## StyleGAN — Official TensorFlow Implementation
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![Teaser image](./stylegan-teaser.png)
**Picture:** *These people are not real – they were produced by our generator that allows control over different aspects of the image.*
This repository contains the official TensorFlow implementation of the following paper:
> **A Style-Based Generator Architecture for Generative Adversarial Networks**<br>
> Tero Karras (NVIDIA), Samuli Laine (NVIDIA), Timo Aila (NVIDIA)<br>
> https://arxiv.org/abs/1812.04948
>
> **Abstract:** *We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces.*
For business inquiries, please visit our website and submit the form: [NVIDIA Research Licensing](https://www.nvidia.com/en-us/research/inquiries/)
**★★★ NEW: [StyleGAN2-ADA-PyTorch](https://github.com/NVlabs/stylegan2-ada-pytorch) is now available; see the full list of versions [here](https://nvlabs.github.io/stylegan2/versions.html) ★★★**
## Resources
Material related to our paper is available via the following links:
- Paper: https://arxiv.org/abs/1812.04948
- Video: https://youtu.be/kSLJriaOumA
- Code: https://github.com/NVlabs/stylegan
- FFHQ: https://github.com/NVlabs/ffhq-dataset
Additional material can be found on Google Drive:
| Path | Description
| :--- | :----------
| [StyleGAN](https://drive.google.com/open?id=1uka3a1noXHAydRPRbknqwKVGODvnmUBX) | Main folder.
| ├ [stylegan-paper.pdf](https://drive.google.com/open?id=1v-HkF3Ehrpon7wVIx4r5DLcko_U_V6Lt) | High-quality version of the paper PDF.
| ├ [stylegan-video.mp4](https://drive.google.com/open?id=1uzwkZHQX_9pYg1i0d1Nbe3D9xPO8-qBf) | High-quality version of the result video.
| ├ [images](https://drive.google.com/open?id=1-l46akONUWF6LCpDoeq63H53rD7MeiTd) | Example images produced using our generator.
| │ ├ [representative-images](https://drive.google.com/open?id=1ToY5P4Vvf5_c3TyUizQ8fckFFoFtBvD8) | High-quality images to be used in articles, blog posts, etc.
| │ └ [100k-generated-images](https://drive.google.com/open?id=100DJ0QXyG89HZzB4w2Cbyf4xjNK54cQ1) | 100,000 generated images for different amounts of truncation.
| │    ├ [ffhq-1024x1024](https://drive.google.com/open?id=14lm8VRN1pr4g_KVe6_LvyDX1PObst6d4) | Generated using Flickr-Faces-HQ dataset at 1024×1024.
| │    ├ [bedrooms-256x256](https://drive.google.com/open?id=1Vxz9fksw4kgjiHrvHkX4Hze4dyThFW6t) | Generated using LSUN Bedroom dataset at 256×256.
| │    ├ [cars-512x384](https://drive.google.com/open?id=1MFCvOMdLE2_mpeLPTiDw5dxc2CRuKkzS) | Generated using LSUN Car dataset at 512×384.
| │    └ [cats-256x256](https://drive.google.com/open?id=1gq-Gj3GRFiyghTPKhp8uDMA9HV_0ZFWQ) | Generated using LSUN Cat dataset at 256×256.
| ├ [videos](https://drive.google.com/open?id=1N8pOd_Bf8v89NGUaROdbD8-ayLPgyRRo) | Example videos produced using our generator.
| │ └ [high-quality-video-clips](https://drive.google.com/open?id=1NFO7_vH0t98J13ckJYFd7kuaTkyeRJ86) | Individual segments of the result video as high-quality MP4.
| ├ [ffhq-dataset](https://drive.google.com/open?id=1u2xu7bSrWxrbUxk-dT-UvEJq8IjdmNTP) | Raw data for the [Flickr-Faces-HQ dataset](https://github.com/NVlabs/ffhq-dataset).
| └ [networks](https://drive.google.com/open?id=1MASQyN5m0voPcx7-9K0r5gObhvvPups7) | Pre-trained networks as pickled instances of [dnnlib.tflib.Network](./dnnlib/tflib/network.py).
|    ├ [stylegan-ffhq-1024x1024.pkl](https://drive.google.com/uc?id=1MEGjdvVpUsu1jB4zrXZN7Y4kBBOzizDQ) | StyleGAN trained with Flickr-Faces-HQ dataset at 1024×1024.
