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MIT Unveils Gen AI Tool That Generates High Res Images 30 Times Faster

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Re: MIT Unveils Gen AI Tool That Generates High Res Images 30 Times Faster

#51

Post by albert_e »

spi wrote: post #46
mattnewton wrote: post #18

Earlier quoted context omitted.

I believe this is similar to the Latent Consistency Modeling approach, where it’s a replacement for the “diffusion” process, not the underlying weights. Basically, they have a more efficient process for pulling images out of the weights, not necessarily a set of new weights.

The weights are different, because the model is different. As jzbontar below mentions, the crucial point is that the random noise mask is the same. The diffusion models are trained to turn random noise to an image, and they are deterministic at that - the same noise leads to the same image. What the authors did here was to find a smart way of training a new model able to "simulate" in a single step what diffusion ach…

Oh the images and prompts we see in article are from the training data?

Pardon my ignorance ...

Does MIT model then not work as a general text-to-image model to generate novel images based on arbitrary new text prompts that it has not seen before?

Re: MIT Unveils Gen AI Tool That Generates High Res Images 30 Times Faster

#52

Post by FrustratedMonky »

error9348 wrote: post #17
FrustratedMonky wrote: post #4

Makes me wonder that on any given day someone could find way to improve something, and OpenAI value will evaporate quickly. Seems like these startups with high valuations, are more tenuous even than the startups of years past.

Could be. Could also be that LLM is to OpenAI what information retrieval is to Google. A lot is publicly known in the information retrieval space, but google still dominates.

That is good point. The original Google designs are public, those were papers that were published. Anybody could create a new 'google', its just about getting name recognition and users. Guess example is DuckDuckGo, or Bing, they throw money at advertising just to get users, but underlying search isn't the problem.

Re: MIT Unveils Gen AI Tool That Generates High Res Images 30 Times Faster

#53

Post by amelius »

notresidenter wrote: post #49
onion2k wrote: post #39

Earlier quoted context omitted.

Are you suggesting AI generates images of people with more than 10 fingers because there are too many pictures of people with more than 10 fingers in the input data? That seems unlikely..

People holding hands, people high-fiving, etc.

Yes, but if AI cannot solve the fingers problem, then can it reliably generate images of things which we have relatively few example images of? We have an enormous amount of images of hands.

Re: MIT Unveils Gen AI Tool That Generates High Res Images 30 Times Faster

#54

Post by imranq »

p1esk wrote: post #28
aurareturn wrote: post #26

Earlier quoted context omitted.

It gives an idea in investors' minds that OpenAI's models can be matched or be "good enough" by open source models.

OpenAI has been at least one year ahead of everyone else in everything they have released do far. And there’s no sign they are stopping any time soon (e.g. GPT5 is expected this year).

GPT-5 will likely only see improvements in deductive reasoning like in math, I think it there are diminishing returns if all GPT-5 is a larger transformer than GPT-4

Re: MIT Unveils Gen AI Tool That Generates High Res Images 30 Times Faster

#55

Post by Kon-Peki »

BadHumans wrote: post #10
FrustratedMonky wrote: post #4

Makes me wonder that on any given day someone could find way to improve something, and OpenAI value will evaporate quickly. Seems like these startups with high valuations, are more tenuous even than the startups of years past.

OpenAI's value doesn't just come from their tech. Copyright Shield is a deal closer from business types I have spoken to. Telling a company, "if anyone we took data from has a problem they have to deal with our lawyers and not you" is music because what companies want most is stability and reliance. It's the entire reason SLAs are such a big deal in B2B.

https://openai.com/policies/business-terms

Section 10 deals with the indemnification they are offering. There are a lot of limitations. It's definitely not terrible, but it's not remotely close to what a business actually wants in an indemnification agreement.

In 10.1 (the indemnification from them to you) does not include "hold harmless", but in 10.2 (the indemnification from you to them) it does. That's not an accident, and those aren't meaningless words ;)

If you get sued and notify OpenAI of the suit, their lawyers take over completely. You must do anything they ask (as long as it is "reasonable"), including sitting for depositions, preserving evidence, participating in the discovery process, etc. If you want to have any involvement beyond being told what to do, you have to hire your own lawyers at your own expense. And at the end of it all, they will come to a settlement with the other side. As long as it is "reasonable", you must sign it.

https://openai.com/policies/service-terms

> If there is a conflict between the Service Terms and your Agreement, the Service Terms will control

> This indemnity does not apply where: (i) Customer or Customer’s End Users knew or should have known the Output was infringing or likely to infringe, (ii) Customer or Customer’s End Users disabled, ignored, or did not use any relevant citation, filtering or safety features or restrictions provided by OpenAI, (iii) Output was modified, transformed, or used in combination with products or services not provided by or on behalf of OpenAI, (iv) Customer or its End Users did not have the right to use the Input or fine-tuning files to generate the allegedly infringing Output, (v) the claim alleges violation of trademark or related rights based on Customer’s or its End Users’ use of Output in trade or commerce, and (vi) the allegedly infringing Output is from content from a Third Party Offering.

