  With the advancement of neural generative capabilities, the art community has
actively embraced GenAI (generative artificial intelligence) for creating
painterly content. Large text-to-image models can quickly generate
aesthetically pleasing outcomes. However, the process can be non-deterministic
and often involves tedious trial-and-error, as users struggle with formulating
effective prompts to achieve their desired results. This paper introduces a
prompting-free generative approach that empowers users to automatically
generate personalized painterly content that incorporates their aesthetic
preferences in a customized artistic style. This approach involves utilizing
``semantic injection'' to customize an artist model in a specific artistic
style, and further leveraging a genetic algorithm to optimize the prompt
generation process through real-time iterative human feedback. By solely
relying on the user's aesthetic evaluation and preference for the artist
model-generated images, this approach creates the user a personalized model
that encompasses their aesthetic preferences and the customized artistic style.
