Personalizing Text-to-Image Generation via Aesthetic Gradients (Paper+Code)
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...