Controlnet API Usage Guide.md

title: Controlnet API Usage Guide
slug: 6DCWoM_fbbp1XuSHH8vVc
createdAt: Thu Jul 18 2024 06:12:37 GMT+0000 (Coordinated Universal Time)
updatedAt: Thu Jul 18 2024 13:40:04 GMT+0000 (Coordinated Universal Time)

Controlnet API Usage Guide

Introduction

This document will guide developers on how to use the aonweb library to call the Controlnet API, which is used for voice cloning and text-to-speech conversion.

Prerequisites

• Node.js environment
• aonweb library installed
• Valid Aonet APPID

Basic Usage

1. Import Required Modules

import { AI, AIOptions } from 'aonweb';

2. Initialize AI Instance

const ai_options = new AIOptions({
    appId: 'your_app_id_here',
    dev_mode: true
});

const aonweb = new AI(ai_options);

3. Prepare Input Data Example

const data = {
   input:{
      "seed": 6,
      "image": "https://replicate.delivery/pbxt/IYQCkyANILbqCWObhtFANxUyuVIMsLw7pyky9eFlz17MBG9c/house.png",
      "scale": 9,
      "steps": 20,
      "prompt": "a modernist house in a nice landscape",
      "structure": "seg",
      "low_threshold": 100,
      "high_threshold": 200,
      "image_resolution": "512"
    }
};

const data = {
   input:{
      "seed": 6,
      "image": "https://replicate.delivery/pbxt/IYQDgL9mAeNkgdFCTCb0qXKDHnL8a84VArZBRGhBYRsqc1vn/1200.jpeg",
      "scale": 9,
      "steps": 20,
      "prompt": "a metallic cyborg bird",
      "structure": "canny",
      "low_threshold": 100,
      "high_threshold": 200,
      "image_resolution": "512"
    }
};

const data = {
   input:{
      "seed": 20,
      "image": "https://replicate.delivery/pbxt/IYQLHLFDraqCrjDUoiwpM9xBhQM1eQVHbxBiNxcbwctUamzb/user_1.png",
      "scale": 9,
      "steps": 20,
      "prompt": "a photo of a brightly colored turtle",
      "structure": "scribble",
      "low_threshold": 100,
      "high_threshold": 200,
      "image_resolution": "512"
    }
};

4. Call the AI Model

const price = 8; // Cost of the AI call
try {
    const response = await aonweb.prediction("/predictions/ai/controlnet", data, price);
    // Handle response
    console.log("Controlnet result:", response);
} catch (error) {
    // Error handling
    console.error("Error generating :", error);
}

Parameter Description

• seed Number,Provide the seeds required for model inference
• image String,Please provide the image file that needs to be processed.
• scale Number,Scale for classifier-free guidance
• steps Number,Provide the steps required for model inference
• prompt String, Please provide the prompt that needs to be inferred.
• structure String,Controlnet structure to condition on
• low_threshold Number,[canny only] Line detection low threshold
• high_threshold Number,[canny only] Line detection high threshold
• image_resolution String,Resolution of output image (will be scaled to this as its smaller dimension)
• num_outputs Number,Number of images to output (higher values may OOM),default:1
• eta Number,Controls the amount of noise that is added to the input data during the denoising diffusion process. Higher value -> more noise
• negative_prompt String,Provide the negative prompt required for model inference

Notes

• Ensure that the provided image URL is publicly accessible and of good quality to achieve the best recognition results.
• The API may take some time to process the input and generate the result, consider implementing appropriate wait or loading states.
• Handle possible errors, such as network issues, invalid input, or API limitations.
• Adhere to the terms of use and privacy regulations, especially when handling image samples of others.

Example Response

The API response will contain the results of the image recognition or other relevant information. Parse and use the response data according to the actual API documentation.