what are the effects ?

Improve characters already trained in AnimagineXLV3.
It is effective for items where caption or trigger words are not effective.
but It is not always effective.
LoRA dedicated to that character would naturally be more effective.

Example of Image

kafka from honkai star rail has "sunglasses"
but that "sunglasses" is not responsive with prompt "sunglasses".
if you add sunglasses in prompt, kafka has sunglasses on eyes or wearing normal sunglasses.
but just add this LoRA, improved strange sunglasses problem.
and also improved outfit that arm isn't in sleeves.

hu tao from genshin impact has "flower ornament" on hat
but that "flower oranament" …

the main usage for the target

Something that is hard to describe by prompts.
e.g.) clothes' pattern, specific hair style

inherently incompatible

special tags written in AnimagineXLV3's page.
especially, years' tag. combined use diminishes effectiveness.
but it doesn't mean completely disappear effectiveness to use

how to use ?

effective strength - "-1.0~1.0"
recommend strength - "-0.5~0.5"

in this case, "-" values give better results. so you should try to "-" values.

which is suitable ?

depends on the dataset been trained.
basically, rank32 or rank?? is suitable.
a smaller number of ranks is still effective if there is a lot of trained data.
e.g.) genshin impact, blue archive, azur lane ?

this time, the content is serious

AnimagineXLV3 is good model but it has some overfitting problem.
sometimes, Have you seen that the output image was generated with excess or deficiency?

why does it happen ?

they are from how AnimagineXLV3 was training.
AnimagineXLV3 was training by relatively newer images
but it cause incorrectly overfitting in some character. so I try to get reduce it.

how to reduce overfitting

fight overfitting with overfitting.
overfitting is not problem if it's correctly overfitting in output character.
so change incorrectly overfitting data to correctly overfitting data by difference on AnimagineXLV3 and AnimagineXLV2.

how to made it ?

It made by difference on AnimagineXLV3 and AnimagineXLV2.