just train with ohwx woman
just 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.
Fare these 1800 images are consistent?
Ffor 1800 images i recommend train up to 15 epoch , take a checkpoint every epoch and compare later
LThanks for the response! They are high quality screenshots from a show with a unique art style, consistent in style only. All of them are uniquely captioned in the same format to describe the image, with a triggerword for the style at the start of the caption. I'll try as you suggested. One more question: Can I train 10 epochs, and with the resulting model, continue training for another 5 epochs in a separate instance (using the resulting model as a base) to get the same or similar result as if I were training 15 epochs from the start? A checkpoint per epoch would require me to regularly offload the models onto another server due to disk space, risking messing this up and epochs not saving.
LAlso, although maybe not particularly relevant. They are actually 2 sets of the same 900 images in different aspect ratios, according to my own testing and other testing I've seen, training on different aspect ratios improves results. Particularly with style finetunes. Never tried it with this many images though.
LThat was exactly it.
torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 20.00 MiB. GPU 0 has a total capacity of 79.20 GiB of which 16.62 MiB is free. Process 2510593 has 79.18 GiB memory in use.,
training was successful on batch size=2 on h100 sxm
This thing is demanding indeed, consumed like 77gb vram
torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 20.00 MiB. GPU 0 has a total capacity of 79.20 GiB of which 16.62 MiB is free. Process 2510593 has 79.18 GiB memory in use.,
training was successful on batch size=2 on h100 sxm
This thing is demanding indeed, consumed like 77gb vram
Fwith our config 10+5 = 15 from 0