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Imagen, a text-to-image diffusion model

gweb-research-imagen.appspot.com

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Re: Imagen, a text-to-image diffusion model

#341

Post by zarzavat »

shadowgovt wrote: post #302
pxmpxm wrote: post #227

Earlier quoted context omitted.

> Randomly pick one. How does the model back out the "certain people would like to pretend it's a fair coin toss that a randomly selected nurse is male or female" feature? It won't be in any representative training set, so you're back to fishing for stock photos on getty rather than generating things.

Yep, that's the hard problem Google is not comfortable releasing the API to this until they have it solved.

But why is it a problem? The AI is just a mirror showing us ourselves. That’s a good thing. How does it help anyone to make an AI that presents a fake world so that we can pretend that we live in a world that we actually don’t? Disassociation from reality is more dangerous than bias.

Re: Imagen, a text-to-image diffusion model

#342

Post by dougabug »

benreesman wrote: post #333
dougabug wrote: post #329

Earlier quoted context omitted.

This characterization is not really accurate. OpenAI has had almost a 2 year lead with GPT-3 dominating the discussion of LLMs (large language models). Google didn’t release its paper on the powerful PaLM-540b model until recently. Similarly, CLiP, Glide, DALL-E, and DALL-E2 have been incredibly influential in visual-language models. Imagen, while highly impressive, definitely is a catch-up piece of work (as was PaLM…

I agree that it’s still a jump ball in a rapidly moving field, I was saying Google is far ahead, not that they’ve won. And I don’t think whatever iteration of PaLM was cooking at the time GPT-3 started getting press would have looked to shabby. I think Google crushed OpenAI on both GPT and DALL-E in short order because OpenAI published twice and someone had had enough.

That’s pretty speculative and dubious (the holding back part) given the heavy bias to publication culture at Google Research and DeepMind. OpenAI has hardly been “crushed” here; PaLM and Imagen are solid, incremental advances, but given what came before them, not Earth-shattering.

If I were going to cite evidence for Alphabet’s “supremacy” in AI, I would’ve picked something more novel and surprising such as AlphaFold, or perhaps even Gato.

It’s not clear to me that Google has anything which compares to Reality Labs, although this may simply be my own ignorance.

Nvidia surely scooped Google with Instant Neural Graphics Primitives, in spite of Google publishing dozens of (often very interesting) NeRF papers. It’s not a war, all these works build on one another.

Re: Imagen, a text-to-image diffusion model

#343

Post by benreesman »

joshcryer wrote: post #336
benreesman wrote: post #273

I apologize in advance for the elitist-sounding tone. In my defense the people I’m calling elite I have nothing to do with, I’m certainly not talking about myself. Without a fairly deep grounding in this stuff it’s hard to appreciate how far ahead Brain and DM are. Neither OpenAI nor FAIR ever has the top score on anything unless Google delays publication . And short of FAIR? D2 lacrosse. There are exceptions to such…

Who does it serve for plebs to be shown the approach openly? I don't know that it does a disservice to anyone by showing the approach. But in general it is likely more due in part to the fact that it's going to happen anyway, if we can share our approaches and research findings, we'll just achieve it sooner.

Once upon a time you could lie only a little bit and Stanford would give you the whole ImageNet corpus. I know because, uh, a friend told me.

I’ve got no interest in moralizing on this, but if any of the big actors wanted to they could put a meaningful if not overwhelming subset of the corpus on S3, put the source code on GitHub, and you could on a modest budget see an epoch or 3.

I’m not holding my breath.

Re: Imagen, a text-to-image diffusion model

#344

Post by alphabetting »

benreesman wrote: post #333
dougabug wrote: post #329

Earlier quoted context omitted.

