-image synthesis. Despite being trained on a vast dataset, the CLIP model has been found to exhibit biases against certain protected attributes, such as gender and race. While previous research has focused on the impact of such biases on image classification, there has been little investigation into their effects on CLIP-based generative tasks. In this paper, we aim to address this gap in the literature by uncovering the queries for which the CLIP model introduces biases in the text-based image editing task. Through a series of experiments, we demonstrate that these biases can have a significant impact on the quality and content of the generated images. To mitigate these biases, we propose a debiasing technique that does not require retraining either the CLIP model or the underlying generative model. Our results show that our proposed framework can effectively reduce the impact of biases in CLIP-based image editing models. Overall, this paper highlights the importance of addressing biases in CLIP-based generative tasks and provides practical solutions that can be readily adopted by researchers and practitioners working in this area.
¹