  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.
