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An op-for-op PyTorch reimplementation of DeepMind's BigGAN model with the pre-trained weights from DeepMind. This repository contains an op-for-op PyTorch reimplementation of DeepMind's BigGAN that was released with the paper Large Scale GAN Training for High Fidelity Natural Image Synthesis. This PyTorch implementation of BigGAN is provided with the pretrained 128x128, 256x256 and 512x512 models by DeepMind. We also provide the scripts used to download and convert these models from the TensorFlow Hub models. This reimplementation was done from the raw computation graph of the Tensorflow version and behave similarly to the TensorFlow version (variance of the output difference of the order of 1e-5). This implementation currently only contains the generator as the weights of the discriminator were not released (although the structure of the discriminator is very similar to the generator so it could be added pretty easily.
Features
- This repo was tested on Python 3.6 and PyTorch 1.0.1
- PyTorch pretrained BigGAN can be installed from pip
- This repository provide direct and simple access to the pretrained "deep" versions of BigGAN for 128, 256 and 512 pixels resolutions
- Load DeepMind's pre-trained weights
- BigGANConfig is a class to store and load BigGAN configurations
- Convert an output tensor from BigGAN in a list of images
Project Samples
