earlyoom VS latent-diffusion

> The system is not supposed to 'lock up' when you run out of physical RAM. If it does, something is wrong. It might become slower as pages are flushed to disk but it shouldn't be terrible unless you are really constrained and thrashing. If the Kernel still can't allocate memory, you should expect the OOMKiller to start removing processes. It should not just 'lock up'. Something is wrong.

I don't why but locking up is my usual experience for Desktop Linux for many years and distros, and I remember seeing at least one article explaining why. The only real solution is calling the OOMKiller early either with a daemon or SysRq.

> It should not take minutes. Should happen really quickly once thresholds are reached and allocations are attempted. What is probably happening is that the system has not run out of memory just yet but it is very close and is busy thrashing the swap. If this is happening frequently you may need to adjust your settings (vm.overcommit, vm.admin_reserve_kbytes, etc). Or even deploy something like EarlyOOM (https://github.com/rfjakob/earlyoom). Or you might just need more RAM, honestly.

Yeah. Exactly. But as the thread says, why aren't those things set up automatically?

It's not that simple: some defaults may differ, and some features may arrive at different times (if ever). For example, earlyoom has been enabled on Workstation since F32, but the KDE Plasma spin got it one release later.

Most answers will answer your question, but you can do better and avoid the freezes in the first place. IME almost every time the system froze up and didn't come back in a few seconds it was out of memory. The obvious solution is to add memory, but you can use Early OOM to kill hungry processes if you're running out of memory instead.

It sounds like you're running out of memory though, so if your OS's OOM killer isn't working as well as it should, you can try earlyoom as an alternative.

Swap is indeed supposed to prevent this AFAIK. You can though try some tools like EarlyOOM and see if it helps : https://github.com/rfjakob/earlyoom

Stable Diffusion XL (SDXL) is the latest text-to-image generation model developed by Stability AI, based on the latent diffusion techniques. SDXL has the potential to create highly realistic images for media, entertainment, education, and industry domains, opening new ways in practical uses of AI imagery.

Here's a link to the early latent-diffusion git, that might be able to create a blank model (I haven't tested it): https://github.com/CompVis/latent-diffusion

Latent diffusion could work for you: https://github.com/CompVis/latent-diffusion (https://arxiv.org/abs/2112.10752)

There are a lot but the one implemented as LDSR in most stable guis is this one. https://github.com/CompVis/latent-diffusion

CompVis/latent-diffusion: High-Resolution Image Synthesis with Latent Diffusion Models (github.com)

The "retrieval based model" refers to https://github.com/CompVis/latent-diffusion#retrieval-augmen..., which uses ScaNN to train a knn embedding searcher.

Stability is basically https://github.com/CompVis/latent-diffusion + training data.

Found relevant code at https://github.com/CompVis/latent-diffusion + all code implementations here

DDIM and PLMS were the original samplers. They were part of Latent Diffusion's repository. They stand for the papers that introduced them, Denoising Diffusion Implicit Models and Pseudo Numerical Methods for Diffusion Models on Manifolds.

yes, https://github.com/CompVis/latent-diffusion