GitHub - fal-ai/aura-sr: AuraSR: GAN-based Super-Resolution for real-world (original) (raw)

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

Usage

from aura_sr import AuraSR

aura_sr = AuraSR.from_pretrained()

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)

Reduce Seam Artifacts

upscale_4x upscales the image in tiles that do not overlap. This can result in seams. Use upscale_4x_overlapped to reduce seams. This will double the time upscaling by taking an additional pass and averaging the results.