  Generative Adversarial Networks (GANs) are the driving force behind the
state-of-the-art in image generation. Despite their ability to synthesize
high-resolution photo-realistic images, generating content with on-demand
conditioning of different granularity remains a challenge. This challenge is
usually tackled by annotating massive datasets with the attributes of interest,
a laborious task that is not always a viable option. Therefore, it is vital to
introduce control into the generation process of unsupervised generative
models. In this work, we focus on controllable image generation by leveraging
GANs that are well-trained in an unsupervised fashion. To this end, we discover
that the representation space of intermediate layers of the generator forms a
number of clusters that separate the data according to semantically meaningful
attributes (e.g., hair color and pose). By conditioning on the cluster
assignments, the proposed method is able to control the semantic class of the
generated image. Our approach enables sampling from each cluster by Implicit
Maximum Likelihood Estimation (IMLE). We showcase the efficacy of our approach
on faces (CelebA-HQ and FFHQ), animals (Imagenet) and objects (LSUN) using
different pre-trained generative models. The results highlight the ability of
our approach to condition image generation on attributes like gender, pose and
hair style on faces, as well as a variety of features on different object
classes.
