yeah i paused just to test, i have 171 faces with same hairstyle different emotions, 53 body differe
yeah i paused just to test, i have 171 faces with same hairstyle different emotions, 53 body different poses, a bit of different hair styles
Fwhat you want to teach?
Ka person, trying to keep the same hairstyle, but also the option to change the hairstyle with prompts
Fok it is easy. 171 images are all from same person right?
Fa bit of different hair styles are ok
Fso what you should do is
Kyeah mostly same hair cut, the ones with full body have different haircuts
Fjust train with ohwx woman
Fthen when prompting
Fdescribe the hair style in details
Fand it should work 95%+
Kok, ill keep training from epoch 10 forward, and ill try that in a bit, thanks so much
Fyou are welcome
Fi trained with 256 images
Fand 70 epoch was best
Fand it is excellent quality
Kexcelent, yeah thats what ill try to do
SFor LoRA training with 15 images and an epoch of 200 on RTXA6000, it takes around 7 hours. Shall we change the epoch value to 15 or 10? Is the caption mandatory?
Fthat would be undertrained
Fwe use ohwx man as caption
Fi mean ohwx + class
Fit reads from folder name
SBut for lora training is 7hr mandatory?
SAnd epoch should be 200
Fyes flux is slow to train .
F200 is best number for 15 images
Fbut if you need speed
Fyou can increae learning rate and reduce epoch
Fand sacrifice some quality
SOk sir 
DI have a photo with dimensions 1080 x 1920 (from TikTok). How can I make it 1024 x 1024? Can I directly follow a tutorial like in a video using the Ultimate-Batch image pre-processing tool? Do I need to check (mark) 'resize without cropping (center image and add white background)'?
Or do I need to edit it manually first using an application like Photoshop?
Or do I need to edit it manually first using an application like Photoshop?
Fyou can use this
Ffirst do 1024x1024 cropo
Fthen resize
Uhello 
For finetuning we used flux dev so far, I was wondering if we could use hidream , chroma or wan 2.1 txt to img for finetuning instead of flux dev?
For finetuning we used flux dev so far, I was wondering if we could use hidream , chroma or wan 2.1 txt to img for finetuning instead of flux dev?
Fyes all can be used i think
PDr, I was trying to use Flux Schnell with your Kohya configs used in Flux Dev finetunning. The model that it saves is too large (24GB) even though Schnell has less than 17GB and I was trying to train with 50 images only. Do you have any idea what could be wrong?
Fschnell is not 17gb
F
Fyou are probably using quantized + T5
PI thought it would be faster to train
Fnope
Aalways train on fp32
Athen inference on fp16<=
Athe logic is to save time on inference not on training for the best quality
LGood morning, gentlemen, i've been using Tier1_48_GB_Faster_v2.json for dreambooth on runpod and constantly getting the training terminated on text encoders training part
So,
subprocess.CalledProcessError: Command '['/opt/environments/python/kohya/bin/python3.10', '/workspace/kohya_ss/sd-scripts/sdxl_train.py', '--config_file', '/workspace/kohya_ss/outputs/config_dreambooth-20250719-133856.toml', '--max_grad_norm=0.0', '--no_half_vae', '--train_text_encoder', , '--learning_rate_te2=0']' returned non-zero exit status 2.
As i understand kohya wants non-zero parameter for text_encoder2
should i insert the same as on te1, 0.000003?
So,
subprocess.CalledProcessError: Command '['/opt/environments/python/kohya/bin/python3.10', '/workspace/kohya_ss/sd-scripts/sdxl_train.py', '--config_file', '/workspace/kohya_ss/outputs/config_dreambooth-20250719-133856.toml', '--max_grad_norm=0.0', '--no_half_vae', '--train_text_encoder', , '--learning_rate_te2=0']' returned non-zero exit status 2.
As i understand kohya wants non-zero parameter for text_encoder2
should i insert the same as on te1, 0.000003?
Fhi what is the exact message?
Fit could be out of RAM
LHey guys. I'm currently training a flux style finetune using kohya with a dataset consisting of around 1800 images total. When leaving the max epoch to 0 (default) it was automatically set to 6. This seemed strange to me, I've never used a dataset this large. I'm using the "Batch_Size_7_48GB_GPU_46250MB_29.1_second_it_Tier_1" preset as provided by Dr. Furkan. Should I tweak another variable like learning rate for a dataset this big? I set max epochs to 10 and it says it will train for 20 hours on an A6000 GPU. I thought I'd ask before I have it train for 20 hours for it to end up being trash.
LCanceled it for now just in case someone more experienced tells me I've made a fatal error.