YouTube Thumbnail Generator

An AI-powered YouTube thumbnail generator that leverages Stable Diffusion and Large Language Models (LLM) to create custom, eye-catching thumbnails based on a given topic.

Table of Contents

• Features
• Requirements
• Installation
• Usage
• Configuration
• Detailed Function Descriptions
• Customization
• Logging
• Troubleshooting
• Contributing
• License

Features

• Intelligent Thumbnail Ideation: Utilizes LLM to generate creative thumbnail concepts and asset prompts.
• High-Quality Asset Generation: Employs Stable Diffusion 3 to create visually appealing assets.
• Automated Thumbnail Assembly: Combines generated assets into cohesive thumbnails.
• Background Removal: Supports automatic background removal from assets for cleaner integration.
• Text Overlay: Adds customizable text to thumbnails for increased engagement.
• Image Optimization: Ensures output images are optimized for YouTube's requirements.

Requirements

• Python 3.7+
• gradio_client
• Pillow
• rembg
• colorama
• Custom LLM implementation (llm/llama.py)

Installation

1. Clone this repository:

   git clone https://github.com/Likhithsai2580/yt_thumbnail_creator.git
   cd yt_thumbnail_creator
   

2. Install the required packages:

   pip install gradio_client Pillow rembg colorama
   

3. Set up the custom LLM implementation:

   • Ensure you have the llm/llama.py file in your project directory.
   • Follow any additional setup instructions for your specific LLM implementation.

4. Configure environment variables:

   • Set ASSET_DIR_PATH to specify where generated assets should be stored.
   • (Optional) Set any API keys or credentials required for Stable Diffusion or your LLM.

Usage

1. Run the script:

   python yt_thumb_gen.py --topic "Your Topic"
   

2. The generated thumbnail will be saved to the path specified in THUMBNAIL_PATH.

Configuration

Modify the following variables in the script to customize the thumbnail generation:

• THUMBNAIL_PATH: Path where the final thumbnail will be saved.
• FONT_SIZE: Default font size for text on thumbnails.
• ASSET_DIR_PATH: Directory to store generated assets.
• THUMBNAIL_SIZE: Size of the generated thumbnail (default: 1280x720).

Detailed Function Descriptions

generate_image(prompt, name, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, api_name)

Generates an image using the most efficient way and saves it as a PNG file.

generate_asset(prompt, name, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps)

Generates an asset using the most efficient way and saves it as a PNG file.

extract_code(response)

Extracts Python code from LLM responses for further processing.

generate_assets(topic)

Generates all necessary assets for a thumbnail based on the given topic.

add_to_thumbnail(thumbnail, asset_path, x, y)

Adds an asset to the thumbnail at the specified (x, y) coordinates.

add_text_to_thumbnail(thumbnail, text, position, font_size, color)

Adds text to the thumbnail with customizable position, font size, and color.

save_thumbnail(thumbnail)

Saves the final thumbnail to the specified path.

remove_bg_from_asset(asset_path)

Removes the background from an asset using the rembg library.

Customization

To generate thumbnails for different topics:

1. Open yt_thumb_gen.py in your preferred text editor.
2. Locate the __main__ section at the bottom of the script.
3. Modify the topic variable with your desired thumbnail topic.
4. Save the file and run the script as described in the Usage section.

Logging

The script uses colorized logging via the colorama library to provide clear feedback on the thumbnail generation process. Different colors are used to highlight various stages and potential issues:

• Green: Successful operations
• Yellow: Warnings or important information
• Red: Errors or critical issues
• Blue: Processing steps

Troubleshooting

• Asset Generation Fails: Ensure you have proper API credentials for Stable Diffusion and that your internet connection is stable.
• LLM Errors: Check that your custom LLM implementation is correctly set up and that any required models are properly loaded.
• File Permission Issues: Verify that the script has write permissions for the ASSET_DIR_PATH and THUMBNAIL_PATH directories.
• Missing Dependencies: Run pip install -r requirements.txt to ensure all required packages are installed.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

• Stable Diffusion for providing the image generation capabilities.
• The developers of rembg for the background removal functionality.
• All contributors who have helped shape and improve this project.

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For any questions or support, please open an issue on the GitHub repository.