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This folder contains models trained for the two characters oyama mahiro and oyama mihari. |
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Trigger words are |
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- oyama mahiro |
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- oyama mihari |
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To get anime style you can add `aniscreen` |
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At this point I feel like having oyama in the trigger is probably a bad idea because it seems to cause more character blending. |
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### Dataset |
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Total size 338 |
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screenshots 127 |
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- Mahiro: 51 |
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- Mihari: 46 |
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- Mahiro + Mihari: 30 |
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fanart 92 |
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- Mahiro: 68 |
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- Mihari: 8 |
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- Mahiro + Mihari: 16 |
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Regularization 119 |
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For training the following repeat is used |
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- 1 for Mahiro and reg |
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- 2 for Mihari |
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- 4 for Mahiro + Mihari |
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### Base model |
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[NMFSAN](https://huggingface.co/Crosstyan/BPModel/blob/main/NMFSAN/README.md) |
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### LoRA |
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Please refer to [LoRA Training Guide](https://rentry.org/lora_train) |
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- training of text encoder turned on |
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- network dimension 64 |
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- learning rate scheduler constant |
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- learning rate 1e-4 and 1e-5 (two separate runs) |
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- batch size 7 |
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- clip skip 2 |
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- number of training epochs 45 |
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### Comparaison |
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learning rate 1e-4 |
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![grid-00010-492069042](https://huggingface.co/alea31415/YuriDiffusion/resolve/main/onimai/samples/grid-00010-492069042.png) |
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learning rate 1e-5 |
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![grid-00017-492069042](https://huggingface.co/alea31415/YuriDiffusion/resolve/main/onimai/samples/grid-00017-492069042.png) |
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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. |
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### Examples |
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![00026-4010692159](https://huggingface.co/alea31415/YuriDiffusion/resolve/main/onimai/samples/00026-4010692159.png) |
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![00030-286171376](https://huggingface.co/alea31415/YuriDiffusion/resolve/main/onimai/samples/00030-286171376.png) |
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![00034-2431887953](https://huggingface.co/alea31415/YuriDiffusion/resolve/main/onimai/samples/00034-2431887953.png) |
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