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Existing attribute editing methods treat semantic attributes as binary,resulting in a single edit per attribute. However, attributes such aseyeglasses, smiles, or hairstyles exhibit a vast range of diversity. In thiswork, we formulate the task of \textit{diverse attribute editing} by modelingthe multidimensional nature of attribute edits. This enables users to generatemultiple plausible edits per attribute. We capitalize on disentangled latentspaces of pretrained GANs and train a Denoising Diffusion Probabilistic Model(DDPM) to learn the latent distribution for diverse edits. Specifically, wetrain DDPM over a dataset of edit latent directions obtained by embedding imagepairs with a single attribute change. This leads to latent subspaces thatenable diverse attribute editing. Applying diffusion in the highly compressedlatent space allows us to model rich distributions of edits within limitedcomputational resources. Through extensive qualitative and quantitativeexperiments conducted across a range of datasets, we demonstrate theeffectiveness of our approach for diverse attribute editing. We also showcasethe results of our method applied for 3D editing of various face attributes.

Exploring Attribute Variations in Style-based GANs using Diffusion Models