Input

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

npx create-replicate --model=microsoft/bringing-old-photos-back-to-life

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 microsoft/bringing-old-photos-back-to-life using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "microsoft/bringing-old-photos-back-to-life:c75db81db6cbd809d93cc3b7e7a088a351a3349c9fa02b6d393e35e0d51ba799",
  {
    input: {
      HR: false,
      image: "https://replicate.delivery/mgxm/7869b8fd-2393-450b-8fd0-281f00eedef9/a.png",
      with_scratch: false
    }
  }
);
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 microsoft/bringing-old-photos-back-to-life using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "microsoft/bringing-old-photos-back-to-life:c75db81db6cbd809d93cc3b7e7a088a351a3349c9fa02b6d393e35e0d51ba799",
    input={
        "HR": False,
        "image": "https://replicate.delivery/mgxm/7869b8fd-2393-450b-8fd0-281f00eedef9/a.png",
        "with_scratch": False
    }
)
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 microsoft/bringing-old-photos-back-to-life 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": "c75db81db6cbd809d93cc3b7e7a088a351a3349c9fa02b6d393e35e0d51ba799",
    "input": {
      "HR": false,
      "image": "https://replicate.delivery/mgxm/7869b8fd-2393-450b-8fd0-281f00eedef9/a.png",
      "with_scratch": false
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Tweak itReport

This example was created by a different version, microsoft/bringing-old-photos-back-to-life:7c0dc620.

Examples

View more examples

Run time and cost

This model costs approximately $0.020 to run on Replicate, or 50 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 88 seconds. The predict time for this model varies significantly based on the inputs.

Readme

Old Photo Restoration (Official PyTorch Implementation)

Bringing Old Photos Back to Life, CVPR2020 (Oral)

Old Photo Restoration via Deep Latent Space Translation,

Ziyu Wan, Bo Zhang, Dongdong Chen, Pan Zhang, Dong Chen, Jing Liao, Fang Wen

Citation

If you find our work useful for your research, please consider citing the following papers :)

@inproceedings{wan2020bringing,
title={Bringing Old Photos Back to Life},
author={Wan, Ziyu and Zhang, Bo and Chen, Dongdong and Zhang, Pan and Chen, Dong and Liao, Jing and Wen, Fang},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={2747--2757},
year={2020}
}

@article{wan2020old,
  title={Old Photo Restoration via Deep Latent Space Translation},
  author={Wan, Ziyu and Zhang, Bo and Chen, Dongdong and Zhang, Pan and Chen, Dong and Liao, Jing and Wen, Fang},
  journal={arXiv preprint arXiv:2009.07047},
  year={2020}
}