adirik/marigold:1a363593 – Run with an API on Replicate

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adirik /marigold:1a363593

Playground API

Input

image

*file

Input image, use an RGB image for optimal results.

resize_input

boolean

Resize the original input resolution to max resolution.

Default: true

num_infer

integer

(minimum: 1, maximum: 20)

Number of inferences to be ensembled, a higher number gives better results but runs slower.

Default: 10

denoise_steps

integer

(minimum: 1, maximum: 50)

Inference denoising steps, more steps results in higher accuracy but slower inference speed.

Default: 10

regularizer_strength

number

(minimum: 0, maximum: 1)

Ensembling parameter, weight of optimization regularizer.

Default: 0.02

reduction_method

string

Ensembling parameter, method to merge aligned depth maps.

Default: "median"

max_iter

integer

(minimum: 1, maximum: 20)

Ensembling parameter, max optimization iterations.

Default: 5

seed

integer

Seed for reproducibility, set to random if left as None.

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

npx create-replicate --model=adirik/marigold

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

const output = await replicate.run(
  "adirik/marigold:1a363593bc4882684fc58042d19db5e13a810e44e02f8d4c32afd1eb30464818",
  {
    input: {
      max_iter: 5,
      num_infer: 10,
      resize_input: true,
      denoise_steps: 10,
      reduction_method: "median",
      regularizer_strength: 0.02
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

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

output = replicate.run(
    "adirik/marigold:1a363593bc4882684fc58042d19db5e13a810e44e02f8d4c32afd1eb30464818",
    input={
        "max_iter": 5,
        "num_infer": 10,
        "resize_input": True,
        "denoise_steps": 10,
        "reduction_method": "median",
        "regularizer_strength": 0.02
    }
)
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 adirik/marigold 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": "1a363593bc4882684fc58042d19db5e13a810e44e02f8d4c32afd1eb30464818",
    "input": {
      "max_iter": 5,
      "num_infer": 10,
      "resize_input": true,
      "denoise_steps": 10,
      "reduction_method": "median",
      "regularizer_strength": 0.02
    }
  }' \
  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.