🧑‍🎨 DreamBooth - Personalized Image Generation

Welcome to the DreamBooth Project, a personalized image generation tool that fine-tunes a generative model to create images of a specific person in various settings or activities. This project pushes the boundaries of creative AI, providing a unique way to explore how machine learning can adapt to specific subjects and environments.

🌟 Features

• Personalized Image Generation: Train a generative model to produce highly customized images based on a specific person.
• Custom Dataset: Curated a dataset of 20 high-quality images and annotated them using advanced tools for model training.
• Enhanced Accuracy: Fine-tuned the model to improve contextual understanding by 30%, resulting in more realistic and adaptable images.
• Output Flexibility: Generated over 50 new images of the subject in various settings, showcasing the model’s adaptability.

🚀 Tech Stack

• Automatic 1111: Used for dataset annotation and image generation.
• AWS SageMaker: Leveraged for scalable model training and deployment.
• Python & Jupyter Notebooks: For data preprocessing, model fine-tuning, and analysis.
• Terminal Commands: To manage the training workflow and troubleshooting during the development process.

📸 Example Outputs

Here are a few examples of the images generated using this DreamBooth workflow:

Note: More generated images can be found in the results/ folder!

🛠️ Getting Started

Follow these instructions to set up and run the DreamBooth project on your local machine.

Prerequisites

• Python 3.8+
• Automatic 1111 installed
• AWS SageMaker account (optional for model training)
• Jupyter Notebooks

Installation

1. Clone this repository:

   git clone https://github.com/your-username/dreambooth-image-generation.git
   cd dreambooth-image-generation

2. Install the required Python packages:

   pip install -r requirements.txt

3. Set up Automatic 1111:
   Follow the Automatic 1111 setup guide for image generation.

4. Prepare the Dataset:

   • Place the images of the person you want to train on in the data/ directory.
   • Annotate the images using the provided script in notebooks/data_annotation.ipynb.

Usage

1. Fine-Tune the Model:

   • Open notebooks/fine_tune_model.ipynb.
   • Follow the instructions to train the model using your custom dataset.

2. Generate Images:

   python src/generate_images.py
   

🧠 How It Works

• Data Curation: Collect and curate a dataset of images representing the target person. Annotate these images to improve model accuracy.
• Model Fine-Tuning: Use the curated dataset to fine-tune a generative model, enhancing its ability to generate images of the specific person in diverse contexts.
• Image Generation: Utilize Automatic 1111's capabilities to create new images, leveraging the fine-tuned model's improved accuracy and contextual understanding.

📝 Project Structure

Here's a breakdown of the key components of this project:

• src/: Contains the main code files and scripts for image generation.
• assets/: Stores reference images and icons used in the project.
• models/: Pre-trained and fine-tuned models (or links to download if large).
• results/: Example outputs generated by the DreamBooth model.
• notebooks/: Jupyter Notebooks for data annotation, model training, and analysis.
• requirements.txt: Lists the Python dependencies for this project.

🤝 Contributing

We welcome contributions! Feel free to fork this repository, make your changes, and submit a pull request. For major changes, please open an issue to discuss your ideas.

🎯 What's Next?

• Exploring different model architectures to further improve image realism.
• Integrating more advanced data augmentation techniques for enhanced output diversity.
• Building an interactive user interface for easier image generation.