zsxkib / aura-sr

AuraSR: GAN-based Super-Resolution for real-world

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✨ AuraSR: GAN Super-Resolution for Images 🖼️

Replicate

AuraSR is a powerful GAN-based super-resolution tool that enhances image clarity and size. Based on the GigaGAN concept, it excels with specific image types.

See AuraSR in action

🎨 Features

  • Upscales PNG, lossless WebP, and high-quality JPEG XL (90+) images
  • Supports scale factors of 2x, 4x, 8x, 16x, and 32x
  • Efficient processing with adjustable batch sizes

⚠️ Important Notes

AuraSR is powerful but has some limitations:

  1. Best results with PNG, lossless WebP, and high-quality JPEG XL (90+)
  2. Sensitive to compression artifacts
  3. No built-in error correction for image imperfections
  4. Ideal for upscaling AI-generated or high-quality uncompressed images

🛠️ Usage

Input Parameters

  • image: The input image to upscale (PNG, WebP, or high-quality JPEG XL)
  • scale_factor: Upscaling factor (2, 4, 8, 16, or 32)
  • max_batch_size: Number of image tiles processed simultaneously (default: 1)

Example

import replicate

output = replicate.run(
    "zsxkib/aura-sr:<VERSION>",
    input={
        "image": open("path/to/your/image.png", "rb"),
        "scale_factor": 4,
        "max_batch_size": 4
    }
)
print(output)

🙌 Acknowledgements

  • fal.ai for the original AuraSR implementation
  • 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 Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.

🐦 Connect

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