i am not looking for something specific like private consultation, community support would be enoug
i am not looking for something specific like private consultation, community support would be enough
Fyes gold is great
Fworking on hunyan
Ais it worth it tho?
AImage2vid seems more promising for research hours
V@julius @Dr. Furkan Gözükara
Ah, okay. My problem is that I have too many neutral-looking, generic images in my training set. I included them thinking more images = better results, but now I’m noticing some issues.
Face Inpainting Issue
I realized that during image generation, the face initially appears to be smiling in the preview, but by the end, it looks more neutral. It seems like the Face Inpainter is neutralizing the expression.
Questions:
Does the image generation process actually have two stages?
Step 1: Normal image generation
Step 2: Face inpainting
How does the Face Inpainter (YOLO) work?
Does it pick a face from my training images, or is it using something else?
Can I adjust the Face Inpainter?
For example, can I tell it which face image to use as a reference?
Can I turn off the Face Inpainter for testing?
I'd like to see how the results change without it.
Training Set Strategy
I’m planning to retrain the model with a new set of training images—this time, focusing on more expressive (smiling, emotional) faces instead of too many neutral or redundant ones. Right now, I have to generate tons of images just to get the expression I want, which is not ideal.
How can I quickly test if a training image set is good?
I don’t want to spend 45+ hours training again only to realize my dataset was bad.
Can I use a lower-tier config for a quick test?
For example, could I train with "6GB_GPU_5400MB_14.1_second_it_Tier_3_512px.json" on a 48GB A6000 GPU (Massed Compute) just to get a fast idea of what expressions the model generates?
Or would the results be completely different compared to using a high-end config like "48GB_GPU_28200MB_6.3_second_it_Tier_1.json"?
Thanks!!!
Ah, okay. My problem is that I have too many neutral-looking, generic images in my training set. I included them thinking more images = better results, but now I’m noticing some issues.
Face Inpainting Issue
I realized that during image generation, the face initially appears to be smiling in the preview, but by the end, it looks more neutral. It seems like the Face Inpainter is neutralizing the expression.
Questions:
Does the image generation process actually have two stages?Step 1: Normal image generation
Step 2: Face inpainting
How does the Face Inpainter (YOLO) work?Does it pick a face from my training images, or is it using something else?
Can I adjust the Face Inpainter?For example, can I tell it which face image to use as a reference?
Can I turn off the Face Inpainter for testing?I'd like to see how the results change without it.
Training Set Strategy
I’m planning to retrain the model with a new set of training images—this time, focusing on more expressive (smiling, emotional) faces instead of too many neutral or redundant ones. Right now, I have to generate tons of images just to get the expression I want, which is not ideal.
How can I quickly test if a training image set is good?I don’t want to spend 45+ hours training again only to realize my dataset was bad.
Can I use a lower-tier config for a quick test?For example, could I train with "6GB_GPU_5400MB_14.1_second_it_Tier_3_512px.json" on a 48GB A6000 GPU (Massed Compute) just to get a fast idea of what expressions the model generates?
Or would the results be completely different compared to using a high-end config like "48GB_GPU_28200MB_6.3_second_it_Tier_1.json"?
Thanks!!!
F@VioletAnt please watch the entire tutorial. also all tier 1 is equal quality
Fand example dataset shared here
Ftake a look
FPatreon
Get more from SECourses: FLUX, Tutorials, Guides, Resources, Training, Scripts on Patreon
F1 : yes
F2 : it mask and inpaint
F3 : what do you mean
F4 : yes
Fworking on hunyuan
Fwow hunyuan is slow
Fanyone got good hunyuan video prompts to test?
Fteacache difference
Fpretty minimal
F
Mhow can i load a json config file in kohya?
Fevery tutorial showing it 
Fi recommend watch this
FYouTubeSECourses
If you want to train FLUX with maximum possible quality, this is the tutorial looking for. In this comprehensive tutorial, you will learn how to install Kohya GUI and use it to fully Fine-Tune / DreamBooth FLUX model. After that how to use SwarmUI to compare generated checkpoints / models and find the very best one to generate most amazing image...
A@Dr. Furkan Gözükara
ASwarm stopped showing me how the image is made up
AWhat happened? yesterday it was good I didn't change anything
Mweird, i watched this video but i dont recall imporing json configs, lemme check it again
Mbtw i got this error, and the loss is becoming a NaN, would this affect the training?
Fyes Nan means not working
F
Fconfig load
Fno idea
Fmake sure to load dreambooth configs into dreambooth lora into lora tab
ABtw what's TeaCache?
Fincreases speed
AIt says Install button bellow Preferred D type
Fi am testing today
Ashould I implement? I'm on kaggle
M... i generated a 10k steps lora this morning like this, no wonder it screwed up 
Mhow can i fix this error?
Fi would say get a fresh config into lora tab
Falso verify your images
Fany values is fine except nan
Fin loss rate
Vthx! what is fasted config json for a 48gb GPU? Just do fastest fine tune to test if the training image set is good. or does this not make sense as it will create very different results compared to a maximum quality fine tune?
Did I understand right, I can only choose between batch 1 and batch 7 config?
Did I understand right, I can only choose between batch 1 and batch 7 config?
Fbatch size 7 at 1 gpu is fastest config we have atm
Fit slightly reduces quality
Fcompared to batch size 1
Vso fastest would be batch 7 and do reduced size of epoch?
In patreon was written:
Fine Tuning / DreamBooth: 256 Training Images & Batch Size is 7 : Best Epoch is 40 = 256 x 40 / 7 = 1480 steps : Duration is 11 hours 56 minutes = around 4 USD cost
similiar like this?
In patreon was written:
Fine Tuning / DreamBooth: 256 Training Images & Batch Size is 7 : Best Epoch is 40 = 256 x 40 / 7 = 1480 steps : Duration is 11 hours 56 minutes = around 4 USD cost
similiar like this?