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Modern generative models achieve excellent quality in a variety of tasksincluding image or text generation and chemical molecule modeling. However,existing methods often lack the essential ability to generate examples withrequested properties, such as the age of the person in the photo or the weightof the generated molecule. Incorporating such additional conditioning factorswould require rebuilding the entire architecture and optimizing the parametersfrom scratch. Moreover, it is difficult to disentangle selected attributes sothat to perform edits of only one attribute while leaving the others unchanged.To overcome these limitations we propose PluGeN (Plugin Generative Network), asimple yet effective generative technique that can be used as a plugin topre-trained generative models. The idea behind our approach is to transform theentangled latent representation using a flow-based module into amulti-dimensional space where the values of each attribute are modeled as anindependent one-dimensional distribution. In consequence, PluGeN can generatenew samples with desired attributes as well as manipulate labeled attributes ofexisting examples. Due to the disentangling of the latent representation, weare even able to generate samples with rare or unseen combinations ofattributes in the dataset, such as a young person with gray hair, men withmake-up, or women with beards. We combined PluGeN with GAN and VAE models andapplied it to conditional generation and manipulation of images and chemicalmolecule modeling. Experiments demonstrate that PluGeN preserves the quality ofbackbone models while adding the ability to control the values of labeledattributes.

PluGeN: Multi-Label Conditional Generation From Pre-Trained Models