This folder contains models trained for the two characters oyama mahiro and oyama mihari.

Trigger words are
- oyama mahiro
- oyama mihari

To get anime style you can add `aniscreen`

At this point I feel like having oyama in the trigger is probably a bad idea because it seems to cause more character blending.


### Dataset

Total size 338

screenshots 127
- Mahiro: 51
- Mihari: 46
- Mahiro + Mihari: 30

fanart 92
- Mahiro: 68
- Mihari: 8
- Mahiro + Mihari: 16

Regularization 119

For training the following repeat is used
- 1 for Mahiro and reg
- 2 for Mihari
- 4 for Mahiro + Mihari


### Base model

[NMFSAN](https://huggingface.co/Crosstyan/BPModel/blob/main/NMFSAN/README.md)


### LoRA

Please refer to [LoRA Training Guide](https://rentry.org/lora_train)

- training of text encoder turned on
- network dimension 64
- learning rate scheduler constant
- learning rate 1e-4 and 1e-5 (two separate runs)
- batch size 7
- clip skip 2
- number of training epochs 45


### Comparaison

learning rate 1e-4

![grid-00010-492069042](https://huggingface.co/alea31415/YuriDiffusion/resolve/main/onimai/samples/grid-00010-492069042.png)

learning rate 1e-5
![grid-00017-492069042](https://huggingface.co/alea31415/YuriDiffusion/resolve/main/onimai/samples/grid-00017-492069042.png)

Normally with 2 repeats and 45 epochs we should have perfectly learned the character with dreambooth (using typically lr=1e-6), but here with lr=1e-5 it does not seem to work very well. lr=1e-4 produces quite correct results but there is a risk of overfitting.

### Examples

![00034-2431887953](https://huggingface.co/alea31415/YuriDiffusion/resolve/main/onimai/samples/00034-2431887953.png)
![00026-4010692159](https://huggingface.co/alea31415/YuriDiffusion/resolve/main/onimai/samples/00026-4010692159.png)
![00030-286171376](https://huggingface.co/alea31415/YuriDiffusion/resolve/main/onimai/samples/00030-286171376.png)