Foh nevermind. i right clicked and opened in new tab
Fthere is a "open in browser" button
F3 looks better but doesn't seem flexible
Fi feel like 6 is best
Fbut 3 is also decent
AHi AI lovers, I'm new here, thanks for having me Dr. Furkan 
AI want to start experimenting with training, I will do an avatar of my face for starters and have watched Dr. Furkans Tutorials which have been a great help.
AThere are a few settings which are a massive source of confusion for me and I was hoping to get some advice here.
AOne is the Epochs. I am trying to understand how many Epochs I should use. I've seen people use the words steps, samples, epochs, iterations, sometimes as if they are synonyms, sometimes not. So I read explanations and think I understand but the next thing I read just makes me confused again.
AI have seen the formula NUMBER OF IMAGES * 101 for determining epochs. I have 64 images, so that would be 6464. That seems a gigantic number, as in the tutorials I have seen suggested values of 150 and 200 Epochs.
APlease can someone clarify?
I am running Dreambooth on Runpod with 24GB VRAM.
I am running Dreambooth on Runpod with 24GB VRAM.
AAnother source of confusion for me is the classification images. Following the tutorial and suggested settings, when I got to the point to generate the classification images, Dreambooth created 3200 images. This seems a huge number and I have not understood how I can determine what the right amount is. Any pointers on the topic of classification images quantity?
Dopen it in browser
Dx3, y6 looks good. what do you think?
Dx3, y3 follows the reference image/prompt and body posture more precisely in my opinion, while keeping face details.
Dare you going to put out a new video for Lora training? @Furkan Gözükara SECourses
Eselamlar stable diffision kullanmaya çalışıyorumda dreambooth ile yüz taraması yapmaya çalışyorum 4060 laptop versiyonu 8 g vram li ekran kartım var 4 5 gündür birçok yöntem denedim bir türlü beceremiyorum anladığım kadarıyla xformers kurmam gerekiyo ve ekran kartı mimarisinden dolayı otomatik kurulum yapamıyorum internetteki bi çok metodu denedim defalarca yeniden stable diffusion kurmak zorudna kaldım Memory Attention bölümünde xformersı göremedim dün gece bir yöntemle xformerı görmeyi başardım bu seferde "Exception training model: 'import of xformers halted; None in sys.modules'." hatası aldım birçok yöntem denediğim için kafam biraz çorba oldu açıkcsı sanırım çok hızlı güncellendiği için sistemler bulduğum yönlendirmeler düzgün çalışmıyor sanırım xformersmı kurmalıyım başka yöntemler var mıdır yardımcı olma imkanınız var mı acaba
Fhi welcome
Fnumber of epochs 100% depends on your training dataset
Ftherefore best approach is saving checkpoints and comparing after training ended
Fi have done 15+ trainings so far to find best settings
Dthat's great to hear, ill be waiting for your next video. I have learned a lot from your SD videos.
Zgetting this error while trying to run 4x ultrasharp
ZError completing request
_pickle.UnpicklingError: invalid load key, '\x08'.\
_pickle.UnpicklingError: invalid load key, '\x08'.\
Ztrying to get 4x ultrasharp to work in my automatic111, i don't even have the ultrasharp in the esrgan folder anymore, yet they still show up here -
AThanks a lot for your response. So I guess I will try 200 Epochs and go from there. I guess the same applies to the amount of classification images, or is there a reccomended amout or formula for those?
Zi can't even count how many times i've broken automatic1111 already.
Zbetween compatibilty issues, different UI's, upscalers and extras not working right, dreambooth errors
Zbut because of that, i can setup an auto1111 installation in linux and be traiing a mode in about 10-15 minutes
Fwell formula depends on your training set so best way is checkpoint testing with x/y/z
Fi am doing another experiment
Fbatch size 8 xformers 0.0.19 dreambooth style training
Fbatch size makes huge speed increase
Ftesting batch size 10 lets see if rtx 3090 can handle it
Fbatch size 10 failed
Fbatch size 7 , rtx 3090 dreambooth ema xformers lion - didnt cache latents:
Fwow cache latents make huge diff - 6 batch size
Fi am going to sleep hopefully see you tomorrow
Hadditional networks extension？what is that? A name for a extension ？
H
HDownload first
HBut didn’t work
HThanks a lot
Hsolved by loading lora from additional networks
DDo you know what batch size is optimal for 3060 12GB? I have successfully trained using 2 Batch on 3060.
XHFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name':
XAnyone got error like this while using the pretrained model on colab?
AGood morning! I have a question regarding training with Dreambooth. I used the same dataset of 180 pictures of myself and trained using sd_1.5 and Deliberate as the base, with the same parameters. However, for each case, I generated the class dataset (photo of man) with its corresponding model. The results are as follows: when using the model trained with SD1.5, my face doesn't come out very well. When using the model trained with Deliberate, my face still doesn't come out very well. However, when I do the following checkpoint merge, the result is really good: (Primary: Deliberate, Secondary: Custom_SD1.5, Tertiary: SD_1.5, Add Difference, M=0.5).
In other words, I add the difference between my model trained with SD_1.5 and the original 1.5 model to the model I want to use as a base (Deliberate), and this is much better than fine-tuning the Deliberate model with my dataset. Does anyone have any idea why this is happening?
In other words, I add the difference between my model trained with SD_1.5 and the original 1.5 model to the model I want to use as a base (Deliberate), and this is much better than fine-tuning the Deliberate model with my dataset. Does anyone have any idea why this is happening?
HD:\SD\stable-diffusion-webui\venv\lib\site-packages\torchvision\transforms\functional_tensor.py:5: UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be removed in 0.17. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in torchvision.transforms.v2.functional.