AuraSR-v2

3.0K
312
1 language
license:apache-2.0
by
fal
Image Model
OTHER
New
3K downloads
Early-stage
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Quick Summary

GAN-based Super-Resolution for upscaling generated images, a variation of the GigaGAN paper for image-conditioned upscaling.

Code Examples

Usagebash
$ pip install aura-sr
Usagebash
$ pip install aura-sr
Usagebash
$ pip install aura-sr
Usagebash
$ pip install aura-sr
Usagebash
$ pip install aura-sr
Usagebash
$ pip install aura-sr
Usagebash
$ pip install aura-sr
Usagepython
from aura_sr import AuraSR

aura_sr = AuraSR.from_pretrained("fal/AuraSR-v2")
Usagepython
from aura_sr import AuraSR

aura_sr = AuraSR.from_pretrained("fal/AuraSR-v2")
Usagepython
from aura_sr import AuraSR

aura_sr = AuraSR.from_pretrained("fal/AuraSR-v2")
Usagepython
from aura_sr import AuraSR

aura_sr = AuraSR.from_pretrained("fal/AuraSR-v2")
Usagepython
from aura_sr import AuraSR

aura_sr = AuraSR.from_pretrained("fal/AuraSR-v2")
Usagepython
from aura_sr import AuraSR

aura_sr = AuraSR.from_pretrained("fal/AuraSR-v2")
Usagepython
from aura_sr import AuraSR

aura_sr = AuraSR.from_pretrained("fal/AuraSR-v2")
Usagepython
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)
Usagepython
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)
Usagepython
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)
Usagepython
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)
Usagepython
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)
Usagepython
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)
Usagepython
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)

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