Stable Diffusion AMD guide

Docker guide

Using Docker seems to be the easiest solution, and it works on any OS.

1. Install Docker. https://docker.com
2. Go to https://hub.docker.com/r/rocm/pytorch/tags
3. Pull the latest image, and run "the master test".
4. If it works, great! Follow the tard guide inside the running image.
5. If it fails with hipErrorNoBinaryForGpu: You'll have to compile PyTorch for your GPU. See the corresponding section. If you're on Arch, try the unofficial repository method.

Manual guide (Linux only)

Arch (unofficial repository)

Add the arch4edu repository as per https://wiki.archlinux.org/title/unofficial_user_repositories#arch4edu.
Now:

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sudo pacman -S python-pytorch-opt-rocm python-torchvision
git clone https://github.com/kali-linex/arch-fmt8.git
cd arch-fmt8 && makepkg -si && cd .. && rm -rf arch-fmt8
cd /wherever/you/want/to/put/the/stable/diffusion/repo
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui
python3 -m venv --system-site-packages venv

Add export MIOPEN_DEBUG_COMGR_HIP_PCH_ENFORCE=0 to webui-user.sh.
Run ./webui.sh.
It should work now. If it has very bad performance (About 1-1.5 it/s expected for RX580 8GB, newer cards will be faster), webui.sh might have installed its own pytorch and torchvision. Run these commands (bash or zsh):

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source venv/bin/activate
if [ "$(python -c 'import torch; print(torch.cuda.is_available())')" = False ]; then pip uninstall pytorch torchvision; echo "Uninstalled virtualenv torch & torchvision; should be good now"; else echo "Something else is wrong or your GPU is just slow"; fi

Arch (compiling from source)

1.) Install rocminfo so we can set AMDGPU_TARGETS, which will slightly speed up compile times.
run: rocminfo | grep Name
You are looking for gfx####, then followed by your gpu on the next line. Once you have it do:
export AMDGPU_TARGETS="gfx####

2.) Install Rocm on Arch using this command:
paru -S rocm-hip-sdk rocm-opencl-sdk I remember having issues with yay while running this, I would play it safe and use paru.
This will take a long time to compile, pray that it actually finishes. If you run out of ram during compiling, you will have to adjust Ninja flags.

Universal

3.) Load in to the environment
conda activate ldm

4.) Go to torch's website
Scroll down to the Install Torch prompt.
Choose pip for Package
ROCm x.x.x for Compute Platform
Copy the command at the bottom.
Run the command, or edit the environment file if you know how. I don't know how to use links in them.
This should work for KoboldAI too.

I was having issues crashing randomly when running the optimized txt2img.py, but txt2img_gradio.py, (and I'm guessing the normal txt2img.py) seems to be stable, just can't generate 512x512. I do 384x384 and that doesn't give out of memory errors.
Takes around 40 seconds with the gradio script after the first gen, at 70 sampling steps.

The master test

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import torch
print(torch.cuda.is_available())
a = torch.tensor([1., 2.], device='cuda')
b = torch.tensor([3., 4.], device='cuda')
print(a + b)

If ROCm supports your GPU and everything is working fine, you should see the following output or similar:

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True
tensor([4., 6.], device='cuda:0')

Now, this can fail in multiple ways (perhaps even more, these are documented as of now):

• Outputs False on the first line, throws an exception on the second: Either ROCm or the ROCm-compatible PyTorch wasn't installed correctly.
• hipErrorNoBinaryForGpu: compile PyTorch from source. See below
• Segmentation fault (core dumped) - Uh oh. TODO documentation
  6.) Running python scripts/txt2img.py or any of the other scripts depending on your vram should now work and use the gpu.

Building PyTorch from source

On Arch

PYTORCH_ROCM_ARCH=gfx#### paru -S python-pytorch-rocm. Take the gfx#### value from the first step of the manual guide. This will install PyTorch globally, if you (most likely) want to put it in a local environment, follow the general guide.

Into a conda environment

1. Clone (or download) https://github.com/pytorch/pytorch
2. Activate the environment. conda activate ldm/ldx/ldo
3. Navigate to the pytorch repo. Check out whatever release/bleeding edge branch you want to install.
4. export PYTORCH_ROCM_ARCH=gfx####
5. python tools/amd/build_amd.py
6. export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"} && python setup.py install This step might fail on some errors. Google them and patch them, it depends on your distro and compiler version. For Arch users, here is a github issue with a collection of common errors.
7. You're done! Run the master test to ensure everything is working.

Troubleshooting

If you get a screenful of seemingly random HIP compilation errors once trying to generate an image (either in the webui or the bare scripts), export MIOPEN_DEBUG_COMGR_HIP_PCH_ENFORCE=0.

Relevant links

• https://github.com/RadeonOpenCompute/ROCm_Documentation/blob/master/Deep_learning/Deep-learning.rst Has a guide about compiling PyTorch for a specific graphics architecture.
• https://github.com/rocm-arch/python-pytorch-rocm/issues/44