This is a PyTorch implementation of the paper
Analyzing and Improving the Image Quality of StyleGAN
which introduces **StyleGAN 2**.
StyleGAN 2 is an improvement over **StyleGAN** from the paper
A Style-Based Generator Architecture for Generative Adversarial Networks.
And StyleGAN is based on **Progressive GAN** from the paper
Progressive Growing of GANs for Improved Quality, Stability, and Variation.
All three papers are from the same authors from NVIDIA AI.

*Our implementation is a minimalistic StyleGAN 2 model training code.
Only single GPU training is supported to keep the implementation simple.
We managed to shrink it to keep it at less than 500 lines of code, including the training loop.*

**🏃 Here’s the training code: experiment.py.**

*These are $64 \times 64$ images generated after training for about 80K steps.*

We’ll first introduce the three papers at a high level.

Generative adversarial networks have two components; the generator and the discriminator. The generator network takes a random latent vector ($z \in \mathcal{Z}$) and tries to generate a realistic image. The discriminator network tries to differentiate the real images from generated images. When we train the two networks together the generator starts generating images indistinguishable from real images.

Progressive GAN generates high-resolution images ($1080 \times 1080$) of size.
It does so by *progressively* increasing the image size.
First, it trains a network that produces a $4 \times 4$ image, then $8 \times 8$ ,
then an $16 \times 16$ image, and so on up to the desired image resolution.

At each resolution, the generator network produces an image in latent space which is converted into RGB, with a $1 \times 1$ convolution. When we progress from a lower resolution to a higher resolution (say from $4 \times 4$ to $8 \times 8$ ) we scale the latent image by $2\times$ and add a new block (two $3 \times 3$ convolution layers) and a new $1 \times 1$ layer to get RGB. The transition is done smoothly by adding a residual connection to the $2\times$ scaled $4 \times 4$ RGB image. The weight of this residual connection is slowly reduced, to let the new block take over.

The discriminator is a mirror image of the generator network. The progressive growth of the discriminator is done similarly.

*$2\times$ and $0.5\times$ denote feature map resolution scaling and scaling.
$4\times4$, $8\times4$, … denote feature map resolution at the generator or discriminator block.
Each discriminator and generator block consists of 2 convolution layers with leaky ReLU activations.*

They use **minibatch standard deviation** to increase variation and
**equalized learning rate** which we discussed below in the implementation.
They also use **pixel-wise normalization** where at each pixel the feature vector is normalized.
They apply this to all the convolution layer outputs (except RGB).

StyleGAN improves the generator of Progressive GAN keeping the discriminator architecture the same.

It maps the random latent vector ($z \in \mathcal{Z}$) into a different latent space ($w \in \mathcal{W}$), with an 8-layer neural network. This gives an intermediate latent space $\mathcal{W}$ where the factors of variations are more linear (disentangled).

Then $w$ is transformed into two vectors (** styles**) per layer,
$i$, $y_i = (y_{s,i}, y_{b,i}) = f_{A_i}(w)$ and used for scaling and shifting (biasing)
in each layer with $\text{AdaIN}$ operator (normalize and scale):

To prevent the generator from assuming adjacent styles are correlated, they randomly use different styles for different blocks. That is, they sample two latent vectors $(z_1, z_2)$ and corresponding $(w_1, w_2)$ and use $w_1$ based styles for some blocks and $w_2$ based styles for some blacks randomly.

Noise is made available to each block which helps the generator create more realistic images. Noise is scaled per channel by a learned weight.

All the up and down-sampling operations are accompanied by bilinear smoothing.

*$A$ denotes a linear layer.
$B$ denotes a broadcast and scaling operation (noise is a single channel).
StyleGAN also uses progressive growing like Progressive GAN*

StyleGAN 2 changes both the generator and the discriminator of StyleGAN.

They remove the $\text{AdaIN}$ operator and replace it with the weight modulation and demodulation step. This is supposed to improve what they call droplet artifacts that are present in generated images, which are caused by the normalization in $\text{AdaIN}$ operator. Style vector per layer is calculated from $w_i \in \mathcal{W}$ as $s_i = f_{A_i}(w_i)$.

Then the convolution weights $w$ are modulated as follows. ($w$ here on refers to weights not intermediate latent space, we are sticking to the same notation as the paper.)

