OshiNoKo-characters-LoHa/LoCon/FullCkpt 推しの子 || Hoshino Ai / Hoshino Aquamarine / Hoshino Ruby / Arima Kana / Saito Miyako / Kurokawa Akane / Kotobuki Minami / Shiranui Frill / Sumi Yuki / ...版本FullCkpt Ep01 (ID: 45551)

UPDATE: Following the common practice, the most recent version is trained on NAI with clip skip 2

The advantage of my models is clearly the ability to put multiple characters in a same image

(I was considering training a full model with the same dataset as well, but the LoHa seems to be reasonably good at this point

The other models are otherwise trained on ACertainty
Please check the version notes for further details including the clip skip to use

Normally you should be able to use the lycoris models on a lot of different checkpoints. In case of incompatibility, you can lower lycoris weight or better, merge your base model with AC/nai.

More checkpoints can be found on the associated hugging face repository https://huggingface.co/alea31415/oshinoko

Screenshots and fan arts are respectively tagged with aniscreen and fanart. Using aniscreen gives both stronger anime style and better resemblance. Using aniscreen on style models may cause weird effects.

You may want to use ; to separate characters (see the prompts of the example images)

Characters

EP01

• HoshinoAi (add hair ornament if you want)

• AmemiyaGoro

• TendojiSarina

• Aquamarine baby

• Aquamarine child

• Aquamarine

• Ruby baby

• Ruby child

• Ruby (add red ribbon if you want)

• ArimaKana child

• ArimaKana (I suppose you should add some hat)

• SaitoIchigo

• Miyako

• GotandaTaishi

• KurokawaAkane

Main improvement of EP02

• Aquamarine

• Ruby

Added in EP04

• NarushimaMelt

• KaburagiMasaya

• KichijoujiYoriko

• ShiranuiFrill

• KotobukiMinami

Added at the end of the season

• MemCho

• KumanoNobuyuki

• SumiYuki

• MorimotoKengo

• Pieyon

Comments on the most recent model:

Pieyon is not learned very well as can be seen from the example images, showing this is a more difficult task than learning the appearance of the characters

The star-shaped eyes are learned pretty well in the newest model, probably too well that it sometimes wants to put it on other characters, especially when we do inpainting

You may want to avoid this by putting related keywords in negative

I suppose this suggests a sort of character blending

Someone reported to me the EP04 version has more important character blending compared to the EP02 version

I am not sure how it would be the case for this new one as I do not have an objective way to judge this

Hopefully it works fine for you

Dataset composition

fanart 2432

screenshots

• EP 01 1268

• EP 02 357

• EP 03,04 777

• EP 05-11 2582

regularization ~35K

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I don't need support or credit, but I would be glad to know that you are using the models I trained and find it useful.

Moreover, I would like to advocate for more franchise models.

You can take a look at my workflow https://github.com/cyber-meow/anime_screenshot_pipeline if you are interested.
You may also check this nice page https://civitai.com/articles/262/directory-of-girlpacks-character-packs for a collection of such models,

描述:

Trained on clip skip 1.

Up to this point, Aqua, Ruby, Kana, and Akane are only trained with limited fan arts, so the quality of these characters will be largely improved later after more episodes come out.

The model is trained for 42647 steps and there are in total 4 checkpoints here

This is the 32783 step version.

If you want to put the star in the correct eye for Aqua and Ruby you would have higher chance to success with 32783 and 42647 step checkpoints. However, the last one seems to be quite overfitted. It would be much more difficult to achieve that with earlier checkpoints and extracted LoCons. For comparison please see the last four images in the examples.

On the other hand, for some characters with few training images, TendojiSarina notably, you may want to use earlier checkpoints or LoCon with lower weight to gain more flexibility.

You may want to add red ribbon for Ruby.

训练词语: See description