Dreamweaver

Dreamweaver is an innovative text-to-image generation project designed to bring your imagination to life. Using advanced machine learning models, Dreamweaver allows users to input descriptive text and generate high-quality, visually engaging images based on the prompt.

Table of Contents About the Project Features Installation Usage Technologies Used Project Structure Contributing License Contact About the Project Dreamweaver leverages the power of deep learning and computer vision to create images from textual descriptions. It combines natural language processing (NLP) techniques with generative models to generate rich, contextual images based on user input.

Features Generate images from textual descriptions Customizable output resolution Supports a wide range of image styles and moods User-friendly interface Multi-language support for input text Installation To set up Dreamweaver locally, follow the instructions below:

Clone the repository:

bash Copy code git clone https://github.com/your-username/dreamweaver.git Navigate to the project directory:

bash Copy code cd dreamweaver Install dependencies:

bash Copy code pip install -r requirements.txt Run the application:

bash Copy code python app.py Make sure to set up the necessary API keys (if using external models) in the .env file.

Usage Enter a descriptive sentence in the input field. Click on "Generate Image." Wait for the image to be generated and displayed on the screen. You can download or share the image. Here’s an example input:

text Copy code "A serene landscape with mountains and a river flowing under a sunset sky." Dreamweaver will generate an image that closely resembles this description.

Technologies Used Python for backend development TensorFlow / PyTorch for deep learning models Flask / Django for building the web application OpenAI / Stable Diffusion for generating images from text HTML / CSS / JavaScript for frontend Project Structure bash Copy code dreamweaver/ │ ├── app.py # Main application script ├── models/ # Directory for storing trained models ├── static/ # Static assets like CSS and JS files ├── templates/ # HTML templates for the web interface ├── requirements.txt # List of dependencies └── README.md # Project documentation (this file) Contributing Contributions are welcome! Here’s how you can help:

Fork the repository. Create a new branch: git checkout -b feature-branch-name. Commit your changes: git commit -m 'Add some feature'. Push to the branch: git push origin feature-branch-name. Open a pull request. Please ensure your code follows the established coding conventions and passes tests before submitting.

License This project is licensed under the MIT License - see the LICENSE file for details.

Contact For any inquiries or suggestions, please feel free to contact:

Unnayan Mishra itsunnayan12@gmail.com