Stylegan | Awesome Repository

StyleGAN is a TensorFlow-based generative adversarial network framework designed for the synthesis of high-resolution synthetic imagery. It utilizes a style-based generator architecture to create realistic visual assets from latent vectors, focusing on the production of high-fidelity images.

The system incorporates style mixing and stochastic noise injection to control visual attributes and fine-grained details. It uses adaptive instance normalization and progressive resolution upsampling to manage image quality and variety across different resolutions.

The framework covers the full lifecycle of generative modeling, including image dataset preprocessing via multi-resolution binary data streaming and model training on multi-GPU hardware. It also provides evaluation tools to measure image fidelity and disentanglement using metrics such as Frechet Inception Distance and Perceptual Path Length.

Features

Generative Adversarial Image Synthesis - Provides a framework for generative adversarial image synthesis to produce high-resolution, realistic visual assets.
Latent Space Generative Models - Employs a latent space generative model that transforms latent vectors into intermediate styles for visual attribute control.
Synthetic Media Generators - Produces realistic synthetic imagery using generative adversarial networks for high-fidelity visual representations.
Generative Model Training Tools - Provides tools for training generative models on custom image datasets using multi-GPU hardware.

Features

Generative Adversarial Image Synthesis - Provides a framework for generative adversarial image synthesis to produce high-resolution, realistic visual assets.
Latent Space Generative Models - Employs a latent space generative model that transforms latent vectors into intermediate styles for visual attribute control.
Synthetic Media Generators - Produces realistic synthetic imagery using generative adversarial networks for high-fidelity visual representations.
Generative Model Training Tools - Provides tools for training generative models on custom image datasets using multi-GPU hardware.