Gradient StyleGAN2 Template Repo

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This is a Github template repo you can use to create your own copy of the forked StyleGAN2 sample from NVLabs. The NVLabs sources are unchanged from the original, except for this README paragraph, and the addition of the workflow yaml file.

Once you create your own copy of this repo and add the repo to a project in your Paperspace Gradient account, you will be able to commit changes, and have the changes automatically validated by running a Gradient workflow using your updates.

There are a few prerequisites you will need to have in place:

1. Create a Paperspace account with a Gradient subscription that supports GPU machine access, and Gradient github integration.
2. You will need a defined Gradient dataset in your account named demo-dataset. You can go through the Workflow Demo which will automatically create demo-dataset in your account. You can learn more about Gradient datasets here.
3. Create your own copy of this repo using the "Use this template" button in Github.
4. Create a github connected project by opening the Projects tab, pressing the "LINK A GITHUB REPO" button, and following the instructions on the next page.
5. You next need to authorize Gradient to access your github account if you have not done so already.
6. Once github permissions are set up for Gradient, return to the Gradient project page and select the repo you created in step 3.
7. At this point Gradient will check that the Gradient github app is associated with the repo. If it is not, you will need to return to github and add the Gradient app to the repo.
8. After adding the Gradient app to the repo return to the projects tab again and complete the process of associating the repo with a project.
9. Now make a small change to the repo such as adding a blank line to the end of this README, then add, commit, and push your changes to your repo.
10. After pushing a change you should see a new workflow created, and an initial workflow run, under your repo's project in the Gradient web interface.

StyleGAN2 — Official TensorFlow Implementation

Analyzing and Improving the Image Quality of StyleGAN
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, Timo Aila

Paper: http://arxiv.org/abs/1912.04958
Video: https://youtu.be/c-NJtV9Jvp0

Abstract: The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. In particular, we redesign generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent vectors to images. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably detect if an image is generated by a particular network. We furthermore visualize how well the generator utilizes its output resolution, and identify a capacity problem, motivating us to train larger models for additional quality improvements. Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality.

For business inquiries, please contact [email protected]
For press and other inquiries, please contact Hector Marinez at [email protected]

★★★ NEW: StyleGAN2-ADA-PyTorch is now available; see the full list of versions here ★★★

Additional material
StyleGAN2	Main Google Drive folder
├  stylegan2-paper.pdf	High-quality version of the paper
├  stylegan2-video.mp4	High-quality version of the video
├  images	Example images produced using our method
│  ├  curated-images	Hand-picked images showcasing our results
│  └  100k-generated-images	Random images with and without truncation
├  videos	Individual clips of the video as high-quality MP4
└  networks	Pre-trained networks
├  stylegan2-ffhq-config-f.pkl	StyleGAN2 for FFHQ dataset at 1024×1024
├  stylegan2-car-config-f.pkl	StyleGAN2 for LSUN Car dataset at 512×384
├  stylegan2-cat-config-f.pkl	StyleGAN2 for LSUN Cat dataset at 256×256
├  stylegan2-church-config-f.pkl	StyleGAN2 for LSUN Church dataset at 256×256
├  stylegan2-horse-config-f.pkl	StyleGAN2 for LSUN Horse dataset at 256×256
└ ⋯	Other training configurations used in the paper

Requirements

• Both Linux and Windows are supported. Linux is recommended for performance and compatibility reasons.
• 64-bit Python 3.6 installation. We recommend Anaconda3 with numpy 1.14.3 or newer.
• We recommend TensorFlow 1.14, which we used for all experiments in the paper, but TensorFlow 1.15 is also supported on Linux. TensorFlow 2.x is not supported.
• On Windows you need to use TensorFlow 1.14, as the standard 1.15 installation does not include necessary C++ headers.
• One or more high-end NVIDIA GPUs, NVIDIA drivers, CUDA 10.0 toolkit and cuDNN 7.5. To reproduce the results reported in the paper, you need an NVIDIA GPU with at least 16 GB of DRAM.
• Docker users: use the provided Dockerfile to build an image with the required library dependencies.

StyleGAN2 relies on custom TensorFlow ops that are compiled on the fly using NVCC. To test that your NVCC installation is working correctly, run:

nvcc test_nvcc.cu -o test_nvcc -run
| CPU says hello.
| GPU says hello.

On Windows, the compilation requires Microsoft Visual Studio to be in PATH. We recommend installing Visual Studio Community Edition and adding into PATH using "C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Auxiliary\Build\vcvars64.bat".

