I think I'm qualified to comment on this subject as I've made over 200 MLP character LoRAs for SDXL-based models (mostly Illustrious, though I started with PDXL 6 and have recently shifted to NoobAI).
One thing I have found is that a small, varied and well-tagged dataset is far better than a large one with just passable tagging and not that much variety. I generally use around 30 distinct images of the character (a minimum of around a dozen pics and a maximum of around 50) when training my LoRAs.
After I gather the images for the dataset, I run them all through an autotagger and then touch up its results (adding a tag for the character, removing tags that don't apply, adding missing ones).
I think I have the capacity to train LoRASs for Flux.2, but I have no clue how to tag images for one (at the moment), so I haven't done anything of that sort yet.
One thing I have found is that a small, varied and well-tagged dataset is far better than a large one with just passable tagging and not that much variety. I generally use around 30 distinct images of the character (a minimum of around a dozen pics and a maximum of around 50) when training my LoRAs.
After I gather the images for the dataset, I run them all through an autotagger and then touch up its results (adding a tag for the character, removing tags that don't apply, adding missing ones).
I think I have the capacity to train LoRA