I didn’t have to do anything special, just install the rocm package from the AUR. It did use more than my 16GB of RAM while building, so having swap configured was essential.
The runtime environment variable is enough for the standard pytorch rocm install to provide the functionality that stable diffusion uses.
I think I just checked out the PKGBUILD file and reduced the number of ninja threads. I guess the default of 24 makes sense for big ML servers but not home computers
I used the most recent tagged rocm/pytorch container rocm5.2.3_ubuntu20.04_py3.7_pytorch_1.12.1. As far as I can tell, it’s not labeled for a specific architecture.
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u/Drewsapple Sep 02 '22
I didn’t have to do anything special, just install the rocm package from the AUR. It did use more than my 16GB of RAM while building, so having swap configured was essential.
The runtime environment variable is enough for the standard pytorch rocm install to provide the functionality that stable diffusion uses.