PrimaCora
PrimaCora t1_izgzw9a wrote
Reply to comment by johnslegers in [P] Using LoRA to efficiently fine-tune diffusion models. Output model less than 4MB, two times faster to train, with better performance. (Again, with Stable Diffusion) by cloneofsimo
It's in the repo, but yeah, they have a way to merge to models, and to merge multiple dream booth trainings into one.
PrimaCora t1_ixg4a8p wrote
Reply to comment by MUSEy69 in [D] What advanced models would you like to see implemented from scratch? by itsstylepoint
Popped in to say something similar. Having its dataset not be half improperly cropped images and proper tags (like from booru's) could help, but the initial cost is massive.
PrimaCora t1_jb5rojz wrote
Reply to comment by JrdnRgrs in [R] We found nearly half a billion duplicated images on LAION-2B-en. by von-hust
The quality issues came more from the fact they square cropped everything. A photo of a guy wearing a crown isn't great to learn from when he's looking like King Charles I.
The duplication just leads to over fitting. If you train a model on one picture, it's going to make that picture pretty dang good. If you train on millions and have a dozen duplicates, it's going to favor those duplicates pretty heavily. And other combinations, like a duplicate photo that has the unique keyword Zhanfuur, would be the only thing it could make it you just input that keyword.
If they retrain with the new bucketing, it should alleviate the crop issue. Deduplication would help reduce over fit. Both together should lead to better quality, size variation, and variety of text input (hopefully for that last one).