Text to Image Generator

Prompt to Pixels

A powerful yet beginner-friendly web application that transforms
natural language prompts into stunning images using AI

---

🌟 Overview

This project demonstrates how to integrate Stable Diffusion AI models into a Python web application using Flask and Hugging Face Diffusers. Built with simplicity in mind, it provides an intuitive interface for generating high-quality images from text descriptions, making AI-powered image generation accessible to everyone.

✨ Features

Core Functionality

• ✅ Text-to-Image Generation - Transform natural language prompts into images
• ✅ High-Quality Output - Powered by Stable Diffusion v1.5
• ✅ Image Download - Save generated images locally
• ✅ Responsive Design - Works seamlessly on desktop and mobile
• ✅ Real-time Preview - Instant image display upon generation

Technical Features

• 🔄 Flexible Hardware Support - CPU and GPU compatibility
• 📦 Local Storage - Generated images saved to filesystem
• 🎯 RESTful API - Clean API endpoints for integration
• ⚡ Optimized Pipeline - Efficient model loading and inference
• 🛡️ Error Handling - Robust error management and logging

---

🔧 Tech Stack

Layer	Technology	Purpose
Frontend	HTML5, CSS3, JavaScript	User interface and interactions
Backend	Python 3.8+, Flask	Web server and API endpoints
AI Model	Stable Diffusion v1.5	Text-to-image generation
ML Framework	PyTorch, Diffusers	Model inference
Storage	Local Filesystem	Generated image storage

Dependencies

Flask >= 2.0.0
torch >= 2.0.0
diffusers >= 0.21.0
transformers >= 4.30.0
accelerate >= 0.20.0
pillow >= 9.0.0

---

📦 Prerequisites

Before you begin, ensure you have the following installed:

• Python 3.8 or higher - Download Python
• pip - Python package installer (comes with Python)
• Git - Download Git
• (Optional) CUDA - For GPU acceleration

System Requirements

Component	Minimum	Recommended
RAM	8GB	16GB+
Storage	10GB free	20GB+ free
GPU	None (CPU works)	NVIDIA GPU with 6GB+ VRAM

---

🚀 Installation

1. Clone the Repository

git clone https://github.com/ARUNAGIRINATHAN-K/text-to-image-generator.git
cd text-to-image-generator

2. Create Virtual Environment (Recommended)

# Windows
python -m venv venv
venv\Scripts\activate

# Linux/Mac
python3 -m venv venv
source venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

Or install manually:

pip install flask torch diffusers transformers accelerate pillow

4. Create Required Directories

mkdir generated_images
mkdir static/css static/js

---

💡 Usage

Running the Application

1. Start the Flask server:

python app.py

2. Open your browser and navigate to:

http://localhost:5000

3. Generate images:
   • Enter a descriptive text prompt (e.g., "a beautiful sunset over mountains")
   • Click "Generate Image"
   • Wait for processing (may take 30-60 seconds on CPU)
   • View and download your generated image

Example Prompts

✨ "a serene lake surrounded by autumn trees, digital art"
🌆 "futuristic cityscape at night, cyberpunk style"
🐱 "cute cat wearing a wizard hat, watercolor painting"
🏔️ "majestic mountain peak with clouds, photography"

---

📁 Project Structure

text-to-image-generator/
├── app.py                      # Main Flask application
├── requirements.txt            # Python dependencies
├── README.md                   # Project documentation
├── .gitignore                  # Git ignore rules
├── generated_images/           # Output directory for images
├── static/
│   ├── css/
│   │   └── style.css          # Custom styles
│   └── js/
│       └── script.js          # Frontend JavaScript
├── templates/
│   └── index.html             # Main HTML template
└── models/                    # Model cache directory (auto-created)

---

🔌 API Reference

Generate Image

Endpoint: POST /generate

Request Body:

{
  "prompt": "your text description here"
}

Response:

{
  "success": true,
  "image_url": "/generated_images/image_1234567890.png",
  "timestamp": "2025-11-07T10:30:00"
}

cURL Example:

curl -X POST http://localhost:5000/generate \
  -H "Content-Type: application/json" \
  -d '{"prompt": "a beautiful landscape"}'

---

⚙️ Configuration

Environment Variables

Create a .env file in the root directory:

FLASK_APP=app.py
FLASK_ENV=development
FLASK_DEBUG=1
MODEL_NAME=runwayml/stable-diffusion-v1-5
IMAGE_WIDTH=512
IMAGE_HEIGHT=512
INFERENCE_STEPS=50

Customizing Generation Parameters

Edit app.py to adjust:

# Image dimensions
image = pipe(
    prompt,
    height=512,  # Adjust height
    width=512,   # Adjust width
    num_inference_steps=50,  # Quality vs speed tradeoff
    guidance_scale=7.5  # Prompt adherence
).images[0]

---

🤖 Model Information

Stable Diffusion v1.5

• Model ID: runwayml/stable-diffusion-v1-5
• Type: Text-to-Image Diffusion Model
• License: CreativeML Open RAIL-M
• Size: ~4GB
• Resolution: 512×512 (default)

---

🔧 Troubleshooting

Common Issues

Issue: Model downloading is slow

Solution: First run downloads ~4GB model. Be patient or use faster internet.

Issue: Out of memory error

Solution: Reduce image size or use CPU instead of GPU
pipe.to("cpu")

Issue: Port 5000 already in use

Solution: Change port in app.py:
app.run(port=5001)

Issue: Generated images look poor quality

Solution: Increase inference steps in configuration (50-100 recommended)

📊 Project Stats

---

🗺️ Roadmap

• Add multiple model support
• Implement image-to-image generation
• Add batch generation feature
• Create Docker container
• Add prompt suggestions
• Implement user authentication
• Add generation history
• Create mobile app

---

Made by Arunagirinathan K

⭐ Star this repo if you find it helpful!

Report Bug • Request Feature