Then it’s demodulated by normalizing, where $i$ is the input channel, $j$ is the output channel, and $k$ is the kernel index.

Path length regularization encourages a fixed-size step in $\mathcal{W}$ to result in a non-zero, fixed-magnitude change in the generated image.

StyleGAN2 uses residual connections (with down-sampling) in the discriminator and skip connections in the generator with up-sampling (the RGB outputs from each layer are added - no residual connections in feature maps). They show that with experiments that the contribution of low-resolution layers is higher at beginning of the training and then high-resolution layers take over.

```
148import math
149from typing import Tuple, Optional, List
150
151import numpy as np
152import torch
153import torch.nn.functional as F
154import torch.utils.data
155from torch import nn
```

This is an MLP with 8 linear layers. The mapping network maps the latent vector $z \in \mathcal{W}$ to an intermediate latent space $w \in \mathcal{W}$. $\mathcal{W}$ space will be disentangled from the image space where the factors of variation become more linear.

`158class MappingNetwork(nn.Module):`

`features`

is the number of features in $z$ and $w$`n_layers`

is the number of layers in the mapping network.

`172 def __init__(self, features: int, n_layers: int):`

`177 super().__init__()`

Create the MLP

```
180 layers = []
181 for i in range(n_layers):
```

`183 layers.append(EqualizedLinear(features, features))`

Leaky Relu

```
185 layers.append(nn.LeakyReLU(negative_slope=0.2, inplace=True))
186
187 self.net = nn.Sequential(*layers)
```

`189 def forward(self, z: torch.Tensor):`

Normalize $z$

`191 z = F.normalize(z, dim=1)`

Map $z$ to $w$

`193 return self.net(z)`

*$A$ denotes a linear layer.
$B$ denotes a broadcast and scaling operation (noise is a single channel).
toRGB also has a style modulation which is not shown in the diagram to keep it simple.*

The generator starts with a learned constant. Then it has a series of blocks. The feature map resolution is doubled at each block Each block outputs an RGB image and they are scaled up and summed to get the final RGB image.

`196class Generator(nn.Module):`

`log_resolution`

is the $\log_2$ of image resolution`d_latent`

is the dimensionality of $w$`n_features`

number of features in the convolution layer at the highest resolution (final block)`max_features`

maximum number of features in any generator block

`212 def __init__(self, log_resolution: int, d_latent: int, n_features: int = 32, max_features: int = 512):`

`219 super().__init__()`

`224 features = [min(max_features, n_features * (2 ** i)) for i in range(log_resolution - 2, -1, -1)]`

Number of generator blocks

`226 self.n_blocks = len(features)`

Trainable $4 \times 4$ constant

`229 self.initial_constant = nn.Parameter(torch.randn((1, features[0], 4, 4)))`

First style block for $4 \times 4$ resolution and layer to get RGB

```
232 self.style_block = StyleBlock(d_latent, features[0], features[0])
233 self.to_rgb = ToRGB(d_latent, features[0])
```

Generator blocks

```
236 blocks = [GeneratorBlock(d_latent, features[i - 1], features[i]) for i in range(1, self.n_blocks)]
237 self.blocks = nn.ModuleList(blocks)
```

$2 \times$ up sampling layer. The feature space is up sampled at each block

`241 self.up_sample = UpSample()`

`w`

is $w$. In order to mix-styles (use different $w$ for different layers), we provide a separate $w$ for each generator block. It has shape `[n_blocks, batch_size, d_latent]1.`input_noise`

is the noise for each block. It’s a list of pairs of noise sensors because each block (except the initial) has two noise inputs after each convolution layer (see the diagram).

