This document will guide developers on how to use the `aonet` library to invoke the AnyText API, which is used for text generation and editing before rendering in images(Text).
## Prerequisites
- Node.js environment
-`aonweb` library installed
- Valid Aonet APPID
## Installation
Ensure the `aonet` library is installed. If not, you can install it using npm:
```bash
npm install aonet
```
## Usage Instructions
### 1. Import the `aonet` Library
```javascript
constAI=require("aonet");
```
### 2. Configure Options
Create an `options` object containing your APPID:
```javascript
constoptions={
appid:"your_APPID"
};
```
Make sure to replace `"your_APPID"` with your actual Aonet APPID.
### 3. Initialize AI Instance
Initialize the AI instance using the configuration options:
```javascript
constaonet=newAI(options);
```
### 4. Invoke AnyText API
Use the `prediction` method to call the FunASR API:
- Ensure the provided image URL is publicly accessible and of good quality for optimal recognition results.
- The API may take some time to process the image and generate results,
- Handle potential errors, such as network issues, invalid input, or API limitations.
- Adhere to terms of use and privacy regulations, especially when processing image containing sensitive information.
- Enter the descriptive prompts (supporting both Chinese and English) in the Prompt. Each line of text to be generated should be enclosed in double quotes, then hand-draw the position for each line of text in sequence to generate an image. The quality of the generated image depends critically on the drawing of the text positions, so please do not draw them too casually or too small. The number of positions must match the number of text lines, and each position’s size should match the length or height of the corresponding text line as closely as possible.
## Example Response
The API response will include the image content after text generation or text editing. Parse and use the response data according to the actual API documentation.
## Advanced Usage
- Implement batch image processing by processing multiple image files in a loop or concurrently.
- Add a user interface to allow users to upload their image files or provide image URLs.
- Implement real-time tex recognition by integrating the API into live image streams.
- Integrate post-processing features for text, such as punctuation addition, semantic analysis, or sentiment analysis.
- Consider implementing multi-language support to handle image in different languages as needed.
By following this guide, you should be able to effectively use the AnyText API for automatic speech recognition in your applications. If you have any questions or need further clarification, feel free to ask.
This document describes how to use the aonet library to call the IDM-VTON AI model. This model is used for virtual try-on, allowing specified clothing images to be applied to human images.
This document describes how to use the aonweb library to call the IDM-VTON AI model. This model is used for virtual try-on, allowing specified clothing images to be applied to human images.
## Prerequisites
...
...
@@ -17,60 +17,50 @@ This document describes how to use the aonet library to call the IDM-VTON AI mod
-`aonweb` library installed
- Valid Aonet APPID
## Installation
Ensure that the aonet library is installed. If it is not installed yet, you can install it using npm:
```bash
npm install aonet
```
## Basic Usage
### 1. Import the AI Class
### 1. Import Required Modules
```js
constAI=require("aonet");
import{AI,AIOptions}from'aonweb';
```
### 2. Configure Options
Create an options object containing authentication information:
console.error("Error in IDM-VTON process:",error);
// Error handling...
}
}
...
...
@@ -138,4 +128,4 @@ runIDMVTON();
## Conclusion
By following this guide, you should be able to successfully integrate and use the IDM-VTON AI model for virtual try-on application development. If you encounter any issues or need further assistance, please refer to the official aonet documentation or contact technical support.
By following this guide, you should be able to successfully integrate and use the IDM-VTON AI model for virtual try-on application development. If you encounter any issues or need further assistance, please refer to the official aonweb documentation or contact technical support.
This document will guide developers on how to use the aonet library to call the XTTS-V2 API, which is used for voice cloning and text-to-speech conversion.
This document will guide developers on how to use the aonweb library to call the XTTS-V2 API, which is used for voice cloning and text-to-speech conversion.
## Prerequisites
...
...
@@ -17,65 +17,50 @@ This document will guide developers on how to use the aonet library to call the
-`aonweb` library installed
- Valid Aonet APPID
## Installation
## Basic Usage
Ensure that the aonet library is installed. If it is not installed yet, you can install it using npm:
```bash
npm install aonet
```
## Steps to Use
### 1. Import the aonet library
### 1. Import Required Modules
```js
constAI=require("aonet");
import{AI,AIOptions}from'aonweb';
```
### 2. Configure Options
Create an options object that includes your APPID:
### 2. Initialize AI Instance
```js
constoptions={
appid:"your APPID"
};
```
Make sure to replace "your APPID" with your actual Aonet APPID.
### 3. Initialize AI Instance
Initialize the AI instance using the configuration options:
constai_options=newAIOptions({
appId:'your_app_id_here',
dev_mode:true
});
```js
constaonet=newAI(options);
constaonweb=newAI(ai_options);
```
### 4. Call the XTTS-V2 API
Use the `prediction` method to call the XTTS-V2 API:
