ariel415el/simplify_contours | Run with an API on Replicate

Input

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

npx create-replicate --model=ariel415el/simplify_contours

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";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run ariel415el/simplify_contours using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "ariel415el/simplify_contours:cc99e2d1a9ea714e5c36346608b979026fb6c139bc4290a1595a59a33012cd91",
  {
    input: {
      cutoff: 0.001,
      input_image: "https://replicate.delivery/mgxm/3aa3f264-6539-4e18-8a55-c8b5f982327a/contour.png"
    }
  }
);

// 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 ariel415el/simplify_contours using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "ariel415el/simplify_contours:cc99e2d1a9ea714e5c36346608b979026fb6c139bc4290a1595a59a33012cd91",
    input={
        "cutoff": 0.001,
        "input_image": "https://replicate.delivery/mgxm/3aa3f264-6539-4e18-8a55-c8b5f982327a/contour.png"
    }
)
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 ariel415el/simplify_contours 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": "ariel415el/simplify_contours:cc99e2d1a9ea714e5c36346608b979026fb6c139bc4290a1595a59a33012cd91",
    "input": {
      "cutoff": 0.001,
      "input_image": "https://replicate.delivery/mgxm/3aa3f264-6539-4e18-8a55-c8b5f982327a/contour.png"
    }
  }' \
  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/ariel415el/simplify_contours@sha256:cc99e2d1a9ea714e5c36346608b979026fb6c139bc4290a1595a59a33012cd91 \
  -i 'cutoff=0.001' \
  -i 'input_image="https://replicate.delivery/mgxm/3aa3f264-6539-4e18-8a55-c8b5f982327a/contour.png"'

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 r8.im/ariel415el/simplify_contours@sha256:cc99e2d1a9ea714e5c36346608b979026fb6c139bc4290a1595a59a33012cd91
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "cutoff": 0.001,
      "input_image": "https://replicate.delivery/mgxm/3aa3f264-6539-4e18-8a55-c8b5f982327a/contour.png"
    }
  }' \
  http://localhost:5000/predictions

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

Output

Generated in

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Tweak itReport View full prediction

This output was created using a different version of the model, ariel415el/simplify_contours:a316fb81.

Examples

View more examples

Run time and cost

This model costs approximately $0.00010 to run on Replicate, or 10000 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 CPU hardware. Predictions typically complete within 1 seconds.

Readme

This model doesn't have a readme.