[latentspacer]

lol, unless I’m wrong, that is not how model development works

a ‘major training run’ only becomes major after you sample from it iteratively every few thousand steps, check its good, fix your pipeline, then continue

almost by design, major training runs don’t fail

if I had to guess, like most labs. they’ve probably had to reallocate more time and energy to their image models than expected since the AI image editing market has exploded in size this year, and will do video later

[rhdunn]

It could be that they weren't able to produce stable video -- i.e. getting a consistent look across frames. Video is more complex than image because of this. If their architecture couldn't handle that properly then no amount of training would fix it.

If they found that their architecture worked better on static images then it is better to pivot to that than wasting the effort. Especially if you have a trained model that is good at producing static images and bad at generating video.