21, but the style is very consistent. I could add more but I'm just trying to get a feel for the set
21, but the style is very consistent. I could add more but I'm just trying to get a feel for the settings
VYou really need to look at the training log in flux gym, all the settings are different. I believe it uses Adam8bit, and a slightly faster learning rate. I also think training is in fp8.
V
Sare you referring to the train.bat file?
VI think that’s it. If you right click and open in a text program you can see the settings.
VDid you assign a trigger word during training/captioning and use it in both images when testing?
Fyou should add more
Fwe cant train fp8
Fall trainings are done at bf16
Fkohya doesnt know how to do fp8 training :d
Wnew Kohya with Torch 2.5 speed increase about 20% - 25%
W
WI used 3090
Famazing
Fi dont have yet. our kohya configs are transferable to onetrainer
Mwhere can i find that khoya configs
F
Full Instructions, Configs, Installers, Information and Links Shared Post (the one used in the tutorial) 
https://www.patreon.com/posts/112099700Patreon
Get more from SECourses: FLUX, Tutorials, Guides, Resources, Training, Scripts on Patreon
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...
Fit has both lora and fine tuning configs
Mthank you Dr.
V
V@Saxon The training parameters are all here. Just match them in kohya. The lora network dim was trained at rank 4 btw.
accelerate launch ^
--mixed_precision bf16 ^
--num_cpu_threads_per_process 1 ^
sd-scripts/flux_train_network.py ^
--pretrained_model_name_or_path "C:\pinokio\api\fluxgym.git\models\unet\flux1-dev.sft" ^
--clip_l "C:\pinokio\api\fluxgym.git\models\clip\clip_l.safetensors" ^
--t5xxl "C:\pinokio\api\fluxgym.git\models\clip\t5xxl_fp16.safetensors" ^
--ae "C:\pinokio\api\fluxgym.git\models\vae\ae.sft" ^
--cache_latents_to_disk ^
--save_model_as safetensors ^
--sdpa --persistent_data_loader_workers ^
--max_data_loader_n_workers 2 ^
--seed 42 ^
--gradient_checkpointing ^
--mixed_precision bf16 ^
--save_precision bf16 ^
--network_module networks.lora_flux ^
--network_dim 4 ^
--optimizer_type adafactor ^
--optimizer_args "relative_step=False" "scale_parameter=False" "warmup_init=False" ^
--split_mode ^
--network_args "train_blocks=single" ^
--lr_scheduler constant_with_warmup ^
--max_grad_norm 0.0 ^--sample_prompts="C:\pinokio\api\fluxgym.git\outputs\k44\sample_prompts.txt" --sample_every_n_steps="500" ^
--learning_rate 8e-4 ^
--cache_text_encoder_outputs ^
--cache_text_encoder_outputs_to_disk ^
--fp8_base ^
--highvram ^
--max_train_epochs 16 ^
--save_every_n_epochs 4 ^
--dataset_config "C:\pinokio\api\fluxgym.git\outputs\k44\dataset.toml" ^
--output_dir "C:\pinokio\api\fluxgym.git\outputs\k44" ^
--output_name k44 ^
--timestep_sampling shift ^
--discrete_flow_shift 3.1582 ^
--model_prediction_type raw ^
--guidance_scale 1 ^
--loss_type l2 ^
accelerate launch ^
--mixed_precision bf16 ^
--num_cpu_threads_per_process 1 ^
sd-scripts/flux_train_network.py ^
--pretrained_model_name_or_path "C:\pinokio\api\fluxgym.git\models\unet\flux1-dev.sft" ^
--clip_l "C:\pinokio\api\fluxgym.git\models\clip\clip_l.safetensors" ^
--t5xxl "C:\pinokio\api\fluxgym.git\models\clip\t5xxl_fp16.safetensors" ^
--ae "C:\pinokio\api\fluxgym.git\models\vae\ae.sft" ^
--cache_latents_to_disk ^
--save_model_as safetensors ^
--sdpa --persistent_data_loader_workers ^
--max_data_loader_n_workers 2 ^
--seed 42 ^
--gradient_checkpointing ^
--mixed_precision bf16 ^
--save_precision bf16 ^
--network_module networks.lora_flux ^
--network_dim 4 ^
--optimizer_type adafactor ^
--optimizer_args "relative_step=False" "scale_parameter=False" "warmup_init=False" ^
--split_mode ^
--network_args "train_blocks=single" ^
--lr_scheduler constant_with_warmup ^
