No model is practical if it can't be used outside the training and test sets. This is when the model is deployed into production.
In this stage, we normally load all the model's operation and trained weights, wait for new unknown data, and when it arrives, we feed it through all the chained functions of the model, informing the outcomes of the output layer or operation via a web service, printing to standard output, and so on.
Then, we will have a final task - to interpret the results of the model in the real world to constantly check whether it works in the current conditions. In the case of generative models, the suitability of the predictions is easier to understand because the goal is normally the representation of a previously known entity.