meepo-pro-player/morphling | Run with an API on Replicate

meepo-pro-player / morphling

Cold

Public
43.7K runs
L40S

Iterate in playground

Run with an API

Playground API Examples README Versions

Input

image

*file

Input image

mask_image

*file

Input mask, need to be RGB(255, 255, 255) = filled mask

style_image

*file

Input style image

prompt

string

Shift + Return to add a new line

Prompt

negative_prompt

string

Shift + Return to add a new line

Negative prompt

num_samples

integer

Number of samples to generate

Default: 1

controlnet_conditioning_scale

number

(minimum: 0, maximum: 1)

control scale

Default: 0.7

control_guidance_start

number

(minimum: 0, maximum: 1)

Controlnet guidance start

Default: 0.01

control_guidance_end

number

(minimum: 0, maximum: 1)

Controlnet guidance end

Default: 1

guidance_scale

number

Style guidance scale

Default: 7.5

num_inference_steps

integer

Number of inference steps

Default: 30

seed

integer

Seed

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

npx create-replicate --model=meepo-pro-player/morphling

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";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run meepo-pro-player/morphling using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "meepo-pro-player/morphling:80c84cfa3d068f209cfdeb035225fe4d046273c2808f2713b3f7e8e95e2ce353",
  {
    input: {
      num_samples: 1,
      guidance_scale: 7.5,
      num_inference_steps: 30,
      control_guidance_end: 1,
      control_guidance_start: 0.01,
      controlnet_conditioning_scale: 0.7
    }
  }
);

// 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 meepo-pro-player/morphling using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "meepo-pro-player/morphling:80c84cfa3d068f209cfdeb035225fe4d046273c2808f2713b3f7e8e95e2ce353",
    input={
        "num_samples": 1,
        "guidance_scale": 7.5,
        "num_inference_steps": 30,
        "control_guidance_end": 1,
        "control_guidance_start": 0.01,
        "controlnet_conditioning_scale": 0.7
    }
)
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 meepo-pro-player/morphling 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": "meepo-pro-player/morphling:80c84cfa3d068f209cfdeb035225fe4d046273c2808f2713b3f7e8e95e2ce353",
    "input": {
      "num_samples": 1,
      "guidance_scale": 7.5,
      "num_inference_steps": 30,
      "control_guidance_end": 1,
      "control_guidance_start": 0.01,
      "controlnet_conditioning_scale": 0.7
    }
  }' \
  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 $0.057 to run on Replicate, or 17 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 Nvidia L40S GPU hardware. Predictions typically complete within 59 seconds. The predict time for this model varies significantly based on the inputs.

Readme

This model doesn't have a readme.