StabilityMatrix VS automatic

On Windows, StabilityMatrix (https://github.com/LykosAI/StabilityMatrix) is a very easy way to get any (or all) of those wrappers installed without conflicts.

do you have a good pc/laptop with a good GPU? is so start with this. A1111 WebUI is no longer being updated so heres a new one https://github.com/LykosAI/StabilityMatrix/ this site you can download checkpoints and loras you have to sign up (its free, and once you do that click on the eye and click everything) https://civitai.com/ you can get prompts from this site (use the search) https://playgroundai.com/ Edited: took out the direct link out and reposted

I then thought, well I just do a new fresh install of A1111. I did 3 different fresh installs and did an install with https://github.com/LykosAI/StabilityMatrix as a portable installation to try and eliminate any outside interference. No luck all installs make pictures horrible, I mean bad, even the portable installation.

If you have a computer powerful enough to run SD, you can install one of the "software" from Stable Diffusion > Local install, the most popular ones are A111, Vlad and comfyUI (but I would advise to start with the first two, as comfyUI may be too complex at the begining). You can install them via StabilityMatrix ; it's a software that manage the installation of the popular webui, their requirements, and their versions; if you're afraid to manage the installation of pyton/miniconda/other things required to run it. NMKD is also a good alternative, as it should work right out of the box (it can even download the base model, if I remember correctly). If you haven't a computer good enough, look at Stable diffusion > Online Websites.

I'd install either via https://github.com/LykosAI/StabilityMatrix or https://civitai.com/models/27574/super-easy-ai-installer-tool.

You can view our source code on our GitHub

Update v1.1 of Stability Matrix has arrived!

> it won't ever be a viable option

For production workloads, I generally agree. It's an unsupported hack with a questionable future, I wouldn't do anything money-making with it.

However, for tinkering and consumer workloads, it already works pretty well. Enough of cuDNN and cuBLAS work to run PyTorch and in turn, Stable Diffusion with https://github.com/lshqqytiger/ZLUDA - there's even a fairly user-friendly setup process already in https://github.com/vladmandic/automatic .

I was able to get a personal non-ML related project working on my AMD card in just a few minutes, which saved me a lot of development time before I then deployed the production workload on NV hardware (this is probably why AMD pulled the plug on the project - it's almost more of a boost to NV than anything else, AMD really need people to be writing code on ROCm to deploy on AMD datacenter hardware).

While I currently use SD.Next[1], I have tested ComfyUI locally with my AMD card. The UI can be daunting, but you learn quite a great deal about how a Stable Diffusion pipeline works. In addition some innovations and advances find their way into ComfyUI first.

[1] https://github.com/vladmandic/automatic

I used SD.NEXT ( https://github.com/vladmandic/automatic ) and https://civitai.com/models/82098/add-more-details-detail-enhancer-tweaker-lora and epicphotogasm_lastUnicorn

I found this thread on GitHub talking about how this was fixed in the latest version with an optional setting. I tried enabling it, as they mentioned, but it just resulted in an immediate CUDA out of memory error when starting generation. So it seems I'm actually needing the shared memory, which I assume is my issue.

As always, do check out our more detailed changelog, give us a quick install from our Repo, and stop by our Discord Server for any questions or help you may need.

SD.Next

Today I finally wanted to give SDXL a chance, so I set everythin up according to Vladmandic's Wiki https://github.com/vladmandic/automatic/wiki/SD-XL

I'm trying to get SDXL working on Vlad's SDNext, but I keep getting the error in the title when trying to run basic operations. I'm not sure what's going on, I followed his guide for it to a T.

We used an inference container based on SDNext, along with a custom worker written in Typescript that implemented the job processing pipeline. The worker used HTTP to communicate with both the SDNext container and with our batch framework.