`243 def forward(self, w: torch.Tensor, input_noise: List[Tuple[Optional[torch.Tensor], Optional[torch.Tensor]]]):`

Get batch size

`253 batch_size = w.shape[1]`

Expand the learned constant to match batch size

`256 x = self.initial_constant.expand(batch_size, -1, -1, -1)`

The first style block

`259 x = self.style_block(x, w[0], input_noise[0][1])`

Get first rgb image

`261 rgb = self.to_rgb(x, w[0])`

Evaluate rest of the blocks

`264 for i in range(1, self.n_blocks):`

Up sample the feature map

`266 x = self.up_sample(x)`

Run it through the generator block

`268 x, rgb_new = self.blocks[i - 1](x, w[i], input_noise[i])`

Up sample the RGB image and add to the rgb from the block

`270 rgb = self.up_sample(rgb) + rgb_new`

Return the final RGB image

`273 return rgb`

*$A$ denotes a linear layer.
$B$ denotes a broadcast and scaling operation (noise is a single channel).
toRGB also has a style modulation which is not shown in the diagram to keep it simple.*

The generator block consists of two style blocks ($3 \times 3$ convolutions with style modulation) and an RGB output.

`276class GeneratorBlock(nn.Module):`

`d_latent`

is the dimensionality of $w$`in_features`

is the number of features in the input feature map`out_features`

is the number of features in the output feature map

`291 def __init__(self, d_latent: int, in_features: int, out_features: int):`

`297 super().__init__()`

First style block changes the feature map size to `out_features`

`300 self.style_block1 = StyleBlock(d_latent, in_features, out_features)`

Second style block

`302 self.style_block2 = StyleBlock(d_latent, out_features, out_features)`

*toRGB* layer

`305 self.to_rgb = ToRGB(d_latent, out_features)`

`x`

is the input feature map of shape`[batch_size, in_features, height, width]`

`w`

is $w$ with shape`[batch_size, d_latent]`

`noise`

is a tuple of two noise tensors of shape`[batch_size, 1, height, width]`

`307 def forward(self, x: torch.Tensor, w: torch.Tensor, noise: Tuple[Optional[torch.Tensor], Optional[torch.Tensor]]):`

First style block with first noise tensor.
The output is of shape `[batch_size, out_features, height, width]`

`315 x = self.style_block1(x, w, noise[0])`

Second style block with second noise tensor.
The output is of shape `[batch_size, out_features, height, width]`

`318 x = self.style_block2(x, w, noise[1])`

Get RGB image

`321 rgb = self.to_rgb(x, w)`

Return feature map and rgb image

`324 return x, rgb`

*$A$ denotes a linear layer.
$B$ denotes a broadcast and scaling operation (noise is single channel).*

Style block has a weight modulation convolution layer.

`327class StyleBlock(nn.Module):`

`d_latent`

is the dimensionality of $w$`in_features`

is the number of features in the input feature map`out_features`

is the number of features in the output feature map

`340 def __init__(self, d_latent: int, in_features: int, out_features: int):`

`346 super().__init__()`

Get style vector from $w$ (denoted by $A$ in the diagram) with an equalized learning-rate linear layer

`349 self.to_style = EqualizedLinear(d_latent, in_features, bias=1.0)`

Weight modulated convolution layer

`351 self.conv = Conv2dWeightModulate(in_features, out_features, kernel_size=3)`

Noise scale

`353 self.scale_noise = nn.Parameter(torch.zeros(1))`

Bias

`355 self.bias = nn.Parameter(torch.zeros(out_features))`

Activation function

`358 self.activation = nn.LeakyReLU(0.2, True)`

`x`

is the input feature map of shape`[batch_size, in_features, height, width]`

`w`

is $w$ with shape`[batch_size, d_latent]`

`noise`

is a tensor of shape`[batch_size, 1, height, width]`

`360 def forward(self, x: torch.Tensor, w: torch.Tensor, noise: Optional[torch.Tensor]):`

Get style vector $s$

`367 s = self.to_style(w)`

Weight modulated convolution

`369 x = self.conv(x, s)`

Scale and add noise

```
371 if noise is not None:
372 x = x + self.scale_noise[None, :, None, None] * noise
```

Add bias and evaluate activation function

`374 return self.activation(x + self.bias[None, :, None, None])`

*$A$ denotes a linear layer.*

Generates an RGB image from a feature map using $1 \times 1$ convolution.

