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Gigantic futanari

#Gigantic penis
Herramientas de IA
  • Generador de Anime con IA
  • Generador de Figuras Anime
  • Generador de Hojas de Personaje
  • Generador de Portadas de Revista
  • Generador de Peluches
  • Generador de Estatuas Gigantes

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Gigantic futanari - AI Model cover image
Subido en
9 abr 2024, 11:41
Utiliza
72.3k
Reseñas
Excelente  (6)
Palabras clave

gigantic_cock, gigantic_eja, giganic_dark

Permisos
  • Permitir la generación y el intercambio de imágenes
  • Permita que los usuarios descarguen su modelo
  • Usos comerciales

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double anal penetration (DAP) / ダブルアナル挿入 - AI Model cover image

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double anal penetration (DAP) / ダブルアナル挿入
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Descripción

UpdatedUsed PonyXL to train this one. Updated dataset to achieve better performance. It enhanced this concept on PonyXL and related models.Adding a concept: gigantic_darkAbout V3.0Updated dataset and used multires technique in this version. It would bring more light and shadow with quality tag, such as masterpiece, highly_detailed, best_quality ,etc.Bewared that may affect the result of generation. You may get chaos result if you use these tags and overweight them.Multires technique parameter:multires_noise_iterations="6"multires_noise_discount=0.3IMPORTANT: Now I set the trigger words as gigantic_penis and gigantic_penis_ejaculation.About V2.0In v2.0, I removed all the images with black bar censor and retrain this locon. That will do help to get better result. The annoying censor bar won't be there.I set batch size = 2 to acclerate training. The final results are good.IntroductionThis LyCORIS is used to generate futanari with gigantic penis. If you put it in a long picuture like (1024X768), you can get a massive long cock. You may find the faces are blurry but you can fix it with inpaint or img to img. Adding hyper futanari LyCORIS can improve the texture of penis.How to useI recommand using it in weight 0.75-0.85. When working with other loras, it is not easy to control. But you can get what you want after some tries.You may get good result in lower weight when use it alone. Stiil, it needs more tests.Training details:Traingset is of about 145 images. They are mirrored before training. Half of them are chosen as regularization images. Total steps are about 8800.Regularization = trueresolution=512batch_size=2epoch=10network_dim=32network_alpha=32clip_skip=2Using AdamW8bits as optimizer:lr="1e-4"unet_lr="1e-4"text_encoder_lr="1e-5"Locon parameters:conv_dim=4conv_alpha=4

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