which template you used?
which 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
SUnable to understand which tutorial to watch to do finetuning training on my 4090 Locally.
I tried watching the one with the Yellow Thumbnail.
I ran all 3 in order but GUI is not booting up
I tried watching the one with the Yellow Thumbnail.
I ran all 3 in order but GUI is not booting up
Fwhat is your python version?
S3.10 right now.
Fthen you must be doing something wrong somewhere
Fcan you make a quick video how do you install
SSure, Doing it immediately
SHere
Fit didnt work
Fyou have error
F
Fyour python installation is wrong
SHow do I solve?
Funinstall and reinstall as in this video exactly
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...
SI am a very newbie to this, I have no idea how to uninstall. Should I just keep following the tutorial?
SI had done this Last night and other apps seem to work fine
Fwell the issue occurs because of your installation with inside users admin app data
Funinstall your python
Ffrom control panel
Freinstall as in video
Factually follow video exactly
Fyour previous applications may get broken after this
Fjust reinstall them
Fif you upgrade gold member i can connect your pc and help
SUpgraded!
Fawesome please open anydesk
SShared on Personal
Fnow working 
Danyone knows where to change Mixed Precision in koya? i dont see any setting for it
