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AuraSR v2

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.

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Model source

Kind
Other
File size
2.30 GB
Version
vmain
Updated
Oct 1, 2026
Downloads
894
Tasks
Image to image
Source
Hugging Face

Source description

AuraSR-v2

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/AuraSR-v2")
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_overlapped(image)

Related resources

1 connection

Sources

1 source
Hugging FacePrimaryVersion main
huggingface.co/fal/AuraSR-v2/tree/main

Verified Oct 1

Model artifacts

2 artifacts

model.ckpt

ckpt · 2.30 GB · SHA-256 8028efd10725…8b7a · Hugging Face

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model.safetensors

safetensors · 2.30 GB · SHA-256 8a80b4546f3f…4dde · Hugging Face

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Source excerpts

2 excerpts

--- license: apache-2.0 tags: - art - pytorch - super-resolution pipeline_tag: image-to-image --- # AuraSR-v2 ![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/AuraSR-v2") ``` ```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_overlapped(image) ```

Source context: 894 downloads · 340 likes · Pipeline image-to-image · Library transformers · Repo fal/AuraSR-v2