Basic AI-Powered Image Generation

Overview

This Jupyter notebook based project demonstrates a basic AI-powered image generation technique using a combination of pre-trained models including InceptionV3 and CLIP.

Prerequisites

• Python 3.8+
• TensorFlow
• Transformers
• OpenCV (cv2)
• NumPy

Installation

pip install tensorflow transformers opencv-python numpy

Features

• Image preprocessing
• Noise generation
• Virtual image creation using text prompts
• Image blending

Usage

1. Specify the base image path
2. Enter a text prompt describing the desired image modification
3. Run the script to generate a blended image

Example

base_img = "path/to/your/image.jpg"
text_prompt = "Add a sunset effect"
virtual = virtual_image(base_img, text_prompt)
cv2.imwrite("generated_image.jpg", virtual)

How It Works

The script uses:

• CLIP model for text understanding
• InceptionV3 for feature extraction
• Gradient descent for image generation
• Blending techniques to combine original and generated images

Limitations

• Requires pre-trained models
• Image generation quality depends on input image and text prompt
• Computationally intensive

Contributing

Contributions are welcome! Please submit pull requests or open issues.