This library implements variational autoencoder models under a unified PyTorch framework, enabling benchmark experiments and fair comparisons across different VAE architectures. It supports training models with the same autoencoding neural network architecture, integrates with MLflow, wandb, and Comet for experiment monitoring, and allows model sharing via the HuggingFace Hub.