This model is trained for 12 characters from Kuma Kuma Kuma Bear (くまクマ熊ベアー) + 5 characters from Saving 80,000 Gold in Another World for My Retirement (老後に備えて異世界で8万枚の金貨を貯めます)

Why do I train the two animes together?

I feel these two animes (light novels actually) have so much similarity that I really want to make some crossovers.

For examples please see https://civitai.com/models/37632/kumabear-roukin8-characters-fullckpt

Moreover there is no reason to do single anime either. I plan to add shinmai renkinjutsushi no tenpo keiei next.

Trigger Words

KumaBear

• Atla
• Cliff
• Eleanora
• Fina
• Flora
• Gentz
• Misana
• Noire
• Shia
• Shuri
• Telmina
• Yuna

Roukin8

• Adelaide
• Beatrice
• Colette
• Sabine
• YamanoMitsuha

Styles (may not be very effective)

• aniscreen
• fanart
• light novel
• official art
• ..., style(s) of your favorite model if know how to merge things properly

To get everything right you may need additional trigger words for outfits and ornaments. Here are some suggestions

• If you want to get the bear costume of Yuna you may add kigurumi, bear hood, animal hood, animal costume, hand puppet etc.
• Add Red bow for Fina/Shuri/Noire
• Add twin drill for Shia
• Add double bun for Flora
• Add scrunchie for telmina

Kumakyuu and Kumayuru are not tagged, but you may get something that look right by prompting with bears, stuffed animal etc.
Interestingly I can hardly take off the hood of Yuna during the early phase of training, but it becomes possible after longer training (actually now Yuna by default does not have hood though almost all the images of her have hood on!)
Many characters are missing from the two animes. I may update the KumaBear one at the end of the season with the following characters

• kumakyuu
• kumayuru
• Lurina
• Farrat (king)
• Kitia (queen)
• Karin
• Sanya
• Helen
• Ans
• Mylene
• Cattleya

Dataset

• KumaBear 5113
  • anime screenshots 5042
  • fanart 37
  • official art 15
  • novel illustration 19
• Roukin8 2948 (screenshots only)
• Regularization ~30K

Training

• First trained for 9739 steps, resumed and trained for another 20494 steps
• clip skip 1, resolution 512, batch size 8, on top of JosephusCheung/ACertainty
• 2.5e-6 cosine scheduler, Adam8bit, conditional dropout 0.08