Prediction

wzesk/littoral_refine:86a86582a6f0095ecb14ac420ebbc1be453b84553427520b0299baedba8258f9

Model

wzesk/littoral_refine:86a86582

sakvnqa3ehrj20cmejy8nx3v6g

Status

Succeeded

Source

Web

Hardware

A100 (80GB)

Total duration

19.4s

Created

5 months ago

Input

img_path
smoothing: 2
mask_filepath
simplification: 0.5

{
  "img_path": "https://raw.github.com/Wzesk/littoral_refine/2b77e90e1701e9da1ecf9142724ac1007848e9d1/sample/20241211T052119_20241211T052515_T43NCE_sr.png",
  "smoothing": 2,
  "mask_filepath": "https://raw.github.com/Wzesk/littoral_refine/2b77e90e1701e9da1ecf9142724ac1007848e9d1/sample/20241211T052119_20241211T052515_T43NCE_mask.png",
  "simplification": 0.5
}

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

const output = await replicate.run(
  "wzesk/littoral_refine:86a86582a6f0095ecb14ac420ebbc1be453b84553427520b0299baedba8258f9",
  {
    input: {
      img_path: "https://raw.github.com/Wzesk/littoral_refine/2b77e90e1701e9da1ecf9142724ac1007848e9d1/sample/20241211T052119_20241211T052515_T43NCE_sr.png",
      smoothing: 2,
      mask_filepath: "https://raw.github.com/Wzesk/littoral_refine/2b77e90e1701e9da1ecf9142724ac1007848e9d1/sample/20241211T052119_20241211T052515_T43NCE_mask.png",
      simplification: 0.5
    }
  }
);

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

output = replicate.run(
    "wzesk/littoral_refine:86a86582a6f0095ecb14ac420ebbc1be453b84553427520b0299baedba8258f9",
    input={
        "img_path": "https://raw.github.com/Wzesk/littoral_refine/2b77e90e1701e9da1ecf9142724ac1007848e9d1/sample/20241211T052119_20241211T052515_T43NCE_sr.png",
        "smoothing": 2,
        "mask_filepath": "https://raw.github.com/Wzesk/littoral_refine/2b77e90e1701e9da1ecf9142724ac1007848e9d1/sample/20241211T052119_20241211T052515_T43NCE_mask.png",
        "simplification": 0.5
    }
)
print(output)

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

Run wzesk/littoral_refine 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": "wzesk/littoral_refine:86a86582a6f0095ecb14ac420ebbc1be453b84553427520b0299baedba8258f9",
    "input": {
      "img_path": "https://raw.github.com/Wzesk/littoral_refine/2b77e90e1701e9da1ecf9142724ac1007848e9d1/sample/20241211T052119_20241211T052515_T43NCE_sr.png",
      "smoothing": 2,
      "mask_filepath": "https://raw.github.com/Wzesk/littoral_refine/2b77e90e1701e9da1ecf9142724ac1007848e9d1/sample/20241211T052119_20241211T052515_T43NCE_mask.png",
      "simplification": 0.5
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

5.4 seconds

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