What’s the best program or extension to use right now to make a sd model of myself
What’s the best program or extension to use right now to make a sd model of myself
SI will take any recommendations
Fyes but it gets worse as you train also if same concept it will bleed
Fonetrainer
FYouTubeSECourses
In this tutorial, I am going to show you how to install OneTrainer from scratch on your computer and do a Stable Diffusion SDXL (Full Fine-Tuning 10.3 GB VRAM) and SD 1.5 (Full Fine-Tuning 7GB VRAM) based models training on your computer and also do the same training on a very cheap cloud machine from MassedCompute if you don't have such compute...
�I trained a flux lora on cicitai, and for testing i went with a real person. On generations, skin gets plastic and shiny...not like my dataset. Using heun beta 30 steps. Curious about what might cause this
Fcivitai training weak
Fsee my latest tutorial i shown myself trained result
FYouTubeSECourses
FusionX: The BEST AI Video Model? + FLUX Hyper-Realistic Upscaling (One-Click Setup!). Struggling to create high-quality AI videos and hyper-realistic images? This tutorial is your ultimate solution! I'm introducing the incredible new Wan 2.1 FusionX model and a game-changing 2x latent upscaler for the FLUX model, all made incredibly simple with...
F13:48 Generating a High-Quality Image with The Official FLUX Preset
14:50 Using Automatic Face Segmentation & Inpainting with FLUX
16:05 The Ultimate Upgrade: Applying The FLUX 2x Latent Upscaler Preset
16:32 Final Result: Comparing Standard vs. 2x Upscaled Image Quality
16:50 Outro & Sneak Peek of The New Ultimate Video Processing App
14:50 Using Automatic Face Segmentation & Inpainting with FLUX
16:05 The Ultimate Upgrade: Applying The FLUX 2x Latent Upscaler Preset
16:32 Final Result: Comparing Standard vs. 2x Upscaled Image Quality
16:50 Outro & Sneak Peek of The New Ultimate Video Processing App
MScammer
THi
TUsing a different model than Flux with my Lora looses the likeness. Is it possible to train a LoRA based on a Flux Model other than the Flux itself ?
Fyes any flux variant
Fyou can train
Tshould I check and verify the weigths ect as in the 1st place mixing my lora with that model is altering my model details or it'll be done automatically?
Fwell it will be different
Fif you have trained on another base model
Fbut probably with flux i assume it would work decent
Fsince flux fine tunes are not so much different
Fusually
LHave any examples to share? If not that’s fine. Just curious how “plastic” it is. Plastic type skin is pretty inherent with Flux regardless. I have read there are some Loras that can help, I am testing way too many different things at the moment so can’t suggest.
And everything I have read training to Flux 1 Dev is by far the best.
What other models are you training when you say it looses its likeness?
And everything I have read training to Flux 1 Dev is by far the best.
What other models are you training when you say it looses its likeness?
PDr Furkan, I saw you released an update for SDXL finetunning today, 20/june. Are you planning any update for FLUX finetunning or your last release for FLUX are the best yet?
Fi recently tested flux fine tuning with more than 20 experiments
Fnone yielded better 
PDr using your tutorial and trainning a FLUX finetunning in Massed Compute, about choosing the GPU, is there a better GPU to choose? For better I'm only considering faster trainning and the relation cost x benefit, I know that the prices increase. I don't understand one thing, if I choose a H100 x8, or a 8 H100 boards, is it eight times faster than an 1 H100? or it doesn't work this way? Can you tell me what is the best board option in Massed Compute, ignoring price, for a 100 images finetunning of a person?
Ffor lora it will be almost 8 x
Fbut for dreambooth last time i tested it wasnt working properly
Fbest price performance is RTX a6000 ADA i think
PIn your post, about Dreambooth finetunning, you said: "...single RTX 4090 is almost same speed as RTX A6000 for Fine Tuning." Does it mean that if I have a RTX 4090 can I train, locally, at the same speed as a RTX a6000 that has 48gb of vram, double vram than mine 4090?
F4090 is faster when fits into VRAM. current not fitting but with block swapping it matches almost
Fwhat is your second / it ?
F5090 is almost twice faster than A6000 since fits into VRAM fully
PSo, if I choose the file 16GB_GPU_15150MB_11.6_second_it_Tier_1 to train a FLUX finetunning, my 4090 is better than a RTX A6000? Is it right? I imagine that I can't choose the 24GB_GPU_22900MB_7.3_second_it_Tier_1 because it's to close to use the full amount of memory, right?
PDr, is 6.36s/it a good velocity to 16GB_GPU_15150MB_11.6_second_it_Tier_1 config in a 4090, locally?
Fyes really good
PHi! Does anyone know if it's possible to upload a fine-tuned model to Replicate to use it via API? And if someone knows, could you give me a mini step-by-step tutorial on how to do it?
Thanks a lot!
Thanks a lot!
Fyes possible
Fbut you aint gonna find tutorial it is only useful for companies
�if i were to train a finetune on say norwegian women, to better capture their looks, say i have 5 models with maybe 10 photos each i use for this. would this be an example of where we use captions? @Furkan Gözükara SECourses
Fyes for general fine tuning you can use captions
Fbut you need more photos
�Ok. The other plan was to train 5 woman loras, then use all on low weight, without triggerword and prompt the womans looks.
Feach lora may overwrite each other but you can try
PThank you very much! I'm going to try anyway because I really need it, if anyone knows of something that could help me, I would truly appreciate it.
FHey everyone, looking for a quick sanity check on my training parameters before I commit my resources. I've done my best to prep everything properly.
Model: Flux (via fluxgym)
Goal: A flexible character LoRA with two distinct styles (style_A and style_B).
Dataset: 98 high-quality, curated images.
Variant A (Core/Black Hair): ~55 images
Variant B (Secondary/Lighter Hair): ~43 images
Shot Distribution: ~14% full-body, ~38% medium, ~48% close-ups.
My Proposed Parameters: Repeats: 4, Epochs: 24, Batch Size: 2, Total Steps: ~5040 (approx. 51 steps per image), Unet LR: 1e-4, Text Encoder LR: 5e-5, Scheduler: cosine, Save every N epochs: 2
Does this recipe look solid for a high-quality Flux character LoRA? Any red flags or suggestions? Thanks!
Model: Flux (via fluxgym)
Goal: A flexible character LoRA with two distinct styles (style_A and style_B).
Dataset: 98 high-quality, curated images.
Variant A (Core/Black Hair): ~55 images
Variant B (Secondary/Lighter Hair): ~43 images
Shot Distribution: ~14% full-body, ~38% medium, ~48% close-ups.
My Proposed Parameters: Repeats: 4, Epochs: 24, Batch Size: 2, Total Steps: ~5040 (approx. 51 steps per image), Unet LR: 1e-4, Text Encoder LR: 5e-5, Scheduler: cosine, Save every N epochs: 2
Does this recipe look solid for a high-quality Flux character LoRA? Any red flags or suggestions? Thanks!
Fwell i only can recommend our researched training config
Fbut image distribution is sounding good
Fhowever different styles will get bleed into each other
FThanks, I really appreciate you taking the time.
Your point about style bleeding is exactly why I set up the style_A and style_B tags in my captions—I'm hoping that gives me the control to call them separately.
you mentioned your researched training config. Is that publicly available anywhere? I'd love to read up on it and compare it with my setup to make sure I'm on the right track.
Your point about style bleeding is exactly why I set up the style_A and style_B tags in my captions—I'm hoping that gives me the control to call them separately.
you mentioned your researched training config. Is that publicly available anywhere? I'd love to read up on it and compare it with my setup to make sure I'm on the right track.