`377class ToRGB(nn.Module):`

`d_latent`

is the dimensionality of $w$`features`

is the number of features in the feature map

`389 def __init__(self, d_latent: int, features: int):`

`394 super().__init__()`

`397 self.to_style = EqualizedLinear(d_latent, features, bias=1.0)`

Weight modulated convolution layer without demodulation

`400 self.conv = Conv2dWeightModulate(features, 3, kernel_size=1, demodulate=False)`

Bias

`402 self.bias = nn.Parameter(torch.zeros(3))`

Activation function

`404 self.activation = nn.LeakyReLU(0.2, True)`

`x`

is the input feature map of shape`[batch_size, in_features, height, width]`

`w`

is $w$ with shape`[batch_size, d_latent]`

`406 def forward(self, x: torch.Tensor, w: torch.Tensor):`

Get style vector $s$

`412 style = self.to_style(w)`

Weight modulated convolution

`414 x = self.conv(x, style)`

Add bias and evaluate activation function

`416 return self.activation(x + self.bias[None, :, None, None])`

This layer scales the convolution weights by the style vector and demodulates by normalizing it.

`419class Conv2dWeightModulate(nn.Module):`

`in_features`

is the number of features in the input feature map`out_features`

is the number of features in the output feature map`kernel_size`

is the size of the convolution kernel`demodulate`

is flag whether to normalize weights by its standard deviation`eps`

is the $\epsilon$ for normalizing

```
426 def __init__(self, in_features: int, out_features: int, kernel_size: int,
427 demodulate: float = True, eps: float = 1e-8):
```

`435 super().__init__()`

Number of output features

`437 self.out_features = out_features`

Whether to normalize weights

`439 self.demodulate = demodulate`

Padding size

`441 self.padding = (kernel_size - 1) // 2`

`444 self.weight = EqualizedWeight([out_features, in_features, kernel_size, kernel_size])`

$\epsilon$

`446 self.eps = eps`

`x`

is the input feature map of shape`[batch_size, in_features, height, width]`

`s`

is style based scaling tensor of shape`[batch_size, in_features]`

`448 def forward(self, x: torch.Tensor, s: torch.Tensor):`

Get batch size, height and width

`455 b, _, h, w = x.shape`

Reshape the scales

`458 s = s[:, None, :, None, None]`

`460 weights = self.weight()[None, :, :, :, :]`

where $i$ is the input channel, $j$ is the output channel, and $k$ is the kernel index.

The result has shape `[batch_size, out_features, in_features, kernel_size, kernel_size]`

`465 weights = weights * s`

Demodulate

`468 if self.demodulate:`

`470 sigma_inv = torch.rsqrt((weights ** 2).sum(dim=(2, 3, 4), keepdim=True) + self.eps)`

`472 weights = weights * sigma_inv`

Reshape `x`

`475 x = x.reshape(1, -1, h, w)`

Reshape weights

```
478 _, _, *ws = weights.shape
479 weights = weights.reshape(b * self.out_features, *ws)
```

Use grouped convolution to efficiently calculate the convolution with sample wise kernel. i.e. we have a different kernel (weights) for each sample in the batch

`483 x = F.conv2d(x, weights, padding=self.padding, groups=b)`

Reshape `x`

to `[batch_size, out_features, height, width]`

and return

`486 return x.reshape(-1, self.out_features, h, w)`

Discriminator first transforms the image to a feature map of the same resolution and then runs it through a series of blocks with residual connections. The resolution is down-sampled by $2 \times$ at each block while doubling the number of features.

`489class Discriminator(nn.Module):`

`log_resolution`

is the $\log_2$ of image resolution`n_features`

number of features in the convolution layer at the highest resolution (first block)`max_features`

maximum number of features in any generator block

`502 def __init__(self, log_resolution: int, n_features: int = 64, max_features: int = 512):`

`508 super().__init__()`

Layer to convert RGB image to a feature map with `n_features`

number of features.

```
511 self.from_rgb = nn.Sequential(
512 EqualizedConv2d(3, n_features, 1),
513 nn.LeakyReLU(0.2, True),
514 )
```

`519 features = [min(max_features, n_features * (2 ** i)) for i in range(log_resolution - 1)]`

Number of discirminator blocks

`521 n_blocks = len(features) - 1`

Discriminator blocks

```
523 blocks = [DiscriminatorBlock(features[i], features[i + 1]) for i in range(n_blocks)]
524 self.blocks = nn.Sequential(*blocks)
```

