Imagen, a text-to-image diffusion model

https://gweb-research-imagen.appspot.com/

by keveman • 4 years ago

988 634 4 years ago

gweb-research-imagen.appspot.com

neolander• 4 years ago

It really does look better than DALL-E, at least from the images on the site. Hard to believe how quickly progress is being made to lucid dreaming while awake.

Jyaif• 4 years ago

Jesus Christ. Unlike DALL-E 2, it gets the details right. It also can generate text. The quality is insanely good. This is absolutely mental.

not2b2• 4 years ago

Yes, the posted results are really good, but since we can't play with it we don't know how much cherry picking has been done.

addajones• 4 years ago

This is absolutely amazingly insane. Wow.

james-redwood• 4 years ago

Metacalculus, a mass forecasting site, has steadily brought forward the prediction date for a weakly general AI. Jaw-dropping advances like this, only increase my confidence in this prediction. "The future is now, old man."

https://www.metaculus.com/questions/3479/date-weakly-general...

sydthrowaway3• 4 years ago

How can we prepare for this?

This will result in mass social unrest.

refulgentis• 4 years ago

You think so? I'm very high on the Kool-Aid, image generation and text transformation models are core parts of my workflow. (Midjourney, GPT-3)

It's still an unruly 7 year old at best. Results need to be verified. Prompt engineering and a sense of creativity are core competencies.

aaaaaaaaaaab2• 4 years ago

Stock up on guns, ammo, cigarettes, water filters, canned food, and toilet paper.

dougmwne8• 4 years ago

I think the serious answer is that it is yet another labor multiplier like electricity and software. Our tech since the industrial revolution has allowed us to elevate ourselves from a largely agrarian society to space and cyberspace. AI, by all appearances, continues to be a tool, just the latest in a long line of better tools. It still requires a human to provide intent and direction. Right now in my job, I command the collect output of a million medieval scribes. In the future I will command a million Michelangelos.

Should ML/AI deliver on the wildest promises, it will be like a SpaceX Starship for the mind.

tpmx7• 4 years ago

I don't see how this gets us (much) closer to general AI. Where is the reasoning?

_joel• 4 years ago

Perhaps the confluence of NLP and something generative?

quirino• 4 years ago

I think this serves at least as a clear demonstration of how advanced the current state of AI is. I had played with GPT-3 and that was very impressive but I couldn't even dream something as good as D-ALLE 2 was already possible.

6gvONxR4sf7o4• 4 years ago

Big pretrained models are good enough now that we can pipe them together in really cool ways and our representations of text and images seem to capture what we “mean.”

chias• 4 years ago

"The future is already here — It’s just not very evenly distributed"

daenz5• 4 years ago

While we leave an in-depth empirical analysis of social and cultural biases to future work, our small scale internal assessments reveal several limitations that guide our decision not to release our model at this time.

Some of the reasoning:

Preliminary assessment also suggests Imagen encodes several social biases and stereotypes, including an overall bias towards generating images of people with lighter skin tones and a tendency for images portraying different professions to align with Western gender stereotypes. Finally, even when we focus generations away from people, our preliminary analysis indicates Imagen encodes a range of social and cultural biases when generating images of activities, events, and objects. We aim to make progress on several of these open challenges and limitations in future work.

Really sad that breakthrough technologies are going to be withheld due to our inability to cope with the results.

ceeplusplus4• 4 years ago

The ironic part is that these "social and cultural biases" are purely from a Western, American lens. The people writing that paragraph are completely oblivious to the idea that there could be other cultures other than the Western American one. In attempting to prevent "encoding of social and cultural biases" they have encoded such biases themselves into their own research.

kevinh• 4 years ago

What makes you think the authors are all American?

tantalor• 4 years ago

https://en.wikipedia.org/wiki/Moral_relativism

not2b2• 4 years ago

It seems you've got it backwards: "tendency for images portraying different professions to align with Western gender stereotypes" means that they are calling out their own work precisely because it is skewed in the direction of Western American biases.

Mockapapella• 4 years ago

Transformers are parallelize-able, right? What’s stopping a large group of people from pooling their compute power together and working towards something like this? IIRC there were some crypto projects a while back that we’re trying to create something similar (golem?)

visarga7• 4 years ago

There are the Eleuther.ai and BigScience projects working on public foundation models. They have a few releases already and currently training GPT-3 sized models.

joshcryer6• 4 years ago

There are people working on reproducing the models, see here for Dall-E 2 for example: https://github.com/lucidrains/DALLE2-pytorch

It's often not worth it to decentralize the computation of the trained model though but it's not hard to get donated cycles and groups are working on it. Don't fret because Google isn't releasing the API/code. They released the paper and that's all you need.

sinenomine10• 4 years ago

You really need a decent infiniband-linked cluster to train large models.

user3939382• 4 years ago

Translation: we need to hand-tune this to not reflect reality but instead the world as we (Caucasian/Asian male American woke upper-middle class San Fransisco engineers) wish it to be.

Maybe that's a nice thing, I wouldn't say their values are wrong but let's call a spade a spade.

ceejayoz5• 4 years ago

"Reality" as defined by the available training set isn't necessarily reality.

For example, Google's image search results pre-tweaking had some interesting thoughts on what constitutes a professional hairstyle, and that searches for "men" and "women" should only return light-skinned people: https://www.theguardian.com/technology/2016/apr/08/does-goog...

Does that reflect reality? No.

(I suspect there are also mostly unstated but very real concerns about these being used as child pornography, revenge porn, "show my ex brutally murdered" etc. generators.)

barredo• 4 years ago

I know you're anon trolling, but the authors' names are:

Chitwan Saharia, William Chan, Saurabh Saxena†, Lala Li†, Jay Whang†, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho†, David Fleet†, Mohammad Norouzi

josho• 4 years ago

Translation: AI has the potential to transform society. When we release this model to the public it will be used in ways we haven’t anticipated. We know the model has bias and we need more time to consider releasing this to the public out of concerns that this transformative technology further perpetuate mistakes that we’ve made in our recent past.

Ar-Curunir• 4 years ago

Except "reality" in this case is just their biased training set. E.g. There's more non-white doctors and nurses in the world than white ones, yet their model would likely show an image of white person when you type in "doctor".

JohnBooty2• 4 years ago

    Translation: we need to hand-tune this to not reflect reality

Is it reflecting reality, though?

Seems to me that (as with any ML stuff, right?) it's reflecting the training corpus.

Futhermore, is it this thing's job to reflect reality?

    the world as we (Caucasian/Asian male American woke 
    upper-middle class San Fransisco engineers) wish it to be

Snarky answer: Ah, yes, let's make sure that things like "A giant cobra snake on a farm. The snake is made out of corn" reflect reality.

Heartfelt answer: Yes, there is some of that wishful thinking or editorializing. I don't consider it to be erasing or denying reality. This is a tool that synthesizes unreality. I don't think that such a tool should, say, refuse to synthesize an image of a female POTUS because one hasn't existed yet. This is art, not a reporting tool... and keep in mind that art not only imitates life but also influences it.

userbinator3• 4 years ago

Indeed. As the saying goes, we are truly living in a post-truth world.

holmesworcester2• 4 years ago

"As we wish it to be" is not totally true, because there are some places where humanity's iconographic reality (which Imagen trains on) differs significantly from actual reality.

One example would be if Imagen draws a group of mostly white people when you say "draw a group of people". This doesn't reflect actual reality. Another would be if Imagen draws a group of men when you say "draw a group of doctors".

In these cases where iconographic reality differs from actual reality, hand-tuning could be used to bring it closer to the real world, not just the world as we might wish it to be!

I agree there's a problem here. But I'd state it more as "new technologies are being held to a vastly higher standard than existing ones." Imagine TV studios issuing a moratorium on any new shows that made being white (or rich) seem more normal than it was! The public might rightly expect studios to turn the dials away from the blatant biases of the past, but even if this would be beneficial the progressive and activist public is generations away from expecting a TV studio to not release shows until they're confirmed to be bias-free.

That said, Google's decision to not publish is probably less about the inequities in AI's representation of reality and more about the AI sometimes spitting out drawings that are offensive in the US, like racist caricatures.

visarga7• 4 years ago

The big labs have become very sensitive with large model releases. It's too easy to make them generate bad PR, to the point of not releasing almost any of them. Flamingo was also a pretty great vison-language model that wasn't released, not even in a demo. PaLM is supposedly better than GPT-3 but closed off. It will probably take a year for open source models to appear.

runnerup• 4 years ago

The largest models which generate the headline benchmarks are never released after any number of years, it seems.

Very difficult to replicate results.

godelski2• 4 years ago

That's because we're still bad about long-tailed data and that people outside the research don't realize that we're first prioritizing realistic images before we deal with long-tailed data (which is going to be the more generic form of bias). To be honest, it is a bit silly to focus on long-tailed data when results aren't great. That's why we see the constant pattern of getting good on a dataset and then focusing on the bias in that dataset.

I mean a good example of this is the Pulse[0][1] paper. You may remember it as the white Obama. This became a huge debate and it was pretty easily shown that the largest factor was the dataset bias. This outrage did lead to fixing FFHQ but it also sparked a huge debate with LeCun (data centric bias) and Timnit (model centric bias) at the center. Though Pulse is still remembered for this bias, not for how they responded to it. I should also note that there is human bias in this case as we have a priori knowledge of what the upsampled image should look like (humans are pretty good at this when the small image is already recognizable but this is a difficult metric to mathematically calculate).

It is fairly easy to find adversarial examples, where generative models produce biased results. It is FAR harder to fix these. Since this is known by the community but not by the public (and some community members focus on finding these holes but not fixing them) it creates outrage. Probably best for them to limit their release.

[0] https://arxiv.org/abs/2003.03808

[1] https://cdn.vox-cdn.com/thumbor/MXX-mZqWLQZW8Fdx1ilcFEHR8Wk=...

xmonkee• 4 years ago

I was hoping your conclusion wasn't going to be this as I was reading that quote. But, sadly, this is HN.

tines10• 4 years ago

This raises some really interesting questions.

We certainly don't want to perpetuate harmful stereotypes. But is it a flaw that the model encodes the world as it really is, statistically, rather than as we would like it to be? By this I mean that there are more light-skinned people in the west than dark, and there are more women nurses than men, which is reflected in the model's training data. If the model only generates images of female nurses, is that a problem to fix, or a correct assessment of the data?

If some particular demographic shows up in 51% of the data but 100% of the model's output shows that one demographic, that does seem like a statistics problem that the model could correct by just picking less likely "next token" predictions.

Also, is it wrong to have localized models? For example, should a model for use in Japan conform to the demographics of Japan, or to that of the world?

karpierz2• 4 years ago

It depends on whether you'd like the model to learn casual or correlative relationships.

If you want the model to understand what a "nurse" actually is, then it shouldn't be associated with female.

If you want the model to understand how the word "nurse" is usually used, without regard for what a "nurse" actually is, then associating it with female is fine.

The issue with a correlative model is that it can easily be self-reinforcing.

daenz5• 4 years ago

I think the statistics/representation problem is a big problem on its own, but IMO the bigger problem here is democratizing access to human-like creativity. Currently, the ability to create compelling art is only held by those with some artistic talent. With a tool like this, that restriction is gone. Everyone, no matter how uncreative, untalented, or uncommitted, can create compelling visuals, provided they can use language to describe what they want to see.

So even if we managed to create a perfect model of representation and inclusion, people could still use it to generate extremely offensive images with little effort. I think people see that as profoundly dangerous. Restricting the ability to be creative seems to be a new frontier of censorship.

godelski2• 4 years ago

But is it a flaw that the model encodes the world as it really is

I want to be clear here, bias can be introduced at many different points. There's dataset bias, model bias, and training bias. Every model is biased. Every dataset is biased.

