Materials discovery is an enormous open problem in the field of materials informatics and a wide variety of techniques have been used to address it. Some methods include large-scale combinatorial synthesis simulations to create crystals that are synthesizable and stable. However, in order for combinatorial synthesis methods to be successful, thousands or millions of compounds must be generated in order to identify useful candidates. Furthermore, known materials may only account for a minuscule fraction of the possible number of synthesizable and stable crystals . Discovering new structures in an efficient and effective way requires a method of processing enormous amounts of data, quickly identifying patterns that are present within the dataset, and extrapolating those patterns outside of the dataset so that new materials can be discovered. Generative models have been implemented for a variety of topics, including composition , molecules , and crystal structures . In this work, we focus on crystal structure generative models.