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
Implementation of Imagen, Google's text-to-image neural network, in PyTorch
51–60 of 123 posts
Re: Implementation of Imagen, Google's text-to-image neural network, in PyTorch
#52What'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...
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
#53Earlier 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 $30k?
Assuming an average training time of 1 week as you said, that gives us about 280k$.
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
#54Earlier 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.
Re: Implementation of Imagen, Google's text-to-image neural network, in PyTorch
#55What'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...
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
#56I guess the stock photos industry will be disrupted by this.
Re: Implementation of Imagen, Google's text-to-image neural network, in PyTorch
#57What 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…
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
#58What'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
Re: Implementation of Imagen, Google's text-to-image neural network, in PyTorch
#59Earlier 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.
Re: Implementation of Imagen, Google's text-to-image neural network, in PyTorch
#60Of course the implementation isn’t the issue. It’s the training data and the compute machines. Open source is pretty meaningless here
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.