> still run on a device like the iPhone, which weights about 170g [1]? The human brain is 8 times heavier.
Why are you comparing the weight? I'm sorry but this is a bizarre comparison. This isn't even apples to oranges, this is apples to a telephone pole.
I'll also throw in that a single Nvidia H100 is 1200 grams. Unless you have a "Bracket with screws" which will add 20 grams (who wouldn't want an extra 20grams of intelligence?).
Like 70%+ of the human brain is water. The human brain needs a massive network of systems to transport nutrients/oxygen which is irrelevant to logical processing.
Similarly the majority of the iphone weight is the battery and frame. The weight of the processing chip is _grams_.
Besides the conflicting variables with the weight, the way that ML works on a physical level is completely different from the human brain.
In their second sentence they have the most honest response I've seen so far at least: " averaged across 4 diverse customer tasks, fine-tunes based on our new model are _slightly_ stronger than GPT-4, as measured by GPT-4 itself."
Does anyone know where I should look if I want to detect specific sounds? Like a smoke alarm, food bowl dispenser (its very distinct), cat meowing, 3d printer collision, that sort of thing?
> Realistically, even with Turbo+LCM, you're still going to 4+ steps (often 8+), with CFG, for reasonable one-generation quality anywhere close to the images people generated at 50+ steps without Turbo/LCM.
For sure the only reason I considered comparing it that way was because the linked repo appears to also be going for a similar approach with 1 step/image on the pi.
From my own experience I've had a hard ever getting a decent image below 6~8steps, but this repo seems more focused on getting it to run in a reasonable amount of time at all, which understandably requires the minimal "maybe passable" settings.
I'm tired of media acting like the flipper is some kind of "super special hacking tool", it is very literally getting it banned in some places when all of it's internals are easy and common radios (Not to knock the flipper, it is conveniently well packaged).
You just needed to be able to send crafted BLE packets, this attack doesn't have anything specific to the flipper at all.
I was just using that as a reference. Stable diffusion will run well with almost any relatively modern gpu.
You don't have to use a 4090, you'll still get double digit performance with a 3060 or whatnot.
> for people who can only otherwise afford Raspberry Pis ;)
You can rent a 4090 for 0.7USD/1hr, or get an A100 for 1.1USD/hr. And if your project is a display + raspberry pi then those costs will dwarf the rental cost.
> I found this claiming an A100 can generate 1 image/s.
The article you linked is over a year old. Needless to say there have been a LOT of optimizations in the last year.
Back then it was common to use 50+ steps for many of the common samplers. Current methods use a few steps like 1. This OnnxStream are using SDXL-turbo, and you can combine LCM and a few other methods to go very fast.
The reason it's so much faster now is the OnnxStream is only using a single step.
However even if you only get 1 image/s with whatever GPU you have I stand by my original statement that unless you want to do it for the cool factor (which is very valid), pre-calculating them makes more sense.
This project is a fun POC but it's not very practical for that type of application.
A 4090 can generate over 100 images a second with turbo+lcm and a few techniques, you can make 2 days worth of images in 1 seconds. You could make a years worth in roughly 3 minutes and put them on the sd card
> which means even fewer people finetuning those models.
Finetunes rarely led to "Top 5 performance" for the small ones.
Previously the top 10+ were all 70B, with maybe a few 30B in there. There were nearly no 13B's, let alone 7B.
The Zephyr-7b-β was one of the best 7B mistral 0.1 finetunes the past month and a half, and that didn't beat most 70B's.
Even at 7B there are few foundational models as even those take a relatively large amount of money. The only decent one for months has been 7B mistral which again didn't come that close to 70B performance.
I know the hugging face leaderboard isn't wildly accurate.
But the top models right now are almost all under 70B. Most are 7B, and the top is 10B. If the benchmarks are even remotely accurate then this is rather wild.
Apparently multiple groups found different "secret sauces", names upstage and whatever UNA is?
AFAIK there's no public sdk for it, only a single third party game is in development and it's by the developers of garry's mod (one of the biggest third party source "1" games).
I'd consider it still proprietary, at that point you'd want to add Snowdrop (ubisoft), RAGE (Rockstar), REDengine (CDPR), Frostbite (EA), etc.
Why are you comparing the weight? I'm sorry but this is a bizarre comparison. This isn't even apples to oranges, this is apples to a telephone pole.
I'll also throw in that a single Nvidia H100 is 1200 grams. Unless you have a "Bracket with screws" which will add 20 grams (who wouldn't want an extra 20grams of intelligence?).
Like 70%+ of the human brain is water. The human brain needs a massive network of systems to transport nutrients/oxygen which is irrelevant to logical processing.
Similarly the majority of the iphone weight is the battery and frame. The weight of the processing chip is _grams_.
Besides the conflicting variables with the weight, the way that ML works on a physical level is completely different from the human brain.