Text-to-Image Generator

The Text-to-Image Generator is a deep learning project designed to generate images based on textual descriptions. By leveraging a neural network model, this application transforms descriptive text prompts into corresponding images, enabling creative and visual expression directly from natural language input.

Table of Contents

• Overview
• Features
• Technologies Used
• Installation
• Usage
• Contributing
• License

Overview

This project aims to convert descriptive text prompts into visually accurate images, serving a wide range of applications, from creative artwork generation to realistic visualizations based on user descriptions. The system uses a deep learning model trained on text-image pairs to understand and visualize text concepts effectively.

Features

• Text-to-Image Conversion: Generates images from user-provided textual descriptions.
• Customizable Output: Ability to adjust image parameters for more control over output styles.
• Efficient Processing: Optimized for faster generation with high-quality results.
• Interactive Interface: User-friendly interface for text input and image viewing.

Technologies Used

• Python: Core programming language.
• PyTorch / TensorFlow: Frameworks for deep learning model training and deployment.
• Stable Diffusion or DALL-E (optional): Model backbones for text-to-image generation.
• Streamlit: For creating an interactive web interface (if applicable).
• CUDA: For GPU acceleration (if applicable).

Installation

To set up the project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/PyaeLinn01/Text-to-Image-Generator.git

2. Install the required packages:

   Navigate to the project directory and install dependencies:

   pip install -r requirements.txt

3. Set up the model:

   • Download any necessary pre-trained models if specified in the project (e.g., stable-diffusion-v1).
   • Place the model files in the designated directory within the project.

4. Run the application:

   For a Streamlit interface (if provided), start the application by running:

   streamlit run app.py

Usage

Enter a descriptive text prompt into the input field to generate an image based on the text. Adjust parameters as desired for different visual effects, and click "Generate" to see the output.

Contributing

Contributions are welcome! To contribute, please fork the repository and create a pull request with a description of your proposed changes.

License

This project is licensed under the MIT License.

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