  The impressive capacity shown by recent text-to-image diffusion models to
generate high-quality pictures from textual input prompts has leveraged the
debate about the very definition of art. Nonetheless, these models have been
trained using text data collected from content-based labelling protocols that
focus on describing the items and actions in an image but neglect any
subjective appraisal. Consequently, these automatic systems need rigorous
descriptions of the elements and the pictorial style of the image to be
generated, otherwise failing to deliver. As potential indicators of the actual
artistic capabilities of current generative models, we characterise the
sentimentality, objectiveness and degree of abstraction of publicly available
text data used to train current text-to-image diffusion models. Considering the
sharp difference observed between their language style and that typically
employed in artistic contexts, we suggest generative models should incorporate
additional sources of subjective information in their training in order to
overcome (or at least to alleviate) some of their current limitations, thus
effectively unleashing a truly artistic and creative generation.
