zsxkib / aura-sr

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

Cold

Public
2.2K runs
L40S
GitHub
Paper
License

Run with an API

Playground API Examples README Versions

Input

Run this model in Node.js with one line of code:

npx create-replicate --model=zsxkib/aura-sr

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run zsxkib/aura-sr using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "zsxkib/aura-sr:7231d40ac06e74c6d1bc287309c396fa4791e4cf4fdcb91fb4b191c6c485fc1c",
  {
    input: {
      image: "https://replicate.delivery/pbxt/LAhOYOkn1DRtiuYEaEHtlblWrPkInSBVPfaBJc2gYTa5rI7S/image.png",
      scale_factor: 4,
      max_batch_size: 1
    }
  }
);
console.log(output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run zsxkib/aura-sr using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "zsxkib/aura-sr:7231d40ac06e74c6d1bc287309c396fa4791e4cf4fdcb91fb4b191c6c485fc1c",
    input={
        "image": "https://replicate.delivery/pbxt/LAhOYOkn1DRtiuYEaEHtlblWrPkInSBVPfaBJc2gYTa5rI7S/image.png",
        "scale_factor": 4,
        "max_batch_size": 1
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run zsxkib/aura-sr using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "7231d40ac06e74c6d1bc287309c396fa4791e4cf4fdcb91fb4b191c6c485fc1c",
    "input": {
      "image": "https://replicate.delivery/pbxt/LAhOYOkn1DRtiuYEaEHtlblWrPkInSBVPfaBJc2gYTa5rI7S/image.png",
      "scale_factor": 4,
      "max_batch_size": 1
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/zsxkib/aura-sr@sha256:7231d40ac06e74c6d1bc287309c396fa4791e4cf4fdcb91fb4b191c6c485fc1c \
  -i 'image="https://replicate.delivery/pbxt/LAhOYOkn1DRtiuYEaEHtlblWrPkInSBVPfaBJc2gYTa5rI7S/image.png"' \
  -i 'scale_factor=4' \
  -i 'max_batch_size=1'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/zsxkib/aura-sr@sha256:7231d40ac06e74c6d1bc287309c396fa4791e4cf4fdcb91fb4b191c6c485fc1c
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "image": "https://replicate.delivery/pbxt/LAhOYOkn1DRtiuYEaEHtlblWrPkInSBVPfaBJc2gYTa5rI7S/image.png",
      "scale_factor": 4,
      "max_batch_size": 1
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

We were unable to load these images. Please make sure the URLs are valid.

{
  "input": "https://replicate.delivery/pbxt/LAhOYOkn1DRtiuYEaEHtlblWrPkInSBVPfaBJc2gYTa5rI7S/image.png",
  "outut": "https://replicate.delivery/pbxt/XYmwV9fjnU0wTSU3DcqQqeQpQsX3IWNZGrU79wfvftub2jLMB/output.png"
}

Generated in

2.1 seconds

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Examples

View more examples

Run time and cost

This model costs approximately $0.11 to run on Replicate, or 9 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia L40S GPU hardware. Predictions typically complete within 111 seconds. The predict time for this model varies significantly based on the inputs.

Readme

✨ AuraSR: GAN Super-Resolution for Images 🖼️

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:

Best results with PNG, lossless WebP, and high-quality JPEG XL (90+)
Sensitive to compression artifacts
No built-in error correction for image imperfections
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

Questions or feedback? Follow me on Twitter @zsakib_ and let’s chat!