Attribute Diffusion: Diffusion Driven Diverse Attribute Editing

Image attribute editing is a widely researched area fueled by the recent advancements in deep generative models. Existing methods treat semantic attributes as binary and do not allow the user to generate multiple variations of the attribute edits. This limits the applications of editing methods in the real world, e.g., exploring multiple eyeglass variations on an e-commerce platform. In this work, we present a technique to generate a collection of diverse attribute edits and a principled way to explore them. Generation and controlled exploration of attribute variations is challenging as it requires fine control over the attribute styles while preserving other attributes and the identity of the subject. Capitalizing on the attribute disentanglement property of the latent spaces of pre-trained GANs, we represent the attribute edits in this space. Next, we train a diffusion model to model these latent directions of edits. We propose a coarse-to-fine sampling strategy to explore these variations in a controlled manner. Extensive experiments on various datasets establish the effectiveness and generalization of the proposed approach for the generation and controlled exploration of diverse attribute edits. Code is available at - project page.