pollinations

modnet

Warm

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

Public

886.1K runs

Run with an API

Pricing

GitHub

Paper

Playground API Examples README Versions

Prediction

pollinations/modnet:f052240c839fa37e4eabebfefb8919f7816957703edb9cf7922de89a239fb6c7

Model

pollinations/modnet:f052240c

ephgfuoadfcu5ht2krflfwkxxy

Status

Succeeded

Source

Web

Hardware

–

Total duration

2m 29s

Created

almost 3 years ago by @voodoohop

Input

image

{
  "image": "https://replicate.delivery/pbxt/HoW8AeYS7KdWdDyasWuk2DNyXlstzrQKqaBkShVvF54n38lM/Screenshot%202022-11-18%20at%2001.10.34.png"
}

Install Replicate’s Node.js client library:

npm install replicate

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 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:f052240c839fa37e4eabebfefb8919f7816957703edb9cf7922de89a239fb6c7",
  {
    input: {
      image: "https://replicate.delivery/pbxt/HoW8AeYS7KdWdDyasWuk2DNyXlstzrQKqaBkShVvF54n38lM/Screenshot%202022-11-18%20at%2001.10.34.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

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:f052240c839fa37e4eabebfefb8919f7816957703edb9cf7922de89a239fb6c7",
    input={
        "image": "https://replicate.delivery/pbxt/HoW8AeYS7KdWdDyasWuk2DNyXlstzrQKqaBkShVvF54n38lM/Screenshot%202022-11-18%20at%2001.10.34.png"
    }
)

# To access the file URL:
print(output.url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output.read())

To learn more, take a look at the guide on getting started with Python.

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": "pollinations/modnet:f052240c839fa37e4eabebfefb8919f7816957703edb9cf7922de89a239fb6c7",
    "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.

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/pollinations/modnet@sha256:f052240c839fa37e4eabebfefb8919f7816957703edb9cf7922de89a239fb6c7 \
  -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.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/pollinations/modnet@sha256:f052240c839fa37e4eabebfefb8919f7816957703edb9cf7922de89a239fb6c7
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

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

Output

Generated in

2.5 seconds

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