Prediction

jingyunliang/swinir:660d922d33153019e8c263a3bba265de882e7f4f70396546b6c9c8f9d47a021a

Model

jingyunliang/swinir:660d922d

xnboddbbsmatzzhuis26tconxa

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

4.5s

Created

over 1 year ago by @fofr

Input

jpeg: 40
image
noise: 15
task_type: Real-World Image Super-Resolution-Large

{
  "jpeg": 40,
  "image": "https://replicate.delivery/pbxt/KK4WleeNoUgjF8PXPPLBLbpBUgRwsoS9EGdBMpwhs0PfLuU0/low-quality-landscape.jpg",
  "noise": 15,
  "task_type": "Real-World Image Super-Resolution-Large"
}

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/pbxt/KK4WleeNoUgjF8PXPPLBLbpBUgRwsoS9EGdBMpwhs0PfLuU0/low-quality-landscape.jpg",
      noise: 15,
      task_type: "Real-World Image Super-Resolution-Large"
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", 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/pbxt/KK4WleeNoUgjF8PXPPLBLbpBUgRwsoS9EGdBMpwhs0PfLuU0/low-quality-landscape.jpg",
        "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": "jingyunliang/swinir:660d922d33153019e8c263a3bba265de882e7f4f70396546b6c9c8f9d47a021a",
    "input": {
      "jpeg": 40,
      "image": "https://replicate.delivery/pbxt/KK4WleeNoUgjF8PXPPLBLbpBUgRwsoS9EGdBMpwhs0PfLuU0/low-quality-landscape.jpg",
      "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.

Output

Generated in

4.5 seconds

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