Welcome to the Generative Zoo, a collection of (so far) 27 diverse models aggregated in one repository. Our main objectives are:
This repository is designed to grow, continually incorporating new models and advancements in generative modeling, with a big focus on Computer Vision tasks.
To ensure consistency and usability across all models in the Generative Zoo, the following implementation rules were defined:
The 27 implemented models can be divided in seven major categories: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Denoising Diffusion Probabilistic Models (DDPMs), Score-based Generative Models (SGMs), Autoregressive Models, and Flow-based Models. The seventh category comprises Stable Diffusion models, which were separated from traditional diffusion models since they are not trained from scratch. The table below provides a summary of the models implemented in the Generative Zoo.
Category | Model | Source |
---|---|---|
VAEs | Vanilla VAE | arXiv |
Conditional VAE | OpenReview | |
Hierarchical VAE | arXiv | |
GANs | Adversarial VAE | arXiv |
Vanilla GAN | arXiv | |
Conditional GAN | arXiv | |
CycleGAN | arXiv | |
Prescribed GAN | arXiv | |
Wasserstein GAN with Gradient Penalty | arXiv | |
DDPMs | Vanilla DDPM | arXiv |
Conditional DDPM | arXiv | |
Diffusion AE | arXiv | |
SGMs | Vanilla SGM | arXiv |
NCSNv2 | arXiv | |
Autoregressive | VQ-VAE + Transformer | arXiv |
VQ-GAN + Transformer | arXiv | |
PixelCNN | arXiv | |
Flow | Vanilla Flow | arXiv |
RealNVP | arXiv | |
Glow | arXiv | |
Flow++ | arXiv | |
Flow Matching | arXiv | |
Conditional Flow Matching | arXiv | |
Rectified Flows | arXiv | |
Stable Diffusion | Stable Diffusion + LoRA | arXiv |
ControlNet | arXiv | |
InstructPix2Pix | arXiv |
You can implement your own dataloaders, but the following ones are already provided in the zoo, as well as tools to automatically download the datasets or instructions on how to get them.
Grayscale Datasets | RGB Datasets |
---|---|
MNIST | CIFAR-10 |
FashionMNIST | CIFAR-100 |
ChestMNIST++ | SVHN |
OctMNIST++ | Places365 |
PneumoniaMNIST++ | DTD |
TissueMNIST++ | TinyImageNet |
Horse2Zebra | |
ImageNet-1K |
@misc{GenerativeZoo,
author = {Francisco Caetano},
title = {GenerativeZoo: A Model Zoo for Generative Models},
journal = {GitHub repository},
howpublished = {\url{https://github.com/caetas/GenerativeZoo}},
year = {2024},
}