santa hat
deerdeer nosedeer glow
Sign In

Yadokari Style LoRA

222
2k
10
Type
LoRA
Stats
1,093
Reviews
Published
Feb 22, 2023
Base Model
SD 1.5
Training
Steps: 10,960
Epochs: 8
Hash
AutoV2
C195A16077
default creator card background decoration
SDXL Training Contest Participant
Machi's Avatar
Machi

This LoRA facilitates imitating the style of Yadokari (also spelled Yadkari, or やどかり), an illustrator best known for their work on character art and illustrations for the browser game and media franchise Kantai Collection.

Prompting

No activation tokens are required to use this LoRA model.

If the style of the eyes ends up looking a bit lifeless, try adding/strengthening "finely detailed iris".

v6 models

For v6 models I recommend using weights between 0.3-0.9, depending on how stylized you want it to be.

Character feature recall for Yadokari's characters can be hit-and-miss, due to low frequency of each individual character in the dataset. Regardless, prompting for features will typically allow proper recall and it has learned some aspects. This is (partially) addressed by the v8 model, but at the cost of ease-of-use and versatility.

v8 models

For v8 models I recommend using weights between 0.2-0.6, depending on how stylized you want it to be.

Character feature recall is improved from v6 models due to better tagging & higher text encoder learn rate, but this also means images more easily start to look like the training set. Most notably, simpler backgrounds & rigging appear more easily.

Dataset

About 92 images of artworks by Yadokari were scraped from Danbooru, consisting of mostly KanColle but there are also fanarts of other IPs and fully original works. In addition, the training set also included 50 unique colored 2D works scraped from Yadokari's Pixiv and Twitter. Some artworks were rotated to be more upright orientation if the image did not contain very clear elements of gravity that would seem unnatural if rotated. Duplicates, or closely related images (for instance, intermediate drafts of later artworks, crops of other artworks) were pruned.

Tagging was done via wd14-swinv2-v2 with threshold 0.15.