AI Picture Generator

Script for generating images based on experiments with model selection

A convenient console tool for generating images based on a text description (promt) in Python using the g4f library. It supports several models: Bing, DeepAI, Flux and Pollinations.

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Content

• Functionality
• Requirements
• Installation
• Launch and Use
• Error Handling
• Settings and Extensions

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Functionality

1. Choosing a generation model

   • Bing (recommended)
   • DeepAI
   • Flux
   • Pollinations

2. The main cycle

   • User input of promt

   • Commands:

     • exit, выход, quit — terminate the script
     • /model — change the model without restarting

3. Result generation and output

   • API request:

     response = client.images.generate(
         model=model,
         prompt=prompt,
         response_format="url"
     )
     image_url = response.data[0].url
     

   • Auto-copying of the link to the clipboard (pyperclip.copy)

   • Beep on successful generation (winsound.MessageBeep)

4. Error handling

   • Interception of exceptions and message output:

      Generation error: <error text>
     

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Requirements

• Python 3.7+
• Windows (for winsound operation)

Dependencies

• g4f
• pyperclip
• winsound (included in the Windows standard library)

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Installation

1. Clone the repository:

   git clone https://github.com/KiriVoll/AI-Picture-Generator.git
   cd AI-Picture-Generator
   

2. Install the dependencies:

   pip install g4f pyperclip
   

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Launch and Use

1. Run the script with the command:

   python AIpictureGenerator.py
   

2. At the first launch, select the generation model (1-4).

3. Enter the promt in the console and press Enter.

4. After generation:

   • The link to the image will be copied to the clipboard.
   • You will hear a beep.

5. To change the model, enter the command /model.

6. Use quit, exit or выход to exit.

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Error Handling

The script intercepts all exceptions that occur when accessing the API and outputs them in a clear format. This allows you to continue working after an unsuccessful request.

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Settings and Extensions

• Response formats: add support for base64 or byte data
• GUI: implement a graphical interface with the tkinter or PyQt libraries
• Logging: save the history of requests and responses to a file
• Parallel queries: implement batch generation of promts

❤️ Support

If you find this useful, feel free to star ⭐ the repo or share your improvements!