`527 self.std_dev = MiniBatchStdDev()`

Number of features after adding the standard deviations map

`529 final_features = features[-1] + 1`

Final $3 \times 3$ convolution layer

`531 self.conv = EqualizedConv2d(final_features, final_features, 3)`

Final linear layer to get the classification

`533 self.final = EqualizedLinear(2 * 2 * final_features, 1)`

`x`

is the input image of shape`[batch_size, 3, height, width]`

`535 def forward(self, x: torch.Tensor):`

Try to normalize the image (this is totally optional, but sped up the early training a little)

`541 x = x - 0.5`

Convert from RGB

`543 x = self.from_rgb(x)`

Run through the discriminator blocks

`545 x = self.blocks(x)`

Calculate and append mini-batch standard deviation

`548 x = self.std_dev(x)`

$3 \times 3$ convolution

`550 x = self.conv(x)`

Flatten

`552 x = x.reshape(x.shape[0], -1)`

Return the classification score

`554 return self.final(x)`

Discriminator block consists of two $3 \times 3$ convolutions with a residual connection.

`557class DiscriminatorBlock(nn.Module):`

`in_features`

is the number of features in the input feature map`out_features`

is the number of features in the output feature map

`567 def __init__(self, in_features, out_features):`

`572 super().__init__()`

Down-sampling and $1 \times 1$ convolution layer for the residual connection

```
574 self.residual = nn.Sequential(DownSample(),
575 EqualizedConv2d(in_features, out_features, kernel_size=1))
```

Two $3 \times 3$ convolutions

```
578 self.block = nn.Sequential(
579 EqualizedConv2d(in_features, in_features, kernel_size=3, padding=1),
580 nn.LeakyReLU(0.2, True),
581 EqualizedConv2d(in_features, out_features, kernel_size=3, padding=1),
582 nn.LeakyReLU(0.2, True),
583 )
```

Down-sampling layer

`586 self.down_sample = DownSample()`

Scaling factor $\frac{1}{\sqrt 2}$ after adding the residual

`589 self.scale = 1 / math.sqrt(2)`

`591 def forward(self, x):`

Get the residual connection

`593 residual = self.residual(x)`

Convolutions

`596 x = self.block(x)`

Down-sample

`598 x = self.down_sample(x)`

Add the residual and scale

`601 return (x + residual) * self.scale`

Mini-batch standard deviation calculates the standard deviation across a mini-batch (or a subgroups within the mini-batch) for each feature in the feature map. Then it takes the mean of all the standard deviations and appends it to the feature map as one extra feature.

`604class MiniBatchStdDev(nn.Module):`

`group_size`

is the number of samples to calculate standard deviation across.

`616 def __init__(self, group_size: int = 4):`

```
620 super().__init__()
621 self.group_size = group_size
```

`x`

is the feature map

`623 def forward(self, x: torch.Tensor):`

Check if the batch size is divisible by the group size

`628 assert x.shape[0] % self.group_size == 0`

Split the samples into groups of `group_size`

, we flatten the feature map to a single dimension
since we want to calculate the standard deviation for each feature.

`631 grouped = x.view(self.group_size, -1)`

Calculate the standard deviation for each feature among `group_size`

samples

`635 std = torch.sqrt(grouped.var(dim=0) + 1e-8)`

Get the mean standard deviation

`637 std = std.mean().view(1, 1, 1, 1)`

Expand the standard deviation to append to the feature map

```
639 b, _, h, w = x.shape
640 std = std.expand(b, -1, h, w)
```

Append (concatenate) the standard deviations to the feature map

`642 return torch.cat([x, std], dim=1)`

The down-sample operation smoothens each feature channel and scale $2 \times$ using bilinear interpolation. This is based on the paper Making Convolutional Networks Shift-Invariant Again.

`645class DownSample(nn.Module):`

```
656 def __init__(self):
657 super().__init__()
```

Smoothing layer

`659 self.smooth = Smooth()`

`661 def forward(self, x: torch.Tensor):`

Smoothing or blurring

`663 x = self.smooth(x)`

Scaled down

`665 return F.interpolate(x, (x.shape[2] // 2, x.shape[3] // 2), mode='bilinear', align_corners=False)`

The up-sample operation scales the image up by $2 \times$ and smoothens each feature channel. This is based on the paper Making Convolutional Networks Shift-Invariant Again.

