Do I need to adjust the epochs accordingly?
Do I need to adjust the epochs accordingly?
Fno epoch same
Fit handles it
Fif your nujmber of images not divisible
Fyou will get lesser epoch
Fbut same step count total
DI see.
DUnderstood! 
F256 images lora testing
Fbatch size 7
F
Fgood quality
F
Fbut fine tuning way better i think we will see grids
F
F
F
Fupgraded my internet to 1 gbit 
Ffaster speed really helps to reinstall apps and verify they are broken or working
DThis just keeps getting better and better 
Fye really amazing results
Fbest checkpoints will clash
Fi am generating grids right now
DI've got a feeling I'm going to run out of space on massed compute lol. Can I move the completed checkpoints to drive and delete them while the training is ongoing?
F
DHow things started
Fnoting the best checkpoints
Fye when i look bad the resemblance was too bad :d
DFlux has spoilt us all lol
Ftrue
Fnow sd 3.1 has to be good
DHow many epochs did you train for 256 image BS7 (fine-tune)?
DI have 225 imgs & I'm now saving every 2 epoch 'cuz it overtrains super fast
Fbest one is 50 epoch
Fit sounds your LR too high
For images are too duplicate :d
DUsing 4e-6, or 0.000004, which was recommended. But hey, 50*256 would be 12,800 steps! Do you mean BS7 would mean 12,800/7 ~ 1,828 steps approx?
Fi will publish my grids generating them
Falso my findings
DNow tuning two checkpoints side by side, one on flux_dev, another on dev_de_distill.
DThe de-distill trains a tad bit faster it seems.
DThis is what I made earlier with Flux Pro 1.1
DSame prompt for Flux_dev:
D
DWish we had access to Pro as well.
DI'm training a lora tree people 2 same class 1 different class with regularization on flux-dev-de-distill, lets see if regularization images works with flux-dev-de-distill
Dthe regularization are person class
Dthe bleeding between subjects was fixed with flux-dev-de-distill but idk yet if works with regularization
D@Dr. Furkan Gözükara flux-dev-de-distill what I saw was that at 30 epoch's the resemblances was reduced with regularization images, lets see if a full 160 epochs fixes that.