This is my first time doing any sort of Stable Diffusion training so I went through a lot of trial and error. Here are my findings in case it helps anyone.

Training

All parameters are provided in the accompanying JSON files.

Trained on 138 curated images, repeated 8 times (1104 total images / 3 batchsize = 368 iterations)
- I pruned most images with white backgrounds because I felt they might have been negatively impacting my results early on, but in hindsight I think that was bad training parameters instead.
Dataset was tagged with WD1.4 interrogator. Shuffling was disabled.
- mutsuki, blue archive were added to the start of each caption.
Two variants included; one trained at 512px max resolution, and another trained at 768px max resolution. All other params identical.
Trained on RTX 4090 for about 2min30sec (512px variant) and 6min30sec (768px variant)
- I tried using higher batch sizes with the 512px variant for faster training, but the results seemed noticably worse.
- Small batch sizes seem to work better even when you have the VRAM for 10 or 12, so I instead put the VRAM towards training a higher resolution variant.

Usage

Mutsuki needs a few tags to be summoned reliably. Some common tags in her dataset: 1girl, halo, side ponytail, long hair, white hair, purple eyes, jacket, red skirt, light grin, small breasts

You can add or ignore mutsuki, blue archive; while they were in her captions, they don't seem to be particularly strong for some reason.

You can use the 512px or 768px variants. I want to say the 768px one is better, but it's hard to say definitively. Give both a shot and post your findings.

Weight 0.80-1.05 should work well depending on model.