I thought I would share my experience this evening with the group here seeing that I still excited as hell for getting this hodgepodge to work at all.
I have been playing with the machineMl version of stable diffusion on Windows for a while now (we won’t go into the reasons why, but I have the 6800XT, which is not well suited to this use case)
Automatic11111 on MachineML is dog slow, but I got some interesting results. So today I cleared out an old SSD, wired it up and installed a clean ubuntu. Following this guide I managed to get rocM running and the difference is like chalk and cheese. Or rather Impulse and Warp drive. Totally crazy!
So for you AMD Radeon users out there. There is hope. It is in Linux, but it is there.
Indeed. I’m in complete awe by this technology. It’s an amazing pass-time that tickles the creative side. As for getting an idea of how different cards and system compare, you can check out https://vladmandic.github.io/sd-extension-system-info/pages/benchmark.html
I also have an 6800 XT, and the performance on that particular benchmark is around 9 it/s. Something like this looks to be a rough indication.
AMD Cards:
NVIDIA Cards:
Included my current numbers, any optimisation advice would be much appreciated 😉
I think those numbers are roughly the same I get. It varies a bit from time to time. I also wouldn’t know how to improve it, to be honest.
I managed to find an extra iteration or two without sacrificing to much stability.
6.06 / 7.59 / 9.11
Running with Doggettx selected as the optimiser in the optimiser config inside automatic1111.
I installed the google perftools as suggested in this thread
“sudo apt install libgoogle-perftools-dev”
And then added the following memory management options as suggested in this thread
by exporting: “export PYTORCH_CUDA_ALLOC_CONF=garbage_collection_threshold:0.6,max_split_size_mb:128”
Made the following changes to my web-user.sh
Uncommented the command line options as follows:
export COMMANDLINE_ARGS=“–medvram --upcast-sampling”
And added the following lines to the end of the file
export LD_PRELOAD=libtcmalloc.so
export PYTORCH_CUDA_ALLOC_CONF=garbage_collection_threshold:0.6,max_split_size_mb:128
Those are some great notes, thanks for sharing.
No problem, backup and be careful. I think AMD still has a lot that can be done in the rocm drivers themselves. There should be gains left to make. Nvidia is not helping with their pricing either. Which should see more users on AMD. Hopefully better support for us. I am pleasantly surprised by what the card can do. I got it for gaming at 1440p, where it was the best bang for buck. The AI stuff is a cool bonus.
I run the tests out of interest. I am leaving some performance on the table due to my launch options, but I need those to avoid to many out of memory and other errors.
Normal Test:
Extensive Test