|    ├ [stylegan-celebahq-1024x1024.pkl](https://drive.google.com/uc?id=1MGqJl28pN4t7SAtSrPdSRJSQJqahkzUf) | StyleGAN trained with CelebA-HQ dataset at 1024×1024.
|    ├ [stylegan-bedrooms-256x256.pkl](https://drive.google.com/uc?id=1MOSKeGF0FJcivpBI7s63V9YHloUTORiF) | StyleGAN trained with LSUN Bedroom dataset at 256×256.
|    ├ [stylegan-cars-512x384.pkl](https://drive.google.com/uc?id=1MJ6iCfNtMIRicihwRorsM3b7mmtmK9c3) | StyleGAN trained with LSUN Car dataset at 512×384.
|    ├ [stylegan-cats-256x256.pkl](https://drive.google.com/uc?id=1MQywl0FNt6lHu8E_EUqnRbviagS7fbiJ) | StyleGAN trained with LSUN Cat dataset at 256×256.
|    └ [metrics](https://drive.google.com/open?id=1MvYdWCBuMfnoYGptRH-AgKLbPTsIQLhl) | Auxiliary networks for the quality and disentanglement metrics.
|       ├ [inception_v3_features.pkl](https://drive.google.com/uc?id=1MzTY44rLToO5APn8TZmfR7_ENSe5aZUn) | Standard [Inception-v3](https://arxiv.org/abs/1512.00567) classifier that outputs a raw feature vector.
|       ├ [vgg16_zhang_perceptual.pkl](https://drive.google.com/uc?id=1N2-m9qszOeVC9Tq77WxsLnuWwOedQiD2) | Standard [LPIPS](https://arxiv.org/abs/1801.03924) metric to estimate perceptual similarity.
|       ├ [celebahq-classifier-00-male.pkl](https://drive.google.com/uc?id=1Q5-AI6TwWhCVM7Muu4tBM7rp5nG_gmCX) | Binary classifier trained to detect a single attribute of CelebA-HQ.
|       └ ⋯ | Please see the file listing for remaining networks.
## Licenses
All material, excluding the Flickr-Faces-HQ dataset, is made available under [Creative Commons BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) license by NVIDIA Corporation. You can **use, redistribute, and adapt** the material for **non-commercial purposes**, as long as you give appropriate credit by **citing our paper** and **indicating any changes** that you've made.
For license information regarding the FFHQ dataset, please refer to the [Flickr-Faces-HQ repository](https://github.com/NVlabs/ffhq-dataset).
`inception_v3_features.pkl` and `inception_v3_softmax.pkl` are derived from the pre-trained [Inception-v3](https://arxiv.org/abs/1512.00567) network by Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna. The network was originally shared under [Apache 2.0](https://github.com/tensorflow/models/blob/master/LICENSE) license on the [TensorFlow Models](https://github.com/tensorflow/models) repository.
`vgg16.pkl` and `vgg16_zhang_perceptual.pkl` are derived from the pre-trained [VGG-16](https://arxiv.org/abs/1409.1556) network by Karen Simonyan and Andrew Zisserman. The network was originally shared under [Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/) license on the [Very De
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stylegan图像生成:一个强大的生成对抗网络(GAN)模型,用于图像生成
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一个强大的生成对抗网络(GAN)模型,用于图像生成。通过stylegan,你可以生成具有高度逼真性的艺术风格图像,为你的创意提供了无限可能。
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stylegan-master.zip (32个子文件)
stylegan-master
dataset_tool.py 30KB
generate_figures.py 9KB
metrics
__init__.py 350B
perceptual_path_length.py 5KB
linear_separability.py 10KB
metric_base.py 6KB
frechet_inception_distance.py 3KB
LICENSE.txt 19KB
run_metrics.py 4KB
dnnlib
__init__.py 799B
util.py 13KB
tflib
__init__.py 524B
tfutil.py 9KB
network.py 29KB
autosummary.py 7KB
optimizer.py 10KB
submission
__init__.py 392B
_internal
run.py 2KB
submit.py 11KB
run_context.py 4KB
training
__init__.py 350B
networks_progan.py 17KB
training_loop.py 15KB
loss.py 10KB
dataset.py 12KB
networks_stylegan.py 33KB
misc.py 10KB
train.py 16KB
stylegan-teaser.png 1.56MB
README.md 20KB
pretrained_example.py 2KB
config.py 671B
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