The Celebrity Image Generation project leverages deep learning to generate realistic images of celebrities. Using a Flask backend and Python, this project provides a user-friendly interface for generating and exploring AI-generated celebrity faces.
This project showcases the power of Generative Adversarial Networks (GANs) in generating high-quality celebrity images. The system allows users to customize various facial attributes such as age, hairstyle, and gender, providing a highly interactive experience.
- Image Generation: Generate high-quality images of celebrities.
- Customization: Modify facial attributes (e.g., age, hairstyle) for generated images.
- User Interface: A simple web interface to interact with the model.
- API Access: Expose an API endpoint for generating images programmatically.
- Backend: Flask, Python
- Machine Learning: TensorFlow, Keras, PyTorch
- Frontend: HTML, CSS, JavaScript (optional)
- Others: Docker (for containerization)
- Python 3.7+
- Virtual environment (recommended)
- Docker (optional)
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Clone the repository:
git clone https://github.com/your-username/celebrity-image-generation.git cd celebrity-image-generation
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Create a virtual environment and activate it:
python -m venv venv source venv/bin/activate # On Windows use `venv\Scripts\activate`
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Install dependencies:
pip install -r requirements.txt
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Set up environment variables:
Create a
.env
file in the root directory and add any necessary configuration variables. -
Run the application:
flask run
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Access the application:
Open your browser and navigate to http://localhost:5000.
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Web Interface: Interact with the model using the provided web interface.
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API: Use the API to generate images programmatically.
POST /api/generate Content-Type: application/json { "attributes": { "age": 30, "hairstyle": "short", "gender": "female" } }
The model is based on GANs (Generative Adversarial Networks) trained on a dataset of celebrity images. For more details on the model architecture and training process, refer to the Model Documentation.
We welcome contributions! Please read our Contributing Guidelines for more details.
This project is licensed under the MIT License - see the LICENSE file for details.