Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)

Optimus: the first large-scale pre-trained VAE language model

[CVPR 2021 Oral] Official PyTorch implementation of Soft-IntroVAE from the paper "Soft-IntroVAE: Analyzing and Improving Introspective Variational Autoencoders"

[ICCV2025]LeanVAE: An Ultra-Efficient Reconstruction VAE for Video Diffusion Models

VAE with RealNVP prior and Super-Resolution VAE in PyTorch. Code release for https://arxiv.org/abs/2006.05218.

Generative models (GAN, VAE, Diffusion Models, Autoregressive Models) implemented with Pytorch, Pytorch_lightning and hydra.

moai is a PyTorch-based AI Model Development Kit (MDK) created to improve data-driven model workflows, design and reproducibility.

Official PyTorch implementation of A Quaternion-Valued Variational Autoencoder (QVAE).

Pytorch implementation of GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection

Implementation of mutual learning model between VAE and GMM.

🤖 | A collection of comprehensive PyTorch tutorials covering fundamental and advanced deep learning concepts

Dirichlet-Variational Auto-Encoder by PyTorch

Codebase for the paper: Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts

A Variational Autoencoder in PyTorch for the CelebA Dataset.

Pytorch implementation of Gaussian Mixture Variational Autoencoder GMVAE

Pytorch implementation of a Variational Autoencoder (VAE) that learns from the MNIST dataset and generates images of altered handwritten digits.

Convolutional Variational Autoencoder in Pytorch benchmarked on CelebA Dataset

Implementation of LiteVAE

Mapping properties to molecules in QM7-X

A PyTorch implementation of various deep generative models, including Diffusion (DDPM), GAN, cGAN, and VAE.