rocm-gfx803 VS stable-diffusion-cpu

That specific card has current support for rocm and that is supported by at least tensorflow and torch, plus many other less known/used libraries like cupy, although you are correct in the fact that support sucks in the long run, I have a GPU that is known to be useful and that has continued COMMUNITY support because AMD cut the support with rocm 4.0, thanks to Xuhuisheng for the patch to make the rx580 work with current rocm despite AMD lack of support, what open source can accomplish https://github.com/xuhuisheng/rocm-gfx803

You'll also need to compile pytorch and torchvision for gfx803, although I recommend you install the whl files from here inside your venv because it's a massive pain to compile them on non-Ubuntu (I tried)

I followed the guide from here: https://github.com/xuhuisheng/rocm-gfx803

There is an unofficial build of ROCm 5.2.0 + pytorch + torchvision with GFX8 support added back in. I have no idea if it works. Perhaps someone who knows Docker/Conda could get SD working with those files.

Created with stable-diffusion-cpu on an HP Proliant server, CPU-only, dual Xeon, 16c/32t.

I found this repo early on and have been using it to run inference on my M1 Pro MBP. https://github.com/ModeratePrawn/stable-diffusion-cpu

For me it runs at about 3.5 seconds per iteration per picture at 512x512.

There is also a fork that uses metal here and is much faster: https://github.com/magnusviri/stable-diffusion/tree/apple-si...

Download the code from the Github repo https://github.com/ModeratePrawn/stable-diffusion-cpu and unzip it. Open it on an editor (e.g. VS Code)

For CPU, try: https://github.com/ModeratePrawn/stable-diffusion-cpu