AI Art Generator

A Django-based web application that generates unique digital artwork using a simplified implementation of diffusion models. This project combines deep learning with web development to create an interactive art generation platform.

Features

• AI-Powered Art Generation: Create unique digital artwork using a diffusion model
• Customizable Parameters: Control the generation with prompts and seed values
• Art Gallery: Browse previously generated artworks
• User Authentication: Save and manage your generated artworks (optional)
• Responsive Design: Works on desktop and mobile devices

Technology Stack

• Backend: Django (Python web framework)
• AI Model: PyTorch-based diffusion model
• Frontend: HTML5, CSS3, JavaScript
• Database: SQLite (default, can be configured for others)
• Image Processing: PIL/Pillow

Installation

Prerequisites

• Python 3.8 or higher
• pip (Python package manager)

Steps

1. Clone the repository

   git clone <repository-url>
   cd art_generator

2. Create a virtual environment

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Set up the database

   python manage.py migrate

5. Create a superuser (optional)

   python manage.py createsuperuser

6. Run the development server

   python manage.py runserver

7. Access the application Open your browser and navigate to http://localhost:8000

Usage

1. Generate Artwork:

   • Visit the home page
   • Optionally provide a text prompt to guide generation
   • Optionally specify a seed value for reproducible results
   • Click "Generate Art" to create your artwork

2. Browse Gallery:

   • Visit the gallery page to see all generated artworks
   • View details like seed values, prompts, and creation dates

3. Manage Account (if authenticated):

   • Your generated artworks will be associated with your account
   • Access additional features if implemented

Project Structure

art_generator/
├── art_project/          # Django project settings
├── art_app/             # Main Django application
│   ├── models.py        # Database models
│   ├── views.py         # Application logic
│   ├── urls.py          # URL routing
│   └── admin.py         # Admin interface configuration
├── diffusion_model/     # AI model implementation
│   └── diffusion.py     # Diffusion model code
├── templates/           # HTML templates
│   ├── home.html        # Main generation interface
│   └── gallery.html     # Artwork gallery
├── static/              # Static files (CSS, JS, images)
├── manage.py            # Django management script
└── requirements.txt     # Python dependencies

Installation Guide for AI Art Generator

Requirements

Software Requirements

• Python 3.8 or higher
• pip (Python package manager)
• Virtual environment tool (venv or virtualenv)

Hardware Requirements

• Minimum 4GB RAM (8GB recommended)
• At least 1GB free disk space
• CPU (GPU optional but not required for this simplified implementation)

Python Package Requirements

Create a requirements.txt file with the following content:

Django==4.2.7
torch==2.0.1
torchvision==0.15.2
Pillow==10.0.1
numpy==1.24.3

Installation Steps

Step 1: Clone or Create Project Directory

If you have the code in a repository:

git clone <repository-url>
cd art_generator

Or create the directory structure manually:

mkdir art_generator
cd art_generator

Step 2: Set Up Virtual Environment

On macOS/Linux:

python -m venv venv
source venv/bin/activate

On Windows:

python -m venv venv
venv\Scripts\activate

Step 3: Install Dependencies

Create a requirements.txt file in your project directory with the content above, then run:

pip install -r requirements.txt

Alternatively, install packages individually:

pip install Django==4.2.7
pip install torch==2.0.1 torchvision==0.15.2
pip install Pillow==10.0.1
pip install numpy==1.24.3

Step 4: Set Up Django Project

If you haven't already created the project structure:

django-admin startproject art_project .
python manage.py startapp art_app

Create the necessary directories:

mkdir -p diffusion_model templates static

Step 5: Create Project Files

Create the following files with the content provided in the previous implementation:

1. diffusion_model/diffusion.py - The diffusion model implementation
2. art_app/models.py - Database models
3. art_app/views.py - Application views
4. art_app/urls.py - URL routing for the app
5. templates/home.html - Main page template
6. templates/gallery.html - Gallery template

Update the main URLs file art_project/urls.py to include the app URLs.

