EMLART-GP - Tutorial GECCO 2024

This is a repository that serves as support for the Tutorial at GECCO 2024: Evolutionary Art and Design in the Machine Learning Era

Installation

Start by cloning the repository and enter the repository folder by issuing the following terminal commands:

git clone https://github.com/jncor/emlart-gp-tutorial.git
cd emlart-gp-tutorial/

This setup assumes and recomends a conda version greater than 23.3.1. The following commands will install the required packages to execute the code:

conda create --name emlart-gp-tutorial python=3.11
conda activate emlart-gp-tutorial 
conda install -c conda-forge tensorflow
python -m pip install git+https://github.com/openai/CLIP.git 
conda install matplotlib scikit-image pytorch-lightning -c pytorch        

You will also need the weights for the models used by the provided scripts from the following link:

models

unzip the file ensuring that a folder called "models" is inside the cloned repository

models/
tensorgp/
stablediffusion_examples/
emlart_gp.py
image_evaluator.py
laion_aesthetics.py
...

Usage

To execute the emlart-gp approach use the following command on the terminal in the repository folder:

python emlart_gp.py <starting random seed number> <#of runs> <# of generations> <text prompt>

E.g.

python emlart_gp.py 10 1 30 "sunset, bright colors" 

To execute the image evaluator based on Laion aesthetics machine learning model and cosine similarity with the a provided prompt via OpenAI Clip you should issue the following command:

python image_evaluator.py <path to the image> <text prompt>

E.g.

python image_evaluator.py "image_examples/stability-ai-out-0.png" "an image of a red square, a blue square and a yellow square"