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For Runpod/Vast instructions see Cloud Setup

For Google Colab see Train_Colab.ipynb

Install Python

Install Python 3.10 from here if you do not already have Python 3.10.x installed.

https://www.python.org/downloads/release/python-3109/

https://www.python.org/ftp/python/3.10.9/python-3.10.9-amd64.exe

Download and install Git from git-scm.com.

or Git for windows

Make sure Python 3.10 shows on your command window:

python --version

You should see Python 3.10.something. 3.10.5, 3.10.9, etc. It needs to be 3.10.x.

If you have Python 3.10.x installed but your command window shows another version (3.8.x, 3.9.x) ask for assistance in the discord. ...or you can try setting the path to the 310 binaries before running the windows_setup.cmd if you know what you're doing.

SET PYTHON=C:\Python310\python.exe

(you'll have to locate python 310 on your system on your own if you)

Clone this repo

Clone the repo from normal command line then change into the directory:

git clone https://github.com/victorchall/EveryDream2trainer

Then change into the folder:

cd EveryDream2trainer

Windows

While still in the command window, run windows_setup.cmd to create your venv and install dependencies.

windows_setup.cmd

Double check your python version again after setup by running these two commands:

activate_venv.bat
python --version

Again, this should show 3.10.x

Finally, install CUDA 11.8 from this link: https://developer.nvidia.com/cuda-11-8-0-download-archive

Local docker container

    docker compose up

And you can either get a shell via:

    docker exec -it everydream2trainer-docker-everydream2trainer-1 /bin/bash

Or go to your browser and hit http://localhost:8888. The web password is test1234 but you can change that in docker-compose.yml.

Your current source directory will be moutned to the Jupyter notebook.

Local Linux install

Preinstallation

  • Make sure you have python3.10 installed. Often this is python3 so check with python3 -V
  • Make sure Linux Nvidia driver is up to date and working. Check that nvidia-smi is working and shows your GPU.
    Steps to update the driver may depend on the Linux distribution you use. For Ubuntu, use Gnome and open Softwrae & Updates, go to the additional drivers tab and select Using NVIDIA driver metapackage from nvidia-driver-530 (proprietary). Currently 530 is the latest version, but you can use latest at your time of install. You will need to use the proprietary driver.
  • Install Cuda 11.8. You can use this link for Ubuntu: https://developer.nvidia.com/cuda-11-8-0-download-archive?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=22.04&target_type=deb_local for Ubuntu 22.04 for instance. Your install may vary depending on Linux distribution, make sure to select appropriate options.
  • Most problems arise from improper driver or cuda install and will not successfully run nvidia-smi (various errors like command not found or driver mismatch). Make sure nvidia-smi runs and prints your GPU information before continuing.
  • Install git with sudo apt-get git
  • Suggested: Enable git lfs support, follow instructions: https://github.com/git-lfs/git-lfs/blob/main/INSTALLING.md
  • Optional: Enable remote desktop and/or SSH (see instructions for your distribution)
  • Optional if you'd rather use conda instead of VENV: Install miniconda, wget the appropriate bash script then run it: https://docs.conda.io/en/latest/miniconda.html#linux-installers

ED2 setup

  • git clone https://github.com/victorchall/EveryDream2trainer
  • cd EveryDream2trainer
  • python3 -m venv venv
  • source venv/bin/activate

At this point python -V should return 3.10 and which python should return something like /home/username/EveryDream2trainer/venv/bin/python so you can just use python to run instead of python3 if you like. YMMV based on distribution.

  • pip install -r requirements.txt

Should be good to go.

Conda should be something like this (untested)

  • git clone https://github.com/victorchall/EveryDream2trainer
  • cd EveryDream2trainer
  • conda create --name ed2 python=3.10
  • conda activate ed2
  • pip install -r requirements.txt

Ensure BitsandBytes can find CUDA

Bitsandbytes (AdamW8Bit, etc) needs to find the location of your CUDA libraries. It attempts to find it in a few locations, but this likely will require an extra step. This may vary slightly based on distribution.

Find the cuda library location: find / -name libcudart.so* 2>/dev/null

example result: /home/freon/EveryDream2trainer/venv/lib/python3.10/site-packages/nvidia/cuda_runtime/lib/libcudart.so.11.0 /home/freon/ml-ed2/venv/lib/python3.10/site-packages/nvidia/cuda_runtime/lib/libcudart.so.11.0

Set path hint for bitsandbytes (should work with either example above) export LD_LIBRARY_PATH=/home/freon/EveryDream2trainer/venv/lib/python3.10/site-packages/nvidia/cuda_runtime/lib You may wish to set this up as part of a startup script such as your ~/.bashrc to make sure it is set on every login.