Vector drawings are innately interactive as they preserve creational cues. Despitethis desirable property they remain relatively under explored due to the difficultiesin modeling complex vector drawings. This is in part due to the primarily sequential and auto-regressive nature of existing approaches failing to scale beyond simpledrawings. In this paper, we define generative models over highly complex vectordrawings by first representing them as “stroke-clouds” – sets of arbitrary cardinality comprised of semantically meaningful strokes. The dimensionality of thestrokes is a design choice that allows the model to adapt to a range of complexities.We learn to encode these set of strokes into compact latent codes by a probabilisticreconstruction procedure backed by De-Finetti’s Theorem of Exchangability. Theparametric generative model is then defined over the latent vectors of the encodedstroke-clouds. The resulting “Latent stroke-cloud generator (LSG)” thus capturesthe distribution of complex vector drawings on an implicit set space. We demonstrate the efficacy of our model on complex drawings (a newly created Animeline-art dataset) through a rangeof generative tasks.