Step 6: Configure Django Settings

Update art_project/settings.py to include:

INSTALLED_APPS = [
    'django.contrib.admin',
    'django.contrib.auth',
    'django.contrib.contenttypes',
    'django.contrib.sessions',
    'django.contrib.messages',
    'django.contrib.staticfiles',
    'art_app',  # Add this line
]

# Add at the bottom of the file
import os
STATIC_URL = '/static/'
STATICFILES_DIRS = [os.path.join(BASE_DIR, 'static')]
TEMPLATES = [
    {
        'BACKEND': 'django.template.backends.django.DjangoTemplates',
        'DIRS': [os.path.join(BASE_DIR, 'templates')],  # Add this line
        'APP_DIRS': True,
        # ... other settings
    },
]

Step 7: Set Up Database

python manage.py makemigrations
python manage.py migrate

Step 8: Create Superuser (Optional)

python manage.py createsuperuser

Follow the prompts to create an admin account.

Step 9: Run the Development Server

python manage.py runserver

Step 10: Access the Application

Open your web browser and go to:

• Main application: http://localhost:8000
• Admin interface: http://localhost:8000/admin (if you created a superuser)

Troubleshooting Common Issues

Issue: "ModuleNotFoundError" for Django

Solution: Ensure your virtual environment is activated and Django is installed:

pip install Django==4.2.7

Issue: "ModuleNotFoundError" for torch

Solution: Install PyTorch with the correct version:

pip install torch==2.0.1 torchvision==0.15.2

Issue: "TemplateDoesNotExist" error

Solution: Check your TEMPLATES setting in settings.py and ensure templates directory exists.

Issue: "CSRF verification failed"

Solution: Add CSRF token to your AJAX requests or configure Django settings appropriately.

Issue: Slow image generation

Solution: This is expected with the CPU implementation. For better performance, consider:

1. Using a GPU-enabled PyTorch version
2. Reducing image size in the diffusion model settings
3. Implementing caching

Verifying Installation

To verify everything is installed correctly, run:

python manage.py check

This should output: "System check identified no issues (0 silenced)."

Production Deployment Notes

For production deployment, you should:

1. Set DEBUG = False in settings.py
2. Set up a proper database (PostgreSQL recommended)
3. Configure a production web server (e.g., Gunicorn with Nginx)
4. Set up static file serving
5. Implement proper security measures

Additional Resources

• Django Documentation
• PyTorch Documentation
• Pillow Documentation

Your AI Art Generator should now be ready to use!

Customization

Model Parameters

You can adjust the diffusion model parameters in diffusion_model/diffusion.py:

• image_size: Output image dimensions (default: 128x128)
• timesteps: Number of diffusion steps (default: 100)
• Generation steps in the generate() method

Styling

Modify the CSS in the HTML templates or add static CSS files to customize the appearance.

Database

By default, the project uses SQLite. To use another database, update the DATABASES setting in art_project/settings.py.

Performance Considerations

• The current implementation uses a CPU-based simplified diffusion model
• For faster generation, consider:
  • Using a GPU-accelerated PyTorch installation
  • Implementing a more efficient model architecture
  • Adding caching mechanisms
  • Using background task processing (e.g., Celery)

Limitations

• The current diffusion model is simplified for demonstration purposes
• Image resolution is limited to 128x128 pixels in the default implementation
• Generation may take several seconds on CPU

Future Enhancements

• Integration with more advanced models (e.g., Stable Diffusion)
• Higher resolution output
• Style transfer capabilities
• Image editing and manipulation features
• Social features (sharing, commenting, liking)
• Advanced user profiles and collections

Troubleshooting

Common Issues

1. Dependency conflicts:

   • Ensure you're using a virtual environment
   • Check Python version compatibility

2. Database errors:

   • Run python manage.py migrate to apply migrations

3. Image generation errors:

   • Verify all dependencies are installed correctly
   • Check available memory

Getting Help

If you encounter issues not covered here, please check:

• Django documentation: https://docs.djangoproject.com/
• PyTorch documentation: https://pytorch.org/docs/

License

This project is provided for educational purposes. Please check the license terms of any external models or libraries you integrate.

Contributing

Contributions are welcome! Please feel free to submit pull requests or open issues for bugs and feature requests.