Description

Machine learning training based on generative adversarial networks.

Requirements

• Google Drive account

Getting started

Open this link on a new tab, follow the instructions on the screen and enjoy !

• https://colab.research.google.com/github/NicolasGrimonpont/Machine-Learning-Playground/blob/main/notebooks/training-model-from-scratch.ipynb

Pre-trained networks

Pre-trained networks are stored as *.pkl files that can be referenced using local filenames or URLs:

# Generate curated MetFaces images without truncation (Fig.10 left)
python generate.py --outdir=out --trunc=1 --seeds=85,265,297,849 \
    --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/metfaces.pkl

# Generate uncurated MetFaces images with truncation (Fig.12 upper left)
python generate.py --outdir=out --trunc=0.7 --seeds=600-605 \
    --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/metfaces.pkl

# Generate class conditional CIFAR-10 images (Fig.17 left, Car)
python generate.py --outdir=out --trunc=1 --seeds=0-35 --class=1 \
    --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/cifar10.pkl

Training new networks

In its most basic form, training new networks boils down to:

python train.py --outdir=~/training-runs --gpus=1 --data=~/datasets/custom --dry-run
python train.py --outdir=~/training-runs --gpus=1 --data=~/datasets/custom

Configuration

Base config	Description
auto (default)	Automatically select reasonable defaults based on resolution and GPU count. Serves as a good starting point for new datasets, but does not necessarily lead to optimal results.
stylegan2	Reproduce results for StyleGAN2 config F at 1024x1024 using 1, 2, 4, or 8 GPUs.
paper256	Reproduce results for FFHQ and LSUN Cat at 256x256 using 1, 2, 4, or 8 GPUs.
paper512	Reproduce results for BreCaHAD and AFHQ at 512x512 using 1, 2, 4, or 8 GPUs.
paper1024	Reproduce results for MetFaces at 1024x1024 using 1, 2, 4, or 8 GPUs.
cifar	Reproduce results for CIFAR-10 (tuned configuration) using 1 or 2 GPUs.
cifarbaseline	Reproduce results for CIFAR-10 (baseline configuration) using 1 or 2 GPUs.

Development

This is a research reference implementation and is treated as a one-time code drop. As such, we do not accept outside code contributions in the form of pull requests.

License

This repository is open-sourced software licensed under the MIT license.

Acknowledgements

This work is made possible by the impressive recent work of NVidia researchers and other previous research.