  Modern generative models achieve excellent quality in a variety of tasks
including image or text generation and chemical molecule modeling. However,
existing methods often lack the essential ability to generate examples with
requested properties, such as the age of the person in the photo or the weight
of the generated molecule. Incorporating such additional conditioning factors
would require rebuilding the entire architecture and optimizing the parameters
from scratch. Moreover, it is difficult to disentangle selected attributes so
that to perform edits of only one attribute while leaving the others unchanged.
To overcome these limitations we propose PluGeN (Plugin Generative Network), a
simple yet effective generative technique that can be used as a plugin to
pre-trained generative models. The idea behind our approach is to transform the
entangled latent representation using a flow-based module into a
multi-dimensional space where the values of each attribute are modeled as an
independent one-dimensional distribution. In consequence, PluGeN can generate
new samples with desired attributes as well as manipulate labeled attributes of
existing examples. Due to the disentangling of the latent representation, we
are even able to generate samples with rare or unseen combinations of
attributes in the dataset, such as a young person with gray hair, men with
make-up, or women with beards. We combined PluGeN with GAN and VAE models and
applied it to conditional generation and manipulation of images and chemical
molecule modeling. Experiments demonstrate that PluGeN preserves the quality of
backbone models while adding the ability to control the values of labeled
attributes.
