sjain200 / fastclipstyler

Personalized Image Filters at Your Fingertips

Cold

Public
97 runs
CPU
GitHub
Paper

Run with an API

Playground API Examples README Versions

Prediction

sjain200/fastclipstyler:ebd37d9c

Model

sjain200/fastclipstyler:a8f6565c

h4b8ya0ftnrge0cgx42sg9r2n0

Status

Succeeded

Source

Web

Hardware

Total duration

0.7s

Created

8 months ago by @sanjanajainsj

Input

image
style_desc: A sketch with black pencil

{
  "image": "https://replicate.delivery/mgxm/e4500aa0-f71b-42ff-a540-aadb44c8d1b2/face.jpg",
  "style_desc": "A sketch with black pencil"
}

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 sjain200/fastclipstyler using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "sjain200/fastclipstyler:ebd37d9c9ad6f43171277c2ed2db5f90b47d448a4e20ae5f672ffbb870e0b243",
  {
    input: {
      image: "https://replicate.delivery/mgxm/e4500aa0-f71b-42ff-a540-aadb44c8d1b2/face.jpg",
      style_desc: "A sketch with black pencil"
    }
  }
);
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 sjain200/fastclipstyler using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "sjain200/fastclipstyler:ebd37d9c9ad6f43171277c2ed2db5f90b47d448a4e20ae5f672ffbb870e0b243",
    input={
        "image": "https://replicate.delivery/mgxm/e4500aa0-f71b-42ff-a540-aadb44c8d1b2/face.jpg",
        "style_desc": "A sketch with black pencil"
    }
)
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 sjain200/fastclipstyler 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": "ebd37d9c9ad6f43171277c2ed2db5f90b47d448a4e20ae5f672ffbb870e0b243",
    "input": {
      "image": "https://replicate.delivery/mgxm/e4500aa0-f71b-42ff-a540-aadb44c8d1b2/face.jpg",
      "style_desc": "A sketch with black pencil"
    }
  }' \
  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/sjain200/fastclipstyler@sha256:ebd37d9c9ad6f43171277c2ed2db5f90b47d448a4e20ae5f672ffbb870e0b243 \
  -i 'image="https://replicate.delivery/mgxm/e4500aa0-f71b-42ff-a540-aadb44c8d1b2/face.jpg"' \
  -i 'style_desc="A sketch with black pencil"'

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/sjain200/fastclipstyler@sha256:ebd37d9c9ad6f43171277c2ed2db5f90b47d448a4e20ae5f672ffbb870e0b243
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "image": "https://replicate.delivery/mgxm/e4500aa0-f71b-42ff-a540-aadb44c8d1b2/face.jpg",
      "style_desc": "A sketch with black pencil"
    }
  }' \
  http://localhost:5000/predictions

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

Output

Generated in

0.7 seconds

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