vetkastar/gradient-overlay – Run with an API on Replicate

vetkastar / gradient-overlay

Cold

Public
2.6K runs
CPU

Run with an API

Playground API Examples README Versions

Input

image

*file

Upload the image to which the gradient will be applied.

gradient_type

*string

Choose the gradient type: linear or radial.

start_color

string

Shift + Return to add a new line

Starting color of the gradient in HEX format (e.g., #FF0000 for red). Leave empty for transparent.

end_color

string

Shift + Return to add a new line

Ending color of the gradient in HEX format (e.g., #0000FF for blue). Leave empty for transparent.

angle

integer

(minimum: 0, maximum: 360)

Angle of the gradient in degrees (0 to 360).

Default: 0

opacity

integer

(minimum: 0, maximum: 100)

Overall opacity of the gradient from 0 to 100.

Default: 50

color_boundary

integer

(minimum: 0, maximum: 100)

Position of the boundary between start and end colors (0 to 100).

Default: 50

gradient_only

boolean

If enabled, outputs only the gradient without overlaying on the original image.

Default: false

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

npx create-replicate --model=vetkastar/gradient-overlay

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 vetkastar/gradient-overlay using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "vetkastar/gradient-overlay:904e162c9bfb016d61ab188af9aa04410adc32a2374175de8a1594d6c01f37e3",
  {
    input: {
      angle: 0,
      opacity: 50,
      gradient_only: false,
      gradient_type: "linear",
      color_boundary: 50
    }
  }
);

// 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 vetkastar/gradient-overlay using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "vetkastar/gradient-overlay:904e162c9bfb016d61ab188af9aa04410adc32a2374175de8a1594d6c01f37e3",
    input={
        "angle": 0,
        "opacity": 50,
        "gradient_only": False,
        "gradient_type": "linear",
        "color_boundary": 50
    }
)
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 vetkastar/gradient-overlay 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": "904e162c9bfb016d61ab188af9aa04410adc32a2374175de8a1594d6c01f37e3",
    "input": {
      "angle": 0,
      "opacity": 50,
      "gradient_only": false,
      "gradient_type": "linear",
      "color_boundary": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

No output yet! Press "Submit" to start a prediction.

Run time and cost

This model costs approximately $0.0013 to run on Replicate, or 769 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 13 seconds.

Readme

This model doesn't have a readme.