Hi, how many epochs are recommended for a 15 pictures training with dreamboth?
Hi, how many epochs are recommended for a 15 pictures training with dreamboth?
Fat least 200
Fbut you can go up to 300 and compare
SThank you doctor
EHonestly I'm always wondering how to know the number of epochs and such. Should we just aim for a certain # of total steps and play with the # of epochs and repeats?
For example, with 15 images 200-300 epochs but how many images repeats? Or is it better 1 epoch and tons of repeats?
For example, with 15 images 200-300 epochs but how many images repeats? Or is it better 1 epoch and tons of repeats?
EI also wonder if with like 15 portrait images we should be using captions or not 
EAlso wondering because the sdxl kohya 48gb config uses 1 epoch
SHey, for flux full finetuning is there a solution faster than L40S for the 48gb config file ? Thanks
Frepeat is always 1 for flux
Fwe had to use repeat for sdxl to use more reg images due to how kohya bad coded reg images logic
Fbecause with sdxl we want to use more reg images
Fand only way is more repeat with kohya
Fwhat do you mean?_
EAhh I see. Then for sdxl is it better to use onetrainer instead in your opinion? Considering I have only 8 portrait images and the 5000 reg images of your patreon. Also unsure if I should use captions or not
Thank you
Thank you
SI mean would an H100 run the training significantly faster than the L40S ? I'm currently at 4.8sec/iteration
Fyes onetrainer also has EMA
Fwhich works good for SDXL
Fyes it is faster
Fare you batch size 7?
F4.8 sec is low for H100
EOk for onetrainer, will try.
Is FLUX really that much better than SDXL though even for just 8 training images? Wondering because it's so slow to run on my m2 macbook pro..
Is FLUX really that much better than SDXL though even for just 8 training images? Wondering because it's so slow to run on my m2 macbook pro..
Fvery very better
Fso much difference
EUrgh ok. Because on mac I need to use a weird flux model or else tons of errors. Model is flux1-dev-Q8_0.gguf 
Fi would use mac if only i was developing an app for iphone :d
EMeh, for developing from backend to frontend it's great, ai stuff it's shit lol
ECompatibility is atrocious
EEspecially with arm/sillicon chips
Fyep that is why
EWhat is the best workflow for preparing images?
Let's say I have portrait images that are mediocre quality and not 1024x1024. Which of the following is the best workflow:
Workflow A: joycaption,supir 5x upscale with caption,outpaint image sides,downscale to 1024x1024
Workflow B: joycaption,supir 5x upscale with caption,downscale to 1024x1024 with transparent sides
Workflow C: joycaption,supir 5x upscale with caption,downscale to 1024x1024 with black or white sides
Let's say I have portrait images that are mediocre quality and not 1024x1024. Which of the following is the best workflow:
Workflow A: joycaption,supir 5x upscale with caption,outpaint image sides,downscale to 1024x1024
Workflow B: joycaption,supir 5x upscale with caption,downscale to 1024x1024 with transparent sides
Workflow C: joycaption,supir 5x upscale with caption,downscale to 1024x1024 with black or white sides
EAlso, I'm unsure which caption type + length I should use with joycaption
EAnd for having square images, for my current dataset, I had used Fooocus to outpaint the sides since the original images were portrait, but would it be acceptable or good to just resize to 1024x1024 and have the sides be black,white or transparent..? Or will that confuse the training?
ESorry for many questions, thank you very much for the help!
SNo I mean I'm currently using the 48go config with an L40S and getting around4.8 sec. I have a deadline and would need the training to be twice faster, so I wondered if that was achievable with an H100 SXM. Sorry if I explained myself wrong
Ffor flux i dont use captions i dont recommend 
Fah yes go with h100 it will be faster directly
EWell it wasn't for flux specifically, it was to have captions from image to upscale them with SUPIR!
What I'm unsure of is the best workflow to prepare a dataset of mediocre quality to increase its quality...
And also if I can just resize to 1024x1024 a portrait images and just leave the sides transparent
What I'm unsure of is the best workflow to prepare a dataset of mediocre quality to increase its quality...
And also if I can just resize to 1024x1024 a portrait images and just leave the sides transparent
Ffor supir caption matters
Fyou can use joy caption
EYeah I am, just wondering if it's better to use "descriptive" caption type or "training" and the caption length
Also hesitant for more specific words. For example, my photo has "snake bites" piercings which is essentially a ring on each side of the bottom lip, but I'm unsure if I should put "snake bites ring piercings" or just like "two lip ring piercings"
Also hesitant for more specific words. For example, my photo has "snake bites" piercings which is essentially a ring on each side of the bottom lip, but I'm unsure if I should put "snake bites ring piercings" or just like "two lip ring piercings"
EI'm literally trying it out right now on a h100 on massed compute right now
Fno idea you better test
EOk, thank you
Only other thing I'm wondering is this: if I have a portrait image, can I just resize to 1024x1024 images and just leave the sides transparent or white/black or do I absolutely have to outpaint the sides if I want to use this image for training in my dataset?
Thanks!
Only other thing I'm wondering is this: if I have a portrait image, can I just resize to 1024x1024 images and just leave the sides transparent or white/black or do I absolutely have to outpaint the sides if I want to use this image for training in my dataset?
Thanks!
Fyou can leave empty transparent
Eoh really?? Wow.. This will make things so much easier... 
I had spent to much time trying to outpaint something decent for the sides with Fooocus..
Damnit lol, glad I asked thank you very much
I had spent to much time trying to outpaint something decent for the sides with Fooocus.. Damnit lol, glad I asked thank you very much
Fyou are welcome
THi
Fhi
MHello everyone 
I am new to the AI world, started learning around a week ago, and my goal is to create a realistic-looking Instagram influencer with the same face and body (body seems hard but at least face hah). I have a dataset of 70 images, which are all 1024x1024 and good quality I think, and I will be training a lora in Fluxgym because it seems beginner friendly.
I just wanted to ask for your suggestions and advice on a few things. What would you recommend in terms of trains per image, number of epochs, and LoRA rank for this kind of realistic, identity-focused project?
(also thank you very much for all your research and insights @Furkan Gözükara SECourses)
I am new to the AI world, started learning around a week ago, and my goal is to create a realistic-looking Instagram influencer with the same face and body (body seems hard but at least face hah). I have a dataset of 70 images, which are all 1024x1024 and good quality I think, and I will be training a lora in Fluxgym because it seems beginner friendly. I just wanted to ask for your suggestions and advice on a few things. What would you recommend in terms of trains per image, number of epochs, and LoRA rank for this kind of realistic, identity-focused project?
(also thank you very much for all your research and insights @Furkan Gözükara SECourses)
Tstart (and end) with fine tuning, skip LORA step. It's not much harder for beginner, basically same process but drastically better in end result.
If you want to focus on the very best result for body + face train 2 separate models on 2 separate datasets - one focused on body (full, medium shots, poses) the other on face (mid, close shots), then use 2 step generation with face model over image generated on body model.
If you want easier process - balance your dataset well for close and mid shots (30% mid, 70% close)
If you want to focus on the very best result for body + face train 2 separate models on 2 separate datasets - one focused on body (full, medium shots, poses) the other on face (mid, close shots), then use 2 step generation with face model over image generated on body model.
If you want easier process - balance your dataset well for close and mid shots (30% mid, 70% close)
MOkay thank you, I have a 4070 ti super, is that sufficient for fine tuning?