In this work, we compare the use of Generative Adversarial Networks and Diffusion Models in unstructured crystal generation. We illustrate the shortcomings of GANs in this space and show that Diffusion Models may offer a very promising alternative to previous methods. Furthermore, we introduce a standardized crystal embedding representation (CrysTens) that can be used in a wide array of different models.