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StyleGAN2 - Official TensorFlow Implementation

StyleGAN2 - Official TensorFlow Implementation

StyleGAN2

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.

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.
  • TensorFlow 1.14 or 1.15 with GPU support. The code does not support TensorFlow 2.0.
  • On Windows, you need to use TensorFlow 1.14 — TensorFlow 1.15 will not work.
  • 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
| test_nvcc.cu
|    Creating library test_nvcc.lib and object test_nvcc.exp
| 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".

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

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.

Generating images:

# 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.

Projecting images to latent space:

# 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

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.

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:

Training curves

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 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.

GitHub

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