Yes, the real world is also biased. But I want to make sure that there are ways to resolve this issue. It is terribly difficult, especially in a DL framework (even more so in a generative model), but it is possible to significantly reduce the real world bias.

jonny_eh4• 4 years ago

But is it a flaw that the model encodes the world as it really is

Does a bias towards lighter skin represent reality? I was under the impression that Caucasians are a minority globally.

I read the disclaimer as "the model does NOT represent reality".

Imnimo• 4 years ago

If some particular demographic shows up in 51% of the data but 100% of the model's output shows that one demographic, that does seem like a statistics problem that the model could correct by just picking less likely "next token" predictions.

Yeah, but you get that same effect on every axis, not just the one you're trying to correct. You might get male nurses, but they have green hair and six fingers, because you're sampling from the tail on all axes.

SnowHill99024• 4 years ago

It’s the same as with an artist: “hey artist, draw me a nurse.” “Hmm okay, do you want it a guy or girl?” “Don’t ask me, just draw what I’m saying.” The artist can then say: “Okay, but accept my biases.” or “I can’t since your input is ambiguous.”

For a one-shot generative algorithm you must accept the artist’s biases.

skybrian2• 4 years ago

Yes, there is a denominator problem. When selecting a sample "at random," what do you want the denominator to be? It could be "people in the US", "people in the West" (whatever countries you mean by that) or "people worldwide."

Also, getting a random sample of any demographic would be really hard, so no machine learning project is going to do that. Instead you've got a random sample of some arbitrary dataset that's not directly relevant to any particular purpose.

This is, in essence, a design or artistic problem: the Google researchers have some idea of what they want the statistical properties of their image generator to look like. What it does isn't it. So, artistically, the result doesn't meet their standards, and they're going to fix it.

There is no objective, universal, scientifically correct answer about which fictional images to generate. That doesn't all art is equally good, or that you should just ship anything without looking at quality along various axes.

ben_w• 4 years ago

This sounds like descriptivism vs prescriptivism. In English (native language) I’m a descriptivist, in all other languages I have to tell myself to be a prescriptivist while I’m actively learning and then switch back to descriptivism to notice when the lessons were wrong or misleading.

pshc2• 4 years ago

I think it is problematic, yes, to produce a tool trained on data from the past that reinforces old stereotypes. We can’t just handwave it away as being a reflection of its training data. We would like it to do better by humanity. Fortunately the AI people are well aware of the insidious nature of these biases.

webmaven8• 4 years ago

We certainly don't want to perpetuate harmful stereotypes. But is it a flaw that the model encodes the world as it really is, statistically, rather than as we would like it to be? By this I mean that there are more light-skinned people in the west than dark, and there are more women nurses than men, which is reflected in the model's training data. If the model only generates images of female nurses, is that a problem to fix, or a correct assessment of the data?

If the model only generated images of female nurses, then it is not representative of the real world, because male nurses exist and they deserve to not be erased. The training data is the proximate causes here, but one wonders what process ended up distorting "most nurses are female" into "nearly all nurse photos are of female nurses" something amplified a real world imbalance into a dataset that exhibited more bias than the real world, and then training the AI bakes that bias into an algorithm (that may end up further reinforcing the bias in the real world depending on the use-cases).

nomel8• 4 years ago

If you tell it to generate an image of someone eating Koshihikari rice, will it be biased if they're Japanese? Should the skin color, clothing, setting, etc be made completely random, so that it's unbiased? What if you made it more specific, like "edo period drawing of a man"? Should the person draw be of a random skin color? What about "picture of a viking"? Is it biased if they're white?

At what point is statistical significance considered ok and unbiased?

pxmpxm6• 4 years ago

At what point is statistical significance considered ok and unbiased?

Presumably when you're significantly predictive of the preferred dogma, rather than reality. There's no small bit of irony in machines inadvertently creating cognitive dissonance of this sort; second order reality check.

I'm fairly sure this never actually played out well in history (bourgeois pseudoscience, deutsche physik etc), so expect some Chinese research bureau to forge ahead in this particular direction.

meetups323• 4 years ago

One of these days we're going to need to give these models a mortgage and some mouths to feed and make it clear to them that if they keep on developing biases from their training data everyone will shun them and their family will go hungry and they won't be able to make their payments and they'll just generally have a really bad time.

After that we'll make them sit through Legal's approved D&I video series, then it's off to the races.

pxmpxm6• 4 years ago

Underrated comment.

aaaaaaaaaaab2• 4 years ago

Reinforcement learning?

sinenomine10• 4 years ago

This is a stellar idea begging for a new dataset.

alphabetting2• 4 years ago

There is a contingent of AI activists who spend a ton of time on Twitter that would beat Google like a drum with help from the media if they put out something they deemed racist or biased.

Mizza• 4 years ago

So glad the company that spies on me and reads my email for profit is protecting me from pictures that don't look like TV commercials.

astrange24• 4 years ago

Gmail doesn’t read your email for ads anymore. They read it to implement spam filters, and good thing too. Having working spam filters is indeed why they make money though.

joshcryer6• 4 years ago

They're withholding the API, code, and trained data because they don't want it to affect their corporate image. The good thing is they released their paper which will allow easy reproduction.

T5-XXL looks on par with CLIP so we may not see an open source version of T5 for a bit (LAION is working on reproducing CLIP), but this is all progress.

minimaxir3• 4 years ago

T5 was open-sourced on release (up to 11B params): https://github.com/google-research/text-to-text-transfer-tra...

It is also available via Hugging Face transformers.

However, the paper mentions T5-XXL is 4.6B, which doesn't fit any of the checkpoints above, so I'm confused.

riffraff2• 4 years ago

This seems bullshit to me, considering Google translate and google images encode the same biases and stereotypes, and are widely available.

nomel8• 4 years ago

Aren't those old systems?

seaman19212• 4 years ago

yea but now they aren't giving people more data-points to attack them with such nonsense arguments.

swayvil• 4 years ago

it isn't woke enough. Lol.

ccbccccbbcccbb5• 4 years ago

In discussions like this, I always head for the gray-text comments to enjoy the last crumbs of the common sense in this world.

jimmygrapes2• 4 years ago

Are "Western gender stereotypes" significantly different than non-Western gender stereotypes? I can't tell if that means it counts a chubby stubble-covered man with a lip piercing, greasy and dyed long hair, wearing an overly frilly dress as a DnD player/metal-head or as a "woman" or not (yes I know I'm being uncharitable and potentially "bigoted" but if you saw my Tinder/Bumble suggestions and friend groups you'd know I'm not exaggerating for either category). I really can't tell what stereotypes are referred to here.

babyshake• 4 years ago

Indeed. If a project has shortcomings, why not just acknowledge the shortcomings and plan to improve on them in a future release? Is it anticipated that "engineer" being rendered as a man by the model is going to be an actively dangerous thing to have out in the world?

makeitdouble3• 4 years ago

"what could go wrong anyway?"

devindotcom• 4 years ago

Good lord. Withheld? They've published their research, they just aren't making the model available immediately, waiting until they can re-implement it so that you don't get racial slurs popping up when you ask for a cup of "black coffee."

While a subset of our training data was filtered to removed noise and undesirable content, such as pornographic imagery and toxic language, we also utilized LAION-400M dataset which is known to contain a wide range of inappropriate content including pornographic imagery, racist slurs, and harmful social stereotypes

Tossing that stuff when it comes up in a research environment is one thing, but Google clearly wants to implement this as a product, used all over the world by a huge range of people. If the dataset has problems, and why wouldn't it, it is perfectly rational to want to wait and re-implement it with a better one. DALL-E 2 was trained on a curated dataset so it couldn't generate sex or gore. Others are sanitizing their inputs too and have done for a long time. It is the only thing that makes sense for a company looking to commercialize a research project.

This has nothing to do with "inability to cope" and the implied woke mob yelling about some minor flaw. It's about building a tool that doesn't bake in serious and avoidable problems.

concordDance2• 4 years ago

I wonder why they don't like the idea of autogenerated porn... They're already putting most artists out of a job, why not put porn stars out of a job too?

makeitdouble3• 4 years ago

Really sad that breakthrough technologies are going to be withheld due to our inability to cope with the results.

Genuinely, isn't it a prime example of the people actually stopping to think if they should, instead of being preoccupied with whether or not they could ?

jowday• 4 years ago

Much like OpenAIs marketing speak about withholding their models for safety, this is just a progressive-sounding cover story for them not wanting to essentially give away a model they spent thousands of man hours and tens of millions of dollars worth of compute training.

6gvONxR4sf7o4• 4 years ago

It’s wild to me that the HN consensus is so often that 1) discourse around the internet is terrible, it’s full of spam and crap, and the internet is an awful unrepresentative snapshot of human existence, and 2) the biases of general-internet-training-data are fine in ML models because it just reflects real life.

astrange24• 4 years ago

The bias on HN is that people who prioritize being nice, or may possibly have humanities degrees or be ultra-libs from SF, are wrong because the correct answer would be cynical and cold-heartedly mechanical.

Other STEM adjacent communities feel similarly but I don’t get it from actual in person engineers much.

nullc2• 4 years ago

It's wild to me that you'd say that. The people complaining (1) aren't following it up with "so we should make sure to restrict the public from internet access entirely". -- that's what would be required to make your juxtaposition make sense.

Moreover, the model doing things like exclusively producing white people when asked to create images of people home brewing beer is "biased" but it's a bias that presumably reflects reality (or at least the internet), if not the reality we'd prefer. Bias means more than "spam and crap", in the ML community bias can also simply mean _accurately_ modeling the underlying distribution when reality falls short of the author's hopes.

For example, if you're interested in learning about what home brewing is the fact that it uses white people would be at least a little unfortunate since there is nothing inherently white and some home brewers aren't white. But if, instead, you wanted to just generate typical home brewing images doing anything but would generate conspicuously unrepresentative images.

But even ignoring the part of the biases which are debatable or of application-specific impact, saying something is unfortunate and saying people should be denied access are entirely different things.

I'll happily delete this comment if you can bring to my attention a single person who has suggested that we lose access to the internet because of spam and crap who has also argued that the release of an internet-biased ML model shouldn't be withheld.

colordrops• 4 years ago

Why is it wild? How is it contradictory?

bogwog• 4 years ago

I wouldn't describe this situation as "sad". Basically, this decision is based on a belief that tech companies should decide what our society should look like. I don't know what emotion that conjures up for you, but "sadness" isn't it for me.

tomp3• 4 years ago

a tendency for images portraying different professions to align with Western gender stereotypes

There are two possible ways of interpreting interpreting "gender stereotypes in professions".

biased or correct

https://www.abc.net.au/news/2018-05-21/the-most-gendered-top...

https://www.statista.com/statistics/1019841/female-physician...

seaman19212• 4 years ago

Yup this is what happens when people who want headlines nitpick for bullshit in a state-of-the-art model which simply reflects the state of the society. Better not to release the model itself than keep explaining over and over how a model is never perfect.

ccbccccbbcccbb5• 4 years ago

In short, the generated images are too gender-challenged-challenged and underrepresent the spectrum of new normalcy!

planetsprite2• 4 years ago

Literally the same thing could be said about Google images, but google images is obviously avaliable to the public.

Google knows this will be an unlimited money generator so they're keeping a lid on it.

jfoster3• 4 years ago

Given that there's already many competing models in this space prior to any of them having been brought to market, it seems more likely that it will be commoditized.

