About this version (qwen v2)A brushup of the dataset, with better result than V1 for qwen. I will use the same dataset again for zturbo when the base model is released. Trigger word: PENISLORAWhat can this lora do?This lora can add erect penises to both men or women viewed from the front/side. Other angles such as POV may have a backwards penis head. Other things it can now do: Side view of the penisCumming / CumshotsBlowjobs (its captioned for the words "blowjob" and "deepthroat" )What can't it do?No penetration in the training data. Also nothing from POV angle, though there is a few images from above and 1 POV video in the training data.Sometimes blowjobs with cumming have the penis slip out the closed mouth.Recommended SettingsIt works pretty good with the new lightning dyno high model. I'll link to it in my example workflow. I like to use dyno high model (no lightning lora), then for low I use the lightning v2 lora on the regular 2.2 low base model.Dataset84 images at 512x resolution43 videos at 256x resolution(I let DP pick the aspect ratio automatically)This is the same exact dataset as the 2.2 5B model. I made no changes.TrainingI used the default diffusion pipe settings.[optimizer]type = 'adamw_optimi'lr = 2e-5betas = [0.9, 0.99]weight_decay = 0.01eps = 1e-8I was baffled why it was taking so long to train the high until I realized after over 60 hours of training that I had put my videos in the images directory which resulted in the high being trained ONLY only on videos and twice (once with a very high resolution). Once I fixed this, I went back and trained from 11K steps up to around 13K with the images in the training data. The high model was fine without to be honest.For the low, I trained it properly with videos and images the whole way, around 6K steps in I upped the image resolution from 512 to 1024 actually and didn't get an OOM (it fit around 24GB exactly). I trained it to around 10.5K steps. Also I trained the low on the full timestep range (0 to 1 instead of 0 to 0.85) from some advice, it may switch better over from high to low on the speed up lora with low steps.I think I might do another version with more angles such as POV and from the behind to make this work for any situation. In that case I don't think it needs 10K steps per training session, epochs around 5K steps looked fine.The resultsI think it was a combination of improved captioning and 2.2 base model being better. But this lora turned out really well.

Penis Lora (+Blowjob, +Cumshot) [Taz]  - WAN 2.2 14b / 5B / 1.3b T2V & I2V (Wan 2.1 & 2.2) + Qwen + Zimage Turbo

A brushup of the dataset, with better result than V1. Trigger word "PENISLORA"I did not like how dataset had so many augmented/bolt-on style breasts. I used qwen image edit + some breast related loras to brush up the dataset. I also modified some of the faces in the dataset to be more feminine. The result is a much nicer reduction of the female faces and breasts in the generations.I trained it up to 28.5k steps, I liked the results of 21K and 27K steps, ultimately picking 21K steps. I will add 27K steps version to the "training data" download if you wanna try it.

qwen_penislora_v2_000021000.safetensors

qwen_penislora_v2_000027000.zip

Qwen V2

ZImage Turbo V2

ZImage Turbo V1

Qwen V1

WAN 2.2 14B I2V- Low v1.1

WAN 2.2 14B I2V-High v1.1

WAN 2.2 14B I2V-Low v1.0

WAN 2.2 14B I2V-High v1.0

Wan 2.2 14B T2V - HIGH-V1

Wan 2.2 14B T2V - LOW-V1

WAN 2.2 5B ti2v

v0.8 Wan 2.1 14B T2V

v0.7C Wan 2.1 1.3B T2V

v0.7b 1.3B T2V

v0.7a 1.3B T2V

v0.7 14B T2V

v0.6

v0.5

Files with SHA256: 4c6afb725ef7eedc42d6b31e94ef2798e61e66ca9e8b17ed4aa8a81fd0a456fd