Faster Projected GAN

Faster Projected GAN

Faster Projected GAN is an unofficial PyTorch implementation of the advanced model introduced in the paper Faster Projected GAN: Towards Faster Few-Shot Image Generation. This model is designed to facilitate rapid training and high-quality image generation with minimal datasets, proving highly effective when fewer than 100 samples are available.

Key Features

• Rapid Training: Efficiently trains on limited datasets without compromising image quality.
• High-quality Image Generation: Produces visually appealing results suitable for various applications in few-shot image generation.

Performance Demonstration

The following results were achieved on the AnimalFace-cat dataset comprising 160 images (provided by the FastGAN authors) after training for less than 237 kimg:

Integrate with Your Project

To include the Faster Projected GAN in your own Python projects, simply import and initialize the generator and discriminator as follows:

from generator import FasterProjectedGANGenerator
from discriminator import ProjectedGANDiscriminator

# Initialize models
G = FasterProjectedGANGenerator()
D = ProjectedGANDiscriminator()

Try it Online

For those seeking a quick way to experiment with this model without local installation, a Colab notebook is available for immediate use.

Contribution

Contributions are highly encouraged. Users are invited to improve this implementation, address bugs, or add new features by opening issues or submitting pull requests.