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Implementation of Imagen, Google's text-to-image neural network, in PyTorch

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Re: Implementation of Imagen, Google's text-to-image neural network, in PyTorch

#51

What's it going to take to train this on porn? This is something that can be crowdfunded. Look deepnude is a thing and somebody is making money off it: https://app.deepnude.cc/upload

Lol. Not the same thing obviously but your question just reminded me of @robotpornaddict, a neural net that watches porn and tries to describe what it sees.

https://twitter.com/robotpornaddict

Re: Implementation of Imagen, Google's text-to-image neural network, in PyTorch

#52

What's it going to take to train this on porn? This is something that can be crowdfunded. Look deepnude is a thing and somebody is making money off it: https://app.deepnude.cc/upload

This tech is actually pretty bad at human faces - it all appears unnatural and distorted. You get things like the occasional extra earlobe...

Human faces were excluded on purpose from the DALL-E 2 training set in order to prevent misuse. I suppose the same will be the case here (or at least, in public-facing versions of the models).

Given how it renders dog faces, I don't see why it wouldn't be good at human faces too, if trained for it.

Re: Implementation of Imagen, Google's text-to-image neural network, in PyTorch

#53
post #37

Earlier quoted context omitted.

When you say a bit of an issue, how much are we talking?

Four or five figures I'd guess? I'm not clued up on costs/performance for TPU stuff to give a better estimate, but guessing at a week on a 256 TPU pod, call it $30k?

You are off by an order of magnitude at least. 256 TPUs-v4 (not pod), would cost you around 20k$/day. They actually used 512 TPUs (256 for base model + 128 for each of the two superresolution models).

Assuming an average training time of 1 week as you said, that gives us about 280k$.

It's also most likely trained for longer than a week, the base model for Dalle-2 was trained for 100-200k GPU hours, so between 2-4x longer than that, we can guess this is roughly similar.

You also never successfully train everything first try, so all in all, to replicate this work just from the paper, we are talking about at least 500k$.

Re: Implementation of Imagen, Google's text-to-image neural network, in PyTorch

#54
post #47

Earlier quoted context omitted.

There's a section of the internet where you can easily find billions of images or can be generated from moving pictures. Even upscaled. I really didn't expect to have to spell it out. hint: they are all about one thing and there are a lot of eager volunteers to help on those websites. It would be easy to "normalize/clean/classify" because the pictures would have a consistent theme, thus reducing the amount of paramet…

Does anyone actually want porn of fake, AI generated “people”? Seems like most of the demand would be deepfakes of real people, which is both highly unethical and a good way to get your business sued out of existence.

furry porn? vore? transformation? selfcest? there's a ton of niche fetishes that aren't really doable IRL, and only exist as art. generated images would be a game changer.

Re: Implementation of Imagen, Google's text-to-image neural network, in PyTorch

#55

What's it going to take to train this on porn? This is something that can be crowdfunded. Look deepnude is a thing and somebody is making money off it: https://app.deepnude.cc/upload

This tech is actually pretty bad at human faces - it all appears unnatural and distorted. You get things like the occasional extra earlobe...

While the "it will never do as well as humans at this one thing I feel strongly about" bias is still a common one, even on hn, at this point I am fairly certain that we will soon all have to live with the fact that we are no longer all that special in regards to, well, absolutely everything.

One of my more interesting realisations over this development is that being human is apparently a religion to a lot of otherwise secular humans.

Re: Implementation of Imagen, Google's text-to-image neural network, in PyTorch

#57
post #16

What is the reason Google published their research details about Imagen? Why don't they just keep their findings to themselfes and build products on top of them? Public companies can't do stuff just for the fun of it, right? So there must be some commercial reasoning behind it?

Two reasons: 1) Even though it's all technically very impressive, so far there's not a huge amount of commercialization potential here. OpenAI is charging for its GPT-3 model but its revenue is probably negligible next to the hardware costs (sunk + ongoing) to train it in the first place, let alone the researcher salaries they're paying 2) Most of the stunning examples are cherry-picked. These things fail much more o…

I'm currently working fulltime on AI-powered design suite Accomplice (https://accomplice.ai) and if you ask me on a good day I would tell you I do think there's already huge commercial potential. On a bad day, though ;)

My current approach is a "model marketplace" (https://accomplice.ai/models) where the most popular open source text-to-image models (VQGAN+CLIP, Disco Diffusion, DALL-E Mega coming soon…), sit alongside the most popular open source style transfer models, and then finally I have the ability for a user to finetune their own models using a simple drag-and-drop tool (https://accomplice.ai/no-code-model-training).

Using this approach a user has enough models to try or train that they can have a higher hit rate. For example, Accomplice currently has finetuned models for photo realistic people (https://accomplice.ai/models/f58bfa91-bb18-406f-a0e1-db00fcf...), watercolor backgrounds (https://accomplice.ai/models/91b8a080-faca-4ff4-8b11-64b0789...), etc…

So theoretically if there were a searchable marketplace of 100s of different finetuned models people could choose from, they would use it much like an iStockPhoto and be able to create the kind of images they want instead of just downloading them.

But it's of course a constant work in progress. Slowly growing though and lots of promising stuff ahead!

Re: Implementation of Imagen, Google's text-to-image neural network, in PyTorch

#59
post #47

Earlier quoted context omitted.

There's a section of the internet where you can easily find billions of images or can be generated from moving pictures. Even upscaled. I really didn't expect to have to spell it out. hint: they are all about one thing and there are a lot of eager volunteers to help on those websites. It would be easy to "normalize/clean/classify" because the pictures would have a consistent theme, thus reducing the amount of paramet…

Does anyone actually want porn of fake, AI generated “people”? Seems like most of the demand would be deepfakes of real people, which is both highly unethical and a good way to get your business sued out of existence.

There’s also seemingly unlimited content of real people produced consensually by those people. What can DALL-E generate that you can’t find on pornhub or onlyfans??

Re: Implementation of Imagen, Google's text-to-image neural network, in PyTorch

#60

Of course the implementation isn’t the issue. It’s the training data and the compute machines. Open source is pretty meaningless here

In fact, this kind of reverses things, doesn't it?

Open source is built on the assumption that you can do more with source code than with binaries. In the case of AI models, the computed weights of models are what's valuable, and the source code used to achieve them is less useful.

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