V2: Updated dataset and added Wan 2.2With V2 of the LoRA I significantly increased the dataset from approximately 100 images to around 300 images. I also added a significant number of images with a vagina visible to help increase the accuracy when creating that. I also made sure to either crop or edit out any watermarks present on any of the images of the dataset. The LoRA still struggles to generate good looking vaginas in both Qwen and Wan, though it has seen some improvement.Unfortunately, the LoRA still struggles to generate vaginas, but it has improved from V1 and the general realism of the LoRA seems to have gotten better for Qwen with the larger dataset.For Wan 2.2 the LoRA is incredibly realistic, and is great at creating the style and body type, however it can generate disfigured images with bad anatomy at times. It also has a tendency to add watermarks in the corners of images for some reason, and I am not sure why this is happening. I will work on trying to remove that for V3.For V3 I will go through and caption all of the images in the dataset to hopefully be able to fix the watermark issue as well as the sometimes poor anatomy on Wan 2.2. I will also be removing the trigger word for V3.Trigger word: nud3V1: Released for Qwen ImageThis is my first attempt at training a LoRA. I trained it on a bunch of random nude photos that I thought could make a good LoRA. For some reason it does struggle with generating a vagina, but the breasts are typically pretty good. It also leans toward caucasian women with larger breasts.I used it on weights between 0.5 and 1.0 and it seemed to work most of the time. You'll probably have to experiment a bit as I'm not quite sure what a lot of the different settings do. (I'm very new to all of this)Trigger word is "nud3"Open to feedback :)Edit: If you want better looking girls without the added nudity, you can still use the LoRA at a strength of 1 but just don't use the trigger word and it will tone itself down a lot while still keeping some of the aesthetic.

Real_Nud3s [Qwen/Wan 2.2]

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