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stylegan2

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    StyleGAN2 — Official TensorFlow Implementation

    Teaser image

    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 researchinquiries@nvidia.com
    For press and other inquiries, please contact Hector Marinez at hmarinez@nvidia.com

    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.
    • 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
    | 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 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

    @article{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},
      journal = {CoRR},
      volume  = {abs/1912.04958},
      year    = {2019},
    }

    Acknowledgements

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