@Dr. Furkan Gözükara if it's not too much trouble, could you make a linux install script as well?
@Dr. Furkan Gözükara if it's not too much trouble, could you make a linux install script as well?
F
Fi will make for runpod
FThoughts? Testing out new sdxl dreambooth settings, with a merge technique
Ftesting 768
Tlink?
Toh ok, how are you running dreambooth?
FYep dreambooth, but suprisingly the settings somewhat work with lora too
Fi've done about 600+ trainings past 2 months or so
Tiv'e done too many trainings as well. Are you running dreambooth in colab?
FNope on a 4090 on my local system
Tnice, i have a 4090 as well. we are the lucky ones
FYep managed to get it before price surged like crazy
Ffully updated
FFGitHub
I have spent over 24 hours to prepare all these scripts You can download all from here : https://www.patreon.com/posts/94098751 Example 15 second DensePose I generated via my script 15_sec_512_clos...
7missing nodes doesnt appers in manager
7how can I install them
AInstead of using the base sd xl for training with kohya could we use turbovisionxl for superior trainings?
A@Dr. Furkan Gözükara I'm getting this error when Kaggle training now:
Your notebook tried to allocate more memory than is available. It has restarted.
Your notebook tried to allocate more memory than is available. It has restarted.
Athat was after the first 20%/safetensor
Mfocused on this today and so far this works. 1. Use Kohya to merge either two loras in ckpt or into one lora. 2. In adetailer use detection K of 2 with adetailer prompt similar to: person 1 <lora 1> [SEP] person 2 <lora2> 3. depending on the image generated with two people and which face has a stronger face detection then you may have to switch step 2 to get the proper detailing.
Fcomfy ui manager
Fyou can try and compare
Fyou see 29 gb ram right?
AHave you tried this before? Using something other than base sd xl model
Fhavent had chance yet
AYes 29GB max
Aonly thing I did different this time was use stablediffusionapi/juggernaut-xl-v7
Ainstead of sdxl base
AWhats going on with this yolo script
A
A
DWill give it a try
Dany special argument ? keywords ? etc to be used ? Or ohwx / bbuk woman and man keys will be taken from lora automatically ? I dont want to create a ckpt/safetensor , it will limit my usuage to be used with other good models. I will see if thre is possibility of merging them into one lora for two faces. Or @Dr. Furkan Gözükara is it possible to traing lora for two faces with captions ? That will be easy i guess ?
Bsounds interesting, do you mind sharing your training settings?
7They are not showing Up in comfyui manager
7I have already installed all missing custom nodes from that tab, but still missing these
HI am trying to follow the tutorial on https://www.patreon.com/posts/kohya-sdxl-lora-88397937
when I try to 'start training' I get an output
09:59:46-038102 INFO Start training Dreambooth...
09:59:46-039759 INFO Image folder does not exist
not clear to me which Image folder it doesn't like... here is my inputs and my kaggle file... what am I doing wrong?
when I try to 'start training' I get an output
09:59:46-038102 INFO Start training Dreambooth...
09:59:46-039759 INFO Image folder does not exist
not clear to me which Image folder it doesn't like... here is my inputs and my kaggle file... what am I doing wrong?
DClick on prepare training data firts then copy info to folder
Haha did the trick thank you
DYou are welcome
Hdoes anyone know what might be causing the training to fail?
message.txt2.53KB
Await so, you recreate your UI results with the json api call with 40% succesrate ? SUS
DThe number of images must take at least 30 to catche
DYour catche must be finished inside 30 minutes
DYour training killed in 1577 images catched so resuce your training dataset images to 1550
DHow many training inages in your data set
Eon magic animate i get this error and it stuck like this :
pipeline_animation.py:624: FutureWarning: Accessing config attribute
num_channels_latents = self.unet.in_channels
0%| | 0/25 [00:00<?, ?it/s]
pipeline_animation.py:624: FutureWarning: Accessing config attribute
in_channels directly via 'UNet3DConditionModel' object attribute is deprecated. Please access 'in_channels' over 'UNet3DConditionModel's config object instead, e.g. 'unet.config.in_channels'.num_channels_latents = self.unet.in_channels
0%| | 0/25 [00:00<?, ?it/s]
DDuring extract lora from my checkpoint in kaggle i got error out of memory how to solve the problem and use gpu during the process
