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Cumflood

LYCORIS
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Updated

Used PonyXL to train this one. Updated dataset to achieve better performance. It enhanced this concept on PonyXL and related models.

Adding a concept: cum_through

Introduction

This LyCORIS is used to enhance the concept of the scenes with massive cum like cumbath and cumshower. In NAI and other hentai models, it is possible to do that, but hard to get the results you wanted. So I made this.

How to use it

I recommand using it in weight 0.7-0.8. Higher weight may give you overbaked results. Works well with other tatoo loras.

This locon has two trigger words. One is cumbath, use it when you need your character partically submerged in cum. One is cumshower, use it when you need the cum over the body.

Training details:

Traingset is of about 400 images. They are mirrored before training. Half of them are chosen as regularization images. Total steps are about 15000.

I divided the training set into two parts tagged with two different trigger words.

Regularization = true

resolution=768

batch_size=1

epoch=10

network_dim=32

network_alpha=32

clip_skip=2

Using AdamW8bits as optimizer:

  • lr="1e-4"

  • unet_lr="1e-4"

  • text_encoder_lr="1e-5"

Locon parameters:

  • conv_dim=4

  • conv_alpha=4

Version Detail

Pony
5000
10
Updated Used PonyXL to train this one. Updated dataset to achieve better performance. It enhanced this concept on PonyXL and related models. Adding a concept: cum_through Introduction This LyCORIS is used to enhance the concept of the scenes with massive cum like cumbath and cumshower. In NAI and other hentai models, it is possible to do that, but hard to get the results you wanted. So I made this. How to use it I recommand using it in weight 0.7-0.8. Higher weight may give you overbaked results. Works well with other tatoo loras. This locon has two trigger words. One is cumbath, use it when you need your character partically submerged in cum. One is cumshower, use it when you need the cum over the body. Training details: Traingset is of about 400 images. They are mirrored before training. Half of them are chosen as regularization images. Total steps are about 15000. I divided the training set into two parts tagged with two different trigger words. Regularization = true resolution=768 batch_size=1 epoch=10 network_dim=32 network_alpha=32 clip_skip=2 Using AdamW8bits as optimizer: lr="1e-4" unet_lr="1e-4" text_encoder_lr="1e-5" Locon parameters: conv_dim=4 conv_alpha=4 Hide

Project Permissions

Model reprinted from : https://civitai.com/models/18665/cumflood?modelVersionId=403029

Reprinted models are for communication and learning purposes only, not for commercial use. Original authors can contact us to transfer the models through our Discord channel --- #claim-models.

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