stable-diffusion-webui VS dreambooth-training-guide

I'm using a fork of automatic1111 that works with the new ckpt files (still takes some effort to install) https://github.com/MrCheeze/stable-diffusion-webui/tree/sd-2.0

I used this repo: https://github.com/MrCheeze/stable-diffusion-webui/tree/sd-2.0 to test out v.2.0. I guess it's like automatic repo but with some additional scripts and files (that I do not understand) that allows you to put v.2.0 model inside and try. For the time being I managed to run base 768x768 model. (512x512 base model doesn't work for me).

Somebody put up a fork already.

I dont have the time right now, but the rule of thumb for me was 80 unet learning steps for 1 image. Atleast 40 regularization images. Read more about regularization images here: https://github.com/nitrosocke/dreambooth-training-guide

This tutorial for dreambooth training has advice with regard to backgrounds which is probably also applicable to LORA. It recommends including images with solid, non-transparent backgrounds but not using them exclusively. Images that focus on the torso and face are probably most important unless your subject has very distinctive legs and feet. Removing other subjects is a must if you're training for a specific subject.

I found this, I'm going to go through this guide. Seems like I am using far too many images. https://github.com/nitrosocke/dreambooth-training-guide

Nitrosocke's guide already tells how much and what kind of images to use.

Dreambooth lets you do it in less than 100 images. https://github.com/nitrosocke/dreambooth-training-guide These folks say it's 5-15 to train on a person but I've not tested myself. https://www.reddit.com/r/StableDiffusion/comments/10tqy88/were\_launching\_a\_lightningfast\_dreambooth\_service/

See eg this tutorial for styles: https://github.com/nitrosocke/dreambooth-training-guide

The first time I tried to Dreambooth a style it went poorly. Then I found Nitrosocke's Dreambooth Training Guide and realized my problems were caused by a poorly redacted dataset.