If I'm using a synthetic dataset,will it bake the artifacts into the style?
If I'm using a synthetic dataset,will it bake the artifacts into the style?
KI'm trying to train Dreambooth flux on 29 images + regularization. Now 25 epochs have passed and the proximity to the target face is zero... Should I wait further hoping that by the 100th epoch everything will get better or is something interfering with training and I shouldn't try further?
I use kohya-ss
I use kohya-ss
Fi would say something sounds broken
Fwhich config you are using?
KI'm trying Adagrad. 20 blocks swap, which is strange for me VRAM is enough for 18 blocks, but it works slower (8 sec/step at 20 blocks and 10 at 18 blocks). Lr = 1e-5. I don't train the text encoder (I don't know if it's necessary, it's not entirely clear to me how it works when there is already T5). I use regularization and it feels like it rolls back the training...
KI also use the fp8 model as a base and the T5 is also fp8
Fis one of my configs?
KNo, I tried to put together general ideas that were found on reddit.
Ftesting grid
Fmy newest training for a client i think worked perfect
KI'm also wondering why "accelerate" isn't updated in kohya_ss scripts. As I understand, newer versions allow using fp8 weights during training. Or am I wrong?
Fwell masked training causes body disproportions
Faccelerate working fine i think
Fwell when i describe background good enough it usually works
Fduring inference
Fi still prefer just ohwx man
For woman
JIs there any way to get around that?
Fnot that i know
KHmm, without regularization with the same settings it turned out very well, from the 10th epoch the features were slightly visible, by 40 it was already very similar. Now the 90th epoch is underway, I don't see any overfitting yet.
This raises the question of why regularization complicates the process so much... Or is it all because my batch size and step accumulation are set to 1? Does it learn on 1 gradient and roll itself back when it hits regularization?
This raises the question of why regularization complicates the process so much... Or is it all because my batch size and step accumulation are set to 1? Does it learn on 1 gradient and roll itself back when it hits regularization?
KSo should de-distill flux models work better with regularization? Or should the gradient update always contain at least one target image when regularizing so as not to forget it?
KI'll probably try to experiment with Flex.1-alpha and de-distill models in fine-grained learning.
And also I wonder if fine-grained learning with block freezing will work? That is, if I, for example, train a fine model on 4 doubles and 4 singles (or any other configuration), then later, when extracting Lora of rank 256, I can remove all layers except those that were trained, getting a more compact lora, right? Or are others that I did not train also important when extracting lora?
And also I wonder if fine-grained learning with block freezing will work? That is, if I, for example, train a fine model on 4 doubles and 4 singles (or any other configuration), then later, when extracting Lora of rank 256, I can remove all layers except those that were trained, getting a more compact lora, right? Or are others that I did not train also important when extracting lora?
Fwhich guide did you watch?
Fye this is for sdxl
Fwhat you want to train?
Fok then use flux
FYouTubeSECourses
If you want to train FLUX with maximum possible quality, this is the tutorial looking for. In this comprehensive tutorial, you will learn how to install Kohya GUI and use it to fully Fine-Tune / DreamBooth FLUX model. After that how to use SwarmUI to compare generated checkpoints / models and find the very best one to generate most amazing image...
Ffor sdxl
Fwe use reg images
Fto use max reg images you need to use repeating and do 1 epoch
Fonetrainer better for sdxl
Fwatch its tutorial
FYouTubeSECourses
In this tutorial, I am going to show you how to install OneTrainer from scratch on your computer and do a Stable Diffusion SDXL (Full Fine-Tuning 10.3 GB VRAM) and SD 1.5 (Full Fine-Tuning 7GB VRAM) based models training on your computer and also do the same training on a very cheap cloud machine from MassedCompute if you don't have such compute...
T@Furkan Gözükara SECourses IS there any guidance / video from you to teach us how to train Lora for Wan 2.1 ?
�Guys, could a person with tattoo be trained and the Tattoos be reproduced correctly? Would sdxl be able to do that too? Fine tuning @Furkan Gözükara SECourses
TI can tell u it's very hard for SDXL, maybe possible for Flux
Fhopefully will make but i still couldnt have time yet 
Fbut flux is doing i trained dwayne johnson
TCan your video to educate us on Wan 2.1 Lora involving both Local Computer Training and Massed Compute Training ? For now, can u tell me if I can train Wan 2.1 on kohya ss for Lora ?
Fyes will do hopefully
Fsorry for delay so many stuff 
Fwan 2.1 full quality lora is my priority task
�@TrumpYouAll I'm curious if including photos zoomed in on tattoos with correct tagging would improve details and consistency 
Tthanks Dr, I would liek to use Massed Compute as well as local computer to train Wan 2.1 Lora. Thanks Dr ! @Furkan Gözükara SECourses +
TSorry, no knowledge on tattoos but can't be done with SDXL
CHow should you determine which base model to train your dream booth/lora on? Is there a good comparison sheet somewhere
TThe best model to start is always the 'base' model , not any 'trained' model
Fyes will cover both hopefully