generative-models

StarGAN - Official PyTorch Implementation (CVPR 2018)

Collection of generative models in Tensorflow

StarGAN v2 - Official PyTorch Implementation (CVPR 2020)

A probabilistic programming library for Bayesian deep learning, generative models, based on Tensorflow

PyTorch implementation of various methods for continual learning (XdG, EWC, SI, LwF, FROMP, DGR, BI-R, ER, A-GEM, iCaRL, Generative Classifier) in three different scenarios.

Measures and metrics for image2image tasks. PyTorch.

Easily generate thousands of 3D models, images, and animation automatically in Blender for free with Blend_My_NFTs.

Official code for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)

PyTorch implementation for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)

AutoVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss

InstaGAN: Instance-aware Image Translation (ICLR 2019)

Implementations of various VAE-based semi-supervised and generative models in PyTorch

Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

Vector Quantized VAEs - PyTorch Implementation

[CVPR 2020 Workshop] A PyTorch GAN library that reproduces research results for popular GANs.

A U-Net combined with a variational auto-encoder that is able to learn conditional distributions over semantic segmentations.

Noise Conditional Score Networks (NeurIPS 2019, Oral)

[ICCV 2021] Focal Frequency Loss for Image Reconstruction and Synthesis

Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN

Pytorch implementation of JointVAE, a framework for disentangling continuous and discrete factors of variation 🌟