Interesting to test when it does get implemented at some point.
Interesting to test when it does get implemented at some point.
K@Furkan Gözükara SECourses Hi sir, we trained the Lora model using a quality Tier 1 config file with 48GB of VRAM.
We generated an AI character with photo grid, cropped, flipped, and upscaled the images. We created a dataset of about 8 images to train the Lora model.
While generating images from Lora, the close-ups have a really good face, but in wide shots, the face becomes pixelated.
Additionally, the face does not closely resemble the training dataset.
I’m attaching the images as well.
brown background image is generated
white background image is dataset
We generated an AI character with photo grid, cropped, flipped, and upscaled the images. We created a dataset of about 8 images to train the Lora model.
While generating images from Lora, the close-ups have a really good face, but in wide shots, the face becomes pixelated.
Additionally, the face does not closely resemble the training dataset.
I’m attaching the images as well.
brown background image is generated
white background image is dataset
TI think the dataset is not good, the same background and clothes in all the photos? Probably the problem is in the dataset
TBut I don't know, sometimes these things happen 
THow many photos did you train?
K8 images , actually
KWhich is the best way to train a AI generated character then
KAnd which is the best way to create a dataset for AI generated character?, I tried multiple things, the consistent is not so good
KCan someone help me in this it will be really helpfull
�try creating consistent images with pulid first and then train on them
Kthanks nitin , for your suggestion , will try it ,and show the results here
Fi recommend 2 stage
Ffirst train
Fgenerate better images
Fthen collect more images
Fand train again
KShould I train the first trained Lora with a new dataset using more steps, or should I train from scratch using the images generated by the trained Lora as a dataset?
Ffrom scractch with new dataset
Kcool
Mhi, getting this error installing Kohya_FLUX_DreamBooth_v17. Anyone can help?
UHello Furkan, everyone!
I'm trying to get my head around the structure of the Patreon as well as tutorials and have a bit of a hard time to see where to start - will appreciate a pointer to a 101 or a tutorial
My use case WAS => Stable Diffusion 1.5 and some public CivitAI models + my own photography dataset with poses, light and bokehs (150-200 images) = New checkpoint + Dreambooth fine-tuning on a new person (50-100 images) = New checkpoint, that let me create portraits of people in my own recognizable photography style (Rembrandt light, bokehs, ethereal feeling). I used https://github.com/TheLastBen/fast-stable-diffusion workbook on Colab for both trainings (SD15/CIVIT + MyStyle-XYZ.jpg x 200 images) + (Person-XYZ x 100 images). Sample results are attached - these look exactly as real people to the extent the parents can't say if it is made with AI or not. With some experimenting I managed to get to 768 and 896 pixel resolutions here, all with Automatic1111.
What I want is to elevate this to SDXL with 1280 pixels and test out SDXL and CivitAI checkpoints. So, I would have 150-200 1024-1280px images to train my style and then 50-100 images of a person to generate images like (beautiful personXYZ as styleXYZ .... ). I can't find a reliable workbook (Colab or RunPod, paid are OK) with clear instructions. I have a good development background and PhD in CS, but in a slightly different area, so I struggle to get to the point where I have a code that works so that I could experiment. Like I found configs, but how do I set up the environment / deployed OneTrainer - but it won't accept .safetensors model / managed to adjust fast-stable-diffusion to accept SDXL, but it fails to work with GDrive
Any clues, hints or pointers I could use? Thank you so much in advance!
Cheers, Alex
I'm trying to get my head around the structure of the Patreon as well as tutorials and have a bit of a hard time to see where to start - will appreciate a pointer to a 101 or a tutorial
My use case WAS => Stable Diffusion 1.5 and some public CivitAI models + my own photography dataset with poses, light and bokehs (150-200 images) = New checkpoint + Dreambooth fine-tuning on a new person (50-100 images) = New checkpoint, that let me create portraits of people in my own recognizable photography style (Rembrandt light, bokehs, ethereal feeling). I used https://github.com/TheLastBen/fast-stable-diffusion workbook on Colab for both trainings (SD15/CIVIT + MyStyle-XYZ.jpg x 200 images) + (Person-XYZ x 100 images). Sample results are attached - these look exactly as real people to the extent the parents can't say if it is made with AI or not. With some experimenting I managed to get to 768 and 896 pixel resolutions here, all with Automatic1111.
What I want is to elevate this to SDXL with 1280 pixels and test out SDXL and CivitAI checkpoints. So, I would have 150-200 1024-1280px images to train my style and then 50-100 images of a person to generate images like (beautiful personXYZ as styleXYZ .... ). I can't find a reliable workbook (Colab or RunPod, paid are OK) with clear instructions. I have a good development background and PhD in CS, but in a slightly different area, so I struggle to get to the point where I have a code that works so that I could experiment. Like I found configs, but how do I set up the environment / deployed OneTrainer - but it won't accept .safetensors model / managed to adjust fast-stable-diffusion to accept SDXL, but it fails to work with GDrive
Any clues, hints or pointers I could use? Thank you so much in advance!
Cheers, Alex
Fyou dont have proper requirements
Fit is python 3.11 first of all
FYouTubeSECourses
If you are interested in using AI, generative AI applications, open source applications in your computer, than this is the most fundamental and important tutorial that you need. In this tutorial I show and explain how to properly install appropriate Python versions accurately, how to switch between different Python versions, how to install diffe...
Fmy recommendation is this video
Fit uses kohya and you can train on
Frunpod or massed compute
Fwe have installers and config
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...
Fthis is most powerful way of training at the moment
UThank you so much Furkan! I have my best prompts and community models that worked well for SDXL, which is the best and latest SDXL (not base model) tutorial?
Fwell we have SDXL config
Fso follow this tutorial
Fbut when using config
Fuse SDXL config and reg images
Flet me link few more
Freg images dataset
FPatreon
Get more from SECourses: FLUX, Tutorials, Guides, Resources, Training, Scripts on Patreon
FPatreon
Get more from SECourses: FLUX, Tutorials, Guides, Resources, Training, Scripts on Patreon
FYouTubeSECourses
Patreon post for config files 
https://www.patreon.com/posts/89213064
SECourses Discord To Get Full Support
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My LinkedIn
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My Medium
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https://www.patreon.com/posts/89213064
SECourses Discord To Get Full Support
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UPerfect, thank you so much!
Mwhat is the estimated time for a training of a dreambooth with aprox 200 images, 1 batch, 200 epoch and a gtx 4090 with a 64gb ram?
Fi dont know exact timing but lets assume 5.5 second
F5.5 * 200 * 200 seconds
Fif faster it will be lower
MOk thank you !!
F200 epoch probably not needed for that many images
MIn the end, it turned out to be 180 images. I'm using 100 epochs, and right now it's at 50% of the training process at a speed of 6.6s per step. We'll see how the final result turns out
MWhat would be the best way to train a model for interior design? Let me explain.
I need to apply a specific product to a given room. For example, I want a Renaissance-style room with this product on the wall and this product on the floor.
I understand that in this case, it makes sense to train a highly structured model with all the individual products and, at the same time, the same products applied to different rooms. I assume that captions are important in this scenario.
What would be your approach to training a model for this application?
I need to apply a specific product to a given room. For example, I want a Renaissance-style room with this product on the wall and this product on the floor.
I understand that in this case, it makes sense to train a highly structured model with all the individual products and, at the same time, the same products applied to different rooms. I assume that captions are important in this scenario.
What would be your approach to training a model for this application?