GloriosaAI

GloriosaAI is a versatile Python codebase that includes a Generative Adversarial Network (GAN) for the training and generation of AI art.

GitHub: GloriosaAI

Scripts:

• main.py: The selection menu for GloriosaAI

/scripts/

• trainer.py Runs GloriosaAI trainer
• modelout.py Output images from trained models with GloriosaAI
• video_encoder.py Encode a video using GloriosaAI
• image-processor.py Prepare images for GloriosaAI
• preprocessor_data.py Dependency for GloriosaAI
• install_dependencies.py: Install dependencies

trainer.py Hyperparameters:

• Epochs:
  • Controls the number of training iterations.
• Batch Size:
  • Determines the number of data samples processed in each training step.
• Latent Dimension:
  • Defines the size of the latent space in the generative model.
• Generation Interval:
  • Sets how often generated images are saved during training.
• Learning Rate:
  • Governs the step size during gradient descent optimization.
• Use Learning Rate Scheduler:
  • Specifies whether to use a learning rate scheduler during training.
• Random Seed:
  • Seeds the random number generator for reproducibility.

How to Run:

    pip install -r requirements.txt
    python main.py
    chmod +x setup.sh
    

Dependencies:

    TensorFlow==2.14.0
    Numpy==1.26.2
    Matplotlib==3.8.2
    Pillow==10.1.0
    OpenCV-Python==4.8.1.78
    Flask==2.1.1
    

Languages Used in the GloriosaAI Project