Recent text-conditioned image generation models have demonstrated an exceptional capacity to produce diverse and creative imagery with high visual quality. However, when pre-trained on billion-sized datasets randomly collected from the Internet, where potential biased human preferences exist, these models tend to produce images with common and recurring stereotypes, particularly for certain racial groups. In this paper, we conduct an initial analysis of the publicly available Stable Diffusion model and its derivatives, highlighting the presence of racial stereotypes. These models often generate distorted or biased images for certain racial groups, emphasizing stereotypical characteristics. To address these issues, we propose a framework called "RS-Corrector", designed to establish an anti-stereotypical preference in the latent space and update the latent code for refined generated results. The correction process occurs during the inference stage without requiring fine-tuning of the original model. Extensive empirical evaluations demonstrate that the introduced \themodel effectively corrects the racial stereotypes of the well-trained Stable Diffusion model while leaving the original model unchanged.

翻译：近年来，基于文本条件的图像生成模型在生成多样且富有创意的视觉高质量图像方面展现出卓越能力。然而，当这些模型在从互联网随机收集的数十亿级数据集上预训练时（其中存在潜在的偏向性人类偏好），它们往往会产生包含常见且反复出现的刻板印象的图像，尤其是针对特定种族群体。本文对公开可用的Stable Diffusion模型及其衍生模型进行了初步分析，揭示了其中存在的种族刻板印象。这些模型常为特定种族群体生成扭曲或带有偏见的图像，并突出刻板特征。为解决这些问题，我们提出了一种名为"RS-Corrector"的框架，旨在在潜在空间中建立反刻板印象偏好，并通过更新潜在编码以优化生成结果。该校正过程在推理阶段完成，无需对原始模型进行微调。大量实证评估表明，该提出的模型在保持原始模型不变的前提下，有效校正了经过充分训练的Stable Diffusion模型中的种族刻板印象。