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Member of house Burion Secelia Dote from the anime " Mobile Suit Gundam the Witch from Mercury ". Works best around 0.8-1.0 weight . Training done on Pony Diffusion V6 XL . Request form
Fonte do modelo
Descrição da fonte
Member of house Burion Secelia Dote from the anime " Mobile Suit Gundam the Witch from Mercury ". Works best around 0.8-1.0 weight . Training done on Pony Diffusion V6 XL . Request form
Always needed : " seciDo , dark skin, dark-skinned female, aqua eyes , silver hair , short hair " always needed for accuracy. Optionally add " white nails " for her proper nails.
Hairstyle options:
Main Uniform: " parted bangs "
Business Outfit: " forehead"
Outfits/notes :
Uniform Outfit: " seceUni , green jacket , shoulder boards, emblem, partially unzipped, halterneck, white leotard, green sleeves , two-sided sleeves, layered sleeves, green shorts , short shorts, white thighhighs "
Fontes
1 fonteArtefatos de modelo
1 artefatoSeceliaDotePDXL_V1-Manityro-CAME.safetensors
safetensors · 61,1 MB · SHA-256 776fea725d76…a661 · Civitai
BaixarTrechos de fonte
1 trechoSource context: 776 downloads · Type LoCon · Base model Pony
Business Outfit: " seceBui , gold jewelry , hoop earrings , necklace , blue blazer , white shirt, collared shirt, long sleeves, blue skirt , pencil skirt "
Very flexible due to data variety along with the tagging I did and Pony gives good flexibility.
I suggest lower weight when using other outfits to avoid standard outfit bleed.
Outfit can be customize a lot with "open jacket" or removing individual parts so have fun with it.
If you need ideas feel free to explore the samples above :)
Witch from Mercury was my first Gundam anime and I loved it so I wanted to make a lora for as much of the cast as I can. Secelia was a fun one and there was some fun about her between my friends group so I wanted to start with her and make the best version possible with all her looks from the anime. I hope you all have fun and enjoy.
Feedback and reviews are always appreciated.
Training data is in the Lora metadata.
Trained on 97 images.
