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

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Repository files navigation

Twitter: @NorowaretaGemu License: MIT



Python
TensorFlow OpenCV
Git PowerShell Shell Batch

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

Prerequisite Folders:

Trainer

  • training_data/class

Video Encoder

  • output/video_frames (Also created by the Trainer)

ModelOut

  • input/input_models (Copy the architecture and weights of both the discriminator and generator.)

Image-Processor

  • unprocessed_images


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.







Prerequisites:

  • 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

Compiler:

  • PyInstaller

Optional:

  • A dataset of images for training (128 x 128 resolution, RGB format)

GloriosaAI - Art Showcase



How to Run:

pip install -r requirements.txt
pip install opencv-contrib-python

For GPU

nvidia-smi
pip install --upgrade pip
pip install --extra-index-url https://pypi.nvidia.com tensorrt-bindings==8.6.1 tensorrt-libs==8.6.1
pip install -U tensorflow[and-cuda]

For CPU

pip install tensorflow

Additional Packages

pip install numpy
pip install matplotlib
pip install Pillow
pip install opencv-python
pip install pyinstaller
pip install flask
python main.py

To make the setup script executable, run the following command in your terminal:

chmod +x setup.sh



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