pollinations/modnet – Run with an API on Replicate

pollinations / modnet

A deep learning approach to remove background & adding new background image

Cold

Public
670.7K runs
T4
GitHub
Paper

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=pollinations/modnet

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

const output = await replicate.run(
  "pollinations/modnet:da7d45f3b836795f945f221fc0b01a6d3ab7f5e163f13208948ad436001e2255",
  {
    input: {
      image: "https://replicate.delivery/pbxt/HoW8AeYS7KdWdDyasWuk2DNyXlstzrQKqaBkShVvF54n38lM/Screenshot%202022-11-18%20at%2001.10.34.png"
    }
  }
);
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 pollinations/modnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "pollinations/modnet:da7d45f3b836795f945f221fc0b01a6d3ab7f5e163f13208948ad436001e2255",
    input={
        "image": "https://replicate.delivery/pbxt/HoW8AeYS7KdWdDyasWuk2DNyXlstzrQKqaBkShVvF54n38lM/Screenshot%202022-11-18%20at%2001.10.34.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 pollinations/modnet 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": "da7d45f3b836795f945f221fc0b01a6d3ab7f5e163f13208948ad436001e2255",
    "input": {
      "image": "https://replicate.delivery/pbxt/HoW8AeYS7KdWdDyasWuk2DNyXlstzrQKqaBkShVvF54n38lM/Screenshot%202022-11-18%20at%2001.10.34.png"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Install Cog

brew install cog

If you don’t have Homebrew, there are other installation options available.

Pull and run pollinations/modnet using Cog (this will download the full model and run it in your local environment):

cog predict r8.im/pollinations/modnet@sha256:da7d45f3b836795f945f221fc0b01a6d3ab7f5e163f13208948ad436001e2255 \
  -i 'image="https://replicate.delivery/pbxt/HoW8AeYS7KdWdDyasWuk2DNyXlstzrQKqaBkShVvF54n38lM/Screenshot%202022-11-18%20at%2001.10.34.png"'

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

Pull and run pollinations/modnet using Docker (this will download the full model and run it in your local environment):

docker run -d -p 5000:5000 --gpus=all r8.im/pollinations/modnet@sha256:da7d45f3b836795f945f221fc0b01a6d3ab7f5e163f13208948ad436001e2255
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "image": "https://replicate.delivery/pbxt/HoW8AeYS7KdWdDyasWuk2DNyXlstzrQKqaBkShVvF54n38lM/Screenshot%202022-11-18%20at%2001.10.34.png"
    }
  }' \
  http://localhost:5000/predictions

Output

Generated in

2.5 seconds

Tweak itReport

This example was created by a different version, pollinations/modnet:f052240c.

Examples

View more examples

Run time and cost

This model costs approximately $0.0056 to run on Replicate, or 178 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 26 seconds. The predict time for this model varies significantly based on the inputs.

Readme

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Model description

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Intended use

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Ethical considerations

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Caveats and recommendations

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