Exploring Attribute Variations in Style-based GANs using Diffusion
  Models
Rishubh Parihar Prasanna Balaji Raghav Magazine Sarthak Vora Tejan Karmali Varun Jampani R. Venkatesh Babu
Existing attribute editing methods treat semantic attributes as binary,
resulting in a single edit per attribute. However, attributes such as
eyeglasses, smiles, or hairstyles exhibit a vast range of diversity. In this
work, we formulate the task of \textit{diverse attribute editing} by modeling
the multidimensional nature of attribute edits. This enables users to generate
multiple plausible edits per attribute. We capitalize on disentangled latent
spaces of pretrained GANs and train a Denoising Diffusion Probabilistic Model
(DDPM) to learn the latent distribution for diverse edits. Specifically, we
train DDPM over a dataset of edit latent directions obtained by embedding image
pairs with a single attribute change. This leads to latent subspaces that
enable diverse attribute editing. Applying diffusion in the highly compressed
latent space allows us to model rich distributions of edits within limited
computational resources. Through extensive qualitative and quantitative
experiments conducted across a range of datasets, we demonstrate the
effectiveness of our approach for diverse attribute editing. We also showcase
the results of our method applied for 3D editing of various face attributes.