DrNeurohax

Generally, if someone’s being a total asshole so severely that they have to be yeeted with several thousand other unaware bystanders, I expect to see a bunch of examples within the first… 2, maybe 3, links.

If someone can point me to a concise list of examples (actual data), I find it more disturbing that an admin on another server can yeet my account because they make noise on a discord server.I mean, yes, federating is a feature, but why even offer the ability to enroll users? Maybe for a group of friends, or something, but just rando users is nothing but a liability to everyone involved.

I got around 5 links deep for each of the links in the admin’s post, and fuck if I know. There was an argumentative user, but they were articulate and thoughtful. Not dropping slurs or wasting space nonsense, but still bordering on “edgy”. The person pushing the defederation appeared to be bullying them and on a power trip.

It was embarrassing. That’s all I took away. (My opinion can change if someone digs through the shitpiles of nothingness to pull up some actual naughty posts, but that’s not going to be me.)

I almost thought I had written your comment and completely forgot about it. No, I just almost made the exact comment and want that hour of my life back.

If there was some over the top racist rant, I sure didn’t see it. And the admin pushing for the defederation sounds so bizarre. Bizarre is the best word I could come up with because “petty” makes me think it was like high school politics. This is closer to a grade school sandbox argument.

The worst I saw was “defedfags” and it was used in a way that was meant to highlight how they never said anything offensive. Like saying, “If you thought what I said before was offensive, let’s see how you respond to something intended to be negative.”

The crazy thing is that the decision is being made because the admin just liked a post. It’s not even because of the post content - which has nothing controversial and appeared maybe 8 times in my Lemmy/kbin feed yesterday.

Editing to add that this is the article: https://kbin.social/search?q=wakeup+call

Oh, I’ve just been toying around with Stable Diffusion and some general ML tidbits. I was just thinking from a practical point of view. From what I read, it sounds like the files are smaller at the same quality, require the same or less processor load (maybe), are tuned for parallel I/O, can be encoded and decoded faster (and there being less difference in performance between the two), and supports progressive loading. I’m kinda waiting for the catch, but haven’t seen any major downsides, besides less optimal performance for very low resolution images.

I don’t know how they ingest the image data, but I would assume they’d be constantly building sets, rather than keeping lots of subsets, if just for the space savings of de-duplication.

(I kinda ramble below, but you’ll get the idea.)

Mixing and matching the speed/efficiency and storage improvement could mean a whole bunch of improvements. I/O is always an annoyance in any large set analysis. With JPEG XL, there’s less storage needed (duh), more images in RAM at once, faster transfer to and from disc, fewer cycles wasted on waiting for I/O in general, the ability to store more intermediate datasets and more descriptive models, easier to archive the raw photo sets (which might be a big deal with all the legal issues popping up), etc. You want to cram a lot of data into memory, since the GPU will be performing lots of operations in parallel. Accessing the I/O bus must be one of the larger time sinks and CPU load becomes a concern just for moving data around.

I also wonder if the support for progressive loading might be useful for more efficient, low resolution variants of high resolution models. Just store one set of high res images and load them in progressive steps to make smaller data sets. Like, say you have a bunch of 8k images, but you only want to make a website banner based on the model from those 8k res images. I wonder if it’s possible to use the the progressive loading support to halt reading in the images at 1k. Lower resolution = less model data = smaller datasets to store or transfer. Basically skipping the downsampling.

Any time I see a big feature jump, like better file size, I assume the trade off in another feature negates at least half the benefit. It’s pretty rare, from what I’ve seen, to have improvements on all fronts.

Even better, this must be fantastic when you’re training AI models with millions of images. The compression level AND performance should be a game changer.

So you’re saying there are people who DO use “normies” and people that DON’T use “normies”. These are not two groups of people. Shit, I just joined this thread, so that makes ME one of YOU, and there’s OTHERS that aren’t here. Are WE the elitists? Or are THEY the “normies”? YOU said there’s no there’s no US or THEM, so EVERYONE is talking in this thread. ANYONE not in this thread must not exist because I know I exist, so YOU thread posters must exist, but wait, that makes ME an US and YOU a THEM.

(I’m not trying to be snarky, but this argument is exactly as nonsensical.)

That’s a blast from the past!