jingyunliang / swinir

Image Restoration Using Swin Transformer

Warm

Public
5.9M runs
A100 (80GB)
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=jingyunliang/swinir

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 jingyunliang/swinir using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "jingyunliang/swinir:660d922d33153019e8c263a3bba265de882e7f4f70396546b6c9c8f9d47a021a",
  {
    input: {
      jpeg: 40,
      image: "https://replicate.delivery/mgxm/efd1b6b0-4d79-4a42-ab31-2dcd29754a2d/chip.png",
      noise: 15,
      task_type: "Real-World Image Super-Resolution-Large"
    }
  }
);
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 jingyunliang/swinir using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "jingyunliang/swinir:660d922d33153019e8c263a3bba265de882e7f4f70396546b6c9c8f9d47a021a",
    input={
        "jpeg": 40,
        "image": "https://replicate.delivery/mgxm/efd1b6b0-4d79-4a42-ab31-2dcd29754a2d/chip.png",
        "noise": 15,
        "task_type": "Real-World Image Super-Resolution-Large"
    }
)
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 jingyunliang/swinir 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": "660d922d33153019e8c263a3bba265de882e7f4f70396546b6c9c8f9d47a021a",
    "input": {
      "jpeg": 40,
      "image": "https://replicate.delivery/mgxm/efd1b6b0-4d79-4a42-ab31-2dcd29754a2d/chip.png",
      "noise": 15,
      "task_type": "Real-World Image Super-Resolution-Large"
    }
  }' \
  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/jingyunliang/swinir@sha256:660d922d33153019e8c263a3bba265de882e7f4f70396546b6c9c8f9d47a021a \
  -i 'jpeg=40' \
  -i 'image="https://replicate.delivery/mgxm/efd1b6b0-4d79-4a42-ab31-2dcd29754a2d/chip.png"' \
  -i 'noise=15' \
  -i 'task_type="Real-World Image Super-Resolution-Large"'

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/jingyunliang/swinir@sha256:660d922d33153019e8c263a3bba265de882e7f4f70396546b6c9c8f9d47a021a
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "jpeg": 40,
      "image": "https://replicate.delivery/mgxm/efd1b6b0-4d79-4a42-ab31-2dcd29754a2d/chip.png",
      "noise": 15,
      "task_type": "Real-World Image Super-Resolution-Large"
    }
  }' \
  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/mgxm/efd1b6b0-4d79-4a42-ab31-2dcd29754a2d/chip.png",
  "outut": "https://replicate.delivery/mgxm/1e3c0b87-01a7-4795-abac-aaf17479cf84/out.png"
}

Tweak itReport

This example was created by a different version, jingyunliang/swinir:a6655af5.

Examples

View more examples

Run time and cost

This model costs approximately $0.019 to run on Replicate, or 52 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 A100 (80GB) GPU hardware. Predictions typically complete within 14 seconds. The predict time for this model varies significantly based on the inputs.

Readme

SwinIR: Image Restoration Using Swin Transformer

This repository is the official PyTorch implementation of SwinIR: Image Restoration Using Shifted Window Transformer (arxiv, supp). SwinIR ahcieves state-of-the-art performance in - bicubic/lighweight/real-world image SR - grayscale/color image denoising - JPEG compression artifact reduction

Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed images). While state-of-the-art image restoration methods are based on convolutional neural networks, few attempts have been made with Transformers which show impressive performance on high-level vision tasks. In this paper, we propose a strong baseline model SwinIR for image restoration based on the Swin Transformer. SwinIR consists of three parts: shallow feature extraction, deep feature extraction and high-quality image reconstruction. In particular, the deep feature extraction module is composed of several residual Swin Transformer blocks (RSTB), each of which has several Swin Transformer layers together with a residual connection. We conduct experiments on three representative tasks: image super-resolution (including classical, lightweight and real-world image super-resolution), image denoising (including grayscale and color image denoising) and JPEG compression artifact reduction. Experimental results demonstrate that SwinIR outperforms state-of-the-art methods on different tasks by up to 0.14~0.45dB, while the total number of parameters can be reduced by up to 67%.

Citation

@article{liang2021swinir,
    title={SwinIR: Image Restoration Using Swin Transformer},
    author={Liang, Jingyun and Cao, Jiezhang and Sun, Guolei and Zhang, Kai and Van Gool, Luc and Timofte, Radu},
    journal={arXiv preprint arXiv:2108.10257}, 
    year={2021}
}