Using pre-trained networks

Pre-trained networks are stored as *.pkl files on the StyleGAN2 Google Drive folder. Below, you can either reference them directly using the syntax gdrive:networks/<filename>.pkl, or download them manually and reference by filename.

# Generate uncurated ffhq images (matches paper Figure 12)
python run_generator.py generate-images --network=gdrive:networks/stylegan2-ffhq-config-f.pkl \
  --seeds=6600-6625 --truncation-psi=0.5

# Generate curated ffhq images (matches paper Figure 11)
python run_generator.py generate-images --network=gdrive:networks/stylegan2-ffhq-config-f.pkl \
  --seeds=66,230,389,1518 --truncation-psi=1.0

# Generate uncurated car images
python run_generator.py generate-images --network=gdrive:networks/stylegan2-car-config-f.pkl \
  --seeds=6000-6025 --truncation-psi=0.5

# Example of style mixing (matches the corresponding video clip)
python run_generator.py style-mixing-example --network=gdrive:networks/stylegan2-ffhq-config-f.pkl \
  --row-seeds=85,100,75,458,1500 --col-seeds=55,821,1789,293 --truncation-psi=1.0

The results are placed in results/<RUNNING_ID>/*.png. You can change the location with --result-dir. For example, --result-dir=~/my-stylegan2-results.

You can import the networks in your own Python code using pickle.load(). For this to work, you need to include the dnnlib source directory in PYTHONPATH and create a default TensorFlow session by calling dnnlib.tflib.init_tf(). See run_generator.py and pretrained_networks.py for examples.

Preparing datasets

Datasets are stored as multi-resolution TFRecords, similar to the original StyleGAN. Each dataset consists of multiple *.tfrecords files stored under a common directory, e.g., ~/datasets/ffhq/ffhq-r*.tfrecords. In the following sections, the datasets are referenced using a combination of --dataset and --data-dir arguments, e.g., --dataset=ffhq --data-dir=~/datasets.

FFHQ. To download the Flickr-Faces-HQ dataset as multi-resolution TFRecords, run:

pushd ~
git clone https://github.com/NVlabs/ffhq-dataset.git
cd ffhq-dataset
python download_ffhq.py --tfrecords
popd
python dataset_tool.py display ~/ffhq-dataset/tfrecords/ffhq

LSUN. Download the desired LSUN categories in LMDB format from the LSUN project page. To convert the data to multi-resolution TFRecords, run:

python dataset_tool.py create_lsun_wide ~/datasets/car ~/lsun/car_lmdb --width=512 --height=384
python dataset_tool.py create_lsun ~/datasets/cat ~/lsun/cat_lmdb --resolution=256
python dataset_tool.py create_lsun ~/datasets/church ~/lsun/church_outdoor_train_lmdb --resolution=256
python dataset_tool.py create_lsun ~/datasets/horse ~/lsun/horse_lmdb --resolution=256

Custom. Create custom datasets by placing all training images under a single directory. The images must be square-shaped and they must all have the same power-of-two dimensions. To convert the images to multi-resolution TFRecords, run:

python dataset_tool.py create_from_images ~/datasets/my-custom-dataset ~/my-custom-images
python dataset_tool.py display ~/datasets/my-custom-dataset

Projecting images to latent space

To find the matching latent vectors for a set of images, run:

# Project generated images
python run_projector.py project-generated-images --network=gdrive:networks/stylegan2-car-config-f.pkl \
  --seeds=0,1,5

# Project real images
python run_projector.py project-real-images --network=gdrive:networks/stylegan2-car-config-f.pkl \
  --dataset=car --data-dir=~/datasets

Training networks

To reproduce the training runs for config F in Tables 1 and 3, run:

python run_training.py --num-gpus=8 --data-dir=~/datasets --config=config-f \
  --dataset=ffhq --mirror-augment=true
python run_training.py --num-gpus=8 --data-dir=~/datasets --config=config-f \
  --dataset=car --total-kimg=57000
python run_training.py --num-gpus=8 --data-dir=~/datasets --config=config-f \
  --dataset=cat --total-kimg=88000
python run_training.py --num-gpus=8 --data-dir=~/datasets --config=config-f \
  --dataset=church --total-kimg 88000 --gamma=100
python run_training.py --num-gpus=8 --data-dir=~/datasets --config=config-f \
  --dataset=horse --total-kimg 100000 --gamma=100

For other configurations, see python run_training.py --help.

