Text-to-image models, which can generate high-quality images based on textual input, have recently enabled various content-creation tools. Despite significantly affecting a wide range of downstream applications, the distributions of these generated images are still not fully understood, especially when it comes to the potential stereotypical attributes of different genders. In this work, we propose a paradigm (Gender Presentation Differences) that utilizes fine-grained self-presentation attributes to study how gender is presented differently in text-to-image models. By probing gender indicators in the input text (e.g., "a woman" or "a man"), we quantify the frequency differences of presentation-centric attributes (e.g., "a shirt" and "a dress") through human annotation and introduce a novel metric: GEP. Furthermore, we propose an automatic method to estimate such differences. The automatic GEP metric based on our approach yields a higher correlation with human annotations than that based on existing CLIP scores, consistently across three state-of-the-art text-to-image models. Finally, we demonstrate the generalization ability of our metrics in the context of gender stereotypes related to occupations.

翻译：文本到图像模型能够基于文本输入生成高质量图像，近年来已赋能多种内容创作工具。尽管这些生成图像的分布对广泛的下游应用产生显著影响，但其特征尚未被充分理解，尤其是在不同性别的潜在刻板印象属性方面。本文提出一种研究范式（性别呈现差异），利用细粒度的自我呈现属性来分析文本到图像模型中性别呈现的差异。通过探测输入文本中的性别指示词（例如“一位女性”或“一位男性”），我们借助人工标注量化呈现中心属性（例如“一件衬衫”和“一条连衣裙”）的频率差异，并引入新的度量指标：GEP。此外，我们提出一种自动估计此类差异的方法。基于该方法的自动GEP度量与现有CLIP评分相比，与人工标注的相关性更高，且这一结论在三个最先进的文本到图像模型中一致成立。最后，我们展示了所提出度量在与职业相关的性别刻板印象场景中的泛化能力。