rocm-gfx803 VS stable-diffusion-webui-docker

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.

I also use the Stable Diffusion WebUI Docker, I found it really easy to set up.

I'm currently using this docker setup: https://github.com/AbdBarho/stable-diffusion-webui-docker

I used the Docker install from here. It was easy to get Automatic1111's web interface up and running, but I'm trying to add new models and I can't figure out how to do it.

Docker ftw

I'm using A1111 via https://github.com/AbdBarho/stable-diffusion-webui-docker which was such a perfectly simple way to get set up, and I've been having an absolute blast. Turns out that for the most part I do not even need to leverage the 24GB vram on my 3090.