In the space of generative modelling of inorganic crystal structures, studies can be differentiated primarily based on two aspects: crystal structure representation and learning models used for generating the novel crystal structures. One of the earliest works in this domain was the rediscovery of experimentally known VxOy materials when the model was trained without them. In addition to this, 20,000 hypothetical compounds were generated as well. The image based representation used in this work was named iMatGen, where the crystal structure was encoded using two images, cell (length of the cell edges and angles between them) and basis (atomic position within a unit cell). This image based representation was encoded and then decoded to original data using a Variational Autoencoder.