cryptopunks

Introduction

The HuggingNFT project features a LightWeight GAN model designed for the unconditional generation of images, specifically focusing on NFTs like the CryptoPunks collection. The project includes a dataset for training and generating images, available on Hugging Face's platform and OpenSea.

Architecture

The model employs a LightWeight GAN architecture, optimized for generating NFT-style images without specific conditions. This approach allows for efficient and high-quality image generation, making it suitable for creative and artistic applications within the NFT space.

Training

Training Data

The training dataset, specific to CryptoPunks, can be accessed on Hugging Face: CryptoPunks Dataset.

Training Procedure

The training process for the model is detailed in the project's GitHub repository, which provides scripts and instructions for replicating or modifying the training process: GitHub Repository.

Guide: Running Locally

To run the model locally:

1. Clone the Repository:

   git clone https://github.com/AlekseyKorshuk/huggingnft
   cd huggingnft
   

2. Install Dependencies:
   Ensure you have Python and the required libraries installed. You can typically do this with:

   pip install -r requirements.txt
   

3. Download the Dataset:
   The dataset can be downloaded from Hugging Face's dataset hub:
   CryptoPunks Dataset.

4. Run the Training Script:
   Execute the provided training script to start generating images. Detailed instructions are in the GitHub repository.

5. Consider Using Cloud GPUs:
   For better performance, consider using a cloud GPU service such as AWS, GCP, or Azure, which can significantly speed up the training and generation processes.

License

The model and associated files are available under the MIT License, allowing for wide usage and modification. Ensure compliance with this license when using or distributing the model.