StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.

The author's officially unofficial PyTorch BigGAN implementation.

🦋A PyTorch implementation of BigGAN with pretrained weights and conversion scripts.

Pytorch implementation of LARGE SCALE GAN TRAINING FOR HIGH FIDELITY NATURAL IMAGE SYNTHESIS (BigGAN)

Official Implementation of the paper "A U-Net Based Discriminator for Generative Adversarial Networks" (CVPR 2020)

Code for: Transforming and Projecting Images into Class-conditional Generative Networks

Official implementation of FQ-GAN

Data Augmentation optimized for GAN

Pytorch implementation of BigGAN Generator with pretrained weights

MichelAI, video clip creator based on BigGAN

Reimplementation of the Paper: Large Scale GAN Training for High Fidelity Natural Image Synthesis

Multi-Variate Temporal GAN for Large Scale Video Generation

Using Deep Learning to create fake images of games using PyTorch

5th place solution for Kaggle Generative Dog Images competition

Music visualizer using audio and semantic analysis to explore BigGAN latent space.

Exploring Generative Adversarial Networks: Improving training techniques

StyleGAN BigGAN for Motorbike Generation

Uses a Pretrained Network (using Google's Deepmind) to generate images.

Creates unique music videos, using a generative adversarial network.

Improving diversity of class-conditional generative networks (cGANs) for image classification, using sample reweighting and boosting techniques