ThrowITout4321• 4 years ago

I'm one that welcomes their reasoning. I don't consider myself a social justice kind of guy but I'm not keen on the idea that a tool that is suppose to make life better for everyone has a bias towards one segment of society. This is an important issue(bug?) that needs to be resolved. Specially since there is absolutely no burning reason to release it before it's ready for general use.

tyrust• 4 years ago

From the HN rules:

Eschew flamebait. Avoid unrelated controversies and generic tangents.

They provided a pretty thorough overview (nearly 500 words) of the multiple reasons why they are showing caution. You picked out the one that happened to bother you the most and have posted a misleading claim that the tech is being withheld entirely because of it.

sinenomine10• 4 years ago

Really sad that breakthrough technologies are going to be withheld due to our inability to cope with the results.

Indeed it is. Consider this an early, toy version of the political struggle related to ownership of AI-scientists and AI-engineers of the near future. That is, generally capable models.

I do think the public should have access to this technology, given so much is at stake. Or at least the scientists should be completely, 24/7, open about their R&D. Every prompt that goes into these models should be visible to everyone.

dclowd99014• 4 years ago

Even as a pretty left leaning person, I gotta agree. We should see AI’s pollution by human shortcoming akin to the fact that our world is the product of many immoralities that came before us. It sucks that they ever existed, but we should understand that the results are, by definition, a product of the past, and let them live in that context.

andybak3• 4 years ago

Great. Now even if I do get a Dall-E 2 invite I'll still feel like I'm missing out!

rvnx9• 4 years ago

It's always the same with AI research: "we have something amazing but you can't use it because it's too powerful and we think you are an idiot who cannot use your own judgement."

2bitencryption• 4 years ago

I can understand the reasoning behind this, though.

Dall-E had an entire news cycle (on tech-minded publications, that is) that showcased just how amazing it was.

Millions* of people became aware that technology like Dall-E exists, before anyone could get their hands on it and abuse it. (*a guestimate, but surely a close one)

One day soon, inevitably, everyone will have access to something 10x better than Imagen and Dall-E. So at least the public is slowly getting acclimated to it before the inevitable "theater-goers running from a projected image of a train approaching the camera" moment

andybak3• 4 years ago

As someone that spent an evening trying to generate images of Hitler Lego I think they have a point.

sexy_panda2• 4 years ago

Would I have to implement this myself, or is there something ready to run?

UncleOxidant• 4 years ago

I think implementing this yourself is likely not doable unless you have the computing resources of a Google, Amazon or Facebook.

sexy_panda2• 4 years ago

It seems like lucidrains is currently working on an implementation [1] of it.

I would love it.

[1] https://github.com/lucidrains/imagen-pytorch

manchmalscott• 4 years ago

The big thing I’m noticing over DALL-E is that it seems to be better at relative positioning. In a MKBHD video about DALLE it would get the elements but not always in the right order. I know google curated some specific images but it seems to be doing a better job there.

benwikler2• 4 years ago

Totally—Imagen seems better at composition and relative positioning and text, while DALL-E seems better at lighting, backgrounds, and general artistry.

kossTKR• 4 years ago

Yeah Dall-e looks amazing, to a mysterious degree even with hints of humour and irony, while imagen images look cheap, one dimensional and quite ugly to be honest.

Still amazing that we're at a point where that's the case, they're both incredible developments.

visarga7• 4 years ago

Interesting discovery they made

We show that scaling the pretrained text encoder size is more important than scaling the diffusion model size.

There seems to be an unexpected level of synergy between text and vision models. Can't wait to see what video and audio modalities will add to the mix.

gwern6• 4 years ago

I think that's unsurprising. With DALL-E 1, for example, scaling the VAE (the image model generating the actual pixels) hits very fast diminishing returns, and all your compute goes into the 'text encoder' generating the token sequence.

Particularly as you approach the point where the image quality itself is superb and people increasingly turn to attacking the semantics & control of the prompt to degrade the quality ("...The donkey is holding a rope on one end, the octopus is holding onto the other. The donkey holds the rope in its mouth. A cat is jumping over the rope..."). For that sort of thing, it's hard to see how simply beefing up the raw pixel-generating part will help much: if the input seed is incorrect and doesn't correctly encode a thumbnail sketch of how all these animals ought to be engaging in outdoors sports, there's nothing some low-level pixel-munging neurons can do to help much.

visarga7• 4 years ago

I was thinking more about our traditional ResNet50 trained on ImageNet vs CLIP. ResNet was limited to a thousand classes and brittle. CLIP can generalise to new concept combinations with ease. That changes the game, and the jump is based on NLP.

ravi-delia3• 4 years ago

Basically makes sense, no? DALLE-2 suffered from misunderstanding propositional logic, treating prompts as less structured then it should have. That's a text model issue! Compared to that, scaling up the image isn't as important (especially with a few passes).

espadrine• 4 years ago

Is there a way to confirm that this extra processing relates to the language structure, and not the processing of concepts?

I wouldn’t be surprised if the lack of video and 3D understanding in the image dataset training fails to understand things like the fear of heights, and the concept of gravity ends up being learned in the text processing weights.

endisneigh• 4 years ago

I give it a few years before Google makes stock images irrelevant.

sydthrowaway3• 4 years ago

Short Getty images?

tpmx7• 4 years ago

Privately owned by the Getty family.

tpmx7• 4 years ago

Rolling this into Google Docs seems like a nobrainer.

semicolon_storm• 4 years ago

Or rolling this into Google Image Search to create images that match users' search queries on the fly.

Don't like any of the results from the real web? Well how about these we created just for you.

curiousgal3• 4 years ago

Until they pull of the plug on it.

ma2rten5• 4 years ago

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

pphysch• 4 years ago

The entire "content" industry could get eaten by a few hundred people curating + touching-up output from these models.

astrange24• 4 years ago

No, competitive advantage means that it’s impossible to run out of jobs just because someone/something is better at it than you.

(Consumer demand and boredom both being infinite is another thing working against it.)

makeitdouble3• 4 years ago

I really expect them to first make DALL-E and competing networks unfit for commercialization by providing the better choice for free, have stock companies cry in the corner, to just sunset the product a year or two down the road and we're left wandering what to do.

notahacker2• 4 years ago

Tbh imagine this tech combines particularly well with really well curated stock image databases so outputs can be made with recognisable styles, and actors and design elements can be reused across multiple generated images.

If Getty et al aren't already spending money on that possibility, they probably should be.

braingenious• 4 years ago

This is super cool and I want to play with it.

armchairhacker• 4 years ago

Does it do partial image reconstruction like DALL-E2? Where you cut out part of an existing image and the neural network can fill it back in.

I believe this type of content generation will be the next big thing or at least one of them. But people will want some customization to make their pictures “unique” and fix AI’s lack of creativity and other various shortcomings. Plus edit out the remaining lapses in logic/object separation (which there are some even in the given examples).

Still, being able to create arbitrary stock photos is really useful and i bet these will flood small / low-budget projects

xnx• 4 years ago

OpenAi really thought they had done something with DALL-E, then Google's all "hold my beer".

dntrkv2• 4 years ago

OpenAI*

FargaColora2• 4 years ago

This looks incredible but I do notice that all the images are of a similar theme. Specifically there are no human figures.

influxmoment• 4 years ago

I believe DALLE and likely this model excluded images of people so it could not be misused

FargaColora2• 4 years ago

Interesting, I had not understood that!

benwikler2• 4 years ago

Would be fascinated to see the DALL-E output for the same prompts as the ones used in this paper. If you've got DALL-E access and can try a few, please put links as replies!

qclibre22• 4 years ago

See the paper here : https://gweb-research-imagen.appspot.com/paper.pdf Section E : "Comparison to GLIDE and DALL-E 2"

thorum3• 4 years ago

Imagen seems better at capturing details/nuance from the prompt, but subjectively the DALLE-2 images feel more “real” to me. Not sure why. Something about the lighting?

joeycodes3• 4 years ago

Posting a few comparisons here.

https://twitter.com/joeyliaw/status/1528856081476116480?s=21...

rg1114• 4 years ago

Imagen seems more realistic where Dall-E2 is more feel-good.

That is what I feel personally.

dclowd99014• 4 years ago

Looking at these… I can’t help but wonder if these are literal examples of AI imagination?

jandrese• 4 years ago

Is there a way to try this out? DALL-E2 also had amazing demos but the limitations became apparent once real people had a chance to run their own queries.

wmfrov• 4 years ago

Looks like no, "The potential risks of misuse raise concerns regarding responsible open-sourcing of code and demos. At this time we have decided not to release code or a public demo. In future work we will explore a framework for responsible externalization that balances the value of external auditing with the risks of unrestricted open-access."

nomel8• 4 years ago

the risks of unrestricted open-access

What exactly is the risk?

shannifin• 4 years ago

Nice to see another company making progress in the area. I'd love to see more examples of different artistic styles though, my favorite DALL-E images are the ones that look like drawings.

Mo3• 4 years ago

Is the source in public domain already?

fortran77• 4 years ago

At this time we have decided not to release code or a public demo.

Oh well.

y04nn• 4 years ago

Really impressive. If we are able to generate such detailed images, is there anything similar for text to music? I would I though that it would be simpler to achieve than text to image.

nomel8• 4 years ago

Compare the size of a raw image file to a raw music file, to get an idea of the complexity difference.

penneyd• 4 years ago

Think sheet music, not an mp3

redox99• 4 years ago

Our language is much more effective at describing images than music.

tomatowurst3• 4 years ago

why stop at audio? the pinnacle of this would be text-to-videos, equally indistinguishable from real thing.

burlesona• 4 years ago

The way things look when still is much easier to fake than the way things move.

I would expect AI development to follow a similar path to digital media generally, as its following the increasing difficulty and space requirements of digitally representing said media: text < basic sounds < images < advanced audio < video.

What’s more impressive to me is how far ahead text-to-speech is, but I think the explanation is straightforward (the accessibility value has motivated us to work on that for a lot longer).

touringa• 4 years ago

SymphonyNet: https://youtu.be/m4tT5fx_ih8

ml_basics• 4 years ago

Why is this seemingly official Google blog post on this random non-Google domain?

mmh0000• 4 years ago

You mean one of Google's domains?

