| # Waraku Chise (Blue Archive) | |
| Came out pretty well I think. Smaller dataset than Mari, but otherwise very similar settings. | |
| ## Usage | |
| Use any or all of these tags to summon Chise: | |
| `chise, halo, red eyes, blue hair` | |
| Hair and eyes are mostly optional if you describe a bit of her outfit as well. | |
| She naturally likes to make her `:o` expression because most art features her doing that. However I also included images tagged with other expressions. | |
| Use `open mouth`, `closed mouth`, and `parted lips` as necessary to get her to make whatever expressions you want. | |
| For her normal outfit (add as many as necessary): | |
| `braid, japanese clothes, detached sleeves, obi, tabi, geta, sailor collar, blue bow` | |
| Her shoes are weird but they're tagged `geta` and the socks as `tabi`. | |
| For her swimsuit outfit (add as many as necessary): | |
| `side ponytail, swimsuit, striped bikini, see-through, sailor collar, side-tie bikini bottom` | |
| You can also add `pointed ears`, which are usually only visible in her swimsuit outfit. | |
| Weight 1 works fine. Also included epoch 6 in case you find the last epoch to be a bit stubborn with her outfits. | |
| ## Training | |
| *All parameters are provided in the accompanying JSON files.* | |
| - Trained on a set of 119 images split by outfit, repeated 10 times (swimsuit) or 14 times (uniform) for 7 epochs (119 images * 10 or 14 repeats / 3 batch size * 7 epochs = 3178 steps) | |
| - Dataset included a mixture of SFW and NSFW. | |
| - Initially tagged with WD1.4 VITv2 model, then performed heavy pruning and editing. | |
| - Pruned implicit (`oni horns`) or redundant tags and simplified outfits so that they were always tagged with the same handful of tags | |
| - Made sure important traits were present and consitently described, and traits like `halo` were consistent with actual visibility | |
| - Added many facial expression, camera angle, and image composition hints | |
| - Used network_dimension 128 (same as usual) and network_alpha 64 (new) | |
| - This relies on the new alpha | |
| - Trained without VAE. | |