Waraku Chise (Blue Archive)
和楽チセ (ブルーアーカイブ) / 와라쿠 치세 (블루 아카이브) / 和楽千世 (碧蓝档案)
Table of Contents
Preview
Usage
Use any or all of these tags to summon Chise: chise, halo, oni horns, hair flower, red eyes, blue hair
For her normal outfit: braid, japanese clothes, detached sleeves, bridal gauntlets, obi, tabi, geta, sailor collar, blue bow
For her swimsuit alt: side ponytail, swimsuit, striped bikini, see-through, see-through shirt, white sailor collar, side-tie bikini bottom
- You may also add
pointy ears, which are usually only visible in her swimsuit outfit.
Training
Exact parameters are provided in the accompanying JSON files.
- Trained on a set of 119 images; 76 swimsuit, 43 normal.
- 15 repeats for normal outfit
- 9 repeats for swimsuit outfit
- 3 batch size, 4 epochs
(76*9 + 43*15) / 3 * 4= 1772 steps
- 0.0837 loss
- 832x832 training resolution
constant_with_warmupscheduler- Initially tagged with WD1.4 Convnext-v2 model, then heavily edited.
- Pruned redundant tags and simplified outfit tags such that Chise's outfit was always described with the same words
- Ensured unique traits such as
halowere consistent with actual visibility - Added many tags for facial expressions, camera angles, and image composition
- Used network_dimension 128 (same as usual) / network alpha 128 (default)
- Resized to 32dim after training
- Trained without VAE.
- Training dataset available here.
Revisions
- v5b (2023-02-12)
- Completely re-trained to correct issue where caption files were not being loaded correctly. New version should have more flexibility but requires additional outfit prompting.
- v1 (2023-01-26)
- Initial release.
- Issue: caption files were completely ignored during training. As a result, prompting "chise" grants a very strong effect, but the LoRA is highly overfit.
Alternate Version - v1
v1 is also available. This version was improperly configured and ignored caption files, so it was trained on the word chise only. It looks quite good, however the model is very overfit and it is difficult to adjust Chise's outfit.
