C:\StableSwarmUI\Models\Stable-Diffusion @Dr. Furkan Gözükara is tihs correct place to put all my ck
C:\StableSwarmUI\Models\Stable-Diffusion @Dr. Furkan Gözükara is tihs correct place to put all my ckpts ?
K
Kthey fail to load when i put them in the stablediffusion folder
K
Show can edit the ui on forge just add the logo on top? @Dr. Furkan Gözükara
Flooks like image files corrupted
Fyou can look incontext lora flux on swarmui
�@Dr. Furkan Gözükara Where do we put blocks to train in kohya gui?
Mno this just happens in runpod, had to use mimicpc for captioning fo rsome reason
Fit is there check entire interface
Mbtw how can i become a gold member or whatever its called
Fit is only for lora
Mwifi good tho
Kwhere do they go? or do i make a new folder
Fit didnt work?
Fuse uified downloader and see where downloaded
Mnope
A@Dr. Furkan Gözükara SOS 
GOKU releasing soon
GOKU releasing soon 
Fwhich one was goku?
Fjust finished recording flux testing on rtx 5090
Fmaybe because of vpn
AChinese Sora competitor
Ait might have Omni human too
Fawesome
Abtw doctor I saw that 100% accurate lip sync is possible with Live Portrait , do you have a tutorial for it ?
Fi dont have atm. but we have the latest repo installation
Ais it runnable on kaggle? or too heavy
Fif we have kaggle notebook yes
Aare the installers on Patreon? for Massed and Runpod
Fyes
Fexporting new test video
Acan you share brief results here ? how much it/s?
F
Fplease watch video and no skip 
Aamazing doctor will watch tomorrow
RAwesome video. Thanks for sharing it!
FYouTubeSECourses
In this video I have intensively compared RTX 5090 speed on FLUX DEV, FLUX Fill, SD 3.5 Large, SD 3.5 Medium, Stable Diffusion XL (SDXL) and Stable Diffusion 1.5 (SD 1.5) models. For each benchmark, I have compared RTX 5090 against RTX 3090 TI so we see the speed improvement. Moreover, I have tested FP8 vs 16-bit precision for FLUX and SD 3.5 La...
Mi used vpn and it worked, but its not accepting any of my cards, ill try to get a USA prepaid card today
A@Dr. Furkan Gözükara or anyone here tried new Pika Labs Addon feature? I need feedback and opinions
Fi didnt use Pika Labs yet
Mbtw is this gold the highest tier there?
Fyes. what you need? if you need private consultation let me know via private message
Mi 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!!!
