stylegan2 README

The project's README from github.com/NVlabs/stylegan2. As one markdown file: README.md.

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 visit our website and submit the form: NVIDIA Research Licensing

★★★ 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

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:

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

@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.

stylegan2 (NVlabs/stylegan2)

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11.2K ·GitHub
Stylegan2
这是什么？这是 StyleGAN2 的官方 TensorFlow 实现。StyleGAN2 是 NVIDIA 研究团队提出的生成对抗网络，能生成当前最先进的高质量合成图像，并修复了原版 StyleGAN 中典型的伪影问题。
这是开箱即用的应用，还是需要我在命令行里运行的研究代码？这是研究代码：一组基于 TensorFlow 的 Python 脚本（用于训练、生成、投影和评估），不是打包好的应用程序，也不是带 API 的库。
我该怎么用它生成图像？从 Google Drive 文件夹下载一个预训练的 .pkl 网络（例如 FFHQ config-f 模型），将 dnnlib 目录添加到 PYTHONPATH，调用 dnnlib.tflib.init_tf()，然后通过 pickle.load() 加载；也可以直接使用 rungenerator.py。结果保存在 results/ /.png 中。
在开始之前，我需要准备什么硬件和软件环境？你需要 64 位 Python 3.6、TensorFlow 1.14（或仅限 Linux 的 1.15，不能使用 TensorFlow 2.x），以及一块或多块高端 NVIDIA GPU，并安装 CUDA 10.0 工具包和 cuDNN 7.5；要复现论文结果，需要至少 16 GB 显存（DRAM）的 GPU。
运行过程中有哪些需要注意的坑？StyleGAN2 会在运行时用 NVCC 即时编译自定义的 TensorFlow 算子；在 Windows 上，这要求 Microsoft Visual Studio 已加入 PATH，而且官方明确不支持 TensorFlow 2.x。
Linux 和 Windows 都能运行吗？可以，两者都支持，但出于性能和兼容性考虑，推荐使用 Linux；Windows 用户必须使用 TensorFlow 1.14，因为标准的 1.15 安装包缺少所需的 C++ 头文件。
我能把它用于商业用途吗？不能。代码采用 Nvidia Source Code License-NC 发布，这是一种非商业许可证，如需商业使用，必须直接联系 NVIDIA。
我能用消费级 GPU 训练它吗？训练是围绕配备 Tesla V100 的多 GPU NVIDIA DGX-1 系统设计的，仅全分辨率 FFHQ 训练就至少需要 16 GB 显存，因此在普通消费级显卡上并不现实。
开箱即用的预训练模型有哪些？NVIDIA 提供了 config-f 预训练网络，包括 FFHQ 人脸（1024x1024）、LSUN Car（512x384），以及 Cat、Church 和 Horse（256x256），均可从 StyleGAN2 的 Google Drive 文件夹下载 .pkl 文件。
训练数据集是如何准备的？数据集以多分辨率 TFRecords 格式存储；你可以使用随附的数据集转换脚本，将 FFHQ、LSUN 各类别，或你自己的同尺寸正方形图像转换为该格式。
我能把一张真实照片投影到模型的潜空间中吗？可以，仓库中包含一个投影脚本，可以为给定的一组输入图像找到对应的潜向量。
StyleGAN2 与原版 StyleGAN 有什么不同？StyleGAN2 重新设计了生成器的归一化方式，重新审视了渐进式增长，并加入了路径长度正则化项，从而提高了图像质量、消除了典型伪影，并使生成器更易于反演。
What are similar projects?pytorch-CycleGAN-and-pix2pix, pix2pix, CycleGAN, DragGAN, stylegan, DeepLearning, lama, GFPGAN
Which topics is it in?deep-learning-tutorial, generative-adversarial-network, gan, tensorflow, windows-app, image-generation, image-processing, artificial-intelligence, nvidia, computer-vision, cuda, deep-learning
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