AntixK/PyTorch-VAE

Health v2 · maintenance only

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Adoption not part of the score

7665 stars · 1182 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A collection of Variational Autoencoder (VAE) model implementations in PyTorch, including Beta-VAE, VQ-VAE, IWAE, WAE, and others, with a focus on reproducibility. All models are trained on the CelebA dataset for consistent comparison and use PyTorch Lightning for training.

Use cases

• implement a VAE in pytorch
• compare different variational autoencoder architectures
• reproduce results from VAE papers
• learn how VQ-VAE or Beta-VAE works
• train a VAE on CelebA faces
• get reference code for a latent variable generative model

When to choose

• you want clean, comparable reference implementations of many VAE variants
• you are doing research or studying generative latent-variable models
• you want a simple starting point to modify a VAE architecture

When to avoid

• you need a production-ready or actively maintained generative modeling framework
• you want to train on datasets other than CelebA without adapting configs
• you need the latest VAE research beyond the included models

Facets

library · maturity maintenance

deep-learning machine-learning image-processing deep-learning machine-learning image-processing python variational-autoencoder pytorch-lightning paper-implementations generative-models celeba gpu

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markdown · JSON · MCP: product_card(name="AntixK/PyTorch-VAE")