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--- |
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license: apache-2.0 |
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tags: |
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- art |
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- pytorch |
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- super-resolution |
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--- |
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# AuraSR-v2 |
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![aurasr example](https://storage.googleapis.com/falserverless/gallery/aurasr-animated.webp) |
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GAN-based Super-Resolution for upscaling generated images, a variation of the [GigaGAN](https://mingukkang.github.io/GigaGAN/) paper for image-conditioned upscaling. Torch implementation is based on the unofficial [lucidrains/gigagan-pytorch](https://github.com/lucidrains/gigagan-pytorch) repository. |
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## Usage |
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```bash |
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$ pip install aura-sr |
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``` |
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```python |
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from aura_sr import AuraSR |
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aura_sr = AuraSR.from_pretrained("fal/AuraSR-v2") |
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``` |
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```python |
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import requests |
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from io import BytesIO |
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from PIL import Image |
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def load_image_from_url(url): |
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response = requests.get(url) |
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image_data = BytesIO(response.content) |
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return Image.open(image_data) |
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image = load_image_from_url("https://mingukkang.github.io/GigaGAN/static/images/iguana_output.jpg").resize((256, 256)) |
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upscaled_image = aura_sr.upscale_4x_overlapped(image) |
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``` |
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