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docs/gan/index.html
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<li><a href="cycle_gan/index.html">Cycle GAN</a></li> <li><a href="cycle_gan/index.html">Cycle GAN</a></li>
<li><a href="wasserstein/index.html">Wasserstein GAN</a></li> <li><a href="wasserstein/index.html">Wasserstein GAN</a></li>
<li><a href="wasserstein/gradient_penalty/index.html">Wasserstein GAN with Gradient Penalty</a></li> <li><a href="wasserstein/gradient_penalty/index.html">Wasserstein GAN with Gradient Penalty</a></li>
<li><a href="stylegan/index.html">Style GAN 2</a></li>
</ul> </ul>
</div> </div>
<div class='code'> <div class='code'>

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<h1><a href="https://nn.labml.ai/gan/stylegan/index.html">Style GAN 2</a></h1>
<p>This is a <a href="https://pytorch.org">PyTorch</a> implementation of the paper
<a href="https://arxiv.org/abs/1912.04958">Analyzing and Improving the Image Quality of StyleGAN</a>
which introduces <strong>Style GAN2</strong>.
Style GAN2 is an improvement over <strong>Style GAN</strong> from the paper
<a href="https://arxiv.org/abs/1812.04948">A Style-Based Generator Architecture for Generative Adversarial Networks</a>.
And Style GAN is based on <strong>Progressive GAN</strong> from the paper
<a href="https://arxiv.org/abs/1710.10196">Progressive Growing of GANs for Improved Quality, Stability, and Variation</a>.
All three papers are from the same authors from <a href="https://twitter.com/NVIDIAAI">NVIDIA AI</a>.</p>
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<li><a href="gan/cycle_gan/index.html">Cycle GAN</a></li> <li><a href="gan/cycle_gan/index.html">Cycle GAN</a></li>
<li><a href="gan/wasserstein/index.html">Wasserstein GAN</a></li> <li><a href="gan/wasserstein/index.html">Wasserstein GAN</a></li>
<li><a href="gan/wasserstein/gradient_penalty/index.html">Wasserstein GAN with Gradient Penalty</a></li> <li><a href="gan/wasserstein/gradient_penalty/index.html">Wasserstein GAN with Gradient Penalty</a></li>
<li><a href="gan/stylegan/index.html">Style GAN 2</a></li>
</ul> </ul>
<h4>✨ <a href="sketch_rnn/index.html">Sketch RNN</a></h4> <h4>✨ <a href="sketch_rnn/index.html">Sketch RNN</a></h4>
<h4>✨ <a href="rl/index.html">Reinforcement Learning</a></h4> <h4>✨ <a href="rl/index.html">Reinforcement Learning</a></h4>

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labml_nn/__init__.py
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* [Cycle GAN](gan/cycle_gan/index.html) * [Cycle GAN](gan/cycle_gan/index.html)
* [Wasserstein GAN](gan/wasserstein/index.html) * [Wasserstein GAN](gan/wasserstein/index.html)
* [Wasserstein GAN with Gradient Penalty](gan/wasserstein/gradient_penalty/index.html) * [Wasserstein GAN with Gradient Penalty](gan/wasserstein/gradient_penalty/index.html)
* [Style GAN 2](gan/stylegan/index.html)
#### ✨ [Sketch RNN](sketch_rnn/index.html) #### ✨ [Sketch RNN](sketch_rnn/index.html)

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labml_nn/gan/__init__.py
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* [Cycle GAN](cycle_gan/index.html) * [Cycle GAN](cycle_gan/index.html)
* [Wasserstein GAN](wasserstein/index.html) * [Wasserstein GAN](wasserstein/index.html)
* [Wasserstein GAN with Gradient Penalty](wasserstein/gradient_penalty/index.html) * [Wasserstein GAN with Gradient Penalty](wasserstein/gradient_penalty/index.html)
* [Style GAN 2](stylegan/index.html)
""" """

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# [Style GAN 2](https://nn.labml.ai/gan/stylegan/index.html)
This is a [PyTorch](https://pytorch.org) implementation of the paper
[Analyzing and Improving the Image Quality of StyleGAN](https://arxiv.org/abs/1912.04958)
which introduces **Style GAN2**.
Style GAN2 is an improvement over **Style GAN** from the paper
[A Style-Based Generator Architecture for Generative Adversarial Networks](https://arxiv.org/abs/1812.04948).
And Style GAN is based on **Progressive GAN** from the paper
[Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://arxiv.org/abs/1710.10196).
All three papers are from the same authors from [NVIDIA AI](https://twitter.com/NVIDIAAI).

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readme.md
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@ -47,6 +47,7 @@ implementations almost weekly.
* [Cycle GAN](https://nn.labml.ai/gan/cycle_gan/index.html) * [Cycle GAN](https://nn.labml.ai/gan/cycle_gan/index.html)
* [Wasserstein GAN](https://nn.labml.ai/gan/wasserstein/index.html) * [Wasserstein GAN](https://nn.labml.ai/gan/wasserstein/index.html)
* [Wasserstein GAN with Gradient Penalty](https://nn.labml.ai/gan/wasserstein/gradient_penalty/index.html) * [Wasserstein GAN with Gradient Penalty](https://nn.labml.ai/gan/wasserstein/gradient_penalty/index.html)
* [Style GAN 2](https://nn.labml.ai/gan/stylegan/index.html)
#### ✨ [Sketch RNN](https://nn.labml.ai/sketch_rnn/index.html) #### ✨ [Sketch RNN](https://nn.labml.ai/sketch_rnn/index.html)

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setup.py
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@ -5,7 +5,7 @@ with open("readme.md", "r") as f:
setuptools.setup( setuptools.setup(
name='labml-nn', name='labml-nn',
version='0.4.98', version='0.4.99',
author="Varuna Jayasiri, Nipun Wijerathne", author="Varuna Jayasiri, Nipun Wijerathne",
author_email="vpjayasiri@gmail.com, hnipun@gmail.com", author_email="vpjayasiri@gmail.com, hnipun@gmail.com",
description="A collection of PyTorch implementations of neural network architectures and layers.", description="A collection of PyTorch implementations of neural network architectures and layers.",