
# Generate moon gate 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 136 high-quality moon gate images.

#### How to use

```python
# Clone this model 
git clone https://huggingface.co/huggan/fastgan-few-shot-moongate

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-moongate")
# 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-moongate](huggan/few-shot-moongate)

## 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}
}
```
{"license": "mit", "tags": ["huggan", "gan", "unconditional-image-generation"], "datasets": ["huggan/few-shot-moongate"]}
pytorch huggan gan unconditional-image-generation dataset:huggan/few-shot-moongate arxiv:2101.04775 license:mit has_space region:us
TAGS
#pytorch #huggan #gan #unconditional-image-generation #dataset-huggan/few-shot-moongate #arxiv-2101.04775 #license-mit #has_space #region-us 


# Generate moon gate image using FastGAN

## Model description

FastGAN model 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 136 high-quality moon gate images.

#### How to use



#### Limitations and bias

* Converge faster and better with small datasets (less than 1000 samples)

## Training data

few-shot-moongate

## Generated Images

!Example image

### BibTeX entry and citation info


# Generate moon gate image using FastGAN ## Model description

FastGAN model 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 136 high-quality moon gate images. #### How to use #### Limitations and bias

* Converge faster and better with small datasets (less than 1000 samples) ## Training data

few-shot-moongate ## Generated Images

!Example image ### BibTeX entry and citation info
TAGS
#pytorch #huggan #gan #unconditional-image-generation #dataset-huggan/few-shot-moongate #arxiv-2101.04775 #license-mit #has_space #region-us 
 # Generate moon gate image using FastGAN ## Model description

FastGAN model 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 136 high-quality moon gate images. #### How to use #### Limitations and bias

* Converge faster and better with small datasets (less than 1000 samples) ## Training data

few-shot-moongate ## Generated Images

!Example image ### BibTeX entry and citation info
passage: TAGS
#pytorch #huggan #gan #unconditional-image-generation #dataset-huggan/few-shot-moongate #arxiv-2101.04775 #license-mit #has_space #region-us 
# Generate moon gate image using FastGAN## Model description

FastGAN model 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 136 high-quality moon gate images.#### How to use#### Limitations and bias

* Converge faster and better with small datasets (less than 1000 samples)## Training data

few-shot-moongate## Generated Images

!Example image### BibTeX entry and citation info