Basically all of the text-to-image generators available today are diffusion models based around convolutional U-Nets. Google has an (unreleased) one that uses vision transformers.

There is more variety in the text encoder, which turns out to be more important than the diffuser. CLIP is very popular, but large language models like T5 show better performance and are probably the future.

>T5

but isn't it much heavier?

Yup. But in neural networks, bigger is better!

but in industry, don't we want things to be cheap? cost might be a bigger factor than performance.

That's always a balance you'll have to make. You can only run what fits on your available hardware.

Source about Google using ViT?

>You can only run what fits on your available hardware.

Precisely.

I'm guessing he refers to this one: https://parti.research.google/

That's not diffusion though

Thanks a lot for the reply.
Do you have documentation for that ?