yep, reduced to 260mb instead of 1GB, thx
yep, reduced to 260mb instead of 1GB, thx
Fyou are welcome
GHi @Furkan Gözükara SECourses , do you have an idea how to create a consistent character for image generation from a single image?
Fyes 2 step training
F1st traing with single image
Fadd cropped version zoom out etc
Fthen generate images
Fthen train again
GInteresting idea, I'll try it. Thanks
GI saw this tutorial, I wonder if they do it the same way behind the scenes
https://youtu.be/UY_aRiOK6gY?si=6U4TAXFPa0lxPPU0
https://youtu.be/UY_aRiOK6gY?si=6U4TAXFPa0lxPPU0
GWhat about this process using different posing on a single image?
https://youtu.be/MbQv8zoNEfY?si=oufZgsSpJJSBMGD9
https://youtu.be/MbQv8zoNEfY?si=oufZgsSpJJSBMGD9
Fsame logic. if you can get more poses from single image can help
Fbut still i recommend twice training
ASingle Image training is pretty good but will work exponentially better if you can get at least three from different angles from my experience
Ayou will save yourself a lot of time and headache
AIf you have Comfy there are some workflows floating around which will attempt to create these angles from the image - you can randomly hit on some with a few rolls of the dice
Lunder this logic, not having so many images, could you recommend some configuration to train with a minimum of 5 or 10 images, how many steps, etc.
Fit is always same the config
Fonly number of epochs may change
Flike do more epoch
LPerfect! To test then... Thanks
GI'm looking for way to install all needed dependencies with a single script or wrap it up in a docker container for fine tuning flux model, I'm planning to do so on Azure GPU inside Azure ML training clusters. If you've got anything ready which might help me achieving this goal it would be great @Furkan Gözükara SECourses
Fsadly i dont have
Fbut we have installer for runpod and massed compute
Fyou can look runpod
MHas anyone encountered this error while trying to train on an A100 or H100 in RunPod? I've started four machines, and all of them give this error.
raise ValueError("Attempting to unscale FP16 gradients.")
ValueError: Attempting to unscale FP16 gradients.
raise ValueError("Attempting to unscale FP16 gradients.")
ValueError: Attempting to unscale FP16 gradients.
Fyes people had it
Fi think you need to get a new pod
Fi trained on h100 a100 h200
Fdidnt have that error
Fwhich template you used?
MThis one
Mstrange, because I've tested on 4 pods already. I was checking if GPT could help me, but the solutions it provided didn't work
MI ran it on my personal machine without any issues; it's only on RunPod that I encountered this error
Fye this is correct one
Fi recommend massed compute people never have errors there :d
Mi'll try, thanks
JHi there! Just wanted to share with everyone some results of my latest trained Finetuning.
If you want to try it! Here is the repo: https://huggingface.co/JotaTerrasa/BZ4X/tree/main
Trigger word is: BZ4X
101 Image dataset
100 Epoch
Hardware used for training and inference:
GPU: RTX 4090
RAM: 128GB DDR4
CPU: Ryzen 9 5900XT
Motherboard: MSI MPG B550 GAMING PLUS
If you have any tips for improvement, would be greatly appreciated.
Thanks for sharing your knowledge on this @Furkan Gözükara SECourses, you rock!
If you want to try it! Here is the repo: https://huggingface.co/JotaTerrasa/BZ4X/tree/main
Trigger word is: BZ4X
101 Image dataset
100 Epoch
Hardware used for training and inference:
GPU: RTX 4090
RAM: 128GB DDR4
CPU: Ryzen 9 5900XT
Motherboard: MSI MPG B550 GAMING PLUS
If you have any tips for improvement, would be greatly appreciated.
Thanks for sharing your knowledge on this @Furkan Gözükara SECourses, you rock!
MI've been running tests with various training approaches for days without successful results, and I'd like to share my goal to see if someone can help me.
I'm training a model for interior design. Essentially, I need a flexible model that allows me to train a variety of products in different environments. That is, multiple furniture designs applied to different spaces such as kitchens, bathrooms, dining rooms, etc.
My idea is to use captioning to specify what type of product appears in the image.
Then, I want to be able to generate images of that specific product applied in different settings.
For a single product, it's easy—I train a model with that specific item. But for multiple product families, do you think it's feasible to group them all into a single model?
Do you think that through captioning, the model would later be able to identify each product family correctly?
Thanks a lot!
I'm training a model for interior design. Essentially, I need a flexible model that allows me to train a variety of products in different environments. That is, multiple furniture designs applied to different spaces such as kitchens, bathrooms, dining rooms, etc.
My idea is to use captioning to specify what type of product appears in the image.
Then, I want to be able to generate images of that specific product applied in different settings.
For a single product, it's easy—I train a model with that specific item. But for multiple product families, do you think it's feasible to group them all into a single model?
Do you think that through captioning, the model would later be able to identify each product family correctly?
Thanks a lot!
JI would say that lora training would be suitable for this situation, for each product and stack them with Lora stack node & divide weights proportionally.
Then in your prompt you’ll need to call to each product trigger word.
Then in your prompt you’ll need to call to each product trigger word.
Jhttps://youtu.be/hZCTiZi-EPw?si=fTg65vTJCP2tqdxX the Lora training can be done easily from this workflow via ComfyUI, if not you can use Doc’s Lora Tutorial via Kohya GUI
JFor the stacking, you can follow this idea by Mickmumpitz: https://youtu.be/MbQv8zoNEfY?si=gP9b6zPSW5_eBy3y
JMinute 11:47
JLora training by Doc: https://youtu.be/nySGu12Y05k?si=yWTC8jR9x39YvIfZ you must master class prompt and understand what kind of dataset you need for your project.
YouTubeSECourses
Ultimate Kohya GUI FLUX LoRA training tutorial. This tutorial is product of non-stop 9 days research and training. I have trained over 73 FLUX LoRA models and analyzed all to prepare this tutorial video. The research still going on and hopefully the results will be significantly improved and latest configs and findings will be shared. Please wat...
JAnd for the captioning, I would suggest you to be us more precise as possible specially when training Lora with flux, just by natural language, but don’t over complex the semantics.
JAlso, doctor has a really nice captioning Python app script that you can download if you subscribe to his Patreon
JI would strongly recommend you that
Fmultiple certain things wont work with flux
Fit bleeds / collapses
Fbut you can teach a single thing or a style