I talk about deviations from previous approaches in the DESIGN doc (https://github.com/minimaxir/aitextgen/blob/master/DESIGN.md), but to answer the difference between aitextgen and Huggingface Transformers:

Model I/O: aitextgen abstracts some of the boilerplate and supports custom GPT-2 models and importing the old TensorFlow models better.

Training: Completely different from Transformers. Different file processing and encoding, training loop leverages pytorch-lightning.

Generation: Abstracts boilerplate, allowing addition of more utility functions (e.g bolding when printing to console, allow printing bulk text to file). Generation is admittingly not that much different than Transformers, but future iterations will increasingly diverge.