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AuraSR

GAN-based Super-Resolution for upscaling generated images, a variation of the GigaGAN paper for image-conditioned upscaling. Torch implementation is based on the unofficial lucidrains/gigagan-pytorch repository.

TipoOtherVersiónRevision 8b70681Bcalidad

Fuente del modelo

Tipo
Other
Tamaño del archivo
2,30 GB
Versión
vRevision 8b70681
Actualizado
11 oct 2026
Descargas
166
Fuente
Hugging Face

Descripción de la fuente

AuraSR

aurasr example

GAN-based Super-Resolution for upscaling generated images, a variation of the GigaGAN paper for image-conditioned upscaling. Torch implementation is based on the unofficial lucidrains/gigagan-pytorch repository.

Usage

$ pip install aura-sr
from aura_sr import AuraSR

aura_sr = AuraSR.from_pretrained("fal-ai/AuraSR")
import requests
from io import BytesIO
from PIL import Image

def load_image_from_url(url):
    response = requests.get(url)
    image_data = BytesIO(response.content)
    return Image.open(image_data)

image = load_image_from_url("https://mingukkang.github.io/GigaGAN/static/images/iguana_output.jpg").resize((256, 256))
upscaled_image = aura_sr.upscale_4x(image)

Fuentes

1 fuente
Hugging FacePrincipalVersión 8b70681ad0364f3221a9bc8c7ef07531df885509
huggingface.co/fal/AuraSR/tree/8b70681ad0364f3221a9bc8c7ef07531df885509

Verificado 15 ago

Artefactos del modelo

2 artefactos

model.ckpt

ckpt · 2,30 GB · SHA-256 10fd2c90cfa4…74b9 · Hugging Face

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model.safetensors

safetensors · 2,30 GB · SHA-256 ff203dd1037c…a7de · Hugging Face

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Extractos de fuentes

2 extractos

--- license: cc tags: - art - pytorch - super-resolution --- # AuraSR ![aurasr example](https://storage.googleapis.com/falserverless/gallery/aurasr-animated.webp) 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. ## Usage ```bash $ pip install aura-sr ``` ```python from aura_sr import AuraSR aura_sr = AuraSR.from_pretrained("fal-ai/AuraSR") ``` ```python import requests from io import BytesIO from PIL import Image def load_image_from_url(url): response = requests.get(url) image_data = BytesIO(response.content) return Image.open(image_data) image = load_image_from_url("https://mingukkang.github.io/GigaGAN/static/images/iguana_output.jpg").resize((256, 256)) upscaled_image = aura_sr.upscale_4x(image) ```

Source context: 166 downloads · 308 likes · Library transformers · Repo fal/AuraSR