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High-quality, diverse, and photorealistic images can now be generated byunconditional GANs (e.g., StyleGAN). However, limited options exist to controlthe generation process using (semantic) attributes, while still preserving thequality of the output. Further, due to the entangled nature of the GAN latentspace, performing edits along one attribute can easily result in unwantedchanges along other attributes. In this paper, in the context of conditionalexploration of entangled latent spaces, we investigate the two sub-problems ofattribute-conditioned sampling and attribute-controlled editing. We presentStyleFlow as a simple, effective, and robust solution to both the sub-problemsby formulating conditional exploration as an instance of conditional continuousnormalizing flows in the GAN latent space conditioned by attribute features. Weevaluate our method using the face and the car latent space of StyleGAN, anddemonstrate fine-grained disentangled edits along various attributes on bothreal photographs and StyleGAN generated images. For example, for faces, we varycamera pose, illumination variation, expression, facial hair, gender, and age.Finally, via extensive qualitative and quantitative comparisons, we demonstratethe superiority of StyleFlow to other concurrent works.

StyleFlow: Attribute-conditioned Exploration of StyleGAN-Generated Images using Conditional Continuous Normalizing Flows