ユーザーLoRA

SD

Ume (Ittla) | LoCon V3

#オリジナル

#熟女

#尖った耳

#短髪

#

#筋肉

#黒髪

#

#悪魔

#猫目

#オレンジ色の目

#悪魔娘

#引き締まった体

#女の子

#筋肉娘

#悪魔の角

Ume (Ittla) | LoCon V3 - AI Model cover image
アップロード日時
2024/05/02 22:21
採用数
20
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トリガーワード

UmeIttla, colored skin, orange eyes, oni horns, skin-covered horns, short hair, toned, abs

権限について
  • 画像生成と共有を許可
  • ユーザーによるモデルのダウンロードを許可する
  • 生成画像の商用利用を許可する

詳細説明

The Oni OC Ume from the artist Ittla. Works best between 0.8-1.0. Training done on NAI.

Trigger V3: "UmeIttla, colored skin, orange eyes, slit pupils, oni horns, skin-covered horns, short hair" always needed, add "toned" and "abs" for more accurate physique.

Suggestions/notes: V3: Added "skin-covered horns, short hair" to training which fixed hair turning into horns during hires. Trained slightly longer than V2 which helped improve color and consistency. Flexibility on clothing is still good, check samples from outfits ideas.

V2: Trigger: "UmeIttla, colored skin, orange eyes, slit pupils, oni horns" Extra tags were added in training on this version as they were the less stable parts of the character, now those features are more consistent across images. "Slit pupils" are still hit/miss depending on model/hires settings so I suggest higher hires denoise as it can help create cleaner details. Very flexible clothing wise as there is no set outfit. Warning: Works best on animated style models. To use consistently on other less anime models you need to add "Purple skin, black hair" to avoid skin going pale.

A shot at remaking my first model on civitai as a LoCon with new training methods I have learned. It came out as nice upgrade with the helper tags being added to training as that way they stay a lot more consistent at the higher training resolution. I hope you all enjoy.

Feedback and reviews are always appreciated.

Nerdy training numbers (V3): Trained on D8Dreambooth trainer Optimizer: AdawW Dadaptation Training resolution: 768 Unet LR: 1 Tnec LR: 1 Unet weight decay: 0.016 Tenc weight decay: 0.02 35 Epochs - 3710 Steps Trained on 53 images using Reg images.

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