--max_grad_norm 0.0 ^--sample_prompts="C:\pinokio\api\fluxgym.git\outputs\k44\sample_prompts.txt" --sample_every_n_steps="500" ^
--learning_rate 8e-4 ^
--cache_text_encoder_outputs ^
--cache_text_encoder_outputs_to_disk ^
--fp8_base ^
--highvram ^
--max_train_epochs 16 ^
--save_every_n_epochs 4 ^
--dataset_config "C:\pinokio\api\fluxgym.git\outputs\k44\dataset.toml" ^
--output_dir "C:\pinokio\api\fluxgym.git\outputs\k44" ^
--output_name k44 ^
--timestep_sampling shift ^
--discrete_flow_shift 3.1582 ^
--model_prediction_type raw ^
--guidance_scale 1 ^
--loss_type l2 ^
Fhe is actively working on but i havent tested yet
SI went through and changed all the settings I could find, but I wasn't sure how to change the network_dim or network_args. I'm also not clear on the epochs, so I just set it to 16 because I'm not sure what the distinction is between epochs and max_train_epochs.
VI believe network dim id lora rank and alpha, which should be changed from 128 to what you see there. Epoch is like a turn, max epoch is max turns you want. The kohya_ss GitHub wiki tells a bit more about the settings.
VFloras yes
Fi dont know base models
Si see what it was -- I was matching the settings to the dreambooth tab -- network dim is in the lora tab. Is there a reason that I'm too naive to know that these settings would not work well in dreambooth?
Mhey everyone, when i am finetuning stable diffusionXL 1.0 base on a woman's picture. why does the base model always have that indian clothing style. so the problem is that when i am tuninng my model, the model can only perform good while i generate a picture of the character i have trained in indian clothes. Need help here
Jwhat token are you using for the woman? could be the token alone is related somehow to indian clothing styles
Fyou should use some good fine tuned model of sdxl
Fas a base
MRealVisXL_v4 , this is the model i am using as base model
Mi will try to change that
Fthere is v5
Mi am trying to go for the flux dev de destill but i cant find some good training params preset
Fi am testing right now
Fit
F
Mperfect, i will be waiting for it
C@Furkan Gözükara SECourses Just saw your latest post with the sample Dwayne Johnson training set; it is very useful!
I have a question: is there a particular number/proportion of Head Shot, Close Shot, Mid shot and Full body shot you would use? All equal? (e.g. 7 each?). I am not sure how whether some of your images is a Mid shot or a Close shot or Full body shot.
Also, what number of training images would you recommend? (balancing between quality and speed - obviously, 256 images is not practical for many of us on Batch size 1!). Would you say 28 is a good amount? Or would you go with 84 like you did recently for your client?
I have a question: is there a particular number/proportion of Head Shot, Close Shot, Mid shot and Full body shot you would use? All equal? (e.g. 7 each?). I am not sure how whether some of your images is a Mid shot or a Close shot or Full body shot.
Also, what number of training images would you recommend? (balancing between quality and speed - obviously, 256 images is not practical for many of us on Batch size 1!). Would you say 28 is a good amount? Or would you go with 84 like you did recently for your client?
Fi would go with 84 for flux
Fit learns better
Fwell i didnt do any certain number of images for each shot type
Fbut when you go higher number you usually have sufficient for all
Fstill 28 should work good i believe currently testing
L@Furkan Gözükara SECourses I am considering training a LoRA Flux model for product photography (still life photography). I want to train a LoRA that specializes in representing still lifes, including props, lighting, and other details. I have a dataset of 200 images. What Kohya parameters would you recommend? Thanks in advance!