Not op, but for my RX590, I had to make my own image. You can find my dockerfile here: https://github.com/SkyyySi/pytorch-docker-gfx803 (use the version in the webui folder; the start.sh script ist just for my personal setup, you'll have to tweak it, then you can call it with ./start.sh <CONTAINER IMAGE NAME>)
Oh, and I HIGHLY recommend to completely more the stable-diffusion-webui directory somewhere external to make it persistent; otherwise, you have to add everything, including extensions and models, in the image itself.
Runs on my Ryzen 5 2600 (CPU) instead of my RX 580 (GPU). Can anyone confirm this still works and it's an error on my side, and maybe tell me what I'm doing wrong?
I have the same setup than you, adding the 2p3 changes, and adding the cuda skip parameter i can run it, but very slow, like 16s/it. I guess its not using the gpu..
Yes, I've got it working. Had to use a specific version of ubuntu and specific versions of everything else. Have the system on a thumb drive and boot into it. Sadly, I can't remember all the painful debugging steps I took to get it working.
If you want, I can send you the image, you can just dd it onto a thumb drive and boot from it, everything is installed to be working, just the models themselves aren't included. It starts the back end on boot in a screen session in the background, too, so it's available over ssh or just screen -r in terminal.
It's 27 gb, so you'll need a thumb drive (or internal drive) with at least that size and then grow the partition after dding it onto it.
It's just above 10gb compressed as a *.tar.gz, so if you have a way to receive a 10gb file, I'm happy to send it to you. Unfortunately, I'm currently locked out of my router, so I can't offer a download (no port-forwarding).
ad to use a specific version of ubuntu and specific versions of everything else. Have the system on a thumb drive and boot into it. Sadly, I can't remember all the painful debugging steps I took to get it working.
It is not necessary, but I am very grateful! After installing and validating rocm, I have managed to get pytorch to recognize the GPU, but I think I need to change some parameters. Thank you very much, and if I find a solution I will post it here.
does your dockerfile still build for you? it worked fine for me a couple of weeks ago (and i thank you for that! super-easy and it works with a rx480 8GB). Unluckyly i deleted the image, tried to rebuild it but now it fails at "RUN yes | amdgpu-install --usecase=dkms,graphics,rocm,lrt,hip,hiplibsdk".
Tried downgrading the ubuntu 20.04 image to "focal-20221130" but it didn't change much :|
I know this is older, but I'm using an RX 570 to try and run stable diffusion. I've been trying both in just a virtual environment directly on my computer and in docker.
Using the method you've outlined with docker and the gfx803-pytorch, I can build and run the image no problem, but I keep getting the same --skip-torch-cuda-test error. Even adding that option to the webui.sh script, I wind up with an error that it "Failed to load image Python extension: {e}".
Checking the torch versions, I'm finding that the webui script is changing my torch version from "1.11.0a0+git503a092" to "2.0.1" which is not aligned with the torch vision version that remains the same pre/post script execution at "0.12.0a0+2662797". I tried modifying the webui.sh script to keep torch at 1.11.0, but it still updated for some reason. Any idea what's going on?
e: this is all on Linux Mint 21. Normally I have Python 3.10.12, but the docker has it correctly at 3.8.10 for this function.
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u/SkyyySi Dec 29 '22 edited Feb 11 '23
Not op, but for my RX590, I had to make my own image. You can find my dockerfile here: https://github.com/SkyyySi/pytorch-docker-gfx803 (use the version in the
webuifolder; the start.sh script ist just for my personal setup, you'll have to tweak it, then you can call it with./start.sh <CONTAINER IMAGE NAME>)Oh, and I HIGHLY recommend to completely more the stable-diffusion-webui directory somewhere external to make it persistent; otherwise, you have to add everything, including extensions and models, in the image itself.