arxiv:2209.12330

Personalizing Text-to-Image Generation via Aesthetic Gradients

Published on Sep 25, 2022

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Abstract

Aesthetic gradients guide a CLIP-conditioned diffusion model to generate images aligned with user-defined aesthetics using qualitative and quantitative experiments.

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This work proposes aesthetic gradients, a method to personalize a CLIP-conditioned diffusion model by guiding the generative process towards custom aesthetics defined by the user from a set of images. The approach is validated with qualitative and quantitative experiments, using the recent stable diffusion model and several aesthetically-filtered datasets. Code is released at https://github.com/vicgalle/stable-diffusion-aesthetic-gradients

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