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# Yuragi Momoka (Blue Archive) |
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由良木モモカ (ブルーアーカイブ) / 유라기 모모카 (블루 아카이브) / 由良木桃香 (碧蓝档案) |
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[**Download here.**](https://huggingface.co/khanon/lora-training/blob/main/momoka/chara-momoka-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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![Momoka portrait](chara-momoka-v1c.png) |
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![Momoka preview 1](example-001b-v1c.png) |
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![Momoka preview 2](example-002b-v1c.png) |
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![Momoka preview 3](example-003b-v1c.png) |
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## Usage |
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Use any or all of the following tags to summon momoka: `momoka, halo, short twintails, horns, bright pupils, pointy ears, hair ornament, ahoge` |
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- Add `(dragon tail:1.3)` for her tail (even though I'm not quite sure Momoka is truly a dragon?) |
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For her normal outfit: `sleeveless dress, collared dress, blue necktie, white open jacket, off shoulder, loose socks, white shoes` |
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- Add `frilled dress` if the frills at the bottom of her dress are not correctly displayed. |
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For her accessories: `potato chips, bag of chips, holding food` |
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For her smug expression: `smug, open mouth, sharp teeth, :3, :d` |
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- Alternatively, `smug, grin, sharp teeth, smile` for a toothy grin |
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[Here is a list of all tags including in the training dataset, sorted by frequency.](all_tags.txt) |
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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 94 images. |
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- 13 repeats |
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- 3 batch size, 4 epochs |
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- `(94 * 13) / 3 * 4` = 1654 steps |
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- 0.0737 loss |
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- Initially tagged with WD1.4 swin-v2 model. Tags pruned/edited for consistency. |
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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 24 after training |
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- This LoRA seemed very slightly overtrained, perhaps due to smaller dataset, so resizing to 24 appeared a bit better than 32. |
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- Training resolution 832x832. |
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- This one also came out better at 832 vs 768. |
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- It's not clear to me why some LoRAs perform substantially better at 768 and others at 832. |
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- Trained without VAE. |
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- [Training dataset available here.](https://mega.nz/folder/fi5zxDpb#J6ABI5i8ZFnTONVYiRlKHg) |
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## Revisions |
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- v1c (2023-02-19) |
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- Initial release. |
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