i analyze grids
i analyze grids
Cyou mean that for each epoch you save you create all the samples in 28_Realism_Prompts.txt and compare them to keep the best one?
V@Furkan Gözükara SECourses Just finished my flux finetune, very happy with the results.
Dora left, finetune right.
Dora left, finetune right.
VGonna extract lora later and compare all 3.
Fawesome
VVI want to see (Regular) Lora, DORA, Lycoris, Finetune, & Lora (Extracted from finetune).
Fnot yet
VFconfigs are same
Fnow hours of editing waiting me 
F
SWill you cover how to half the size of the checkpoint too in this tutorial? It would be nice if we could use 12gb checkpoints instead of 24gb
V@Furkan Gözükara SECourses The extract lora from the finetune only works on the base model I used to create the finetune from. Any image created with a different model using this lora is just static.
How do I fix this?
How do I fix this?
V
VI fixed it, linked to wrong base model.
V
VThe extracted lora is actually better than the finetune!
Fthat means your model overtrained :d
FPatreon
Get more from SECourses: FLUX, Tutorials, Guides, Resources, Training, Scripts on Patreon
Fdifferent model what you mean?
Fwhich different model
VI picked the wrong base model by accident.
VI mean the colors are nicer and the textures more pronounced over the trained model when using dev paired with lora.
Fyes it is very likely model was overtrained and LoRA extraction reduced this impact
VMaybe so, I used the 200th epoch but at 150 the textures are slighly more pronounced.
Fi also find that epoch good for my 15 images dataset
VYeah it was nice with image prompt, composition, texture quality, layout and overall amount of detail (items placed nicely).
V@Furkan Gözükara SECourses I asked bmatis and he added a feature to the gui that was in the code, the ability to set the max amount of save-state folders to what ever we want. If set to 1 it will be overwritten with the newest save-state made.
Atrying to train on a Flux model but getting this error with it... File "D:\kohya_ss\sd-scripts\library\flux_utils.py", line 81, in analyze_checkpoint_state
max_double_block_index = max(
ValueError: max() arg is an empty sequence
max_double_block_index = max(
ValueError: max() arg is an empty sequence
AIt works fine with the default flux dev, but not this other finetuned dev ccheckpoint
�Does this mean we can now resume training where we left or if I want to train again with more epochs can I just add it and start from where it last trained?
Fyou need to report to kohya
Fyes you can
Fyou can also use latest checkpoint
Sdr Furkan i hope everything is ok , the full tutorial about make drambooth with flux is in process right?
Fyep editing
F
Show this process make dreambooth models intead of loral model make better models thna make the loras? only to undertand how is work
Ffine tuning is training full model
Flora is optimization
Fthus fine tuning better
D@Furkan Gözükara SECourses not sure if kohya and/or you still see this on a closed PR, so here too:
@bmaltais can we add this to gui so many people want to test
all my proof-of-concept samples were at weight 1.
Yes, loss of reg steps is then very low compared to train steps, but this is a function of the reg step prediction already being very close to the target.
you can try at weight 10 or even 1000, but I'd expect that the regularization then overwhelms the training steps, and the model doesn't learn anything anymore.
@bmaltais can we add this to gui so many people want to test
- Specify a large value for --prior_loss_weight option (not dataset config). We recommend 10-1000.
- Set the loss in the training without using the regularization image to be close to the loss in the training using DOP.
all my proof-of-concept samples were at weight 1.
Yes, loss of reg steps is then very low compared to train steps, but this is a function of the reg step prediction already being very close to the target.
you can try at weight 10 or even 1000, but I'd expect that the regularization then overwhelms the training steps, and the model doesn't learn anything anymore.
Vnope it works, just tested it thoroughly, weight 10 works fine just takes longer to train
Deven at 1 I think it dampens training of the concept you do want to change, so even if 10 works I'd be careful and/or try different values
DI was more thinking in the other direction, doing less reg steps than 1:1, because even at 1:1 with weight 1, regularisation is strong
Dbut currently working on sth else so I'd probably not get around to more tests any time soon