Messing around with system python/pip and newly installed versions till all was broken and then looking at documentation.
This was way back on the 00’s and I’m still ashamed on how fast completely I messed it up.

Just figured out there are 10 places called Lisbon dotted around the US, according to the search.

Me with four open cli terminals righ now:
https://i.kym-cdn.com/photos/images/original/001/617/650/91a.jpg

Got one more for you: https://gossip.ink/
I use it via a docker/podman container I’ve made for it: https://hub.docker.com/repository/docker/vluz/node-umi-gossip-run/general

I got cancelled too and chose Hetzner instead. Will not do business with a company that can’t get their filters working decently.

Lovely! I’ll go read the code as soon as I have some coffee.

I do SDXL generation in 4GB at extreme expense of speed, by using a number of memory optimizations.
I’ve done this kind of stuff since SD 1.4, for the fun of it. I like to see how low I can push vram use.

SDXL takes around 3 to 4 minutes per generation including refiner but it works within constraints.
Graphics cards used are hilariously bad for the task, a 1050ti with 4GB and a 1060 with 3GB vram.

Have an implementation running on the 3GB card, inside a podman container, with no ram offloading, 1 vcpu and 4GB ram.
Graphical UI (streamlit) run on a laptop outside of server to save resources.

Working on a example implementation of SDXL as we speak and also working on SDXL generation on mobile.
That is the reason I’ve looked into this news, SSD-1B might be a good candidate for my dumb experiments.

Oh my Gwyn, this comment section is just amazing.

Goddammit! Don’t tell that one, I use it to impress random people at parties.

HateLLM will be a smash. /s

Well done!

That’s wonderful to know! Thank you again.
I’ll follow your instructions, this implementation is exactly what I was looking for.

Absolutely stellar write up. Thank you!

I have a couple of questions.
Imagine I have a powerful consumer gpu card to trow at this solution, 4090ti for the sake of example.
- How many containers can share one physical card, taking into account total vram memory will not be exceeded?
- How does one virtual gpu look like in the container? Can I run standard stuff like PyTorch, Tensorflow, and CUDA stuff in general?

Don’t trust brave, never will.

If at all true this would be world-changing news.

I use this: https://cloudhiker.net/explore

DS1 to DS3, I lost count of the hours.