my research would take at best several days
my research would take at best several days
Cuntil publish gui ?? ohhhh..
Fyep
Fi need to train and test
Cok, so i will use just command by now
CI'm looking for the latest info and best practices for training WAN 2.2 LoRAs, especially for character likeness on a 24GB card.
From what I've gathered so far, it seems the best dataset approach is to train the high-noise model on low-resolution videos (to learn motion) and the low-noise model on high-resolution images (to learn the fine details).
I also saw in the new Musubi-Tuner docs that you can apparently train both high and low noise LoRAs at the same time now, using the --dit (for low) and --dit_high_noise (for high) parameters in a single command. For 24GB VRAM, the key seems to be adding the --lazy_loading option to only keep the active model on the GPU.
Has anyone tried this simultaneous training method yet?
From what I've gathered so far, it seems the best dataset approach is to train the high-noise model on low-resolution videos (to learn motion) and the low-noise model on high-resolution images (to learn the fine details).
I also saw in the new Musubi-Tuner docs that you can apparently train both high and low noise LoRAs at the same time now, using the --dit (for low) and --dit_high_noise (for high) parameters in a single command. For 24GB VRAM, the key seems to be adding the --lazy_loading option to only keep the active model on the GPU.
Has anyone tried this simultaneous training method yet?
JNo need for the lazy-loading as you can just train the two lora's after each other (in command line it auto continues to the 2nd command)
CI know. But y can also Train both same time
JIt probably wont be faster, and you could decide to just turn off your pc after the 1st lora training if you decide that
CProbably you right. I just experiment..we will know much better in few weeks probably..
J+ I may want to use less epochs or learning rate for the other model
Ji noticed the high noise model gets overtrained quicker
C@JonkoXL Do you successfully Train any high and low noise LORA and it's working together with OTHER lora keeps human eyes good quality?
JI think eyes in Wan 2.2. will still be a problem unless we get an even higher base resolution
JJThis is my only Lora I succesfully trained so far
Jhaven't tried it with other loras
CI Train three and when using with others LORA human eyes always problem.
Wan2 1 was always fine
Wan2 1 was always fine
Jhave you tried switching your high and low lora?
Jfor some reason i sometimes do that and apparently it can work out
COo. No..but will try when back home
JI played a bit with Wan 2.1 Lora training too, it seemed easier or more forgiving. But only tested it
C
JOOh nice :p
Jwhat's your learning_rate and how many steps did you train for?
CWan2.1? I don't remember..but I Keep on drive all dataset and configs.
Sthanks - right now im doing Wan2.2 training on my dataset ll see how it turns out
CLet me know please. Share also what datasets you used if you can. Just photo's or mixed or video clips?
Sdue to nda i cant share dataset but these are studio photos and few natural shots of a person. Unfortunetly only photos for now
Mhello, i got out of memory error using massed-compute 2x RTX 6000 ADA
Jtraining what?
M40 images of a human for Flux
SNo LoRA vs LoRA Wan2.2
Sshe is little bit damaged tho its 40 epoch [65img's dataset] 32DIM
Jlearning rate?
Fdid you load lora config into dreambooth or dreambooth into lora?
Fconfig name?
MNo, I loaded dreambooth config into Dreambooth. I tried both 48GB_GPU_28200MB_6.3_second_it_Tier_1 and Batch_Size_7_48GB_GPU_46250MB_29.1_second_it_Tier_1. out of memory errors for both
Fwhat is your images resolution?
Fi recommend restart machine and use this config load into dreambooth
Fwill work 100%
F48GB_GPU_28200MB_6.3_second_it_Tier_1.json
S3e-4
M1024x1024. It works in single GPU, but always memory error when I try to use dual GPU
JI will try 0.000234 next as learning rate
Jjust a little bit more than 2e-4
JAt ~ 1000 steps
JI have no idea if amount of images affects the learning rate, if we keep the steps the same
Smaybeeee
Slet me know 