  In creativity support and computational co-creativity contexts, the task of
discovering appropriate prompts for use with text-to-image generative models
remains difficult. In many cases the creator wishes to evoke a certain
impression with the image, but the task of conferring that succinctly in a text
prompt poses a challenge: affective language is nuanced, complex, and
model-specific. In this work we introduce a method for generating images
conditioned on desired affect, quantified using a psychometrically validated
three-component approach, that can be combined with conditioning on text
descriptions. We first train a neural network for estimating the affect content
of text and images from semantic embeddings, and then demonstrate how this can
be used to exert control over a variety of generative models. We show examples
of how affect modifies the outputs, provide quantitative and qualitative
analysis of its capabilities, and discuss possible extensions and use cases.
