TextToImageAI

A Python-based text-to-image generation tool using Stable Diffusion, designed for easy integration and customization.

Description

TextToImageAI is a powerful yet simple-to-use text-to-image generation tool that leverages the Stable Diffusion model to create high-quality images from text descriptions. It supports both single image generation and multiple variations, with customizable parameters for fine-tuning the generation process.

Features

• 🖼️ Generate high-quality images from text descriptions
• 🎨 Create multiple variations of the same prompt
• 🎯 Customize generation parameters (steps, guidance scale, etc.)
• 🚀 GPU acceleration support for faster generation
• 📁 Automatic file management and organization
• ❌ Negative prompt support for better control
• 🛠️ Error handling and robust implementation

Installation

1. Clone the repository:

git clone https://github.com/SanjeevJMT/Text-to-Image-AI.git
cd TextToImageAI

2. Install the required dependencies:

pip install torch diffusers transformers pillow

Usage

Basic Usage

from image_generator import ImageGenerator

# Initialize the generator
generator = ImageGenerator()

# Generate a single image
image_path = generator.generate_image(
    prompt="A serene mountain landscape at sunset with a lake reflection"
)
print(f"Image saved to: {image_path}")

Advanced Usage

# Generate an image with custom parameters
image_path = generator.generate_image(
    prompt="A beautiful sunset over mountains",
    filename="sunset_mountains.png",
    num_inference_steps=50,
    guidance_scale=7.5,
    negative_prompt="blur, distortion, low quality"
)

# Generate multiple variations
variations = generator.generate_image_variations(
    prompt="A cosmic nebula with vibrant colors",
    num_images=4,
    num_inference_steps=30
)

Configuration

The ImageGenerator class accepts several parameters:

Parameter	Description	Default
model_id	Stable Diffusion model to use	"runwayml/stable-diffusion-v1-5"
output_dir	Directory for saving generated images	"generated_images"
num_inference_steps	Number of denoising steps	50
guidance_scale	How closely to follow the prompt	7.5

System Requirements

• Python 3.7 or higher
• CUDA-capable GPU (recommended)
• Minimum 8GB RAM (16GB recommended)
• ~4GB disk space for model storage

Performance Notes

• First-time usage will download the model (~4GB)
• GPU acceleration significantly improves generation speed
• Higher inference steps produce better quality but take longer
• Guidance scale affects how closely the image follows the prompt

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 Not Licensed yet.

Acknowledgments

• Stable Diffusion for the base model
• Hugging Face for model hosting and diffusers library

Contact

Sanjeev JMT - @SanjeevJMT

Project Link: https://github.com/SanjeevJMT/Text-to-Image-AI