While using generative models to create synthetic network traffic is faster and cheaper than traditional testbeds, synthetic traffic suffers from problems with realism and structural completeness. State of the art traffic generation frameworks usually omit payloads because of the difficulties in representing their high-dimensional data, which makes the synthetic traffic unrealistic and limits its usefulness. This work proposes a two-stage process that takes advantage of the high repetition of some protocols, particularly those used by Industrial Control Systems, to selectively simplify payloads, greatly reducing the number of classes and reducing model loss and consequently the ability of the model to handle sequences of payloads. Model training loss was reduced by 47.796%, and payload class selection was improved up to 69% over state of the art approaches, allowing for more realistic synthetic network traffic with reduced memory and computation overheads.