I thought adafactor had a kind of auto LR. Thanks
I thought adafactor had a kind of auto LR. Thanks
Fwe use static
Fauto lr works good for LLMs where you can truly calculate meaningfull accuracy
V@Furkan Gözükara SECourses Yeah, that is the only downside. The provided drive is not suited for save-states unless you constantly download and delete older files.
VI think there is an option to only have a single saved-state folder, with each new saved-state overwriting the older one. I am asking kohya and bmaltis how to do this.
M@Furkan Gözükara SECourses I want to do some dreambooth training on Flux.1 to teach it more about musculature and anatomy, ChatGPT suggested creating training imagesets that use segmentation masks to isolate the various muscles . Can Flux.1 use segmented images for training like this? Any information is sincerely appreciated! 
V@mostlyhuman I recommend checking this out. https://www.reddit.com/r/DreamBooth/comments/1fz539o/training_on_body_parts_rather_than_the_person_as/
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MThank you, that is exactly what I need, hopefully it works, it seemed like others were having trouble replicating the results. I am a little confused on some of the details I reached out to the OP, hopefully he can shed some light. Thanks again!
VCheck the link and download the lora for research purposes. Keep in mind the lora is not SFW.
MI didnt see a link, it was in his post?
VIts in the reply to the OP.
VThe lora using this method can be found here 
Mperfect, thanks!
VSegmentation such as SAM2 could be used, but you would have to test to see how accurate it is.
MSo is that just a link to his finished model? I was hoping to see a few more examples of how he structures his datasets
Fi dont think you need and probably wouldnt work
V@Furkan Gözükara SECourses It works, just check the unlocked versions with proper nudity. New flux models now know what bobs, vajajays, boobs, rears, etc look like. 
V@mostlyhuman I think he was teased away from explaining it further since the data he was working on was nudity. But you can message him, he can explain in better detail. Maybe even share more examples.
Mahh okay, that tracks
V@Furkan Gözükara SECourses When can we see the newest comparison between dora, finetune, finetune lora and lycoris?
Fby lycoris already inferior in quality
Fdora maybe
VWe must all see its inferiority!
Fi agree i plan to research them too
V@Furkan Gözükara SECourses BTW, I wanted to ask, why do you not have buckets enabled in the training config?
Fi dont recommend
VWhy is that?
Fits impact being unpredictable
VI see.
Fso train at 1024x1024 first
Fthen train with bucket
Fand compare
VCan't you control it by resizing the images to be either 1024px by longest or shortest side? You still keep the max resolution to 1024x1024, but the images are then bucketed more naturally.
Fi think it is best to crop to 1024x1024
Fthen again you can just enable bucket and let kohya handle
Fi really recommend comparing results of both
Flol i have been tutorial recording for now like 5 hours
Fneed to sleep now
Frunpod part didnt finish yet
V
CHey Doc!!
I have done a first fine tune at 200 / 3000 epochs/steps and I would like to train it further.
If I use my last epoch as my starting model in Dreambooth, is there anything, any settings where I am bound to my first training?
Or can I change anything I want (LR, dataset, etc..?) for my next 200 epochs?
Can I even train it on a different Instance and class prompts to add more characters?
I have done a first fine tune at 200 / 3000 epochs/steps and I would like to train it further.
If I use my last epoch as my starting model in Dreambooth, is there anything, any settings where I am bound to my first training?
Or can I change anything I want (LR, dataset, etc..?) for my next 200 epochs?
Can I even train it on a different Instance and class prompts to add more characters?
Fyou can try adding more char but i never tried and likely to fail
Fall you need to do is decide your next number of epochs of training
Fif it was 100 so if you train 200 more it will be 300
CI used 24GB_GPU_23150MB_10.2_second_it_Tier_1.json for my first 200 epoch which has a LR of 4e-06, does it make sense to lower it as I fine tune further to avoid over fitting?
Do you look at the loss or the samples to see if you're about to overfit?.
Do you look at the loss or the samples to see if you're about to overfit?.
Flowering will reduce overfit
Fi never look loss rate
Fuseless
Fi analyze grids