  # whois appspot.com
  [Querying whois.verisign-grs.com]
  [Redirected to whois.markmonitor.com]
  [Querying whois.markmonitor.com]
  [whois.markmonitor.com]
  Domain Name: appspot.com
  Registry Domain ID: 145702338_DOMAIN_COM-VRSN
  Registrar WHOIS Server: whois.markmonitor.com
  Registrar URL: http://www.markmonitor.com
  Updated Date: 2022-02-06T09:29:56+0000
  Creation Date: 2005-03-10T02:27:55+0000
  Registrar Registration Expiration Date: 2023-03-10T00:00:00+0000
  Registrar: MarkMonitor, Inc.
  Registrar IANA ID: 292
  Registrar Abuse Contact Email: abusecomplaints@markmonitor.com
  Registrar Abuse Contact Phone: +1.2086851750
  Domain Status: clientUpdateProhibited (https://www.icann.org/epp#clientUpdateProhibited)
  Domain Status: clientTransferProhibited (https://www.icann.org/epp#clientTransferProhibited)
  Domain Status: clientDeleteProhibited (https://www.icann.org/epp#clientDeleteProhibited)
  Domain Status: serverUpdateProhibited (https://www.icann.org/epp#serverUpdateProhibited)
  Domain Status: serverTransferProhibited (https://www.icann.org/epp#serverTransferProhibited)
  Domain Status: serverDeleteProhibited (https://www.icann.org/epp#serverDeleteProhibited)
  Registrant Organization: Google LLC
  Registrant State/Province: CA
  Registrant Country: US
  Registrant Email: Select Request Email Form at https://domains.markmonitor.com/whois/appspot.com
  Admin Organization: Google LLC
  Admin State/Province: CA
  Admin Country: US
  Admin Email: Select Request Email Form at https://domains.markmonitor.com/whois/appspot.com
  Tech Organization: Google LLC
  Tech State/Province: CA
  Tech Country: US
  Tech Email: Select Request Email Form at https://domains.markmonitor.com/whois/appspot.com
  Name Server: ns4.google.com
  Name Server: ns3.google.com
  Name Server: ns2.google.com
  Name Server: ns1.google.com

jefftk4• 4 years ago

While appspot.com is a Google domain, anyone can register domains under it. It would be similarly surprising to see an official GitHub blog post under someproject.github.io

dekhn2• 4 years ago

I'n not certain but I think it's prelease. The paper says the site should be at https://imagen.research.google/ but that host doesn't respond

jonny_eh4• 4 years ago

appspot.com is the domain that hosts all App Engine apps (at least those that don't use a custom domain). It's kind of like Heroku and has been around for at least a decade.

https://cloud.google.com/appengine

jefftk4• 4 years ago

Spring 2008: 14 years!

mshockwave• 4 years ago

IIRC appspot.com is used by App Engine, one of the earliest SaaS platforms provided by Google.

aidenn0• 4 years ago

This is quite suspicious considering that google AI research has an official blog[1], and this is not mentioned at all there. It seems quite possible that this is an elaborate prank.

1: https://ai.googleblog.com/

jeffbee2• 4 years ago

Not just that ... Google Sheets must be the all-time worst way to distribute 200 short strings.

ShakataGaNai• 4 years ago

All of these AI findings are cool in theory. But until its accessible to some decent amount of people/customers - its basically useless fluff.

You can tell me those pictures are generated by an AI and I might believe it, but until real people can actually test it... it's easy enough to fake. This page isn't even the remotest bit legit by the URL, It looks nicely put together and that's about it. Could have easily put together this with a graphic designer to fake it.

Let be clear, I'm not actually saying it's fake. Just that all of these new "cool" things are more or less theoretical if nothing is getting released.

cellis• 4 years ago

Inference times are key. If it can't be produced within reasonable latency, then there will be no real world use case for it because it's simply too expensive to run inference at scale.

theptip• 4 years ago

There are plenty of usecases for generating art/images where a latency of days or weeks would be competitive with the current state of the art.

For example, corporate graphics design, logos, brand photography, etc.

I really do think inference time is a red herring for the first generation of these models.

Sure, the more transformative use-cases like real-time content generation to replace movies/games, but there is a lot of value to be created prior to that point.

dougmwne8• 4 years ago

There's been much prior work done to take these models down from datacenter size to single GPU size. Given continued work in that area and improving GPU performance it seems like it's just a matter of years before inference can be cheap and local for even the most impressive of generation.

unholiness• 4 years ago

Certificate is expired, anyone have a mirror?

minimaxir3• 4 years ago

Generating at 64x64px then upscaling it probably gives the model a substantial performance boost (training speed/convergence) than working at 256x256 or 1024x1024 like DALL-E 2. Perhaps that approach to AI-generated art is the future.

the__alchemist• 4 years ago

I'll be skeptical until I see it in action, vice pre-selected results.

alimov• 4 years ago

Would it be bad to release this with a big warning and flashing gifs letting people know of the issues it has and note that they are working to resolve them / ask for feedback / mention difficulties related to resolving the issues they identified?

tomatowurst3• 4 years ago

when will there be a "DALL-E for porn" ? or is this domain also claimed by Puritans and morality gate keepers? The most in demand text-to-image is use case is for porn.

astrange24• 4 years ago

Train it yourself. Danbooru is a publicly available explicit dataset.

tomatowurst3• 4 years ago

This is not something you can train on a regular AWS gpu-instance without racking up millions of dollars in bills to my knowledge. Dataset isn't an issue its a capex issue.

marcodiego• 4 years ago

Ok. Now, how about the legality of it generating socially unacceptable images like child porn?

faizshah2• 4 years ago

What's the best open source or pre-trained text to image model?

GaggiX3• 4 years ago

latent diffusion model trained on LAION-400M https://github.com/CompVis/latent-diffusion

faizshah2• 4 years ago

Thank you, this is great.

spyremeown• 4 years ago

Jesus, this is so awesome. I think it’s the first AI that really makes me have that “wow” sensation.

dr_dshiv• 4 years ago

How the fck are things advancing so fast? Is it about to level off …or extend to new domains? What’s a comparable set of technical advances?

dqpb• 4 years ago

This video by Juergen Schmidhuber discusses the acceleration of AI progress:

https://youtu.be/pGftUCTqaGg

astrange24• 4 years ago

Bigger model = better because a lot of performance at this task is memorization or the “lottery ticket hypothesis”.

An impressive advance would be a small model that’s capable of working from an external memory rather than memorizing it.

colinmhayes7• 4 years ago

I wondered why all the pictures at the top had sunglasses on, then I saw a couple with eyes. Still some work to do on this one.

londons_explore6• 4 years ago

Figure 2: Non-cherry picked Imagen samples

Hooray! Non-cherry-picked samples should be the norm.

mistrial9• 4 years ago

Reading a relatively-recent Machine Learning paper from some elite source, and after multiple repititions of bragging and puffery, in the middle of the paper, the charts show that they had beaten the score of a high-ranking algorithm in their specific domain, moving the best consistant result from 86% accuracy to 88% accuracy, somewhere around there. My response was: they got a lot of attention within their world by beating the previous score, no matter how small the improvement was.. it was a "winner take all" competition against other teams close to them; the accuracy of less than 90% is really of questionable value in a lot of real world problems; it was an enormous amount of math and effort for this team to make that small improvement.

What I see is semi-poverty mindset among very smart people who appear to be treated in a way such that the winners get promotion, and everyone else is fired. That this sort of analysis with ML is useful for massive data sets at scale, where 90% is a lot of accuracy, not at all for the small sets of real world, human-scale problems where each result may matter a lot. The amount of years of training that these researchers had to go through, to participate in this apparently ruthless environment, are certainly like a lottery ticket, if you are in fact in a game where everyone but the winner has to find a new line of work. I think their masters live in Redmond, if I recall.. not looking it up at the moment.

gwern6• 4 years ago

What you're missing is that the performance on a pretext task like ImageNet top-1 will transfer outside ImageNet, and as you go further into the high score regime, often a small % can yield qualitatively better results because the underlying NN has to solve harder and harder problems, eliciting true solutions rather than a patchwork of heuristics.

Nothing in a Transformer's perplexity in predicting the next token tells you that at some point it suddenly starts being able to write flawless literary style parodies, and this is why the computer art people become virtuosos of CLIP variants and are excited by new ones, because each one attacks concepts in slightly different ways and a 'small' benchmark increase may unlock some awesome new visual flourish that the model didn't get before.

londons_explore6• 4 years ago

If you worked in a hospital and you managed to increase the survival rate from 86% to 88%, you too would be a hero.

Sure, it's only 2%, but if it's on a problem where everyone else has been trying to make that improvement for a long time, and that improvement means big economic or social gains, then it's worth it.

themeiguoren• 4 years ago

I like focusing on the failure rate instead - going from 14% to 12% is a pretty big jump.

davikr• 4 years ago

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.

minimaxir3• 4 years ago

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)

toxicFork2• 4 years ago

What is the AI dungeon incident?

dougmwne8• 4 years ago

GTP-3 was an erotica virtuoso before it was gagged. There's a serious use case here in endless porn generation. Google would very much like to not be in that business.

That said, you can download Dream by Wombo from the app store and it is one of the top smartphone apps, even though it is a few generations behind state of the art.

astrange24• 4 years ago

The current GPT3 on the OpenAI dashboard is perfectly capable of generating erotica or being racist even if you don’t want it to. They didn’t block it so much as put up a warning dialog when it’s acting up.

Actually, I think they made InstructGPT even better at erotica because it’s trained to be “helpful and friendly”, so in other words they made it a sub.

PheonixPharts2• 4 years ago

It is a shame that such powerful AI had to arrive during an age of prudishness that makes the Victorians seem wild.

gerdesj• 4 years ago

"Google would very much like to not be in that business."

Google is not a hobby project anymore: "don't do evil" or whatever they whittered on about back in the day.

LordDragonfang• 4 years ago

You're aware of nothing but creativity from its users. The people using the technology unethically intentionally don't advertise that they're using it.

There's mountains of ai-generated inauthentic content that companies (including Google) have to filter out of their services. This content is used for spam, click farms, scamming, and even state propaganda operations. GPT-2 made this problem orders of magnitude worse than it used to be, and each iteration makes it harder to filter.

The industry term is (generally) "Coordinated Inauthentic Behavior" (though this includes uses of actual human content). I think Smarter Every Day did a good videos (series?) on the topic, and there are plenty of articles on the topic if you prefer that.

thorum3• 4 years ago

That only lasts until the community copies the paper and catches up. For example the open source DALLE-2 implementation is coming along great: https://github.com/lucidrains/DALLE2-pytorch

lucidrains• 4 years ago

Imagen actually shows some of the components in DALLE2 is unnecessary, so Imagen will end up being easier to build. I'll definitely add the dynamic thresholding trick from Imagen to DALLE2 repository though; that is a finding that should boost any DDPMs using classifier free guidance.

natly3• 4 years ago

I don't buy the ethics but I do buy the obvious PR nightmare that would inevitably happen if journalists could play with this and immediately publish their findings of "racist imagery generated by googles AI". That's all it's about and us complaining is not going to make them change their minds.

hda22• 4 years ago

Then they should be honest about it. They can use all the PR lingo they want but don't flat out lie about it.

Lying about ethics or misattributing their actions to some misguided sense of "social" responsibility puts google in a far worse light in my eyes. I can't help but wonder how many skilled employees were driven off from accepting a position at google because of lies like these.

Gigachad3• 4 years ago

There already is an article calling DALL E racist and it isn’t even public yet. Just imagine what horrible things the general public will get it to spit out.

quasar14• 4 years ago

check out open source alternative dalle-mini: https://huggingface.co/spaces/dalle-mini/dalle-mini

bambax2• 4 years ago

Thanks for the link. Doesn't appear to be very good yet, though.

s17n• 4 years ago

Running inference on one of these models takes like a GPU minute, so they can't just let the public use them.

throwawaaaaay17• 4 years ago

They can absolutely do for it if they charge the public the cost of GPU time.

yeldarb• 4 years ago

Can't be this; Google Colab gives out tons of free GPU usage.

whywhywhywhy• 4 years ago

The AI ethics thing is just a PR larp at this point.

“Oh our tech is so dangerous and amazing it could turn the world upside down” yet we hand it to random Bluechecks on Twitter.

It’s just marketing

fragmede• 4 years ago

You know Twitter and Google are different companies, right?

smaudet• 4 years ago

And that's why we need to be shutting this stuff down, completely.

Someone tried to say there were ethics committees etc the other day...what a bad joke. Who checks the ethics committee is making ethical decisions?

I was told I "didn't know what" I was talking about, excuse from some over-important know-it-all who didn't know what ethics was, i.e. they don't know what they are talking about.

fumblebee• 4 years ago

Another consideration here is that hosting a queryable model like this becomes expensive. I remember a couple of years ago lone developer had to take down his site which hosted a freely accessible version of GPT-2 (3?) model because the bills were running to some $20k. (Chump change for Google, but still).

