finaly i got it working thank you
finaly i got it working
thank you
thank you
Fso what command did you use?
Mthe collad file
Mto use runpodctl on google
FNice idea
FInstalling runpodctl there
Nhow about a video to find out how many images are necessary to train an image via dreambooth. I tried training with 24,36,43 images and really can't tell what is the "sweet spot"...
Fthis is impossible. it depends on your dataset
MHello. New here. Is there a tutorial on what/how to caption dataset for dreambooth or is dreambooth just for faces?
Bhello, nice videos, i am repeatedly watching these dreambooth tutorials
Bso far looks like training with gradient accumulation step 2 and batch size 2 gives weird results at 0.0008 LR
Bwould be very usefull to have a list of optimal paremeters for dreambooth traning somewhere, will be easier to see it compared to searching thorugh the videos
Fhow to caption them depends on you
Fbut do you ask how to use captions? filewords?
Fi suggest not use higher batch size. because it changes how they are getting trained
Ffor faces 1 and 1 works best
Fyou watched this one?
FYouTubeSECourses - Software Engineering Courses
RunPod: https://bit.ly/RunPodIO. Discord : https://bit.ly/SECoursesDiscord. Web UI DreamBooth got epic update and we tested all new features to find the best settings (10). If I have been of assistance to you and you would like to show your support for my work, please consider becoming a patron on 
https://www.patreon.com/SECourses
Playlist of...
https://www.patreon.com/SECoursesPlaylist of...
Byeah i noticed, i am using 2x2 and faces are not getting better
Fi recently trained a style by using settings in this video
Byes i watched both
Bthe previous one
F1x1
Fit yielded very good results
Bjust i rented a nice GPU so i thought 2x2 would be better
Byes for your face it was amazing
Fi think that is necessary when you need high computation power which means fine tuning with thousands of images
Bi traned first on anime , and it went ok, but for human face was too hard
Freally decent quality
Byes, because of special models from civitai
Bok, i will try 1x1 then
Fif you want higher quality
F1024x1024 definitely improvement
Fif your dataset is such
Fi am planning a style teaching video hopefully soon
Flike corridor crew
Bthats very nice
BThere were very little tutorials on dreambooth for runpod
Byours was a lifesaver
BJust would be nice to have a list of parameters, because I am having hard time to find all of them in 1 hour videos
BI watched 2 videos, but just checking which parameters you used would be much faster.. 
BLooking forward for it
MYes, and what they are for. I've tried researching and found that it's either to include or exclude information but nothing definitive on which so maybe it's both?
MMeaning if I have a data set of images with a yellow car in too many of them I would use captions to tell the ai "yellow car" to make sure it doesn't try to train the yellow car? Hopefully that makes sense.
Bif your dataset is not too large, you can just photoshop it out, does not take much time actually
FThe primary reason is
MIt's pretty big actually, and I'd also like to be able to teach it a few things for the same model not just a face, or one object.
FImproving knowledge of model
FSo when you add a keyword
FAnd associated with an image with it