Create README.md

README.md

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+---
+tags:
+- huggan
+- gan
+# 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 fauvism still life 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}
+}
+```