Gigachad3• 4 years ago

I think a lot of people would be perfectly happy to pay per query to access this.

angrysword• 4 years ago

Ethics,racist, LGBT, bla,bla. If we talk about political correct, I really suggest you guys go somewhere else. instead of stay in hacker news. AI generated porn is better that let some people, who do not want to do the porn, doing the porn themselves.

bergenty• 4 years ago

Primarily Indian origin authors on both the DALL-E and this research paper. Just found that impressive considering they make up 1% of the population in the US.

throwaway7432• 4 years ago

https://github.com/lucidrains/imagen-pytorch

CobrastanJorji2• 4 years ago

Is this a joke?

throwaway7432• 4 years ago

atty• 4 years ago

Lucidrains is a champ. If theyre on HN, bravo and thanks for all the reference implementations!

jonahbenton2• 4 years ago

I know that some monstrous majority of cognitive processing is visual, hence the attention these visually creative models are rightfully getting, but personally I am much more interested in auditory information and would love to see a promptable model for music. Was just listening to "Land Down Under" from Men At Work. Would love to be able to prompt for another artist I have liked: "Tricky playing Land Down Under." I know of various generative music projects, going back decades, and would appreciate pointers, but as far as I am aware we are still some ways from Imagen/Dalle for music?

addandsubtract• 4 years ago

I agree. How cool would it be to get an 8 min version of your favorite song? Or an instant DnB remix? Or 10 more songs in the style of your favorite album?

jonahbenton2• 4 years ago

Yeah. I particularly love covers and often can hear in my head X playing Y's song. Would love tools to experiment with that for real.

In practice, my guess is that even though Dall-e level performance in music generation would be stunning and incredible, it would also be tiresome and predictable to consume on any extended basis. I mean- that's my reaction to Dall-e- I find the images astonishing and magical but can only look at them for limited periods of time. At these early stages in this new world the outputs of real individual brains are still more interesting.

But having tools like this to facilitate creation and inspiration by those brains- would be so so cool.

exac• 4 years ago

You can sort of do that with https://fairuseify.ml

astrange24• 4 years ago

I believe we’re lacking someone training up a large music model here, but GPT-style transformers can produce music.

gwern can maybe comment here.

An actually scary thing is that AIs are getting okay at reproducing people’s voices.

gwern6• 4 years ago

Voice synthesis has been going steady. Lots of commercial and hobbyist interest: you can use 15.ai for crackerjack SaaS voice synthesis in a slick free UI; and if you want to run the models yourselves, Tortoise just released a FLOSS stack of remarkable quality.

Music, I'm afraid, appears stuck in the doldrums of small one-offs doing stuff like MIDI. Nothing like the breadth & quality of Jukebox has come out since it, even though it's super-obvious that there is a big overhang there and applying diffusion & other new methods would give you something like much like DALL-E 2 / Imagen for general music.

SemanticStrengh7• 4 years ago

Does it outperform DALL-E V2?

SemanticStrengh7• 4 years ago

Note that there was a close model in 2021 ignored by all https://paperswithcode.com/sota/text-to-image-generation-on-... (on this benchmark) Also what is the score of dalle v2?

SemanticStrengh7• 4 years ago

This competitor might be better for respecting spatial prepositions and photorealism but on a quick look i find the images more uncanny. DALL-E has IMHO better camera POV/distance and is able to make artistic/dreamy/beautiful images. I haven't yet seen this Google model be competitive for art and uncaniness. However progress is great and I might be wrong.

jeffbee2• 4 years ago

Is there anything at all, besides the training images and labels, that would stop this from generating a convincing response to "A surveillance camera image of Jared Kushner, Vladimir Putin, and Alexandria Ocasio-Cortez naked on a sofa. Jeffrey Epstein is nearby, snorting coke off the back of Elvis"?

astrange24• 4 years ago

- The current examples aren’t convincing pictures of “a shiba inu playing a guitar”.

- If you made that picture with actors or in MS Paint, politics boomers on Facebook wouldn’t care either way. They’d just start claiming it’s real if they like the message.

hn_throwaway_994• 4 years ago

As someone who has a layman's understanding of neural networks, and who did some neural network programming ~20 years ago before the real explosion of the field, can someone point to some resources where I can get a better understanding about how this magic works?

I mean, from my perspective, the skill in these (and DALL-E's) image reproductions is truly astonishing. Just looking for more information about how the software actually works, even if there are big chunks of it that are "this is beyond your understanding without taking some in-depth courses".

londons_explore6• 4 years ago

Figure A.4 in the linked paper is a good high level overview of this model. Shame it was hidden away on page 19 in the appendix!

Each box you see there has a section in the paper explaining it in more detail.

hn_throwaway_994• 4 years ago

Uhh, yeah, I'm going to need much more of an ELI5 than that! Looking at Figure A.4, I understand (again, at a very high-level) the first step of "Frozen Text Encoder", and I have a decent understanding of the upsampling techniques used in the last 2 diffusion model steps, but the middle "Text-to-Image Diffusion Model" step that magically outputs a 64x64 pixel image of an actual golden retriever wearing an actual blue checkered beret and red-dotted turtleneck is where I go "WTF??".

rvnx9• 4 years ago

Check https://github.com/multimodalart/majesty-diffusion or https://github.com/lucidrains/DALLE2-pytorch

There is a Google Colab workbook that you can try and run for free :)

This is the image-text pairs behind: https://laion.ai/laion-400-open-dataset/

astrange24• 4 years ago

I mean, from my perspective, the skill in these (and DALL-E's) image reproductions is truly astonishing.

A basic part of it is that neural networks combine learning and memorizing fluidly inside them, and these networks are really really big, so they can memorize stuff good.

So when you see it reproduce a Shiba Inu well, don’t think of it as “the model understands Shiba Inus”. Think of it as making a collage out of some Shiba Inu clip art it found on the internet. You’d do the same if someone asked you to make this image.

It’s certainly impressive that the lighting and blending are as good as they are though.

hn_throwaway_994• 4 years ago

To be clear, I understand the general techniques about (a) how diffusion models can be used to upsample images and generate more photorealistic (or even "cartoon realistic") results and (b) I understand how they can do basic matching of "someone typed in Shiba Inu, look for images of Shiba Inus".

What I don't understand is how they do the composition. E.g. for "A giant cobra snake on a farm. The snake is made out of corn." I think I could understand how it could reproduce the "A giant cobra snake on a farm" part. What I don't understand is how it accurately pictured "The snake is made out of corn." part, when I'm guessing it has never seen images of snakes made out of corn, and the way it combined "snake" with "made out of corn", in a way that is pretty much how I imagined it would look, is the part I'm baffled by.

PheonixPharts2• 4 years ago

these networks are really really big, so they can memorize stuff good.

People tend to really underestimate just how big these models are. Of course these models aren't simply "really really big" MLPs, but the cleverness of the techniques used to build them is only useful at insanely large scale.

I do find these models impressive as examples of "here's what the limit of insane amounts of data, insane amounts of compute can achieve with some matrix multiplication". But at the same time, that's all they are.

What saddens me about the rise of deep neural networks is it is really is the end of the era of true hackers. You can't reproduce this at home. You can't afford to reproduce this one in the cloud with any reasonable amount of funding. If you want to build this stuff your best bet is to go to top tier school, make the right connections and get hired by a mega-corp.

But the real tragedy here is that the output of this is honestly only interesting it if it's the work of some hacker fiddling around in their spare time. A couple of friend hacking in their garage making images of raccoon painting is pretty cool. One of the most powerful, well funded, owners of the likely the most compute resources on the planet doing this as their crowning achievement in AI... is depressing.

ma2rten5• 4 years ago

I get the impression that maybe DALL-E 2 produces slightly more diverse images? Compare Figure 2 in this paper with Figures 18-20 in the DALL-E 2 paper.

benreesman14• 4 years ago

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 a brash generalization, NVIDIA’s group comes to mind, but it’s a very good rule of thumb. Or your whole face the next time you are tempted to doze behind the wheel of a Tesla.

There are two big reasons for this:

- the talent wants to work with the other talent, and through a combination of foresight and deep pockets Google got that exponent on their side right around the time NVIDIA cards started breaking ImageNet. Winning the Hinton bidding war clinched it.

- the current approach of “how many Falcon Heavy launches worth of TPU can I throw at the same basic masked attention with residual feedback and a cute Fourier coloring” inherently favors deep pockets, and obviously MSFT, sorry OpenAI has that, but deep pockets also non-linearly scale outcomes when you’ve got in-house hardware for multiply-mixed precision.

Now clearly we’re nowhere close to Maxwell’s Demon on this stuff, and sooner or later some bright spark is going to break the logjam of needing 10-100MM in compute to squeeze a few points out of a language benchmark. But the incentives are weird here: who, exactly, does it serve for us plebs to be able to train these things from scratch?

meowface• 4 years ago

Not elitist at all; I highly appreciate this post. I know the basics of ML but otherwise am clueless when it comes to the true depths of this field and it's interesting to hear this perspective.

benreesman14• 4 years ago

I used a lot of jargon and lingo and inside baseball in that post, it was intended for people who have deep background.

But if you’re interested I’m happy to (attempt) answers to anything that was jargon: by virtue of HN my answers will be peer-reviewed in real time, and with only modest luck, a true expert might chime in.

ttul• 4 years ago

In short, it’s all about money.

benreesman14• 4 years ago

Yes and no.

For example: the high-frequency trading industry is estimated to have made somewhere between 2-3 billion dollars in all of 2020, profit/earnings. That’s a good weekend at Google.

HFT shops pay well, but not much different to top performers at FAANG.

People work in HFT because without taking a pay cut they can play real ball: they want to try themselves against the best.

Heavy learning people are no different in wanting both a competitive TC but maybe even more to be where the action is.

That’s currently Blade Runner Industries Ltd, but that could change.

f38zf5vdt2• 4 years ago

But the incentives are weird here: who, exactly, does it serve for us plebs to be able to train these things from scratch?

I'm not sure it matters. The history of computing shows that within the decade we will all have the ability to train and use these models.

sinenomine10• 4 years ago

... Unless the possession of capable models becomes a legal liability by that time.

dougabug3• 4 years ago

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-540b).

Google clearly demonstrates their unrivaled capability to leverage massive quantities of data and compute, but it’s premature to declare that they’ve secured victory in the AI Wars.

benreesman14• 4 years ago

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.

joshcryer6• 4 years ago

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.

benreesman14• 4 years ago

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.

Herodotus382• 4 years ago

Is Maxwell's Demon applicable to this scenario? I'm not a physicist but I recently had to look it up after talking with someone and thought it had to do with a specific thermodynamic thought experiment with gas particles and heat differences. Is there is another application I don't understand with computing power?

benreesman14• 4 years ago

You’re absolutely right that I used a sloppy analogy there.

It’s Boltzmann and Szilard that did the original “kT” stuff around underlying thermodynamics governing energy dissipation in these scenarios, and Rolf Landaeur (I think that’s how you spell it) who did the really interesting work on how to apply that thermo work to lower-bounds on energy-expenditure in a given computation.

I said Maxwell’s Demon because it’s the best known example of a deep connection between useful work and computation. But it was sloppy.

chillee• 4 years ago

Neither OpenAI nor FAIR ever has the top score on anything unless Google delays publication.

This is ... very incorrect. I am very certain (95%+) that Google had nothing even close to GPT-3 at the time of its release. It's been 2 full years since GPT-3 was released, and even longer since OpenAI actually trained it.

