Urgh ok. Because on mac I need to use a weird flux model or else tons of errors. Model is flux1-dev-
Urgh 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?
MAnd yeah tbh I watched doctor futkans video about fine tuning vs lora and he also said that lora is inferior
TIdaely you would have to rent A6000, it's 0.3$ per hour and training would take 6-10 hours
MOkay, thats through runpod right
Tno, massedcompute
MOh okay sorry
MI essentially just have to follow doctor furkan’s guide right
Tthat's right
MHah okay, it will be the more complex way then hah, but I want best results possible soo yeah
MThanks timson
Tone last advice - the end result would depend on balance and composition of training data set even more than on the process itself. Training set would represent the features you want the model to learn proportionally. The more features you want to train (body type, poses, facial expressions) the more diverce dataset you woudl need and more training steps it would take to reach optimum training point
TThink more about balance than on amount of good quality images
MOkay, if you have time now, would you like to hop on a call and you can have a glance at the images I have and give your opinion, I can share screen
Tsorry, that would be too much
You can order paid consultation with Dr. Furkan but I advice you to have some experience on your own first with tutorials
You can order paid consultation with Dr. Furkan but I advice you to have some experience on your own first with tutorials
MOkay, thank you regardless 
F100% you can train on that gpu
Fif you have 64 gb ram
Myeah thats the thing unfortunately I only have 2x16 gb 6000mhz cl30
Mif I were to upgrade my ram, can I just order another 2x16 6000mhz kit and have a total of 4x16 or will I be better off with dual channel 2x32
Fi recommend get 2x48 if possible
F4x ram usually causes too much problem
Fbut it depends on motherboard a lot
Moh okay, thanks for the advice, my motherboard is a new model b650 called Eagle AX by Gigabyte, I bought it for my 7800x3d so should be good enough
MI have 70 images in my data set to create my realistic instagram ai model, how many epochs would you recomend to use when fine tuning?
