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StyleGAN – Style Generative Adversarial Networks

Last Updated : 25 Feb, 2025
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Generative Adversarial Networks (GANs) are a type of neural network that consist two neural networks: a generator that creates images and a discriminator that evaluates them. The generator tries to produce realistic data while the discriminator tries to differentiate between real and generated data.

StyleGAN is an advanced generative model developed by NVIDIA designed to generate highly realistic images such as faces of people who don’t exist. Unlike traditional image generation methods StyleGAN provides a unique advantage by giving you control over various levels of image features. This includes broad shapes and structures as well as finer details like texture and lighting. In this article we will learn more about them.

Architecture of StyleGAN

Style GAN uses the baseline GAN architecture and proposed some changes in the generator part of it. However the discriminator architecture is quite similar to baseline GAN. Let’s look at these architectural changes one by one.

Style GAN architecture

1. Baseline Progressive Growing GANs: It means the size of generated image increases gradually from a very low resolution (4×4) to high resolution (1024 x1024). This is done by adding a new block to both the models to support the larger resolution after fitting the model on smaller resolution to make it more stable..

2. Bi-linear Sampling: In StyleGAN bi-linear sampling replaces the nearest neighbor method used in Progressive GAN. This helps achieve smoother images by applying a low-pass filter to the activations during both upsampling and downsampling.

3. Mapping Network and Style Network: Instead of directly feeding a random latent vector (z) to the generator StyleGAN maps the latent vector (z) into another vector (w) using an 8-layer MLP (multi-layer perceptron). This intermediate vector (w) controls different image features such as texture and lighting.

  • After mapping an affine transformation is applied to further modify the features which are then passed through the AdaIN (Adaptive Instance Normalization) layer.
  • AdaIN adjusts the feature maps by normalizing them, then scaling and biasing them using the style vector. This gives StyleGAN more control over image style at different layers.

Generator Architecture of Style GAN vs Traditional Architecture

  • The input to the AdaIN is y = (ys, yb) which is generated by applying (A) to (w). The AdaIN operation is defined by the following equation:

[Tex]AdaIN (x_i, y) = y_{s, i}\left ( \left ( x_i – \mu_i \right )/ \sigma_i \right )) + y_{b, i}   [/Tex]
where each feature map x is normalized separately, and then scaled and biased using the corresponding scalar components from style y. Thus the dimensional of y is twice the number of feature maps  (x) on that layer. The synthesis network contains 18 convolutional layers 2 for each of the resolutions (4×4 – 1024×1024).

4. Generator Improvements: StyleGAN replaces the traditional latent code input with a constant matrix of size 4×4×512. This helps the model focus on the style features rather than random noise improving performance. Additionally Gaussian noise is added at each level of the synthesis network to introduce small stochastic details like freckles, wrinkles and other fine features that enhance realism.

5. Mixing Regularization: To reduce unwanted correlations in the features, mixing regularization randomly mixes two different latent vectors (z1, z2) at various levels of the generator. This forces the network to learn more diverse representations of images.

6. Style Control at Different Resolutions: The synthesis network in StyleGAN provides control over the style at different image resolutions:

coarsefineresult-copy-2
  • Coarse Resolution (4×4 to 8×8): Affects major features like pose and general shape.
  • Middle Resolution (16×16 to 32×32): Affects facial features, hair, eyes, etc.
  • Fine Resolution (64×64 to 1024×1024): Controls finer details like colors and micro-features.

Each resolution level has its own noise component influencing how style changes at that level. For Example: Noise in coarse level cause changes in broader structure while in Fine level cause changes in finer details of image.

7. Feature Disentanglement Studies: StyleGAN also introduces two methods for measuring how well different features are separated:

  • Perceptual Path Length: Measures the perceptual difference between images when interpolating between two latent vectors.
  • Linear Separability: Checks how well features (e.g., male vs. female) can be separated using a linear decision boundary.

These studies highlight that the w space (intermediate mapping) is more easily separable than the z space (latent space) underscoring the power of the mapping network.

Results: 

This generates state -of-the art results on Celeba-HQ dataset. It also proposes a new dataset of human faces called Flicker Face HQ (FFHQ) dataset which have considerably more variation than Celeba-HQ. this style-GAN architecture generates considerably good results also on FFHQ dataset. Below are the results of this architecture on these two dataset. 

Here we calculate FID score using 50, 000 randomly chosen images from the training set and take the lowest distance encountered over the course of training.

Use cases of StyleGANs

There are various applications of StyleGANs in real life:

  • Face Generation and Enhancement: StyleGAN has become highly popular for generating realistic human faces in domains like entertainment, gaming and virtual avatars. It can create faces that don’t belong to real people often used for video games, movies or even avatars for virtual meetings.
  • Fashion Design: It is used to create new clothing designs by blending various style features. Designers can explore different looks, colors, and patterns to generate ideas for collections. This helps speed up the creative process and brings out innovative designs.
  • Data Augmentation in Machine Learning: In machine learning particularly in computer vision StyleGAN is used to generate synthetic data for training purposes. By generating variations of existing images such as faces, vehicles, etc. StyleGAN helps in augmenting datasets where collecting real data is expensive or difficult.
  • Animation and Video Games: StyleGAN’s ability to create detailed and varied facial features has made it useful in the gaming industry, where it’s used for character creation. Games can generate faces of characters or NPCs (non-playable characters) based on different features which enhance realism in games.

In conclusion StyleGAN stands out as a revolutionary tool in the field of image generation offering unparalleled control over both broad and fine features of synthetic images and is useful in creative fields such as art, design and synthetic media where you can generate realistic images and adjust their style creatively.


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