That's not to talk about any of the other things OpenAI/FAIR has released that were SOTA at the time of release (Dall-E 1, JukeBox, Poker, Diplomacy, Codex).

Google Brain and Deepmind have done a lot of great work, but to imply that they essentially have a monopoly on SOTA results and all SOTA results other labs have achieved are just due to Google delaying publication is ridiculous.

gwern6• 4 years ago

Yeah, at the time, GB was still very big on mixture-of-expert models and bidirectional models like T5. (I'm not too enthusiastic about the former, but the latter has been a great model family and even if not GPT-3, still awesome.) DeepMind pivoted faster than GB, based on Gopher's reported training date, and GB followed some time after. But definitely neither had their own GPT-3-scale dense Transformer when GPT-3 was published.

benreesman14• 4 years ago

Any “brash generalization” is clearly going to be grossly incorrect in concrete cases, and while I have a little gossip from true insiders, it’s nowhere near enough to make definitive statements about specific progress on teams at companies that I’ve never worked for.

I did a bit of disclaimer on my original post but not enough to withstand detailed scrutiny. This is sort of the trouble with trying to talk about cutting-edge research in what amounts to a tweet: what’s the right amount of oversimplified, emphatic statement to add legitimate insight but not overstep into being just full of shit.

I obviously don’t know that publication schedules at heavy-duty learning shops are deliberate and factor-in other publications. The only one I know anything concretely about is FAIR and even that’s badly dated knowledge.

I was trying to squeeze into a few hundred characters my very strong belief that Brain and DM haven’t let themselves be scooped since ResNet, based on my even stronger belief that no one has the muscle to do it.

To the extent that my oversimplification detracted from the conversation I regret that.

davelondon• 4 years ago

I'M SQUEEZING MY PAPER!

ALittleLight2• 4 years ago

Interesting to me that this one can draw legible text. DALLE models seem to generate weird glyphs that only look like text. The examples they show here have perfectly legible characters and correct spelling. The difference between this and DALLE makes me suspicious / curious. I wish I could play with this model.

Tehdasi• 4 years ago

Still has the issue with screwing up mechanical objects. In their demo checkout the wheels on the skateboards, all over the place.

sdenton4• 4 years ago

For comparison, most humans can't draw a bicycle:

https://www.wired.com/2016/04/can-draw-bikes-memory-definite...

gpt5• 4 years ago

I only see the problem for the paintings. If you choose a photo it's good. Could be a problem in the source data (i.e. paintings of mechanical objects are imperfect).

GaggiX3• 4 years ago

Imagen takes text embeddings, OpenAI model takes image embeddings instead, this is the reason. There are other models that can generate text: latent diffusion trained on LAION-400M, GLIDE, DALL-E (1).

ALittleLight2• 4 years ago

My understanding of the terms text and image embeddings is that they are ways of representing text or images as vectors. But, I don't understand how that would help with the process of actually drawing the symbols for those letters.

ricardobeat• 4 years ago

I thought the weird text in DALL-E 2 was on purpose to prevent malicious use.

zimpenfish3• 4 years ago

The latent-diffusion[1] one I've been playing with is not terrible at drawing legible text but generally awful at actually drawing the text you want (cf. [2]) (or drawing text when you don't want any.)

[1] https://github.com/CompVis/latent-diffusion.git [2] https://imgur.com/a/Sl8YVD5

the8472• 4 years ago

DALLE1 was able to render text[0]. That DALLE2 isn't probably is a tradeoff introduced by unCLIP in exchange for diverse results. Now the google model is better yet and doesn't have to make that tradeoff.

[0] https://openai.com/blog/dall-e/#text-rendering

syspec• 4 years ago

I'm curious why all of these tools seem to be almost tailored toward making meme images?

The kind of early 2010's, over the top description of something that's ridiculous

benreesman14• 4 years ago

These things can make any image you can define in terms of a corpus of other images. That was true at lower resolution five years ago.

To the extent that they get used for making bored ape images or whatever meme du juor, it says much more about the kind of pictures people want to see.

I personally find the weird deep dreaming dogs with spikes coming out of their heads more mathematically interesting, but I can understand why that doesn’t sell as well.

TaylorPhebillo• 4 years ago

My hunch is that they aren't tailored toward ridiculous images exactly, but if they demonstrated "a woman sitting in a chair reading", it would be really hard to tell if the result was a small modification of an image in the training data. If they demonstrate "A snake made out of corn", I have less concern about the model having a very close training example.

B1FF_PSUVM• 4 years ago

Also, almost 40 years ago, the name of a laser printer capable of 200 dpi.

Almost there, the Apple Laserwriter nailed it at 300 dpi.

Sometimes sneaked an issue of the "SF-Lovers Digest" in between code printouts.

lxe• 4 years ago

Hey I also wrote a neural net that generates perfect images. Here's a static site about it. With images it definitely generated! Can you use it? Is there a source? Hah, of course not, because ethics!

Veedrac• 4 years ago

I thought I was doing well after not being overly surprised by DALL-E 2 or Gato. How am I still not calibrated on this stuff? I know I am meant to be the one who constantly argues that language models already have sophisticated semantic understanding, and that you don't need visual senses to learn grounded world knowledge of this sort, but come on, you don't get to just throw T5 in a multimodal model as-is and have it work better than multimodal transformers! VLM[1] at least added fine-tuned internal components.

Good lord we are screwed. And yet somehow I bet even this isn't going to kill off the they're just statistical interpolators meme.

[1] https://www.deepmind.com/blog/tackling-multiple-tasks-with-a...

benreesman14• 4 years ago

It’s just my opinion but I think the meme you’re talking about is deeply related to other branches of science and philosophy: ranging from the trust old saw about AI being anything a computer hasn’t done yet to deep meditations on the nature of consciousness.

They’re all fundamentally anthropocentric: people argue until they are blue in the face about what “intelligent” means but it’s always implicit that what they really mean is “how much like me is this other thing”.

Language models, even more so than the vision models that got them funded have empirically demonstrated that knowing the probability of two things being adjacent in some latent space is at the boundary indistinguishable from creating and understanding language.

I think the burden is on the bright hominids with both a reflexive language model and a sex drive to explain their pre-Copernican, unique place in the theory of computation rather than vice versa.

A lot of these problems just aren’t problems anymore if performance on tasks supersedes “consciousness” as the thing we’re studying.

ravi-delia3• 4 years ago

I'd argue that there is probably at least one leap in terms of human-level writing which isn't just pure prediction. Humans write with intent, which is how we can maintain long run structure. I definitely write like GPT while I'm not paying attention, but with the executive on the task I outperform it. For all we know this is solvable with some small tweak to architecture, and I rather doubt that a model which has solved this problem need be conscious (though our own solution seems correlated with consciousness), but it is one more step.

axg11• 4 years ago

I firmly believe that ~20-40% of the machine learning community will say that all ML models are dumb statistical interpolators all the way until a few years after we achieve AGI. Roughly the same groups will also claim that human intelligence is special magic that cannot be recreated using current technology.

I think it’s in everyone’s benefit if we start planning for a world where a significant portion of the experts are stubbornly wrong about AGI. As a technology, generally intelligent ML has the potential to change so many aspects of our world. The dangers of dismissing the possibility of AGI emerging in the next 5-10 years are huge.

sinenomine10• 4 years ago

The dangers of dismissing the possibility of AGI emerging in the next 5-10 years are huge.

Again, I think we should consider "The Human Alignment Problem" more in this context. The transformers in question are large, heavy and not really prone to "recursive self-improvement".

If the ML-AGI works out in a few years, who gets to enter the prompts?

woeirua• 4 years ago

You should be much more concerned about the prospect of nuclear war right now than the sudden emergence of an AGI.

Daishiman2• 4 years ago

These ML models aren't capable of generating novel thinking. They allow for extracting knowledge from an existing network. They cannot declare new ideas, identify how to validate them, and gather data and reach conclusions.

skybrian2• 4 years ago

I think it's something like a very intelligent Borgian library of babel. There are all sorts of books in there, by authors with conflicting opinions and styles, due to the source material. The librarian is very good at giving you something you want to read, but that doesn't mean it has coherent opinions. It doesn't know or care what's authentic and what's a forgery. It's great for entertainment, but you wouldn't want to do research there.

For image generation, it's obviously all fiction. Which is fine and mostly harmless if you you know what you're getting. It's going to leak out onto the Internet, though, and there will be photos that get passed around as real.

For text, it's all fiction too, but this isn't obvious to everyone because sometimes it's based on true facts. There's often not going to be an obvious place where the facts stop and the fiction starts.

The raw Internet is going to turn into a mountain of this stuff. Authenticating information is going to become a lot more important.

hooande• 4 years ago

I haven't been overly surprised by any of it. The final product is still the same, no matter how much they scale it up.

All of these models seem to require a human to evaluate and edit the results. Even Co-Pilot. In theory this will reduce the number of human hours required to write text or create images. But I haven't seen anyone doing that successfully at scale or solving the associated problems yet.

I'm pessimistic about the current state of AI research. It seems like it's been more of the same for many years now.

ComputerGuru• 4 years ago

It seems to have the same "adjectives bleed into everything problem" that Dall-E does.

Their slider with examples at the top showed a prompt along the lines of "a chrome plated duck with a golden beak confronting a turtle in a forest" and the resulting image was perfect - except the turtle had a golden shell.

codemonkey-zeta• 4 years ago

Probably just a frontend coding mistake, and not an error in the model, but in the interactive example if you select:

"A photo of a Shiba Inu dog Wearing a (sic) sunglasses And black leather jacket Playing guitar In a garden"

The Shiba Inu is not playing a guitar.

didgeoridoo• 4 years ago

Found the QA tester.

spekcular• 4 years ago

Also, no sunglasses in "A photo of a raccoon wearing sunglasses and a red shirt riding a bike in a garden," and a few similar prompts (e.g. surfing).

astrange24• 4 years ago

There are visible “alignment” issues in some of their examples still. The marble koala DJ in the paper doesn’t use several of the keywords.

They have an example “horse riding an astronaut” that no model produces a correct image for. It’d be interesting if models could explain themselves or print the caption they understand you as saying.

discmonkey• 4 years ago

For people complaining that they can't play with the model... I work at Google and I also can't play with the model :'(

make3• 4 years ago

I mean I don't know how that makes it any better from a reproducibility stand point lol

arthurcolle• 4 years ago

How does that make you feel?

quickthrower22• 4 years ago

Probably like an employee

karmasimida• 4 years ago

I mean inference on this cost not small money.

I don't think they would host this for fun then.

hathym• 4 years ago

off-topic: as a google employee do you have unlimited gce credits?

octocop2• 4 years ago

is your team/division hiring?

Firmwarrior• 4 years ago

Every tech megacorp is always hiring people who can jump through the flaming code hoops just right

interblag• 4 years ago

I think they address some of the reasoning behind this pretty clearly in the write-up as well?

The potential risks of misuse raise concerns regarding responsible open-sourcing of code and demos. At this time we have decided not to release code or a public demo. In future work we will explore a framework for responsible externalization that balances the value of external auditing with the risks of unrestricted open-access.

I can see the argument here. It would be super fun to test this model's ability to generate arbitrary images, but "arbitrary" also contains space for a lot of distasteful stuff. Add in this point:

While a subset of our training data was filtered to removed noise and undesirable content, such as pornographic imagery and toxic language, we also utilized LAION-400M dataset which is known to contain a wide range of inappropriate content including pornographic imagery, racist slurs, and harmful social stereotypes. Imagen relies on text encoders trained on uncurated web-scale data, and thus inherits the social biases and limitations of large language models. As such, there is a risk that Imagen has encoded harmful stereotypes and representations, which guides our decision to not release Imagen for public use without further safeguards in place.

