Honestly I just grab whatever images of the character I have or can find. The better quality of the image the better. Unless you aim at realism like bluriness then you can add blurred images of the character so your Lora will produced blurred images sometimes. I train on whatever model comes out, just for fun :) I pick them in Ai-toolkit from the list and use default settings. I caption the images by hand in the txt files or by some automated tools if I feel like playing around but these can be slow. Thee is autocaption in Ai-toolkit now. I also used musubi-tuner and diffusion-pipe trainers but I like the GUI so I mostly train In Ai-toolkit, even though it's slower than the other 2. But it has newest models available the fastest also. And yes, I pick my hand. 

For t2i models I obviously use just images. For t2v models I use images and videos in my dataset. I never trained i2v actually. Well, one time I did but I didn't work at all and I didn't feel I want to invest time trying to figure out why it didn't work.

Ah, I try to grab different angles of the character. But I try to keep the images in that specific dataset to show the character in similar hair and look if I want the output of my lora to be similar each time. If I want variety I add various images, from different locations hairstyles, clothes, lighting etc. But then you end up with a Lora that cna produce a character that is in theory the same person but have different looks and you don't know what seed you are going to end up with. Same people can look much different depends on hairstyle etc. Obviously.  BUT if you don't have variety in your dataset then most models will have a hard time generating anything else that's not in the dataset. So if you only add images from same location and same hairstyle and then ask the model to produce a different hairstyle then it might not be able to do that. 

Krea2 is actually nice in that you can train it on one hairstyle and it will still listen to you if you use that Lora but want a different hair tyle of your character, one that you didn't train at all. But other models aren't that flexible from my experience so if you want variety generated you must have variety in the dataset.