~Amorous Lesbian Kisses~ Update: Ya'll the Wan version is pretty fire I'm quite pleased with it. I'm gonna try to replicate those results for Hunyuan now! Kisses for Wan: It's been a long time coming, but I've finally successfully created a Wan version of this model! It seems competent for both T2V and I2V. A big key was using 16fps, Wan's native, so if you train Wan I'd definitely recommend that! FWIW my example videos have been interpolated to 32FPS using <a target="_blank" rel="ugc" href="https://github.com/GSeanCDAT/GIMM-VFI">https://github.com/GSeanCDAT/GIMM-VFI</a> which is really excellent. Anyway, I trained it at 480x272, 69 frames at 16fps times 30 videos for 2400 steps at 2e-5 with loraplus of 4 using Musubi Tuner. I removed the leading "amorous kissing" but otherwise the prompting format remains the same:"close up of two young women tongue kissing. The woman on the left has red hair and is wearing a black lace choker, the woman on the right is Indian, with beautiful light skin and long straight black hair."Tongue kissing, making out, kissing, wide shot, medium shot, close up should all be hotwords! Wan especially picked up the "making out" keyword really nicely and if you include it you will get lots of caresses and touches. It also manages tongue interactions better than Hunyuan. My examples were made with Musubi Tuner in about 20 minutes each! I use Musubi with a scheduled CFG, I do the first ten steps and the last three, but every other otherwise. This gains good speed without sacrificing much if any quality! I've also been experimenting with skip layer guidance which is curious and seems to really boost quality. Oh I also use fp8 scaled which is a huge boon. Musubi's implementation is online, which means you start with the full model(not the pre scaled ones). It keeps some smaller but very important params in full precision while quantizing the weights themselves to fp8 maintaining only 2.5% quantization error(vs 12.5% for a naive cast to e4m3fn!). I've ran several same seed comparisons and it's not just good in the numbers, it's consistently the closest results to the full unquantized model of any method I've tried. Comfy has fp8 scaled too but it's done differently(the weights are saved scaled and you just load that) but I hear it's really good too. Hurray for democratizing access! Original/Hunyuan:This has been a tough nut to crack, likely because of the complex hand and tongue movements involved. Base Hunyuan will make simple platonic kisses but not much more. This LORA is focused on creating amorous, sexual kisses and making out between women. It was trained on my RTX 4070 Ti SUPER 16GB with Musubi Tuner in 12 hours. This is the first revision worth sharing, it's not perfect but can definitely make some nice things! Expect updates! Caption/prompting format:"amorous kissing, medium shot of two nude young women tongue kissing and making out with each other in a living room. The woman on the left has her brunette hair in pigtails and a tattoo on her arm while the woman on the right has brunette hair in a ponytail. Behind them a couch with some pillows and some plants can be seen.""amorous kissing, wide shot of two women laying on a gray couch in each other's arms, making out and tongue kissing passionately. They both have brunette hair, one is wearing a colorful haltertop and shorts and the other is wearing a white dress""amorous kissing, close up of two women kissing sensually in front of a bright window. The woman on the left has red hair and is wearing a black jacket, the woman on the right is wearing a beanie and thick black glasses. Both of them are wearing mascara" Small note: "making out" was used to indicate lots of caresses and occasional sexual touches accompanying the kissing, but I don't think it took super well in this first revision! "tongue kissing" was used when there was a lot of visible, outside the mouth tongue action, "kissing" if not as much or it's contained inside the mouth. "wide shot" was used if the full body is visible, "medium shot" for waist up, and "close up" for the close ups. Oh and "passionately" was used as a modifier if the kisses were extra enthusiastic compared to the dataset overall.Recommendations: Weight: 0.8-1.0 Flow shift: ~9.0 @ 544p Guidance: &lt;= 7.0 (Too much creates more issues with hands) Steps: 50 Frames: 61-129 (longer may or may not work, wasn't trained) *Reports and my experiments indicate that Teacache may create issues with the LORA so please try without it if possible. Dataset consisted of 26 high quality videos of women of various ages and races sharing various types of amorous kisses and making out from various distances in various states of undress. The source data was preprocessed with ffmpeg into the training clips which were each 144 frames long at 24fps showing only the action of interest with no scene cuts or dramatic camera movements. Further they were cropped to show only the women in order to add some aspect ratio variation as 95% of the source was 16:9 before processing.Training config:Network dimension: 36 Network alpha: 1 Learning rate: 2.4e-4 Optimizer: came_pytorch.CAME Optimizer args: weight_decay=0.01, eps=(1e-30,1e-16), betas=(0.9,0.999,0.9999) Steps: 2400 Warmup steps: 100 Scheduler: Constant with warmup discrete_flow_shift: 7.0 timestep_sampling: shift VRAM savings: --blocks_to_swap 31, --split_attn, --flash_attn Dataset was listed four times in the toml to allow processing different frame bucket lengths at different resolutions:[general] caption_extension = ".txt" enable_bucket = true bucket_no_upscale = false [[datasets]] video_directory = "/home/blyss/projects/art/extra/dataset/AmorousLesbianKisses" cache_directory = "/home/blyss/projects/art/extra/dataset/AmorousLesbianKisses/cache0" resolution = [480, 272] target_frames = [129] frame_extraction = "head" batch_size = 1 [[datasets]] video_directory = "/home/blyss/projects/art/extra/dataset/AmorousLesbianKisses" cache_directory = "/home/blyss/projects/art/extra/dataset/AmorousLesbianKisses/cache1" resolution = [640, 360] target_frames = [69] frame_extraction = "uniform" frame_sample = 2 batch_size = 1 [[datasets]] video_directory = "/home/blyss/projects/art/extra/dataset/AmorousLesbianKisses" cache_directory = "/home/blyss/projects/art/extra/dataset/AmorousLesbianKisses/cache2" resolution = [848, 480] target_frames = [41] frame_extraction = "uniform" frame_sample = 2 batch_size = 1 [[datasets]] video_directory = "/home/blyss/projects/art/extra/dataset/AmorousLesbianKisses" cache_directory = "/home/blyss/projects/art/extra/dataset/AmorousLesbianKisses/cache3" resolution = [1280, 720] target_frames = [1] frame_extraction = "uniform" frame_sample = 2 batch_size = 2

Amorous Lesbian Kisses

Trained with FP16 base Wan using FP8 scaled mode from Musubi. Date: 2025-04-16T10:42:43 Title: WanKisses Resolution: 1280x720 Architecture: wan2.1/lora Network Dim/Rank: 16.0 Alpha: 16.0 dtype: F16 Module: networks.lora_wan : {'loraplus_lr_ratio': '4'} Learning Rate (LR): 2e-05 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: 38 Batches per epoch: 64 Gradient accumulation steps: 1 Timestep sampling: Shift Discrete Flow Shift 3.0

Files with SHA256: 07ddcf1ce84b7a1655bef7b66971c4474bdce315f075152959c9f63d12c53578