This characterization is not really accurate. OpenAI has had almost a 2 year lead with GPT-3 dominating the discussion of LLMs (large language models). Google didn’t release its paper on the powerful PaLM-540b model until recently. Similarly, CLiP, Glide, DALL-E, and DALL-E2 have been incredibly influential in visual-language models. Imagen, while highly impressive, definitely is a catch-up piece of work (as was PaLM…

I agree that it’s still a jump ball in a rapidly moving field, I was saying Google is far ahead, not that they’ve won. And I don’t think whatever iteration of PaLM was cooking at the time GPT-3 started getting press would have looked to shabby. I think Google crushed OpenAI on both GPT and DALL-E in short order because OpenAI published twice and someone had had enough.

OpenAI and FAIR are definitely in the same league as Google but Google has been all-in on AI from the beginning. They've probably spent well over $100B on AI research. I really enjoyed the Genius Makers book which came out last year from an NYT reporter on history of ML race. Deepmind apparently turned down a FB offer of double what Google was offering.

Re: Imagen, a text-to-image diffusion model

#345

Post by [deleted] »

minimaxir wrote: post #211
davikr wrote: post #132

Interesting and cool technology - but I can't seem to ignore that every high-quality AI art application is always closed, and I don't seem to buy the ethics excuse for that. The same was said for GPT, yet I see nothing but creativity coming out from its users nowadays.

Granted that's a selection bias: you likely won't hear about the cases where legit obscene output occurs. (the only notable case I've heard is the AI Dungeon incident)

[deleted]

Re: Imagen, a text-to-image diffusion model

#347

Post by benreesman »

alphabetting wrote: post #344
benreesman wrote: post #333

Earlier quoted context omitted.

I agree that it’s still a jump ball in a rapidly moving field, I was saying Google is far ahead, not that they’ve won. And I don’t think whatever iteration of PaLM was cooking at the time GPT-3 started getting press would have looked to shabby. I think Google crushed OpenAI on both GPT and DALL-E in short order because OpenAI published twice and someone had had enough.

OpenAI and FAIR are definitely in the same league as Google but Google has been all-in on AI from the beginning. They've probably spent well over $100B on AI research. I really enjoyed the Genius Makers book which came out last year from an NYT reporter on history of ML race. Deepmind apparently turned down a FB offer of double what Google was offering.

Cade Metz is that author and most of it I can only speculate on.

The bits and pieces I saw first hand tie out reasonably well with that account.

Re: Imagen, a text-to-image diffusion model

#348

Post by zone411 »

londons_explore wrote: post #292
ma2rten wrote: post #234

Earlier quoted context omitted.

Google is very conservative about anything that can generate open-ended outputs. Also these models are still very expensive computationally.

They're expensive to train, but not awfully expensive to use. Especially if you have hundreds of images you want to generate (due to the way compute devices tend to get much more efficiency with a large batch size). Google could totally afford it, especially if the feature was hidden behind a button the user had to click, and not just run for every image search.

When diffusion models are used, the inference time could be meaningful. But then this is only 64x64 with upsampling, so probably not too bad.

Re: Imagen, a text-to-image diffusion model

#349

Post by nyolfen »

tines wrote: post #297
magicalist wrote: post #285

Earlier quoted context omitted.

> Could skewing search results, i.e. hiding the bias of the real world Which real world? The population you sample from is going to make a big difference. Do you expect it to reflect your day to day life in your own city? Own country? The entire world? Results will vary significantly.

I'd say it doesn't actually matter, as long as the population sampled is made clear to the user. If I ask for pictures of Japanese people, I'm not shocked when all the results are of Japanese people. If I asked for "criminals in the United States" and all the results are black people, that should concern me, not because the data set is biased but because the real world is biased and we should do something about that.…

> If I asked for "criminals in the United States" and all the results are black people,

curiously, this search actually only returns white people for me on GIS

Re: Imagen, a text-to-image diffusion model

#350

Post by [deleted] »

slg wrote: post #332
rpmisms wrote: post #323

Earlier quoted context omitted.

So, the model should have a knowledge of political correctness, and return multiple results if the first choice might reinforce a stereotype?

I never said anything about political correctness. You implied that you want a model that "provides a reflection of reality". All nurses being female is not "a reflection of reality". It is a distortion of reality because the model doesn't actually understand gender or nurses.

[deleted]
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