Py Torch Studio G A N LLM Model

Introduction

StudioGAN is a PyTorch library offering implementations of notable Generative Adversarial Networks (GANs) for both conditional and unconditional image generation. It provides a unified environment for researchers to compare and evaluate new GAN methodologies.

Architecture

StudioGAN includes a variety of GAN models to serve as benchmarks for image synthesis tasks. It supports a range of architectures, allowing for comprehensive evaluations and comparisons.

Training

StudioGAN offers pre-trained model checkpoints used in benchmark evaluations. These can be utilized to replicate results or as a starting point for further research and development.

Guide: Running Locally

Clone the Repository:

git clone https://github.com/POSTECH-CVLab/PyTorch-StudioGAN.git
cd PyTorch-StudioGAN

Install Dependencies: Ensure you have Python and PyTorch installed, then install additional requirements:
```
pip install -r requirements.txt
```
Run the Script: Execute training or evaluation scripts provided in the repository:
```
python train.py --config your_config.yaml
```

For optimal performance, it is advisable to use cloud GPUs such as AWS EC2 P3 instances or Google Cloud's NVIDIA GPUs.

License

PyTorch-StudioGAN is distributed under the MIT License. Specific portions of the library, including StyleGAN2, StyleGAN2-ADA, and StyleGAN3, are licensed under the NVIDIA source code license. PyTorch-FID is licensed under the Apache License. Detailed license information can be found in the respective files within the repository.

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