`668class UpSample(nn.Module):`

```
678 def __init__(self):
679 super().__init__()
```

Up-sampling layer

`681 self.up_sample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False)`

Smoothing layer

`683 self.smooth = Smooth()`

`685 def forward(self, x: torch.Tensor):`

Up-sample and smoothen

`687 return self.smooth(self.up_sample(x))`

`690class Smooth(nn.Module):`

```
698 def __init__(self):
699 super().__init__()
```

Blurring kernel

```
701 kernel = [[1, 2, 1],
702 [2, 4, 2],
703 [1, 2, 1]]
```

Convert the kernel to a PyTorch tensor

`705 kernel = torch.tensor([[kernel]], dtype=torch.float)`

Normalize the kernel

`707 kernel /= kernel.sum()`

Save kernel as a fixed parameter (no gradient updates)

`709 self.kernel = nn.Parameter(kernel, requires_grad=False)`

Padding layer

`711 self.pad = nn.ReplicationPad2d(1)`

`713 def forward(self, x: torch.Tensor):`

Get shape of the input feature map

`715 b, c, h, w = x.shape`

Reshape for smoothening

`717 x = x.view(-1, 1, h, w)`

Add padding

`720 x = self.pad(x)`

Smoothen (blur) with the kernel

`723 x = F.conv2d(x, self.kernel)`

Reshape and return

`726 return x.view(b, c, h, w)`

This uses learning-rate equalized weights for a linear layer.

`729class EqualizedLinear(nn.Module):`

`in_features`

is the number of features in the input feature map`out_features`

is the number of features in the output feature map`bias`

is the bias initialization constant

`737 def __init__(self, in_features: int, out_features: int, bias: float = 0.):`

`744 super().__init__()`

`746 self.weight = EqualizedWeight([out_features, in_features])`

Bias

`748 self.bias = nn.Parameter(torch.ones(out_features) * bias)`

`750 def forward(self, x: torch.Tensor):`

Linear transformation

`752 return F.linear(x, self.weight(), bias=self.bias)`

This uses learning-rate equalized weights for a convolution layer.

`755class EqualizedConv2d(nn.Module):`

`in_features`

is the number of features in the input feature map`out_features`

is the number of features in the output feature map`kernel_size`

is the size of the convolution kernel`padding`

is the padding to be added on both sides of each size dimension

```
763 def __init__(self, in_features: int, out_features: int,
764 kernel_size: int, padding: int = 0):
```

`771 super().__init__()`

Padding size

`773 self.padding = padding`

`775 self.weight = EqualizedWeight([out_features, in_features, kernel_size, kernel_size])`

Bias

`777 self.bias = nn.Parameter(torch.ones(out_features))`

`779 def forward(self, x: torch.Tensor):`

Convolution

`781 return F.conv2d(x, self.weight(), bias=self.bias, padding=self.padding)`

This is based on equalized learning rate introduced in the Progressive GAN paper. Instead of initializing weights at $\mathcal{N}(0,c)$ they initialize weights to $\mathcal{N}(0, 1)$ and then multiply them by $c$ when using it.

The gradients on stored parameters $\hat{w}$ get multiplied by $c$ but this doesn’t have an affect since optimizers such as Adam normalize them by a running mean of the squared gradients.

The optimizer updates on $\hat{w}$ are proportionate to the learning rate $\lambda$. But the effective weights $w$ get updated proportionately to $c \lambda$. Without equalized learning rate, the effective weights will get updated proportionately to just $\lambda$.

So we are effectively scaling the learning rate by $c$ for these weight parameters.

`784class EqualizedWeight(nn.Module):`

`shape`

is the shape of the weight parameter

`804 def __init__(self, shape: List[int]):`

`808 super().__init__()`

He initialization constant

`811 self.c = 1 / math.sqrt(np.prod(shape[1:]))`

Initialize the weights with $\mathcal{N}(0, 1)$

`813 self.weight = nn.Parameter(torch.randn(shape))`

Weight multiplication coefficient

`816 def forward(self):`

Multiply the weights by $c$ and return

`818 return self.weight * self.c`

This is the $R_1$ regularization penality from the paper Which Training Methods for GANs do actually Converge?.

