Human Aesthetic Preference-Based Large Text-to-Image Model Personalization: Kandinsky Generation as an Example
<|reference_start|>Human Aesthetic Preference-Based Large Text-to-Image Model Personalization: Kandinsky Generation as an Example: 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.<|reference_end|>
@article{zhou2024human,
    title={Human Aesthetic Preference-Based Large Text-to-Image Model
  Personalization: Kandinsky Generation as an Example},
    author={Aven-Le Zhou, Yu-Ao Wang, Wei Wu and Kang Zhang},
    journal={arXiv preprint arXiv:2402.06389},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06389},
    primaryClass={cs.AI cs.HC cs.MM}
}