Recent studies have highlighted biases in generative models, shedding light
on their predisposition towards gender-based stereotypes and imbalances. This
paper contributes to this growing body of research by introducing an evaluation
protocol designed to automatically analyze the impact of gender indicators on
Stable Diffusion images. Leveraging insights from prior work, we explore how
gender indicators not only affect gender presentation but also the
representation of objects and layouts within the generated images. Our findings
include the existence of differences in the depiction of objects, such as
instruments tailored for specific genders, and shifts in overall layouts. We
also reveal that neutral prompts tend to produce images more aligned with
masculine prompts than their feminine counterparts, providing valuable insights
into the nuanced gender biases inherent in Stable Diffusion.

该研究通过引入一个评估协议，旨在自动分析性别指示对稳定扩散图像的影响，从而为生成模型中的偏见和性别失衡问题做出贡献。研究发现性别指示不仅影响性别呈现，还影响生成图像中的物体和布局的表现，揭示出稳定扩散中微妙的性别偏见。