Existing text-to-image generative models reflect or even amplify societal biases ingrained in their training data. This is especially concerning for human image generation where models are biased against certain demographic groups. Existing attempts to rectify this issue are hindered by the inherent limitations of the pre-trained models and fail to substantially improve demographic diversity. In this work, we introduce Fair Retrieval Augmented Generation (FairRAG), a novel framework that conditions pre-trained generative models on reference images retrieved from an external image database to improve fairness in human generation. FairRAG enables conditioning through a lightweight linear module that projects reference images into the textual space. To enhance fairness, FairRAG applies simple-yet-effective debiasing strategies, providing images from diverse demographic groups during the generative process. Extensive experiments demonstrate that FairRAG outperforms existing methods in terms of demographic diversity, image-text alignment, and image fidelity while incurring minimal computational overhead during inference.

本研究介绍了一种名为公平检索增强生成（FairRAG）的新框架，该框架通过从外部图像数据库中检索的参考图像来改善人类生成中的公平性，并通过将参考图像投影到文本空间的轻量级线性模块来实现条件生成，以提高公平性。通过简单而有效的去偏策略，FairRAG 提供来自各种人口群体的图像，从而在生成过程中增加了人口多样性。大量实验表明，FairRAG 在人口多样性、图像-文本对齐和图像保真度方面优于现有方法，并且在推理过程中具有最小的计算开销。

公正的人类生成：公正检索增强