Ouranalysis include examination of the cross-attention maps of the learned prompts at individual time steps of the denoising process.

Text-to-image generative models often present issues regarding fairness with respect to certain sensitive attributes, such as gender or skin tone. This study aims to reproduce the results presented in "ITI-Gen: Inclusive Text-to-Image Generation" by Zhang et al. (2023a), which introduces a model to improve inclusiveness in these kinds of models. We show that most of the claims made by the authors about ITI-Gen hold: it improves the diversity and quality of generated images, it is scalable to different domains, it has plug-and-play capabilities, and it is efficient from a computational point of view. However, ITI-Gen sometimes uses undesired attributes as proxy features and it is unable to disentangle some pairs of (correlated) attributes such as gender and baldness. In addition, when the number of considered attributes increases, the training time grows exponentially and ITI-Gen struggles to generate inclusive images for all elements in the joint distribution. To solve these issues, we propose using Hard Prompt Search with negative prompting, a method that does not require training and that handles negation better than vanilla Hard Prompt Search. Nonetheless, Hard Prompt Search (with or without negative prompting) cannot be used for continuous attributes that are hard to express in natural language, an area where ITI-Gen excels as it is guided by images during training. Finally, we propose combining ITI-Gen and Hard Prompt Search with negative prompting.

Text-to-image generative AI models such as Stable Diffusion are used daily by millions worldwide. However, many have raised concerns regarding how these models amplify racial and gender stereotypes. To study this phenomenon, we develop a classifier to predict the race, gender, and age group of any given face image, and show that it achieves state-of-the-art performance. Using this classifier, we quantify biases in Stable Diffusion across six races, two genders, five age groups, 32 professions, and eight attributes. We then propose novel debiasing solutions that outperform state-of-the-art alternatives. Additionally, we examine the degree to which Stable Diffusion depicts individuals of the same race as being similar to one another. This analysis reveals a high degree of stereotyping, e.g., depicting most middle eastern males as being dark-skinned, bearded, and wearing a traditional headdress. We address these limitations by proposing yet another novel solution that increases facial diversity across genders and racial groups. Our solutions are open-sourced and made publicly available.