DiffusionToolkit VS dreambooth-training-guide

Diffusion Toolkit (https://github.com/RupertAvery/DiffusionToolkit) is an image metadata-indexer and viewer for AI-generated images. It aims to help you organize, search and sort your ever-growing collection, using the data that's right there on the images.

Awesome thanks, I wouldn't have thought to use 'works" I always do JCW or just (Mini Cooper). FYI you can pull the prompt info from the image of you are using Automatic1111 or vladdiffusion, under PNGinfo tab. If not using those repos, something like this works well: https://github.com/RupertAvery/DiffusionToolkit

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.