lightweight-ai/controlnet_test – Run with an API on Replicate

lightweight-ai / controlnet_test

Cold

Public
63 runs
A100 (80GB)

Run with an API

Playground API Examples README Versions

Input

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

npx create-replicate --model=lightweight-ai/controlnet_test

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 lightweight-ai/controlnet_test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lightweight-ai/controlnet_test:2794e598d44a66785e6ff4b4b155dbb79068c9ae3b9cda31d5b96f98cc6507f8",
  {
    input: {
      loras: [],
      width: 1024,
      height: 1024,
      prompt: "A bohemian-style female travel blogger with sun-kissed skin and messy beach waves",
      inpaint: false,
      controlnet: false,
      lora_scales: [],
      num_outputs: 1,
      control_type: "canny",
      control_image: "",
      output_format: "png",
      output_quality: 100,
      prompt_strength: 0.8,
      control_strength: 0.2,
      num_inference_steps: 28
    }
  }
);

// 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 lightweight-ai/controlnet_test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lightweight-ai/controlnet_test:2794e598d44a66785e6ff4b4b155dbb79068c9ae3b9cda31d5b96f98cc6507f8",
    input={
        "loras": [],
        "width": 1024,
        "height": 1024,
        "prompt": "A bohemian-style female travel blogger with sun-kissed skin and messy beach waves",
        "inpaint": False,
        "controlnet": False,
        "lora_scales": [],
        "num_outputs": 1,
        "control_type": "canny",
        "control_image": "",
        "output_format": "png",
        "output_quality": 100,
        "prompt_strength": 0.8,
        "control_strength": 0.2,
        "num_inference_steps": 28
    }
)
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 lightweight-ai/controlnet_test 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": "2794e598d44a66785e6ff4b4b155dbb79068c9ae3b9cda31d5b96f98cc6507f8",
    "input": {
      "loras": [],
      "width": 1024,
      "height": 1024,
      "prompt": "A bohemian-style female travel blogger with sun-kissed skin and messy beach waves",
      "inpaint": false,
      "controlnet": false,
      "lora_scales": [],
      "num_outputs": 1,
      "control_type": "canny",
      "control_image": "",
      "output_format": "png",
      "output_quality": 100,
      "prompt_strength": 0.8,
      "control_strength": 0.2,
      "num_inference_steps": 28
    }
  }' \
  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 runs on Nvidia A100 (80GB) GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

This model doesn't have a readme.