You were in Eden, the garden of God. Every kind of precious stone adorned you: ruby, topaz, and diamond, beryl, onyx, and jasper, sapphire, turquoise, and emerald. Your mountings and settings were crafted in gold, prepared on the day of your creation.
Eden is a sandbox for the Abraham project to test pipelines for creating generative art with machine learning.
In Eden, Abraham is free of the autonomy, originality, and uniqueness criteria required of an autonomous artificial artist. There are no security, privacy, or decentralization constraints.
Once we eat from the tree of knowledge, admitting the possibility of evil (bias, subversion, data poisoning, collusion, and other potential attacks), all development will be transferred to Abraham-mvp, and Eden will be destroyed.
- wrappers of deep learning repositories for generative modeling, and manipulation of images, text, and audio, structured as submodules.
- an API written on top to combine and chain models together.
- examples and demos.
To install, command your terminal the following:
git clone --recurse-submodules https://github.com/abraham-ai/eden cd eden pip install -r requirements.txt
sudo apt install nodejs sudo apt install jupyter labextension install @jupyter-widgets/jupyterlab-manager
Pipfile is also provided if you wish to use pipenv.
external folder contains submodules of dependencies. These can be used directly according to their own instructions, as well as through wrappers contained in the
Many of the dependencies require additional files (mostly pre-trained models) to run. The commandment is:
At this moment, Eden is unstable and scarcely documented. Development is underway. The code is currently provided as-is. In the future, it should be turned into a python package and a Docker container will be helpful as well. Documentation is promised.
A set of work-in-progress examples are found inside the
examples directory, packaged as Jupyter notebooks. In the future, it may make sense to divide these into "templates" (minimal examples that demonstrate how to use core features).