This doesn't contradict anything I have written. The average technical user will be unable to train this exact model (not to mention the supposed future more powerful ones) in their basement in this decade.
Re:Stable Diffusion is a big deal
by angusturner on Thursday September 01, 2022 @08:04PM
(#32685015)
That just feels like such a pessimistic forecast to me. Of course, the current trajectory of improvements in model efficiency and better commercial GPUs / ML-accelerators may hit a wall.
But I would not be surprised if this was trainable on a commercial GPU at home within that time. But I think another important trend that we are seeing is that you don't need to train these models from scratch.
Open-source "foundation models" means that you can usually get away with the much easier task of fine-tuning, as to not throw away / re-learn everything that these large models have already fit.
Edit: I initially said 2-5 years, but on more reflection this does seem optimistic (for training from scratch).
As of about a week ago this tech runs on consumer GPUs. The weights have been downloaded 100s of thousands of times, and fine-tuning / modifying is possible.
Training from scratch is about $500k still, but it will only get cheaper and easier.