clementchadebec/benchmark_VAE

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1995 stars · 178 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Pythae is a PyTorch library that unifies implementations of many Variational Autoencoder (VAE) variants under a common interface, enabling fair benchmarking with identical encoder/decoder architectures. It supports custom autoencoder architectures, distributed training, experiment tracking via wandb/mlflow/comet-ml, and model sharing through the HuggingFace Hub.

Use cases

• benchmark VAE variants under the same neural network architecture
• train a beta-VAE or VQ-VAE on my own dataset with custom encoder and decoder
• reproduce published VAE research results in PyTorch
• compare generative autoencoder models like VAE-GAN and Wasserstein autoencoder
• share and load trained VAE models on the HuggingFace Hub
• run distributed training of a variational autoencoder on larger datasets
• generate new samples from a trained VAE with built-in samplers

When to choose

• you need a unified, reproducible implementation of many VAE variants for fair comparison
• you want to train VAEs with your own encoder/decoder networks and track experiments with wandb, mlflow, or comet-ml
• you want to share or reuse trained VAE models via the HuggingFace Hub
• you need distributed (DDP) training for VAEs on large datasets

When to avoid

• you need general-purpose deep learning tooling beyond autoencoder models
• you want diffusion models or transformer-based generative models rather than VAE-family methods
• you need a production inference server rather than a research and benchmarking library

Facets

library · maturity active

machine-learning deep-learning benchmarking machine-learning deep-learning artificial-intelligence python variational-autoencoder pytorch generative-models reproducible-research huggingface-hub experiment-tracking beta-vae vq-vae normalizing-flows model-training

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