NVlabs/NVAE

Health v2 · maintenance only

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Adoption not part of the score

1092 stars · 176 forks observed · 2026-08-28

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

Official PyTorch implementation of NVAE, a deep hierarchical variational autoencoder for likelihood-based image generation from a NeurIPS 2020 spotlight paper. It achieves state-of-the-art results among non-autoregressive likelihood models on MNIST, CIFAR-10, CelebA 64, and CelebA HQ datasets.

Use cases

• train a hierarchical VAE on CIFAR-10 or MNIST
• generate high-quality face images with a VAE
• reproduce NeurIPS 2020 NVAE paper results
• benchmark likelihood-based generative models in bits per dimension
• experiment with deep hierarchical VAE architectures in PyTorch
• train a generative image model on CelebA HQ 256

When to choose

• you need a strong, well-known VAE baseline for image generation research
• you want tractable, fast sampling with an encoder network unlike flows or autoregressive models
• you are reproducing or extending published hierarchical VAE results

When to avoid

• you need actively maintained code with modern PyTorch versions - the repo has not been updated since late 2022
• you want state-of-the-art image generation today, since diffusion models now outperform it
• you need a production-ready generative model rather than research code

Facets

library · maturity maintenance

machine-learning deep-learning image-processing deep-learning machine-learning image-processing computer-vision python variational-autoencoder pytorch generative-models image-generation research-code neurips-2020 gpu linux

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