If you knew or should have known, you're on your own. If you didn't follow the rules exactly, you're on your own. If you used the output in commerce and they claim a trademark violation, you're on your own.

And lastly, remember from the business terms that OpenAI's lawyers take control and you must do whatever they ask in terms of depositions and discovery? In dealing with all that information, if they see any indication that you no longer qualify for indemnification OpenAI is going to say goodbye, send all the legal bills to you.

It's better than nothing, for sure. But not all that confidence-inspiring.

Re: MIT Unveils Gen AI Tool That Generates High Res Images 30 Times Faster

#56

Post by spi »

albert_e wrote: post #51
spi wrote: post #46

Earlier quoted context omitted.

The weights are different, because the model is different. As jzbontar below mentions, the crucial point is that the random noise mask is the same. The diffusion models are trained to turn random noise to an image, and they are deterministic at that - the same noise leads to the same image. What the authors did here was to find a smart way of training a new model able to "simulate" in a single step what diffusion ach…

Oh the images and prompts we see in article are from the training data? Pardon my ignorance ... Does MIT model then not work as a general text-to-image model to generate novel images based on arbitrary new text prompts that it has not seen before?

Nothing to pardon, asking questions is always the right thing to do :-) I also didn't look into the paper in great details, although I'm quite sure I am not fooling myself, but still take this with a grain of salt.

My understanding is that this paper by MIT doesn't train any new model from scratch. I takes a pretrained model (e.g. StableDiffusion), which however is trained to do "a small step" only: you fix a number of steps (e.g. 1000 in the MIT paper), and ask the model to predict how to "enhance" an image by a certain step (e.g. of size 1/1000); the constants are adjusted so that, if the model is "perfect", you get from pure white noise to an image in the exact number of steps you set. If I remember correctly how diffusion works, in theory you could set this number to any value, including 1, but in practice you need several hundreds to get a good result, i.e. the original StableDiffusion model is only able to fit a small adjustment.

This new paper shows how to "distil" the original model (in this case, StableDiffusion) into another model. However, unlike typical distillation, which is used to compress a big model into a smaller one, in this case the distilled model is basically the same as the one you start with; but it has been trained with a different objective, namely to transform random noise to the prediction that the original model (StableDiffusion) would make in 1000 steps. To do so, it is trained on a very large amount of triples (text, noise, image). But I don't think you can incorporate into this training procedure other "real" images that are not generated by the model you start with, because you don't have a corresponding noise (abstractly, there is no such concept as "corresponding noise" to a given image, because the relation noise -> image depends on the specific model you start with, and this map is not anywhere near invertible, since not all images can be generated by StableDiffusion, or any other model).

Once the model is trained, you can of course give it a new prompt and, in theory, it should generate something rather similar to what StableDiffusion would generate with the same prompt (hopefully, the example displayed on their web page are not from the training set! Otherwise it would be totally useless). But you should never obtain something "totally different" from what StableDiffusion would give you, so in that sense it's not "general", it is "just" a model that imitates StableDiffusion very well while being much faster. Which is already great of course :-)

Re: MIT Unveils Gen AI Tool That Generates High Res Images 30 Times Faster

#57

Post by GaggiX »

ilaksh wrote: post #44
GaggiX wrote: post #16

There are so many similar diffusion model distillation techniques these days that it's become hard to tell the difference between them, this is probably the fourth example I've seen that uses adversarial loss to distill the model, the others being UFOgen, some other work by StabilityAI on SDXL-turbo and similar, SDXL-Lightning by ByteDance. I found this blog page that explains some of the differences: https://sander.…

This is 10 times faster than almost all of the fast image generation models.

I mean, it's not the only one that can run SD-1.5 in one step, so no.

Re: MIT Unveils Gen AI Tool That Generates High Res Images 30 Times Faster

#58

Post by jobigoud »

onion2k wrote: post #39
fzliu wrote: post #38

Earlier quoted context omitted.

The >10 fingers problem should be solved via higher quality data or conditioning rather than a better model architecture.

Are you suggesting AI generates images of people with more than 10 fingers because there are too many pictures of people with more than 10 fingers in the input data? That seems unlikely..

Higher quality could also mean the description attached to the original images in the dataset. If you were to describe a picture of someone you would never specifically call out the fact that they have 5 fingers per hand, we take it for granted, so that kind of information may never appear in the dataset.

But I think what the grant parent means by conditioning is non-textual conditioning, like ControlNet. This will always be more powerful than trying to describe something by text. Think about the description of a character in a novel vs the movie adaptation.

Re: MIT Unveils Gen AI Tool That Generates High Res Images 30 Times Faster

#59

Post by itishappy »

josefx wrote: post #42
Drakim wrote: post #40

Earlier quoted context omitted.

In some pictures not all 5 fingers on a hand are visible, so maybe it appears that humans have a variable amount of fingers?

Humans with less than 5 fingers per hand do indeed exist. How does that lead to a default of 7?

Counting fingers is how humans do it, not necessarily how AI does. On a five-fingered hand, fingers are more likely to have neighbors than not. Why wouldn't the default be infinite fingers?
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