Style G A N Human

Introduction

StyleGAN-Human is a generative adversarial network (GAN) model designed to generate high-quality human images. This model is detailed in the research paper available on arXiv and is hosted on GitHub for public access.

Architecture

StyleGAN-Human builds upon the StyleGAN architecture, which is known for its ability to produce high-resolution, photorealistic images. This model specifically focuses on generating human figures, providing enhanced realism and detail in human image synthesis.

Training

The model was trained using a dataset focused on human images. The training process utilized state-of-the-art techniques in GANs to refine the image quality and realism. Pre-trained weights are available for download, providing a foundation for further experimentation or application development.

Guide: Running Locally

To run StyleGAN-Human locally, follow these basic steps:

1. Clone the Repository: Clone the StyleGAN-Human GitHub repository to your local machine.
2. Install Dependencies: Ensure all necessary Python packages and dependencies are installed.
3. Download Weights: Obtain the pre-trained weights from the provided Google Drive links, link 2, link 3.
4. Configure Environment: Set up your environment to match the model's requirements.
5. Run the Model: Execute the model using your preferred Python environment.

For optimal performance, it is recommended to use cloud GPUs, such as those offered by AWS, GCP, or Azure, given the intensive computational requirements of GANs.

License

The use of StyleGAN-Human is subject to licensing terms provided in the GitHub repository. Please review them to ensure compliance with any usage restrictions or attributions required.