Once we have trained a model with the CrysTens representation, it would be desirable to transform the output of each model at inference time back into a CIF for visualization and analysis purposes. The process of transforming back from the CrysTens representation can also provide insights into the level of "understanding" of the representation itself that each model displays. CrysTens representations generated from CIF files are by definition symmetric, but CrysTens' that are output from generative models are not necessarily symmetric due to the stochastic nature of deep learning algorithms. Instead, we found that even our most symmetrical generated CrysTens' had a small amount of noise that was mitigated by the redundancy of our representation.