Readme
✨ AuraSR v2: Advanced GAN Super-Resolution for Images 🖼️
AuraSR v2 is an improved GAN-based super-resolution tool that enhances image clarity and size. Based on the GigaGAN concept and optimized for real-world applications, it excels with a wide range of image types.
🎨 Features
- Upscales PNG, WebP, JPEG, and other common image formats
- Supports 4x upscaling with improved quality
- Efficient processing with overlapped tile technique
- Optimized for both AI-generated and high-quality photographs
⚠️ Important Notes
AuraSR v2 is more versatile than its predecessor but still has some considerations:
- Excellent results with a wide range of image types, including compressed formats
- Improved handling of compression artifacts
- Enhanced performance on real-world photographs
- Ideal for upscaling both AI-generated and high-quality natural images
🛠️ Usage
Input Parameters
-
image: The input image to upscale (supports various formats including PNG, WebP, JPEG) -
scale_factor: Fixed at 4x upscaling
Example
import replicate
output = replicate.run(
"zsxkib/aura-sr-v2:<VERSION>",
input={
"image": open("path/to/your/image.jpg", "rb"),
}
)
print(output)
🙌 Acknowledgements
- fal.ai for the original AuraSR implementation and v2 improvements
- lucidrains for the unofficial PyTorch implementation of GigaGAN
Citation
If you use this model in your research or applications, please cite the original GigaGAN paper:
@article{DBLP:journals/corr/abs-2303-05511,
author = {Minguk Kang and
Jaesik Park and
Namhyuk Ahn and
Sungsoo Ahn and
Kibeom Hong and
Bohyung Han},
title = {GigaGAN: Large-scale GAN for Text-to-Image Synthesis},
journal = {CoRR},
volume = {abs/2303.05511},
year = {2023},
url = {https://arxiv.org/abs/2303.05511},
eprinttype = {arXiv},
eprint = {2303.05511},
timestamp = {Tue, 14 Mar 2023 17:06:10 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2303-05511.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
License
This model is released under the Apache 2.0 license .
🐦 Connect
Questions or feedback? Follow me on Twitter @zsakib_ and let’s chat!
