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PyTorch and Stable Diffusion on FreeBSD

This tutorial was written on FreeBSD 13.1.

Table of Contents

Notes

For now this only covers those with an NVIDIA card with CUDA support.

You must have the Linuxulator installed for any of this to work. It has only been tested with the default CentOS from ports.

This does not use the official Stable Diffusion repository but the WebUI written by and located at AUTOMATIC1111.

If you wish to install the official repository none of the contents in this documentation should change other than checking out the official repository.

Verify and setup CUDA

This involves two steps the first is to install nv-sglrun in order to check for CUDA support which only works for FreeBSD binaries. The second step is to build uvm_ioctl_override.c to have the same work for Linux binaries.

This work has been done by shkhln you can see his work here libc6-shim. We will not use this directly but the version used for Linux binaries below.

Install libc6-shim to get the nvidia-sglrun binary.

  # pkg install libc6-shim

Note This requires both nvidia-driver and linux-nvidia-libs

Check for support using nvidia-smi. Here's a good writeup on what this utility displays Explained Output of Nvidia-smi Utility.

  # nvidia-smi
  Mon Nov 21 15:25:35 2022
  +-----------------------------------------------------------------------------+
  | NVIDIA-SMI 525.53       Driver Version: 525.53       CUDA Version: N/A      |
  |-------------------------------+----------------------+----------------------+
  <snip>

Now use it with nvidia-sglrun

  # nv-sglrun nvidia-smi
  shim init
  Mon Nov 21 15:25:37 2022       
  +-----------------------------------------------------------------------------+
  | NVIDIA-SMI 525.53       Driver Version: 525.53       CUDA Version: 12.0     |
  |-------------------------------+----------------------+----------------------+
  <snip>

It now says 12.0 for CUDA version instead of N/A. Ignore the driver version the one in ports will work just fine.

Also note the shim init which gives you an indication that the shim is running.

The next part is to build the shim required for Linux binaries located at uvm_ioctl_override.c also written by shkhln.

First install linux-c7-devtools.

  # pkg install linux-c7-devtools

Next get uvm_ioctl_override.c.

  # fetch https://gist.githubusercontent.com/shkhln/40ef290463e78fb2b0000c60f4ad797e/raw/f640983249607e38af405c95c457ce4afc85c608/uvm_ioctl_override.c

Build.

  # /compat/linux/bin/cc --sysroot=/compat/linux -m64 -std=c99 -Wall -ldl -fPIC -shared -o dummy-uvm.so uvm_ioctl_override.c

This will be tested later as the Linux nvidia-smi works due to the Linux NVIDIA libraries.

Initial setup

There are two steps for the initial setup. Installing missing packages and choosing a path.

Packages

You will need to install the following packages:

Package name Purpose
linux-c7-libglvnd libGL.so.1

Binaries

Starting in FreeBSD 13.1 md5sum exists at /sbin/md5sum. The installer requires this binary. A quick and relativly safe solution to this is to add a link to ~/bin for example:

  # mkdir -p ~/bin
  # ln -sf /sbin/md5 ~/bin/md5sum

Ensure that ~/bin is in your $PATH. If you are on FreeBSD 13.1 or newer you do not need to do this.

Installation Path

Pick a path where you want to install everything. To keep things easy we'll install everything under the same path and use ${BASE_PATH} going forward.
This is used for demonstration purposes in this document and not meant to be applied anywhere.

Example.

  # BASE_PATH="/path/to/somewhere/"
  # mkdir ${BASE_PATH}
  # cd ${BASE_PATH}
  1. When we change a directory assume all the following commands are run under that directory unless we change again.
  2. '#' is a shell login does not reflect being root.

Conda

Install miniconda from ports using pkg to install Linux Conda.

  # pkg install linux-miniconda-installer
  # miniconda-installer
  1. Review license and accept license with yes.
  2. For a path enter ${BASE_PATH}/conda
    • Example /path/to/somewhere/conda
  3. When it asks to run conda init select no.
  4. Yes to remove Miniconda3-latest-Linux-x86_64.sh.
  5. Choose got it.

Warning If you hit 'yes' to run conda init or run it later it will add a code block to ~/.bashrc that starts with >>> conda initialize >>>.

Now let's activate Conda.

First let's activate our environment. We need to use BASH here most SH scripts are written in BASH. There can be issues running some SH scripts it's not worth the trouble.

  # bash
  # source ${BASE_PATH}/etc/profile.d/conda.sh
  # conda activate
  (base) #

The (base) lets us know we are in the base environment.

PyTorch

Follow this if you are looking to use PyTorch. If you want to use Stable Diffusion jump to the next section.

To keep things organised and not have to worry about versions we'll create a dedicated environment. All subsequent commands will be run in this environment.

