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 272 high-quality images of aurora.
+
+
+#### How to use
+
+```python
+# You can include sample code which will be formatted
+```
+
+#### 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}
+}
+```