sczhou/lednet – Run with an API on Replicate

sczhou / lednet

Joint Low-light Enhancement and Deblurring in the Dark

Cold

Public
21.4K runs
T4
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=sczhou/lednet

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

const output = await replicate.run(
  "sczhou/lednet:2349ec8d0a40cf4ca6ba62fa2d8239e14cd2e1535e4aafefabe09dd6b7992dd5",
  {
    input: {
      image: "https://replicate.delivery/pbxt/ICZzxE1ed1SXjgj7iV3M2CF9q1HSXQzo0HI6CtWZwYIsFvqU/0013_0016_lolblur_real_input.png",
      model: "lednet"
    }
  }
);

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

output = replicate.run(
    "sczhou/lednet:2349ec8d0a40cf4ca6ba62fa2d8239e14cd2e1535e4aafefabe09dd6b7992dd5",
    input={
        "image": "https://replicate.delivery/pbxt/ICZzxE1ed1SXjgj7iV3M2CF9q1HSXQzo0HI6CtWZwYIsFvqU/0013_0016_lolblur_real_input.png",
        "model": "lednet"
    }
)
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 sczhou/lednet 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": "sczhou/lednet:2349ec8d0a40cf4ca6ba62fa2d8239e14cd2e1535e4aafefabe09dd6b7992dd5",
    "input": {
      "image": "https://replicate.delivery/pbxt/ICZzxE1ed1SXjgj7iV3M2CF9q1HSXQzo0HI6CtWZwYIsFvqU/0013_0016_lolblur_real_input.png",
      "model": "lednet"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

1.0 second

Tweak itReport

Examples

View more examples

Run time and cost

This model costs approximately $0.031 to run on Replicate, or 32 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 T4 GPU hardware. Predictions typically complete within 138 seconds. The predict time for this model varies significantly based on the inputs.

Readme

LEDNet: Joint Low-light Enhancement and Deblurring in the Dark [ECCV 2022]

Paper | Project Page | Video

🚀 LEDNet is a practical algorithm that enhances the quality of nighttime images by boosting luminance and removing blur.

If LEDNet is helpful, please help to ⭐ the [Github Repo]. Thanks!

📋 License This project is licensed under S-Lab License 1.0. Redistribution and use for non-commercial purposes should follow this license.

📝 Citation If our work is useful for your research, please consider citing:

@InProceedings{zhou2022lednet,
    author = {Zhou, Shangchen and Li, Chongyi and Loy, Chen Change},
    title = {LEDNet: Joint Low-light Enhancement and Deblurring in the Dark},
    booktitle = {ECCV},
    year = {2022}
}