  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
