Implementation of Generating Diverse High-Fidelity Images with VQ-VAE-2 in PyTorch

Official Pytorch implementation of the paper "Action-Conditioned 3D Human Motion Synthesis with Transformer VAE", ICCV 2021

PyTorch implementation of VQ-VAE-2 from "Generating Diverse High-Fidelity Images with VQ-VAE-2"

Variational Autoencoder (VAE) with perception loss implementation in pytorch

🧶 Modular VAE disentanglement framework for python built with PyTorch Lightning ▸ Including metrics and datasets ▸ With strongly supervised, weakly supervised and unsupervised methods ▸ Easily configured and run with Hydra config ▸ Inspired by disentanglement_lib

dcgan combined with vae in pytorch!

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

GenRec: Generative Recommender Systems with RQ-VAE semantic IDs, Transformer-based retrieval, and LLM integration. Built on PyTorch with distributed training support.

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

This repository contains the official PyTorch implementation of inkn'hue: Enhancing Manga Colorization from Multiple Priors with Alignment Multi-Encoder VAE

Code, slides, and examples from my generative AI video course... taking you all the way from VAEs to near real-time Stable Diffusion with PyTorch and Hugging Face!

A PyTorch implementation of Vector Quantized Variational Autoencoder (VQ-VAE) with EMA updates, pretrained encoder, and K-means initialization. Efficient discrete representation learning for various data types.

Various Latent Variable Models implementations in Pytorch, including VAE, VAE with AF Prior, VQ-VAE and VQ-VAE with Gated PixelCNN

Authors' PyTorch implementation of "Lossy Image Compression with Quantized Hierarchical VAEs"

[pytorch]Implementation of "Recommender Systems with Generative Retrieval" with RQ-VAE beam search enhancement and comparative analysis with RQ-Kmeans.

pytorch implementation of Spatially Invariant Unsupervised Object Detection with VAE

The model uses the AE-GAN (Autoencoder Generative Adversarial Network) architecture for generating upsampled images. The model is trained on Celeb-A image (1024 x 1024) dataset where input image is of 128x128 and generated image is of shape 480x480.

Modern PyTorch implementation of World Models with interactive notebooks for the Car Racing environment. Features VAE vision model, MDN-RNN memory system, and CMA-ES controller with visualization tools. Complete end-to-end reinforcement learning pipeline with clean, well-documented code.

VAE for Fashion MNIST with PyTorch

PyTorch/skopt based implementation of Bayesian optimization with Gaussian processes - build to optimize latent spaces of VAEs to generate molecules with desired properties

PyTorch implementation of https://github.com/IsaacGuan/3D-VAE with some modifications.

[Pytorch] Minimal implementation of a Variational Autoencoder (VAE) with Categorical Latent variables inspired from "Categorical Reparameterization with Gumbel-Softmax".

PyTorch implementation of the paper "Privacy-preserving datasets by capturing feature distributions with Conditional VAEs" @ BMVC2024

Image Feature extraction using Pytorch with VAE and AE methods

Deep learning fraud detection system using MLP, Autoencoder, and VAE for imbalanced credit card data. Built with PyTorch, it includes SMOTE, RobustScaler preprocessing, FastAPI REST API for real-time predictions, and an interactive dashboard. Features EDA, ROC-AUC/PR-AUC evaluation, and unit tests.

Implementations of various VAE papers with tensorflow 2.0 or pytorch.

Implementation of stick breaking VAE with pytorch

β-VAE implementation with PyTorch

Implementation of a VAE with Inception-V3 layers in PyTorch

Pytorch reproduction of two papers: [1] β-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework, Higgins et al., ICLR, 2017 [2] Understanding disentangling in β-VAE, Burgess et al., arxiv:1804.03599, 2018 [3]