In order to perform efficient material discovery via deep learning, it is important to find a representation that is capable of capturing all aspects of a given crystal structure. CrysTens encodes all of the pertinent values of a structure such as lattice parameters, lattice angles, and space group numbers as well as an interatomic component that is composed of a pairwise distance matrix as well as a dimensional graph for each dimension. The redundant aspects of CrysTens not only allow generative models many opportunities to mitigate noise when generating a crystal but it can also provide a way for measuring the performance of a given model. The variance of generated lattice parameters, lattice angles, space groups, and fractional coordinates is correlated with a model's ability to produce realistic and symmetrical crystals that have parameters similar to those found in the real distribution and even match the real distribution of CGCNN predicted values. We found that Vanilla GANs struggled in this space, often falling victim to training instability and mode collapse that ultimately lead to poor generated CIF quality. Many of these problems were rectified with the implementation of EMD and Wasserstein Loss, creating a WGAN. However, although WGANs did not struggle with the same training instability and mode collapse that Vanilla GANs did, they failed to consistently produce symmetrical crystals. We found that Diffusion Models performed the best in this space. They performed the best in all of our metrics and consistently produced the most realistic-looking and symmetrical CIFs. The enhanced performance of Diffusion Models over GANs holds true in the image synthesis domain as well . The use of Diffusion Models is extremely promising in the field of materials informatics and the improvement of such models could not only provide an efficient method of materials discovery but could revolutionize inverse design as well.