In this paper, aesthetic gradients are proposed as a method for personalizing a CLIP-conditioned diffusion model by using a set of images to guide the generative process towards a custom aesthetic. The approach is validated through qualitative and quantitative experiments using the recently developed stable diffusion model and several aesthetically-filtered datasets.
The following video by koiboi can be useful to explain how you can build one by yourself.
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