That is we try to reduce the L2 norm of gradients of the discriminator with respect to images, for real images ($P_\mathcal{D}$).

`821class GradientPenalty(nn.Module):`

`x`

is $x \sim \mathcal{D}$`d`

is $D(x)$

`836 def forward(self, x: torch.Tensor, d: torch.Tensor):`

Get batch size

`843 batch_size = x.shape[0]`

Calculate gradients of $D(x)$ with respect to $x$.
`grad_outputs`

is set to $1$ since we want the gradients of $D(x)$,
and we need to create and retain graph since we have to compute gradients
with respect to weight on this loss.

```
849 gradients, *_ = torch.autograd.grad(outputs=d,
850 inputs=x,
851 grad_outputs=d.new_ones(d.shape),
852 create_graph=True)
```

Reshape gradients to calculate the norm

`855 gradients = gradients.reshape(batch_size, -1)`

Calculate the norm $\Vert \nabla_{x} D(x)^2 \Vert$

`857 norm = gradients.norm(2, dim=-1)`

Return the loss $\Vert \nabla_x D_\psi(x)^2 \Vert$

`859 return torch.mean(norm ** 2)`

This regularization encourages a fixed-size step in $w$ to result in a fixed-magnitude change in the image.

where $\mathbf{J}_w$ is the Jacobian $\mathbf{J}_w = \frac{\partial g}{\partial w}$, $w$ are sampled from $w \in \mathcal{W}$ from the mapping network, and $y$ are images with noise $\mathcal{N}(0, \mathbf{I})$.

$a$ is the exponential moving average of $\Vert \mathbf{J}^\top_{w} y \Vert_2$ as the training progresses.

$\mathbf{J}^\top_{w} y$ is calculated without explicitly calculating the Jacobian using

`862class PathLengthPenalty(nn.Module):`

`beta`

is the constant $\beta$ used to calculate the exponential moving average $a$

`885 def __init__(self, beta: float):`

`889 super().__init__()`

$\beta$

`892 self.beta = beta`

Number of steps calculated $N$

`894 self.steps = nn.Parameter(torch.tensor(0.), requires_grad=False)`

Exponential sum of $\mathbf{J}^\top_{w} y$ where $[\mathbf{J}^\top_{w} y]_i$ is the value of it at $i$-th step of training

`898 self.exp_sum_a = nn.Parameter(torch.tensor(0.), requires_grad=False)`

`w`

is the batch of $w$ of shape`[batch_size, d_latent]`

`x`

are the generated images of shape`[batch_size, 3, height, width]`

`900 def forward(self, w: torch.Tensor, x: torch.Tensor):`

Get the device

`907 device = x.device`

Get number of pixels

`909 image_size = x.shape[2] * x.shape[3]`

Calculate $y \in \mathcal{N}(0, \mathbf{I})$

`911 y = torch.randn(x.shape, device=device)`

Calculate $\big(g(w) \cdot y \big)$ and normalize by the square root of image size. This is scaling is not mentioned in the paper but was present in their implementation.

`915 output = (x * y).sum() / math.sqrt(image_size)`

Calculate gradients to get $\mathbf{J}^\top_{w} y$

```
918 gradients, *_ = torch.autograd.grad(outputs=output,
919 inputs=w,
920 grad_outputs=torch.ones(output.shape, device=device),
921 create_graph=True)
```

Calculate L2-norm of $\mathbf{J}^\top_{w} y$

`924 norm = (gradients ** 2).sum(dim=2).mean(dim=1).sqrt()`

Regularize after first step

`927 if self.steps > 0:`

Calculate $a$

`930 a = self.exp_sum_a / (1 - self.beta ** self.steps)`

Calculate the penalty

```
934 loss = torch.mean((norm - a) ** 2)
935 else:
```

Return a dummy loss if we can’t calculate $a$

`937 loss = norm.new_tensor(0)`

Calculate the mean of $\Vert \mathbf{J}^\top_{w} y \Vert_2$

`940 mean = norm.mean().detach()`

Update exponential sum

`942 self.exp_sum_a.mul_(self.beta).add_(mean, alpha=1 - self.beta)`

Increment $N$

`944 self.steps.add_(1.)`

Return the penalty

`947 return loss`