That said, I hope they're serious about the "framework for responsible externalization" part, both because it would be really fun to play with this model and because it would be interesting to test it outside of their hand-picked examples.

Siira• 4 years ago

harmful stereotypes and representations

So we can't have this model because of ... the mere possibility of stereotypes? With this logic, humans should all die, as we certainly encode some nasty stereotypes in our brains.

This level of dishonesty to not give back to the community is not unexpected at this point, but seeing apologists here is.

OrangeMusic• 4 years ago

I find this is a really bad precedent. Not far from now they'll achieve "super human" level general AI, and be like "yeah it's too powerful for you, we'll keep it internal".

SemanticStrengh7• 4 years ago

Is brocoliman standing in the way? :(

kvetching2• 4 years ago

Good thing there is a company committed to Open Sourcing these sorts of AI models.

Oh wait.

Google: "it's too dangerous to release to the public"

OpenAI: "we are committed to open source AGI but this model is too dangerous to release to the public"

SnowHill99024• 4 years ago

Could there exist a quine for this?

hahajk• 4 years ago

Off topic, but this caught my attention:

“In future work we will explore a framework for responsible externalization that balances the value of external auditing with the risks of unrestricted open-access.”

I work for a big org myself, and I’ve wondered what it is exactly that makes people in big orgs so bad at saying things.

dougmwne8• 4 years ago

I think they were being careful not to be too quotable there on CNN.

iuppiter• 4 years ago

so cool

qz_kb2• 4 years ago

I have to wonder how much releasing these models will "poison the well" and fill the internet with AI generated images that make training an improved model difficult. After all if every 9/10 "oil painted" image online starts being from these generative models it'll become increasingly difficult to scrape the web and to learn from real world data in a variety of domains. Essentially once these things are widely available the internet will become harder to scrape for good data and models will start training on their own output. The internet will also probably get worse for humans since search results will be completely polluted with these "sort of realistic" images which can ultimately be spit out at breakneck speed by smashing words from a dictionary together...

JayStavis• 4 years ago

Huh, I had never thought of that. Makes it seem like there's a small window of authenticity closing.

The irony is that if you had a great discriminator to separate the wheat from the chaff, that it would probably make its way into the next model and would no longer be useful.

My only recommendation is that OpenAI et al should be tagging metadata for all generated images as synthetic. That would be a really interesting tag for media file formats (would be much better native than metadata though) and probably useful across a lot of domains.

joshspankit4• 4 years ago

The OpenAI access agreement actually says that you must add (or keep?) a watermark on any generated images, so you’re in good company with that line of thinking.

whatshisface• 4 years ago

I don't think it will "poison the well" so much as change it - images that humans like more will get a higher pagerank, so the models trained on Google Images will not so much as degrade as they will detach from reality and begin to follow the human mind they way plausible fiction does.

rhacker4• 4 years ago

Look at carpentry blogs, recipe blogs. Nearly all of it is junk content. I bet if you combined GPT and imagen or dalle2 you could replace all of them. Just provide a betty crocker recipe and let it generate a blog that has weekly updates and even a bunch of images - "happy family enjoying pancakes together"

I can see the future as being devoid of any humanity.

animal_spirits• 4 years ago

The future digital landscape might be void of humanity, but there will still be real humans living next door to you ;)

joshspankit4• 4 years ago

I’d much rather skip the blog format and replace them with an AI that can answer “Please provide a pie recipe like my grandparent’s”, or “I’d like to make these ribs on the BBQ so that they come out flavourful, soft, and a little sweet.”

slimsag• 4 years ago

I wrote a comedic "Best Apache Chef recipe" article[1] mocking these sites.

I guess the concern would be: If one of these recipe websites _was_ generated by an AI, the ingredients _look_ correct to an AI but are otherwise wrong - then what do you do? Baking soda swapped with baking powder. Tablespoons instead of teaspoons. Add 2tbsp of flower to the caramel macchiato. Whoops! Meant sugar.

[0] http://slimsag.com/best-apache-chef-recipe/1438731.htm

exikyut• 4 years ago

"Picture of happy nuclear family enjoying paperclip maximization at the beach"

thelittleone• 4 years ago

Seeing this a lot on youtube also. Scripts pulling in "news" from a source as a script for a robo voice combined with "related" images stitched together randomly.

4m1rk• 4 years ago

Doesn't it increases the value of genuine human-produced content? Or their NFTs!

rmbyrro• 4 years ago

I see the opposite future.

As AI advances, a lot of people will look after experiencing life outside the digital world.

Even digital communication will not be trustworthy anymore with deepfaces and everything else, so people will want to get together more often.

Edit: for the lazy ones, yeah, digital will be a sad and heartless environment...

kimi• 4 years ago

I can see the future as being devoid of any humanity.

Considering how many of the readers of said blog will be scrapers and bots, who will use the results to generate more spammy "content", I think you are right.

walt74• 4 years ago

I can see the future as being devoid of any humanity.

I can see a past where this already happened, to paraphrase Douglas Adams ;)

joshspankit4• 4 years ago

Just yesterday I was speculating that current AI is bad at math because math on the internet is spectacularly terrible.

I think you’re right, and it’s unlikely that we (society) will convince people to label their AI content as such so that scraping is still feasible.

It’s far more likely that companies will be formed to provide “pristine training sets of human-created content”, and quite likely they will be subscription based.

trhway• 4 years ago

“pristine training sets of human-created content”

well, we do have organic/farmed/handcrafted/etc. food. One can imagine information nutrition label - "contains 70% AI generated content, triggers 25% of the daily dopamine release target".

kleer001• 4 years ago

How would that really happen? It seems to me you're assuming that there's no such thing as extant databases of actual oil paintings, that people will stop producing, documenting, and curating said paintings. I think the internet and curated image databases are far more well kept than your proposed model accounts for.

qz_kb2• 4 years ago

My hypothetical example is not really about oil paintings, but the fact these models will surely get deployed and used for stock photos for articles, on art pages etc.

I think this will introduce unavoidable background noise that will be super hard to fully eliminate in future large scale data sets scraped from the web, there's always going to be more and more photorealistic pictures of "cats" "chairs" etc. in the data that are close to looking real but not quite, and we can never really go back to a world where there's only "real" pictures, or "authentic human art" on the internet.

agar2• 4 years ago

The irony is that when the majority of content becomes computer-generated, most of that content will also be computer-consumed.

Neil Stephenson covered this briefly in "Fall; or Dodge In Hell." So much 'net content was garbage, AI-generated, and/or spam that it could only be consumed via "editors" (either AI or AI+human, depending on your income level) that separated the interesting sliver of content from...everything else.

jillesvangurp2• 4 years ago

He was definitely onto something in that book where people also resort to using blockchains to fingerprint their behavior and build an unbreakable chain of authenticity. Later in that book that is used to authorize the hardware access of the deceased and uploaded individuals.

A bit far out there in terms of plot but the notion of authenticating based on a multitude of factors and fingerprints is not that strange. We've already started doing that. It's just that we currently still consume a lot of unsigned content from all sorts of unreliable/untrustworthy sources.

Fake news stops being a thing as soon as you stop doing that. Having people sign off on and vouch for content needs to start becoming a thing. I might see Joe Biden saying stuff in a video on Youtube. But how do I know if that's real or not?

With deep fakes already happening, that's no longer an academic question. The answer is that you can't know. Unless people sign the content. Like Joe Biden, any journalists involved, etc. You might still not know 100% it is real but you can know whether relevant people signed off on it or not and then simply ignore any unsigned content from non reputable sources. Reputations are something we can track using signatures, blockchains, and other solutions.

Interesting with Neal Stephenson that he presents a problem and a possible solution in that book.

Gigachad3• 4 years ago

I wonder if google images could just seed in some generated images when none relevant are found..

VMG• 4 years ago

It will not be limited to the internet. Have you looked at a magazine stand in the last 10 years? The content looks generated (not by AI) even today.

Cheap books, cheap TV and cheap music will be generated.

bowmessage• 4 years ago

I also worry about the potential to further stifle human creativity, e.g. why paint that oil painting of a panda riding a bicycle when I could generate one in seconds?

telesilla• 4 years ago

Our imaginations are gigantic. We'll find something else impressive and engaging to do. Or not care. I'm not worried. Watch children: they find a way to play even when there is nothing.

richrichardsson• 4 years ago

One reason:

A digital picture of an oil painting != an actual oil painting

Of course once someone trains an AI with a robotic arm to do the actual painting, then your worry holds firm.

rg1114• 4 years ago

People training newer models just have to look for the "Imagen" tag or the Dall-E2 rainbow at the corner and heuristically exclude images having these. This is trivial.

Unless you assume there are bad actors who will crop out the tags. Not many people now have access to Dall-E2 or will have access to Imagen.

As someone working in Vision, I am also thinking about whether to include such images deliberately. Using image augmentation techniques is ubiquitous in the field. Thus we introduce many examples for training the model that are not in the distribution over input images. They improve model generality by huge margins. Whether generated images improve generality of future models is a thing to try.

Damn I just got an idea for a paper writing this comment.

zone4113• 4 years ago

In my melody generation system I'm already including melodies that I've judged as "good" (https://www.youtube.com/playlist?list=PLoCzMRqh5SkFwkumE578Y...) in the updated training set. Since the number of catchy melodies that have been created by humans is much, much lower than the number of pretty images, it makes a significant difference. But I'd expect that including AI-generated images without human quality judgement scores in the training set won't be any better than other augmentation techniques.

viraptor• 4 years ago

Unless you assume there are bad actors who will crop out the tags.

I don't know why people do that but lots of randoms on the internet do that and they're not even bad actors per se. The removed signatures from art posted online became a kind of a meme itself. Especially when comic strips are reposted on Reddit. So yeah, we'll see lots of them.

SirHound• 4 years ago

Most images you see from these services will not have a watermark on them. Cropping is trivial.

dclowd99014• 4 years ago

Eventually the only jobs humans will have is training AI to act human. Sounds very Philip K Dick now that I think about it.

actionfromafar2• 4 years ago

The transition will be complete when some AI can fool/bribe the other AIs that its workers are human.

afro88• 4 years ago

I can see a world where in person consumption of creative media (art, music, movies etc), where all devices are to be left at the door, becomes more and more sought after and lucrative.

If the AI models can't consume it, it can't be commoditised and, well, ruined.

ismepornnahi• 4 years ago

Adding a watermark to all AI generated images should be imperative.

abel_• 4 years ago

On the contrary -- the opposite will happen. There's a decent body of research showing that just by training foundation models on their outputs, you amplify their capabilities.

Less common opinion: this is also how you end up with models that understand the concept of themselves, which has high economic value.

Even less common opinion: that's really dangerous.

benlivengood• 4 years ago

I think instead the images people want to put on the Internet will do the same for these models as adversarial training did for AlphaZero; it will learn what kinds of images engage human reaction.

rajnathani• 4 years ago

For better training data in the future: Storing a content hash and author identification (an example proprietary solution right now [0]) of image authors, and having a decentralized reputation system for people/authors would help be the solution for better training data in the future whereby authors can gain reputation/incentives too.

[0] https://creativecloud.adobe.com/discover/article/how-to-use-...

