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# Kyoyama Kazusa (Blue Archive) |
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杏山カズサ (ブルーアーカイブ) / 쿄야마 카즈사 (블루 아카이브) / 杏山和纱 (碧蓝档案) |
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[**Download here.**](https://huggingface.co/khanon/lora-training/blob/main/kazusa/chara-kazusa-v1c.safetensors) |
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## Table of Contents |
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- [Preview](#preview) |
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- [Usage](#usage) |
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- [Training](#training) |
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- [Revisions](#revisions) |
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## Preview |
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![Kazusa preview 1](example-001-v1c-16dim.png) |
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![Kazusa preview 2](example-002-v1c-16dim.png) |
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![Kazusa preview 3](example-003-v1c-16dim.png) |
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![Kazusa preview 4](example-004-v1c-16dim.png) |
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![Kazusa preview 6](example-006-v1c-16dim.png) |
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## Usage |
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- Use any or all of the following tags to summon Kazusa: `kazusa, 1girl, animal ears, colored inner hair, halo, short hair` |
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- For her normal outfit: `black choker, black jacket, black pantyhose, green sailor collar, hooded jacket, white skirt, miniskirt, hairclip, pink neckerchief, school uniform, sneakers` |
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- For a closed jacket, you might need to add `open jacket, white shirt` to the negative prompt. |
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- For raised hood: `hood up` |
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- For her "trying to look cool" expression: `expressionless, blush, sweatdrop, v-shaped eyebrows, embarrassed` |
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- For selfies: `selfie, (reaching towards viewer:1.2)` |
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- For eating: `eating, :t, holding food, food on face` |
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- Accessories: `macaron, cake, fork` |
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## Training |
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*Exact parameters are provided in the accompanying JSON files.* |
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- Trained on a set of 280 images. |
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- 7 repeats |
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- 3 batch size, 4 epochs |
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- `(280 * 7) / 3 * 4` = 2614 steps |
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- Kazusa has a lot of good art so her dataset is much larger than usual. This seems to have let me train for longer without overfitting. |
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- 0.08 loss |
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- Initially tagged with WD1.4 swin-v2 model. Tags pruned/edited for consistency. |
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- [Detailed tagging methodology here.](tagging%20methodology.md) |
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- `constant_with_warmup` scheduler |
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- 1.5e-5 text encoder LR |
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- 1.5e-4 unet LR |
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- 1e-5 optimizer LR |
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- Used network_dimension 128 (same as usual) / network alpha 128 (default) |
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- Resized to 16 after training |
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- Training resolution 832x832. |
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- Trained without VAE. |
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## Revisions |
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- v1c (2023-03-02) |
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- Initial release. |
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