stphtan94117/master_colorization – Run with an API on Replicate

Input

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

npx create-replicate --model=stphtan94117/master_colorization

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 stphtan94117/master_colorization using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "stphtan94117/master_colorization:aa4f2dac8c216e98148af3855d1fa5d5f10d7b61ab9069941547c104b266b39b",
  {
    input: {
      image: "https://replicate.delivery/pbxt/JTqNNxKnxmypTtWNfvuf7IRNRgs64oQkdiIwxKEUTsmI17bD/5c5411bd14c50b0006e839a0.jpeg",
      factor: 65,
      model_type: "stable"
    }
  }
);
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 stphtan94117/master_colorization using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "stphtan94117/master_colorization:aa4f2dac8c216e98148af3855d1fa5d5f10d7b61ab9069941547c104b266b39b",
    input={
        "image": "https://replicate.delivery/pbxt/JTqNNxKnxmypTtWNfvuf7IRNRgs64oQkdiIwxKEUTsmI17bD/5c5411bd14c50b0006e839a0.jpeg",
        "factor": 65,
        "model_type": "stable"
    }
)
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 stphtan94117/master_colorization 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": "aa4f2dac8c216e98148af3855d1fa5d5f10d7b61ab9069941547c104b266b39b",
    "input": {
      "image": "https://replicate.delivery/pbxt/JTqNNxKnxmypTtWNfvuf7IRNRgs64oQkdiIwxKEUTsmI17bD/5c5411bd14c50b0006e839a0.jpeg",
      "factor": 65,
      "model_type": "stable"
    }
  }' \
  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/stphtan94117/master_colorization@sha256:aa4f2dac8c216e98148af3855d1fa5d5f10d7b61ab9069941547c104b266b39b \
  -i 'image="https://replicate.delivery/pbxt/JTqNNxKnxmypTtWNfvuf7IRNRgs64oQkdiIwxKEUTsmI17bD/5c5411bd14c50b0006e839a0.jpeg"' \
  -i 'factor=65' \
  -i 'model_type="stable"'

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/stphtan94117/master_colorization@sha256:aa4f2dac8c216e98148af3855d1fa5d5f10d7b61ab9069941547c104b266b39b
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "image": "https://replicate.delivery/pbxt/JTqNNxKnxmypTtWNfvuf7IRNRgs64oQkdiIwxKEUTsmI17bD/5c5411bd14c50b0006e839a0.jpeg",
      "factor": 65,
      "model_type": "stable"
    }
  }' \
  http://localhost:5000/predictions

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

Output

Generated in

78.8 seconds

Tweak itReport

Run time and cost

This model costs approximately $0.028 to run on Replicate, or 35 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 124 seconds. The predict time for this model varies significantly based on the inputs.