Apple Research unearthed forgotten AI technique and using it to generate images
33 comments
·June 23, 2025bitpush
rTX5CMRXIfFG
That site's target market is what we know as "Apple fanboys". I'm not one to consider 9to5 serious journalism (nor even worthy to post in HN), but even those publications that I consider serious are businesses, too, and need to pander to their markets in order to make money.
danhau
This „4 year old technique“ apparently could give Apple an edge for on-device workloads.
> short: both Apple and OpenAI are moving beyond diffusion, but while OpenAI is building for its data centers, Apple is clearly building for our pockets.
bitpush
The same edge Apple had summarizing notifications so poorly that they had to turn it off?
https://arstechnica.com/apple/2024/11/apple-intelligence-not...
politelemon
> I find it fascinating that Apple-centric media sites are stretching so much to position the company in the AI race.
A glance through the comments also shows HNers doing their best too. The mind still boggles as to why this site is so willing to perform mental gymnastics for a corporate.
kelseyfrog
Forgotten from like 2021? NVAE[1] was a great paper but maybe four years is long enough to be forgotten in the AI space? shrug
1. NVAE: A Deep Hierarchical Variational Autoencoder https://arxiv.org/pdf/2007.03898
bbminner
Right, it is bizzare to read that someone "unearthed a forgotten AI technique" that you happened to have worked with/on when it was still hot - when did I become a fossil? :D
Also, if we're being nitpicky, diffusion model inference has been proven equivalent to (and is often used as) a particular NF so.. shrug
nabla9
They are both variational inference, but Normalizing Flow (NF) is not VAE.
kelseyfrog
If you read the paper, you'll find "More Expressive Approximate Posteriors with Normalizing Flows" is in the methods section. The authors are in fact using (inverse) normalizing flows within the context of VAEs.
The appendix goes on to explain, "We apply simple volume-preserving normalizing flows of the form z′ = z + b(z) to the samples generated by the encoder at each level".
imoverclocked
It’s pretty great that despite having large data centers capable of doing this kind of computation, Apple continues to make things work locally. I think there is a lot of value in being able to hold the entirety of a product in hand.
xnx
Google has a family of local models too! https://ai.google.dev/gemma/docs
coliveira
It's very convenient for Apple to do this: less expenses on costly AI chips, and more excuses to ask customers to buy their latest hardware.
nine_k
Users have to pay for the compute somehow. Maybe by paying for models run in datacenters. Maybe paying for hardware that's capable enough to run models locally.
lostlogin
But also: if Apple's way works, it’s incredibly wasteful.
Server side means shared resources, shared upgrades and shared costs. The privacy aspect matters, but at what cost?
Bootvis
I can upgrade to a bigger LLM I use through an API with one click. If it runs on my device device I need to buy a new phone.
b0a04gl
flows make sense here not just for size but cuz they're fully invertible and deterministic. imagine running same gen on 3 iphones, same output. means apple can kinda ensure same input gives same output across devices, chips, runs. no weird variance or sampling noise. good for caching, testing, user trust all that. fits apple's whole determinism dna and more of predictable gen at scale
yorwba
Normalizing flows generate samples by starting from Gaussian noise and passing it through a series of invertible transformations. Diffusion models generate samples by starting from Gaussian noise and running it through an inverse diffusion process.
To get deterministic results, you fix the seed for your pseudorandom number generator and make sure not to execute any operations that produce different results on different hardware. There's no difference between the approaches in that respect.
MBCook
I wonder if it’s noticeably faster or slower than the common way on the same set of hardware.
nextaccountic
This subject is fascinating and the article is informative, but I wish that HN had a button like "flag", but specific for articles that seems written by AI (well at least the section "How STARFlow compares with OpenAI’s 4o image generator" sounds like it)
CharlesW
FWIW, you can always report any HN quality concerns to hn@ycombinator.com and it'll be reviewed promptly and fairly (IMO).
Veen
It reads like the work of a professional writer who uses a handful of variant sentence structures and conventions to quickly write an article. That’s what professional writers are trained to do.
OhNoNotAgain_99
[dead]
rfv6723
Apple AI team keeps going against the bitter lesson and focusing on small on-device models.
Let's see how this would turn out in longterm.
peepeepoopoo137
"""The bitter lesson""" is how you get the current swath of massively unprofitable AI companies that are competing with each other over who can lose money faster.
furyofantares
I can't tell if you're perpetuating the myth that these companies are losing money on their paid offerings, or just overestimating how much money they lose on their free offerings.
sipjca
somewhat hard to say how the cards fall when the cost of 'intelligence' is coming down 1000x year over year while at the same time compute continues to scale. the bet should be made on both sides probably
furyofantares
10x year over year, not 1000x, right? The 1000x is from this 10x observation having held for 3 years.
echelon
Edge compute would be clutch, but Apple feels a decade too early.
7speter
Maybe for a big llm, but if they add some gpu cores and added a magnitude or 2 more unified memory to their i devices, or shoehorned m socs into high tier iDevices (especially as their lithography process advances), image generation becomes more viable, no? Also, I thought I read somewhere that apple wanted to infer simpler queries locally and switch to datacenter inference when the request was more complicated.
If they approach things this way, and transistor progress continues linearly (relative to the last few years) maybe they can make their first devices that can meet these goals in… 2-3 years?
I find it fascinating that Apple-centric media sites are stretching so much to position the company in the AI race. The title is meant to say that Apple found something unique that other people missed, when the simplest explanation is they started working on this a while back (2021 paper, afterall) and just released it.
A more accurate headline would be - Apple starting to create images using 4 year old techniques.