Depends on the dataset. You can probably get decent results by restricting the modality of the images (faces, cars, bedrooms etc)
I trained from scratch with 4x3090 and while it’s not as good as SD it’s surprisingly better with hands.
I trained from scratch with 4x3090 and while it’s not as good as SD it’s surprisingly better with hands.
I particularly interested in the image generation part (the DDPM/SGM)
There’s code on my GitHub (glid3)
edit: The architecture is identical to SD except I trained on 256px images with cosine noise schedule instead of linear. Using the cosine schedule makes the unet converge faster but can overfit if overtrained.
edit 2: Just tried it again and my model is also pretty bad at hands actually. It does get lucky once in a while though.
I use an open air rig like the ones used for crypto mining. 4x3090 would normally trip the breakers without mods but if you under volt the cards the power draw is just under the limit for a home AC outlet.