LoveMortuus2• 4 years ago

Maybe we'll go back to index based search engines like Yahoo. Could resolve many issues we see today, but I think the biggest question is scalability. Maybe some open source open database system?

kulikalov• 4 years ago

Good looking images will be popular, bad looking images will be disposed on the backyard of the internet. Even if next iterations of these models will be trained on AI-generated images, the dataset will be well filtered by how much people like those images. After all, that's the purpose of any art, right?

gwern6• 4 years ago

I don't think it will be a big deal, for multiple different reasons: https://www.lesswrong.com/posts/uKp6tBFStnsvrot5t/what-dall-...

rhacker4• 4 years ago

Next phase of all this: Image to 3d printable template files compatible with various market available printers.

Print me a racoon in a leather jacket riding a skateboard.

d--b• 4 years ago

One thing that no one predicted in AI development was how good it would become at some completely unexpected tasks while being not so great at the ones we supposed/hoped it would be good.

AI was expected to grow like a child. Somehow blurting out things that would show some increasing understanding on a deep level but poor syntax.

In fact we get the exact opposite. AI is creating texts that are syntaxically correct and very decently articulated and pictures that are insanely good.

And these texts and images are created from a text prompt?! There is no way to interface with the model other than by freeform text. That is so weird to me.

Yet it doesn’t feel intelligent at all at first. You can’t ask it to draw “a chess game with a puzzle where white mates in 4 moves”.

Yet sometimes GPT makes very surprising inferences. And it starts to feel like there is something going on a deeper level.

DeepMind’s AlphaXxx models are more in line with how I expected things to go. Software that gets good at expert tasks that we as humans are too limited to handle.

Where it’s headed, we don’t know. But I bet it’s going to be difficult to tell the “intelligence” from the “varnish”

satokausi• 4 years ago

I doubt 99% of humans can draw a ”chess game with a puzzle where white mates in 4 moves”

bambax2• 4 years ago

Maybe not draw, but we can do an image search for "chess puzzle mate in 4" which gives plenty of results:

https://www.google.com/search?q=chess+puzzle+mate+in+4&tbm=i...

It would be surprising if AI couldn't do the same search and produce a realistic drawing out of any one of the result puzzles.

astrange24• 4 years ago

They can with computer assistance, and this AI sort of has that in that it’s both some “intelligence” and a whole lot of memorized internet, with the issue that we don’t know how to separate those things.

stevage• 4 years ago

It's surprisingly easy to construct such a thing.

If you want to be trivial about it, you can just have white back-rank mate with a rook, and black has 4 pieces to block with.

Layke11232• 4 years ago

Syntactically* I know it's the most trivial of things, but in case you were curious as I often am!

Layke11232• 4 years ago

Oh God! Am I a bot?

planb2• 4 years ago

Seeing the artificial restrictions to this model as well as to DALL-E 2, I can't help but ask myself why the porn industry isn't driving its own research. Given the size of that industry and the sheer abundance of training material, it seems just a matter of time until you can create photo realistic images of yourself with your favourite celebrity for a small fee. Is there anything I am missing? Can you only do this kind of research at google or openai scale?

alexb_3• 4 years ago

Porn is actually a really good litmus test to see if a money/media transfer technology has real promise. Pornography needs exactly 2 things to work well - a way to deliver media, and a way to collect money. If you truly have a system that can do one of those two things better than we currently can, and it's not just empty hype, it will be used for porn. "Empty hype" won't touch that stuff, but real-world usecases will.

Unrelated to the main topic, but this is exactly why I think cryptocurrencies will only be used for illegal activities, or things you may want to hide, and nothing else. Because that's where it has found its usecase in porn.

planb2• 4 years ago

Wow. The last paragraph of my comment looked nearly identical to yours, but I deleted it before submitting because I didn't want to derail. Exactly my thoughts...

rg1114• 4 years ago

Transfer learning is a thing.

But I have not tried making generative models with out-of-distribution data before. Distributions other than main training data.

There are several indie attempts that I am aware of. Mentioning them to the reply of this comment. (In case the comment gets deleted)

The first layers should be general. But the later layers should not behave well to porn images. As they are more specialist layers learning distribution specific visual patters.

Transfer learning is posssible.

rg1114• 4 years ago

1. DeepCreamPy draws over hentai sensor bars if you direct it where the bar is: https://github.com/gguilt/DeepCreamPy

2. hentAI automates the process: https://github.com/natethegreate/hent-AI

3. [NSFW] Should look at this person on Twitter: https://twitter.com/nate_of_hent_ai

4. [NSFW] PornHub released vintage porn videos upscaled to 4k with AI a while back. The called it the "Remastured Project": https://www.pornhub.com/art/remastured

5. [NSFW] This project shows the limit of AI-wthout-big-tech-or-corporate-support projects. This project creates female genitalia that don't exist in the real world. Project is "This Vagina Does Not Exist": https://thisvaginadoesnotexist.com/about.html

nootropicat• 4 years ago

This is completely outside the competency of the current porn industry.

You gave an example of a still image, but it's going to end up with an AI generating a full video according to a detailed text prompt. The porn industry is going to be utterly destroyed.

rishabhjain• 4 years ago

Used some of the same prompts and generated results with open source models, model I am using fails on long prompts but does well on short and descriptive prompts. Results:

https://imgur.com/gallery/6qAK09o

xyzal• 4 years ago

Tangentially related question: what is the best (~latest?) such a network uploaded to Colab one can toy with?

wiz21c• 4 years ago

I find it a bit disturbing that they talk about social impact of totally imaginary pictures of racoon.

Of course, working in a golden lab at Google may twist your views on society.

dougmwne8• 4 years ago

Oh, I would say they are probably underestimating the impact. You only saw the images they thought couldn't raise alarm bells. Anyone will be able to create photorealistic images of anyone doing anything, Anything! This is certainly a dangerous and society altering tech. It won't all be teddy bears and racoons playing poker.

octocop2• 4 years ago

Would be awesome to see a side by side comparison to DALL-E, generating from the same text

mlfn• 4 years ago

It's in the PDF.

geonic• 4 years ago

Can anybody give me short high-level explanation how the model achieves these results? I'm especially interested in the image synthesis, not the language parsing.

For example, what kind of source images are used for the snake made of corn[0]? It's baffling to me how the corn is mapped to the snake body.

[0] https://gweb-research-imagen.appspot.com/main_gallery_images...

dave_sullivan• 4 years ago

Well, first they parse the language into a high level vector representation. Then they take images and add noise and train a model to remove the noise so it can start with a noisy image and produce a clear image from it. Then they train a model to map from the word representation for text to the noisy image representation for the corresponding image. Then they upsample twice to get to good resolution.

So text -> text representation -> most likely noised image space -> iteratively reduce noise N times -> upsample result

Something like that, please correct anything I'm missing.

Re: the snake corn question, it is mapping the "concept" of corn to the concept of a body as represented by intermediary learned vector representations.

DougBTX• 4 years ago

In the paper they say about half the training data was an internal training set, and the other half came from: https://laion.ai/laion-400-open-dataset/

kordlessagain• 4 years ago

Since guidance weights are used to control image quality and text alignment, we also report ablation results using curves that show the trade-off between CLIP and FID scores as a function of the guidance weights (see Fig. A.5a). We observe that larger variants of T5 encoder results in both better image-text alignment, and image fidelity. This emphasizes the effectiveness of large frozen text encoders for text-to-image models

I usually consider myself fairly intelligent, but I know that when I read an AI research paper I'm going to feel dumb real quick. All I managed to extract from the paper was a) there isn't a clear explanation of how it's done that was written for lay people and b) they are concerned about the quality and biases in the training sets.

Having thought about the problem of "building" an artificial means to visualize from thought, I have a very high level (dumb) view of this. Some human minds are capable of generating synthetic images from certain terms. If I say "visualize a GREEN apple sitting on a picnic table with a checkerboard table cloth", many people will create an image that approximately matches the query. They probably also see a red and white checkerboard cloth because that's what most people have trained their models on in the past. By leaving that part out of the query we can "see" biases "in the wild".

Of course there are people that don't do generative in-mind imagery, but almost all of us do build some type of model in real time from our sensor inputs. That visual model is being continuously updated and is what is perceived by the mind "as being seen". Or, as the Gorillaz put it:

  … For me I say God, y'all can see me now
  'Cos you don't see with your eye
  You perceive with your mind
  That's the end of it…

To generatively produce strongly accurate imagery from text, a system needs enough reference material in the document collection. It needs to have sampled a lot of images of corn and snakes. It needs to be able to do image segmentation and probably perspective estimation. It needs a lot of semantic representations (optimized query of words) of what is being seen in a given image, across multiple "viewing models", even from humans (who also created/curated the collections). It needs to be able to "know" what corn looks like, even from the perspective of another model. It needs to know what "shape" a snake model takes and how combining the bitmask of the corn will affect perspective and framing of the final image. All of this information ends up inside the model's network.

Miika Aittala at Nvidia Research has done several presentations on taking a model (imagined as a wireframe) and then mapping a bitmapped image onto it with a convolutional neural network. They have shown generative abilities for making brick walls that looks real, for example, from images of a bunch of brick walls and running those on various wireframes.

Maybe Imagen is an example of the next step in this, by using diffusion models instead of the CNN for the generator and adding in semantic text mappings while varying the language models weights (i.e. allowing the language model to more broadly use related semantics when processing what is seen in a generated image). I'm probably wrong about half that.

Here's my cut on how I saw this working from a few years ago: https://storage.googleapis.com/mitta-public/generate.PNG

Regardless of how it works, it's AMAZING that we are here now. Very exciting!

anoncow• 4 years ago

Another ad for DALL-E.

causi• 4 years ago

One thing I find particularly fascinating is that all the elements of the resulting image have a relatively cohesive art style.

beeskneecaps• 4 years ago

It’s terrifying that all of these models are one colab notebook away from unleashing unlimited, disastrous imagery on the internet. At least some companies are starting to realize this and are not releasing the source code. However they always manage to write a scientific paper and blog post detailing the exact process to create the model, so it will eventually be recreated by a third party.

Meanwhile, Nvidia sees no problem with yeeting stylegan and and models that allow real humans to be realistically turned into animated puppets in 3d space. The inevitable end result of these scientific achievements will be orders of magnitude worse than deepfakes.

Oh, or a panda wearing sunglasses, in the desert, digital art.

isx726552• 4 years ago

I am absolutely terrified of all this for a different reason: all human professions (not just art) will soon be replaced by “good enough” AI, creating a world flooded with auto-generated junk and billions of people trapped permanently in slums, because you can’t compete with free, and no one can earn a living any longer.

It’s an old fear for sure but it seems to be getting closer and closer every day, and yet most of the discussion around these things seems to be variations of “isn’t this cool?”

orblivion• 4 years ago

And then once you take the (probably trivial) step where the computers come up with the ideas for the images, these images won't be interesting anymore because we know a human didn't even make it. It won't be funny in the same way. "Oh that was clever" doesn't make sense anymore. We could reach a new level of jaded.

(Also, hello readers from the year 2032 when all of these predictions sound silly.)

pretendscholar• 4 years ago

As soon as middle class work starts to get automated we will form a new system of resource allocation because all of a sudden the current tax system doesn't work and we go through the mother of all economic crises because no one has any money.

kvetching2• 4 years ago

I, for One, Welcome Our Robot Overlords

butz• 4 years ago

Looking at example pictures it seems that this model has trouble with putting sunglasses on a racoon.

Reiden• 4 years ago

What's the limiting factor for model replication by others? Amount of compute? Model architecture? Quality / Quantity of training data? Would really appreciate insights on the subjects

t0mk• 4 years ago

This would generate great music videos for Bob Dylan songs. I'm thinking Gates of Eden, ".. upon four legged forest cloud, the cowboy angel rides" :D

Imagen, a text-to-image diffusion model

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