You sure it works on Win 11?
You sure it works on Win 11?
Fit works
Fbut use python 3.10
Finstall as i have shown in vide
Fand set it default
Fpython 3.10.11
Athanks, I will try!
Fafter doing that do a fresh install
Athanks onetrainer works now
A!!!
AYay!!!
Fgreat
SYouTubeSECourses
In this tutorial, I am going to show you how to install OneTrainer from scratch on your computer and do a Stable Diffusion SDXL (Full Fine-Tuning 10.3 GB VRAM) and SD 1.5 (Full Fine-Tuning 7GB VRAM) based models training on your computer and also do the same training on a very cheap cloud machine from MassedCompute if you don't have such compute...
Fit is up to date
SI lost my job so i have some extra time on my hands
Stime to learn now i guess
Fi hope you can find asap
Sgoing to try and just do freelance
Swhat is the it/s supposed to be?
Fdepends on gpu
Fwhat is your gpu
Srtx 4090
Si did the 15gb fast preset
F1-1.2 range
Sokay that's what i'm getting was hoping i could get it faster haha
Fyouy need to sacrifice quality with xformers
For batch size + xformers
Soh i can use xformers? i think i need to rewatch and look for that part
Fyes but that can reduce quality
Fxformers + turn off gradient checkpoint
Fwill speed up
Sgradient is under learning rate scaler?
Shmm xformers only took it up to 1.4
Fye you cant get much faster
Fdid you disable gradient checkpoint?
Si did
Fok then that is what you can get
Ftry batch size 2
Fbut it may give out of vram error likely
HI'm currently trying to learn how to train a model like realvisXL (https://civitai.com/models/139562/realvisxl-v40).
I've been training LoRA models, but have no experience with checkpoint training.
To train a target-focused (realism) and also flexible model like realvisXL, do I just need a lot more images followed by detailed captions? Or is there something more to it?
If you have any suggestions or links to share, I'd be really grateful.
I've been training LoRA models, but have no experience with checkpoint training.
To train a target-focused (realism) and also flexible model like realvisXL, do I just need a lot more images followed by detailed captions? Or is there something more to it?
If you have any suggestions or links to share, I'd be really grateful.
Fyou need a big dataset
Fproper captioning
Fand training
HHow big to be exact?
Do I need 100+ image? or 1000+?
And for the captions, is it ok to use captioning tools such as blip or wd1.4? It seems impossible to manually caption thousands of images. Thanks.
Do I need 100+ image? or 1000+?
And for the captions, is it ok to use captioning tools such as blip or wd1.4? It seems impossible to manually caption thousands of images. Thanks.
Fyou want to do a model like real vis XL 4?
Fyou need 10s of thousands high quality image to fine to SDXL base into such model
Fyou can use CogVLM for captioning
Fespecially CogVLM v2
HI'm wondering if the Dreambooth method would be effective for training with 10,000 high-quality images. Based on the publicly available SDXL base model, there are various fine-tuning methods such as Dreambooth, LoRA, HyperNetwork, and Textual Inversion. From what I know, Dreambooth seems to be the most appropriate. I would appreciate it if you could share any specifically recommended methods for the final training process you emphasized, or if there are any noteworthy precedents for training with such a large dataset.
(P.S. I've watched all of your fine-tuning tutorial videos on YouTube multiple times. I'm a big fan! Thank you!)
(P.S. I've watched all of your fine-tuning tutorial videos on YouTube multiple times. I'm a big fan! Thank you!)
Fdreambooth without class images = fine tuning