~Magic Wand for HunyuanVideo(and now with experimental MagicWan2.1)~ Heya! I've updated the Wan version with my much better training settings that I've developed! It should be /much/ better now and I improved the captions to support "wide shot/medium shot/medium close up/close up" like my other models do. From my tests the overall quality, motion and everything is improved significantly. If you have any issues with blurred faces or face out of frame please add "blurred face" or "face out of frame" to your negative! Enjoy! Training was exactly like my WanNipplePlay model: <a target="_blank" rel="ugc" href="https://civarchive.com/models/1590451/both-hands-sensual-nipple-play-selfother?modelVersionId=1799759">https://civarchive.com/models/1590451/both-hands-sensual-nipple-play-selfother?modelVersionId=1799759</a>Experimental MagicWan: Latest version "MagicWan" for Wan2.1 is up! It's experimental and my first attempt at training a LoRA for Wan. I used the same settings and dataset I'd used for 1.5 Hunyuan but lower res. By 3600 steps it was COOKED, but epoch 56 around 2000 steps seems usable though it can definitely be improved! Feedback is welcome and I will definitely be attempting to improve it. Oh and please let me know in the comments if there is any interest in a version for the 1.3B model, I don't use it personally but I'd be happy to train a version for it if it's desired! MagicWand 1.5: New version is up! Featuring much better quality overall, it was trained on a 25% larger dataset at 8e-5 for 3600 steps. It can prompt for corded/cordless (specify either before the color but note that non-white corded models are very limited in dataset and in porn in general so ymmv there) and a bit better for the motion of the vibrator(can append either "vigorously rubbing it up and down" or "grinding herself against it/she is grinding herself against it" to the trigger, results may be seed dependent for grinding especially). For instance: "a nude young woman is reclining on a bed while her girlfriend uses a cordless white MgcWndVb to stimulate her vagina, vigorously rubbing it up and down. She has messy blonde hair and large breasts. Her girlfriend has red hair and is wearing black lingerie. Their faces are close together and they are looking directly into each other's eyes as they share this intense moment of intimacy" It should also handle prompts for couples better. I noticed that "her vagina is wet" doesn't work as well as it did in 1.0 though and instead... kinda makes her squirt a bit sometimes? More testing is needed there. MagicWand 1.0: Good vibrations are coming! Hey all, this is my second LoRA for HunyuanVideo and it turned out /really/ well in my opinion. It produces videos of women either using a magic wand style vibrator on themselves or having one used on them by another man/woman. It was trained at Rank 16 with blurred faces(captioned "blurred out face") so it should be highly compatible with your character LoRA. It learned the motion well enough you can often see the woman's vagina undulating with the vibrations of the wand!Captions/Prompting:"a nude woman is reclining on a kitchen chair with her legs spread while a man standing behind her uses a purple MgcWndVb to stimulate her vagina, rubbing it up and down. She has dark brown hair, small breasts, and her vagina is wet. He has gray hair and is wearing a dark gray shirt. Behind them is a large bed with a black and white painting hanging above it and on the left appears to be a sliding glass door and another painting""a nude woman with dark hair is using a black MgcWndVb to stimulate her vagina. She arches her back and bends forward as she begins having an orgasm. A tattoo is visible on her hip and her bellybutton is pierced."The trigger should be "using a {black/white/purple/pink} MgcWndVb to stimulate her vagina." Several of the data points also showed the woman wet with arousal and were tagged "Her vagina is wet" so that's promptable too, as is the motion of the wand to a degree. It should be capable of creating solo female, female/female, and male/female scenes. The dataset was quite varied and included various positions, ages of women, ethnicities, aspect ratios etc so try it out I hoped to make it versatile!Training notes:I've learned that high resolutions are not needed for training Hunyuan(Sauce: <a target="_blank" rel="ugc" href="https://civarchive.com/articles/11942/training-a-lora-the-right-way">https://civarchive.com/articles/11942/training-a-lora-the-right-way</a>), at least for motion LoRAs. This LoRA was trained on 15 videos that were preprocessed to six seconds long at 24 fps. A simple python script was used to employ Yolov8X-face detection and then apply a heavy Gaussian blur to faces in order to maintain face agnostic behavior(captioned accordingly). These were then VAE encoded to a combination of 424x240@129f and 640x360@41f and trained in about 12 hours on my 4070TI Super with Musubi Tuner. LR was 1.2e-4 with a LoraPlus multiplier of 4 for 2400 steps using CAME optimizer and constant with warmup scheduler and 100 warmup steps. Final Notes: Ever used one of these things yourself or had one used on you? If not, you should try! It will break your mind in the very best way 😉 Oh also, I've got a few utility scripts I use when creating video datasets for like chunking up videos, normalizing frame rate, blurring faces, etc. Nothing special, don't expect too much; they are just simple CLI affairs but if you are like me and use a largely CLI workflow, they could be helpful: <a target="_blank" rel="ugc" href="https://github.com/Sarania/videoprocessingscripts">https://github.com/Sarania/videoprocessingscripts</a> They've been written and tested on Linux but should work on Windows too. Depends on Python3, ffmpeg, opencv, and ultralytics. Feel free to use them or not I just thought they might be useful especially the yolov blurring one!

Magic Wand Vibrator (self/other) (Hunyuan/Wan)

Date: 2025-03-02T07:59:49 Title: MagicWand Resolution: 1280x720 Architecture: hunyuan-video/lora Network Dim/Rank: 16.0 Alpha: 1.0  dtype: BF16 Module: networks.lora : {'loraplus_lr_ratio': '4'} Learning Rate (LR): 0.00012 Optimizer: came_pytorch.CAME.CAME(weight_decay=0.01,eps=(1e-30, 1e-16),betas=(0.9, 0.999, 0.9999)) Scheduler: constant_with_warmup  Warmup steps: 100 Epoch: 40 Batches per epoch: 60 Gradient accumulation steps: 1 Timestep sampling: Shift Discrete flow shift: 7.0

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