  Recent progress in generative models, especially in text-guided diffusion
models, has enabled the production of aesthetically-pleasing imagery resembling
the works of professional human artists. However, one has to carefully compose
the textual description, called the prompt, and augment it with a set of
clarifying keywords. Since aesthetics are challenging to evaluate
computationally, human feedback is needed to determine the optimal prompt
formulation and keyword combination. In this paper, we present a
human-in-the-loop approach to learning the most useful combination of prompt
keywords using a genetic algorithm. We also show how such an approach can
improve the aesthetic appeal of images depicting the same descriptions.
