Style G A N X L

Introduction

StyleGAN-XL is a generative adversarial network (GAN) designed for generating high-quality, diverse images. It builds upon previous StyleGAN architectures to enhance performance, particularly in the generation of high-resolution images across various datasets.

Architecture

StyleGAN-XL improves upon its predecessors with optimizations that allow for more efficient training and better image quality. It employs advanced techniques to stabilize training and enhance the diversity of generated images. The architecture can handle various image resolutions, thereby making it versatile for multiple applications, from generating small-scale images to high-resolution ones.

Training

The model is trained using large-scale datasets, with pre-trained weights available for various resolutions and datasets such as ImageNet, CIFAR-10, FFHQ, and custom datasets like Pokemon. These pre-trained weights can be used to fine-tune the model for specific applications or to explore the capabilities of the model directly.

Guide: Running Locally

1. Clone the Repository:

   • Use git clone to obtain the StyleGAN-XL code from the GitHub repository.

2. Install Dependencies:

   • Ensure you have the necessary Python libraries installed. This typically involves using a package manager like pip to install any required packages listed in the repository's requirements.txt file.

3. Download Pre-trained Weights:

   • Download the desired pre-trained weights from the links provided:
     • ImageNet 16x16
     • ImageNet 32x32
     • ImageNet 64x64
     • ImageNet 128x128
     • CIFAR-10
     • FFHQ 256x256
     • Pokemon 256x256

4. Run the Model:

   • Use the provided scripts to load the model and generate images. Adjust parameters as necessary for your specific use case.

5. Cloud GPUs:

   • For efficient processing, especially for high-resolution images, consider using cloud-based GPU services such as AWS, Google Cloud, or Azure to leverage their computational power.

License

The StyleGAN-XL project is open-source, and its use and distribution are subject to the terms specified in the project's repository. Always refer to the specific licensing terms provided in the repository to ensure compliance.