from scractch with new dataset
from 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
https://discord.com/servers/software-engineering-courses-secourses-772774097734074388
My LinkedIn
https://www.linkedin.com/in/furkangozukara/
My Instagram
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My Medium
@FurkanGozuka...
https://www.patreon.com/posts/89213064
SECourses Discord To Get Full Support
https://discord.com/servers/software-engineering-courses-secourses-772774097734074388
My LinkedIn
https://www.linkedin.com/in/furkangozukara/
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https://www.instagram.com/gozukarafurkan/
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@FurkanGozuka...
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?
Ffull research so best to keep experimenting
AHey @Furkan Gözükara SECourses, I'm enjoying the 5090 videos you've done and had a question. Wondering if you're planning on doing a video comparing 3090 vs 5090 for fine tuning or lora training? Sorry if asked elsewhere discord didn't show if it had or hadn't.
Fyep i will show
Fi made a quick test also
Fi am waiting official pytorh
FReddit
Explore this post and more from the SECourses community
EHi! Don't know which channel to write this on. I'm training a lora and I'm wondering if I can stop the training and start a training on the latest save like you can when training checkpoint?
ESave training state (including optimizer states etc.) when saving models
Save training state (not enabled)
Save training state (including optimizer states etc.) on train end
Save training state at end of training (not enabled)
Resume from saved training state (path to "last-state" state folder)
Saved state to resume training from
Save training state (not enabled)
Save training state (including optimizer states etc.) on train end
Save training state at end of training (not enabled)
Resume from saved training state (path to "last-state" state folder)
Saved state to resume training from
MI have finetuned flux-dev with 180 images of a character and 180 photos from various angles. Maybe it's not the best dataset because it didn’t have varied backgrounds. Almost all the images were just of the face without the body.
I'm having quite a few issues with prompts where I request a stylization. I trained a batch with 100 epochs and have 4 safetensors—I’ve tested them all, and I’m experiencing the same issues across the board.
When I ask for a stylized result, like a cartoon or anime style, it struggles to adhere to the prompt and often generates random things instead.
What could be happening? Thanks a lot!
I'm having quite a few issues with prompts where I request a stylization. I trained a batch with 100 epochs and have 4 safetensors—I’ve tested them all, and I’m experiencing the same issues across the board.
When I ask for a stylized result, like a cartoon or anime style, it struggles to adhere to the prompt and often generates random things instead.
What could be happening? Thanks a lot!
Tthis is normal, nothing is wrong. You trained the model on realism data and it is inclined to generate it.
T18000 steps is a lot, you may want to try earlier checkpoints
Tyou may also try different prompt engineering, generate a lot of images and cherrypick from them secondary dataset to include in traning of second gen model with more stilyzed images in training data
Myes I did it
MI did imagine that. But I watched @Furkan Gözükara SECourses tutorial and saw that he had some incredibly stylized images... I think he also only trained with realistic images. That's where my doubt comes from
Fyes buyt i never tried
Fits option is here
F
Fyes flux is very realistic