We have verified that the results match the paper when training with 1, 2, 4, or 8 GPUs. Note that training FFHQ at 1024×1024 resolution requires GPU(s) with at least 16 GB of memory. The following table lists typical training times using NVIDIA DGX-1 with 8 Tesla V100 GPUs:

Configuration	Resolution	Total kimg	1 GPU	2 GPUs	4 GPUs	8 GPUs	GPU mem
config-f	1024×1024	25000	69d 23h	36d 4h	18d 14h	9d 18h	13.3 GB
config-f	1024×1024	10000	27d 23h	14d 11h	7d 10h	3d 22h	13.3 GB
config-e	1024×1024	25000	35d 11h	18d 15h	9d 15h	5d 6h	8.6 GB
config-e	1024×1024	10000	14d 4h	7d 11h	3d 20h	2d 3h	8.6 GB
config-f	256×256	25000	32d 13h	16d 23h	8d 21h	4d 18h	6.4 GB
config-f	256×256	10000	13d 0h	6d 19h	3d 13h	1d 22h	6.4 GB

Training curves for FFHQ config F (StyleGAN2) compared to original StyleGAN using 8 GPUs:

After training, the resulting networks can be used the same way as the official pre-trained networks:

# Generate 1000 random images without truncation
python run_generator.py generate-images --seeds=0-999 --truncation-psi=1.0 \
  --network=results/00006-stylegan2-ffhq-8gpu-config-f/networks-final.pkl

Evaluation metrics

To reproduce the numbers for config F in Tables 1 and 3, run:

python run_metrics.py --data-dir=~/datasets --network=gdrive:networks/stylegan2-ffhq-config-f.pkl \
  --metrics=fid50k,ppl_wend --dataset=ffhq --mirror-augment=true
python run_metrics.py --data-dir=~/datasets --network=gdrive:networks/stylegan2-car-config-f.pkl \
  --metrics=fid50k,ppl2_wend --dataset=car
python run_metrics.py --data-dir=~/datasets --network=gdrive:networks/stylegan2-cat-config-f.pkl \
  --metrics=fid50k,ppl2_wend --dataset=cat
python run_metrics.py --data-dir=~/datasets --network=gdrive:networks/stylegan2-church-config-f.pkl \
  --metrics=fid50k,ppl2_wend --dataset=church
python run_metrics.py --data-dir=~/datasets --network=gdrive:networks/stylegan2-horse-config-f.pkl \
  --metrics=fid50k,ppl2_wend --dataset=horse

For other configurations, see the StyleGAN2 Google Drive folder.

Note that the metrics are evaluated using a different random seed each time, so the results will vary between runs. In the paper, we reported the average result of running each metric 10 times. The following table lists the available metrics along with their expected runtimes and random variation:

Metric	FFHQ config F	1 GPU	2 GPUs	4 GPUs	Description
fid50k	2.84 ± 0.03	22 min	14 min	10 min	Fréchet Inception Distance
is50k	5.13 ± 0.02	23 min	14 min	8 min	Inception Score
ppl_zfull	348.0 ± 3.8	41 min	22 min	14 min	Perceptual Path Length in Z, full paths
ppl_wfull	126.9 ± 0.2	42 min	22 min	13 min	Perceptual Path Length in W, full paths
ppl_zend	348.6 ± 3.0	41 min	22 min	14 min	Perceptual Path Length in Z, path endpoints
ppl_wend	129.4 ± 0.8	40 min	23 min	13 min	Perceptual Path Length in W, path endpoints
ppl2_wend	145.0 ± 0.5	41 min	23 min	14 min	Perceptual Path Length without center crop
ls	154.2 / 4.27	10 hrs	6 hrs	4 hrs	Linear Separability
pr50k3	0.689 / 0.492	26 min	17 min	12 min	Precision and Recall

Note that some of the metrics cache dataset-specific data on the disk, and they will take somewhat longer when run for the first time.

License

Copyright © 2019, NVIDIA Corporation. All rights reserved.

This work is made available under the Nvidia Source Code License-NC. To view a copy of this license, visit https://nvlabs.github.io/stylegan2/license.html

Citation

@inproceedings{Karras2019stylegan2,
  title     = {Analyzing and Improving the Image Quality of {StyleGAN}},
  author    = {Tero Karras and Samuli Laine and Miika Aittala and Janne Hellsten and Jaakko Lehtinen and Timo Aila},
  booktitle = {Proc. CVPR},
  year      = {2020}
}

Acknowledgements

We thank Ming-Yu Liu for an early review, Timo Viitanen for his help with code release, and Tero Kuosmanen for compute infrastructure.