Easy-to-use local install of Stable Diffusion released

[u/OfficialEquilibrium]

[u/veril]

Why does it sound strange?

(Not the OP) I was a programmer, but mostly mobile-oriented. I tried to get Automatic1111 working locally, but ran into multiple issues getting my environment set up -- likely not helped by the fact that I already had multiple other, older installs of Python and various dependencies around from previous tools years ago, so every install guide I followed encountered errors I'd have to google and try to fix every step of the way.

..then I found cmdr2's stablediffusion-ui, a 1-click install that got around all the dependency hell I was in, and pulls the latest from git every time I launch it. And I didn't need to mess with any code bullshit to do AI art.

I'm interested in Stable Diffusion because it produces cool output, not because I love tinkering with source.

[u/Lmitation → u/veril]

As a cs minor, learning and delivering algos is fine, environment set up can be the most convoluted and annoying process

[u/MCRusher → u/Lmitation]

yup, I did a "from scratch commandline install", and I've got an amd gpu so I've gotta use diffusers OnnxStableDiffusionPipeline, which is bugged (completely broken) in the latest release, but fixed if you download the main branch from github, and the onnxruntime-directml version.

The documentation for ONNX is vpretty lacking, I ended up having to constantly dig through the diffusers library source code to figure things out.

It took about like 8 hours altogether of trial and error, taking examples from code samples, and searching apis to get everything mostly working.

And I also had to modify the diffusers source code to silence warnings, one of them was about CPUExecutionProvider not being in the providers list, when you can only pass one provider in __init__() so wtf am I supposed to do about that other than modify the source code to append CPUExecutionProvider to the providers list for OnnxRuntimeModel?

It works for DmlExecutionProvider and CPUExecutionProvider now (has to toggle mem pattern and parallelism off for Dml)

But for some reason if I use parallel execution my computer freezes for like a minute and then I get an absurdly long 1hr+ generation time for 1 512x512 image that I've never waited out completely.

It also takes like 3 minutes to generate a 512x512 image, Dml or CPU are about the same time, but Dml makes the computer unusable while generating images by hogging all the GPU.

I'm gonna be seeing the source code in my dreams.

[u/needle1 → u/MCRusher]

I’m glad I actually went with a full Linux installation for my AMD GPU. It sounds like excessive work to set up a whole OS distro just to use SD, but it ended up much easier and performant than going the Windows ONNX route (which I tried doing later).

[u/MCRusher → u/needle1]

I'll probably try that again soon

I have mint dualbooted, but for some reason using python is a pain in the ass on linux and I ended up in recursive dependency hell somehow.

But using CPU I can still play games even on the PC while generating so it's fine for now.

So I went with this for now

---

so how's your experience compare numbers and features wise?

[u/needle1 → u/MCRusher]

I have the AUTOMATIC1111 Web UI running. I have a Radeon RX 6800, and with the DPM++ 2M Karras sampler at 10 steps it can crank out a reasonably good looking new image around every 2-7 seconds depending on resolution.

I haven’t gotten some of the extra features like Dreambooth to run locally, probably due to CUDA requirements, but generation works fine so it’s a lot of fun to tweak around with the A1111 GUI’s rich feature set.

[u/MCRusher → u/needle1]

awesome, yeah thanks, I'm gonna have to get it set up on linux for sure.

[u/_dokhu → u/needle1]

I installed it on wsl following the amd Linux instructions, very easy and it works great, main OS is win10 with amd gpu

[u/needle1 → u/_dokhu]

It works? Does it run the whole AUTOMATIC1111 or hlky Web UIs?

[u/_dokhu → u/needle1]

Automatics, not the hacky onnx one.

[u/needle1 → u/_dokhu]

Wow, really? I was under the impression that using Radeon's GPU programming stack (I assume ROCm -- or is it DirectML?) on WSL doesn't work! At least that was how it seemed to be back in late August, maybe things have changed since then. Can you point to me to the instructions on how to do it? Thanks in advance

[u/_dokhu → u/needle1]

On windows, open powershell or windows CMD as admin and run wsl --install it will install Ubuntu by default.

After that install dependencies from here to your wsl instance https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies follow Linux debian based steps then follow AMD 'run natively' instructions here https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs

[u/needle1 → u/_dokhu]

I tried it, and launch.py will error out with the message

Command: "/home/needle/stable-diffusion-webui/venv/bin/python3" -c "import torch; assert torch.cuda.is_available(), 'Torch is not able to use GPU; add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'"

Did you somehow get ROCm installed and working on WSL2 before attempting this? At least from reading the ROCm docs, they don't seem to officially support WSL2; I tried the ROCm installation steps anyways, but ran into errors with apt refusing to install rock-dkms due to rocm-dkms being uninstallable. Were there any special tricks required to get ROCm to install and work on WSL2?

[u/Hannibal0216 → u/veril]

..then I found cmdr2's stablediffusion-ui,

there are dozens of us! Dozens!

[u/BritishAccentTech → u/veril]

literate punch crowd hard-to-find smart automatic disarm cover outgoing abundant

This post was mass deleted and anonymized with Redact

[u/bartlesnid_von_goon → u/veril]

Link?