nitinrwt / stablediff

Text-to-Image Conversion with Hugging Face

This repository contains a machine learning application that converts text into images using the Hugging Face pipeline. The model utilized here is trained to generate images based on textual descriptions, offering a convenient way to visualize text data.

Table of Contents

• Introduction
• Features
• Installation
• Usage
• Contributing
• License

Introduction

In many natural language processing (NLP) tasks, it's beneficial to have a visual representation of textual data. This project aims to bridge the gap between text and image data by leveraging state-of-the-art machine learning models provided by Hugging Face.

Features

• Text-to-Image Conversion: Utilize Hugging Face's powerful pipeline to generate images from textual descriptions.
• Customization: Easily tailor the input text and tweak model parameters to generate different types of images.
• Scalability: The application can handle a variety of text inputs and efficiently produce corresponding visual outputs.
• Modularity: The project is structured for easy integration into existing ML pipelines or applications.

Installation

1. Clone the repository:

   git clone https://github.com/NitinRwt/StableDiff

2. Navigate to the project directory:

   cd your_repo

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

1. Run the application script:

   python main.py

2. Follow the prompts to input the text you want to convert into an image.

3. The application will process the input text and generate the corresponding image output.

4. Explore the generated images and refine your inputs as necessary.

Contributing

Contributions are welcome! If you'd like to improve this project, here's how you can get started:

• Fork the repository.
• Create your feature branch (git checkout -b feature/AmazingFeature).
• Commit your changes (git commit -m 'Add some AmazingFeature').
• Push to the branch (git push origin feature/AmazingFeature).
• Open a pull request.

Please make sure to update tests as appropriate.