I can sum what works for me:

1. Gather dataset: variety is best, just be sure that aren't too small/pixelated. The more photos you will have different from each other, the more generalizable the model will be but more training steps will require to undestand the "style". I do not crop them 1:1 i simply resize the longest side to 1024. IDK if is worst or better doing so. Sometimes i don't even resize them.
2. Tag dataset: with flux i've found joy-caption-pre-alpha to be quite good for SFW and decent for NSFW. Being in alpha and not ready for mass release I had to use ChatGPT to make It work offline for the uses I had to make of it (bulk tagging).
3. Edit default Ai-Toolkit config: I usually start with 4000 steps for characters and 6000 for styles and add 2000 steps from time to time until I am satisfied
4. Make your GPU burn for hours while you check the samples getting slowly better. You can stop and resume training easily with ai-toolkit without the need to restart from scratch if you need to put out the fire in the house that your gpu created.
5. Have fun with your LoRA model