\section*{Ethical Implications}


This work aims to provide users with an effective framework for adapting the output of diffusion models to the preferred aesthetics of the user. While general text-to-image models might be biased towards specific attributes when synthesizing images from text, our approach enables the user to better reflect the desirable effects. On the other hand, malicious agents might try to use generated images to mislead viewers. This is a common issue, existing in other generative modeling approaches. Future research in generative modeling and personalization must continue investigating and revaluing these issues.


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