readme: ---
  tags:
  - huggan
  - gan
  - unconditional-image-generation
  datasets:
  - huggan/few-shot-aurora
  # See a list of available tags here:
  # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12
  # task: unconditional-image-generation or conditional-image-generation or image-to-image
  license: mit
  ---
  
  # Generate aurora image using FastGAN
  
  ## Model description
  
  [FastGAN model](https://arxiv.org/abs/2101.04775) is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-encoder, the model was able to converge after some hours of training for either 100 high-quality images or 1000 images datasets.
  
  This model was trained on a dataset of 272 high-quality images of aurora.
  
  
  #### How to use
  
  ```python
  # Clone this model 
  git clone https://huggingface.co/huggan/fastgan-few-shot-aurora/
  
  def load_generator(model_name_or_path):
      generator = Generator(in_channels=256, out_channels=3)
      generator = generator.from_pretrained(model_name_or_path, in_channels=256, out_channels=3)
      _ = generator.eval()
  
      return generator
      
  def _denormalize(input: torch.Tensor) -> torch.Tensor:
      return (input * 127.5) + 127.5
      
  # Load generator
  generator = load_generator("huggan/fastgan-few-shot-aurora")
  
  # Generate a random noise image
  noise = torch.zeros(1, 256, 1, 1, device=device).normal_(0.0, 1.0)
  with torch.no_grad():
    gan_images, _ = generator(noise)
    
  gan_images = _denormalize(gan_images.detach())
  save_image(gan_images, "sample.png", nrow=1, normalize=True)
  ```
  
  #### Limitations and bias
  
  * Converge faster and better with small datasets (less than 1000 samples)
  
  ## Training data
  
  [few-shot-aurora](https://huggingface.co/datasets/huggan/few-shot-aurora)
  
  ## Generated Images
  
  ![Example image](example.png)
  
  ### BibTeX entry and citation info
  
  ```bibtex
  @article{FastGAN,
    title={Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis},
    author={Bingchen Liu, Yizhe Zhu, Kunpeng Song, Ahmed Elgammal},
    journal={ICLR},
    year={2021}
  }
  ```