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StyleGAN Image Generation Style-Based Generator High-Resolution Open Source

StyleGAN for generating high-resolution photorealistic images using style-based generator architecture with adaptive instance normalization (AdaIN). The architecture uses mapping network to transform random noise to intermediate latent space (W), synthesis network with progressive growing, and style mixing capabilities for fine-grained control. Perfect for learning StyleGAN fundamentals and high-quality image generation. Complete implementation with PyTorch, including adversarial training, style mixing, truncation trick, TensorBoard integration, and comprehensive training tools.

StyleGAN PyTorch Image Generation AdaIN Download Now Jupyter Notebook TensorBoard Get Started
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StyleGAN Image Generation Project - RSK World
StyleGAN Image Generation Project - RSK World
Image Generation StyleGAN Python PyTorch GAN Computer Vision

This project implements StyleGAN for generating high-resolution photorealistic images using style-based generator architecture with adaptive instance normalization (AdaIN). The architecture uses mapping network to transform random noise to intermediate latent space (W), synthesis network with progressive growing from low to high resolution, and style mixing capabilities for fine-grained control over image attributes. Perfect for learning StyleGAN fundamentals and high-quality image generation, featuring PyTorch implementation, adversarial training, style mixing, truncation trick, TensorBoard integration, and comprehensive training tools.

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StyleGAN Architecture

Complete StyleGAN architecture with style-based generator and discriminator networks. Uses mapping network to transform random noise to intermediate latent space (W), synthesis network with progressive growing, and adaptive instance normalization (AdaIN) for style injection.

  • Style-based generator network
  • Mapping network (Z to W space)
  • Synthesis network with progressive growing
  • Adaptive instance normalization (AdaIN)

Style-Based Generation

Generates high-resolution images using style-based architecture with fine-grained control. Maps random noise to intermediate latent space (W) and injects style at different resolutions using AdaIN layers.

  • Intermediate latent space (W)
  • Style injection at multiple resolutions
  • Progressive growing architecture
  • Controllable image attributes

Mapping & Synthesis Networks

Mapping network transforms random noise Z to intermediate latent space W. Synthesis network generates images progressively from low to high resolution with style control at each layer.

  • Mapping network (8 layers)
  • Synthesis network with progressive growing
  • AdaIN layers for style injection
  • High-resolution generation (up to 1024x1024)

Adversarial Training

Complete adversarial training pipeline with generator and discriminator networks. Uses gradient penalty for stable training, truncation trick for quality control, and comprehensive loss monitoring.

  • Generator loss (adversarial)
  • Discriminator loss with gradient penalty
  • Truncation trick support
  • Training stability improvements

TensorBoard Integration

Real-time training visualization with TensorBoard integration. Monitor generator loss, discriminator loss, gradient penalty, view generated image samples at different resolutions, and track training progress.

  • Real-time loss visualization
  • Generator and discriminator loss tracking
  • Generated image samples at multiple resolutions
  • Interactive dashboard

Jupyter Notebook

Interactive Jupyter Notebook for model architecture visualization, training setup examples, image generation, and latent space exploration.

  • Model architecture visualization
  • Training setup examples
  • Image generation examples
  • Latent space visualization

Style Mixing & Interpolation

Mix styles from different latent codes at different layers and generate smooth transitions between images. Supports style mixing at specific layers and interpolation in W space.

  • Style mixing at different layers
  • Linear interpolation in W space
  • Controllable style attributes
  • Smooth image transitions

Data Augmentation

Advanced data augmentation strategies including adaptive augmentation, mixup, cutout, and standard augmentation for improved training stability.

  • Adaptive augmentation
  • Mixup augmentation
  • Cutout augmentation
  • Multiple augmentation levels

Web Interface

Interactive Flask-based web application for generating images. Real-time image generation with customizable parameters and download functionality.

  • Flask-based web app
  • Interactive image generation
  • Real-time generation
  • Download generated images

Multiple Dataset Support

Support for multiple datasets including custom datasets, CelebA, CIFAR-10, and MNIST. Easy dataset switching and custom data loading.

  • Custom dataset support
  • CelebA dataset
  • CIFAR-10 dataset
  • MNIST dataset

Resume Training

Resume training from checkpoints with automatic checkpoint detection. Continue training from the last saved epoch without losing progress.

  • Checkpoint resuming
  • Automatic checkpoint detection
  • Training continuation
  • Progress preservation

Advanced Training

Complete training pipeline with learning rate scheduling, gradient clipping, early stopping, label smoothing, and comprehensive training progress logging.

  • Learning rate scheduling
  • Gradient clipping
  • Early stopping
  • Label smoothing
  • Training progress logging

Training Visualization

Comprehensive training visualization with loss curves (generator, discriminator, gradient penalty), generated image samples at progressive resolutions, style mixing examples, and real-time TensorBoard monitoring.

  • Generator/discriminator loss curves
  • Generated images at multiple resolutions
  • Style mixing visualization
  • Real-time monitoring

Utility Functions

Helper functions for image processing, model utilities, checkpoint management, evaluation metrics, and common development tasks.

  • Image processing utilities
  • Model utility functions
  • Checkpoint management
  • Evaluation helpers

Requirements

The following are the technical requirements for this project:

  • Python 3.8+
  • PyTorch 2.0+
  • NumPy 1.21+
  • Matplotlib 3.5+
  • Jupyter Notebook 1.0.0+
  • TensorBoard 2.10+
  • Pillow 9.0+
  • OpenCV 4.6+

Credits & Acknowledgments

This project is developed for educational purposes and utilizes the following resources:

  • Python - PSF License
  • PyTorch - BSD License
  • RSK World - Project Inspiration
  • GitHub Repository - Source code and documentation
  • A Style-Based Generator Architecture for GANs - Original StyleGAN Paper

Support & Contact

For paid applications, please contact us for integration help or feedback.

  • Support Email: help@rskworld.in
  • Contact Number: +91 9330539277
  • Website: RSKWORLD.in
  • GitHub Project
  • Join Our Discord
  • Slack Support Channel
  • StyleGAN Image Generation Documentation
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Categories

Image Generation StyleGAN Python PyTorch GAN Computer Vision

Technologies

Python 3.8+
PyTorch 2.0+
StyleGAN
AdaIN
Image Generation

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