Create a new environment. This may give a warning about updating Conda you can ignore this and update later if you wish by following the command suggested after activating Conda as above. We will call it pytorch as denoted by the --name argument. After creation we will activate it to install PyTorch into it.

Note Make sure you hit y when it asks you to proceed

  (base) # conda create --name pytorch python=3.10
  (base) # conda activate pytorch

Let's make sure pip is in the right location.

  (pytorch) # which pip
  ${BASE_PATH}/conda/envs/pytorch/bin/pip

Note the (pytorch) in brackets.

This installs the PyTorch distribution with CUDA.

  # pip install torch==1.12.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113

Let's see if it works

  # python3 -c 'import torch; print(torch.cuda.is_available())'
  ${BASE_PATH}/conda/envs/sd/lib/python3.9/site-packages/torch/cuda/__init__.py:83: 
UserWarning: CUDA initialization: CUDA unknown error - this may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero. (Triggered internally at  ../c10/cuda/CUDAFunctions.cpp:109.)  return torch._C._cuda_getDeviceCount() > 0
  False

Now let's try using the shim.

  (pytorch) # LD_PRELOAD="${BASE_PATH}/dummy-uvm.so" python3 -c 'import torch; print(torch.cuda.is_available())'
  True

  (pytorch) # LD_PRELOAD="${BASE_PATH}/dummy-uvm.so" python3 -c 'import torch; print(torch.cuda.get_device_name(0))'
  NVIDIA GeForce RTX 3060

You're free to install Stable Diffusion as well it will use its own env.

Stable Diffusion WebUI

Next we'll install Stable Diffusion This will use a different environment if you installed PyTorch above. It's better to use separate environments so you can avoid version clashing.

Note If you installed PyTorch above it's safe to run conda activate here.

  # conda activate
  (base) # git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui

Note Here is where you will switch the repository for https://github.com/CompVis/stable-diffusion If you want the original program. Swap stable-diffusion-webui for stable-diffusion. You will also need to use environment.yaml instead of environment-wsl2.yaml used below.

  (base) # cd stable-diffusion-webui
  (base) # conda env update --file environment-wsl2.yaml --prune

You can use this command to update packages when required. The --prune command will remove any old packages that aren't used which is very useful for debugging.

Let's switch to the automatic env now.

  (base) # conda activate automatic
  (automatic) # which python3
  ${BASE_PATH}/conda/envs/automatic/bin/python3

Great.

Models

Signup and download the 1.5 Model from Hugging Face

Download either v1-5-pruned-emaonly.ckpt or v1-5-pruned.ckpt depending on which you want to use and your amount of VRAM.

  (automatic) mv sd-v1-4.ckpt ${BASE_PATH}/stable-diffusion-webui/models/Stable-diffusion/model.ckpt

model.ckpt is the default model that will load. You can have as many as you want and load them via the commandline.

Now to install Git to allow launch.py to install dependencies.

  (automatic) # conda install git

Next step is to run launch.py

  (automatic) # LD_PRELOAD=${BASE_PATH}/dummy-uvm.so  python3 launch.py

You will see some output as it installs required dependencies.

Commit hash: 828438b4a190759807f9054932cae3a8b880ddf1
Installing gfpgan
Installing clip
Cloning Stable Diffusion into repositories/stable-diffusion...
Cloning Taming Transformers into repositories/taming-transformers...
Cloning K-diffusion into repositories/k-diffusion...
Cloning CodeFormer into repositories/CodeFormer...
Cloning BLIP into repositories/BLIP...
Installing requirements for CodeFormer
Installing requirements for Web UI
Launching Web UI with arguments: 
${BASE_PATH}/conda/envs/automatic/lib/python3.10/site-packages/psutil/_pslinux.py:513: RuntimeWarning: shared, active, inactive memory stats couldn't be determined and were set to 0  warnings.warn(msg, RuntimeWarning)
LatentDiffusion: Running in eps-prediction mode
DiffusionWrapper has 859.52 M params.
making attention of type 'vanilla' with 512 in_channels
Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
making attention of type 'vanilla' with 512 in_channels
Loading weights [7460a6fa] from ${BASE_PATH}/stable-diffusion-webui/models/Stable-diffusion/model.ckpt
Global Step: 470000
Applying cross attention optimization (Doggettx).
Model loaded.
Loaded a total of 0 textual inversion embeddings.
Embeddings: 
Running on local URL:  http://127.0.0.1:7860

To create a public link, set `share=True` in `launch()`.

That's it! You can browse to http://127.0.0.1:7860 and use the WebUI as you wish!

The launch.py command takes quite a few arguments you can find by running it with --help.

Submitting requests or changes

Feel free to open a ticket with any requests or suggested changes. Pull requests are also welcome.

This document was rendered locally using the excellent instant parser Grip.