You need to have the nightly version of Rust installed. There's an issue linked in the FAQ of the README for the project that has instructions to install it.

https://birchtree.me/content/images/size/w960/2022/03/M1-Ultra-chart.jpeg

Dude, Apple is always claiming fastest in the world .

In this specific case Apple DID claim that they are faster than the "highest end discrete GPU" while in this and most real world tests is roughly equivalent to a midrange Nvidia GPU.

You should ask yourself why Apple is the one who lies and you believe them without checking the reality.

Apple has never said their GPUs were the fastest in the world. Why are you lying?

From the github page:

> The image generation procedure follows the standard configuration: 50 inference steps, 512x512 output image resolution, 77 text token sequence length, classifier-free guidance (batch size of 2 for unet).

The benchmark they used is 50 steps on a 77 character input, outputting 512x512.

I am too stupid to actually try it.

>ERROR: Failed building wheel for tokenizers or error: can't find Rust compiler

WHAT

lol

You never will, given that ultrabook is a trademark of Intel.

If you go to the actual github project you can see the full benchmarks and settings.

86400 seconds a day divided by 18 seconds per waifu. POG

not meaningless for those that use the same sw

I dont see any ultrabook or even 5kg laptop with a M1 ultra either.

Edit: you know what actually you can buy many ultrabooks with the RTX 3060 ( Asus ROG zephyrus G14, Dell XPS, razer blade 14 and many more <20mm thick laptops) while Apple laptops's gpu is at best half a m1ultra.

So yeah talk about fanboys who cant even google.

Ngl, as someone who has run stablediff on my own gpu, 18 seconds could either be god awful, average or good depending on the number of steps in the generation. A 15 step generation on my 2070 only takes like 4 seconds and produces perfectly fine results. Think ive gotta go up to like 50+ before reaching 18 seconds.

Fastest in the world in their class.

I don’t see any ultrabooks with a 3060 in them, nor any even close to as powerful as a fanless 20w one.

Hm, I don't see it that was at all.

If we look at how Intel chips scale, we see that single core performance actually decreases on the largest chips. That's why historically the Xeon Mac Pro would actually have a lower single core performance than the similar generation i5 or i7.

Of course the Xeon would more than make up for it by having tons of cores, more PCIe lanes, support for ECC RAM, etc.

I think it would be fantastic if the M1 Supermega or whatever they end up calling the Mac Pro chip matches the M1 single core performance.

[deleted]

You the real MVP. The github page contains most of the info everyone is complaining the article didn't have.

It's a tablet with a keyboard.

Mac airs are shit.

Half my office got those from IT.

I got a 4lb Asus work station with an A5000... ;p

(Basically I use it to run freaking CAD software but only to review engineering, hell for fun I run blender renders I set up at home and send over to render in the background while I work.

That last part is a great way to out it.

YEP!

Bet it was on a special rig, not a consumer computer.

Knowing Apple, this method and result has a ton of asterisks on it.

It can absolutely draw more than 20W, no?

CUDA and the accompanying cudnn libraries are highly specialised hardware and software libraries for machine learning tasks provided by Nvidia, that they have been working on over the past decade.

It’s the reason Nvidia has such a huge lead in the deep learning community, and the reason that their GPUs are able to command a premium over AMD. Basically all deep learning tools are now designed and benchmarked around Nvidia and CUDA, with some also supporting custom built hardware like Google’s TPUs. AMD is catching up, but the tooling for Nvidia “just works”. This is also the reason people buy those $2000 3090s and 4090s, not for gaming, but for actual work.

Frankly, the two chips are in completely different classes in terms of power draw and what they do (one is a dedicated GPU, the other is a whole SoC), it’s impressive that the M1/M2 even stays competitive.

Prompt yes, anything else, no.

SD version, resolution, passes, channels etc, all massively affect performance.

"I take 25 minutes to drive to work and you take 30 so my car is faster"

Every prompt takes the same amount of time

Yeah, talking about the pro line. If they're stuck at M1 single core speeds at desktop level it'll suck for certain applications.