For on-site generation, I recommend only using models designed for WAN2.1 or earlier. It does not appear that Civitai can get 2.2 models to work. You can use the Universal model with both high and low noise steps, but if you want the best quality, I included both versions as well.This LoRA is trained on conversational language. You can describe your scene in plain English or Chinese, and it should adhere to your prompt well. Below are some inspirations for you if you need it.Positions:<ul><li>On her back</li><li>Kneeling</li></ul>Perspectives:<ul><li>POV</li></ul>Enhancement phrases:<ul><li>She bounces her body up and down</li><li>She bounces her breasts up and down with her hands</li></ul><h3 id="all-showcase-videos-have-the-workflow-metadata-embedded.-you-can-download-the-video-and-drag-it-into-comfyui-to-replicate-them-with-their-prompts-seeds-and-settings.">All showcase videos have the workflow metadata embedded. You can download the video and drag it into ComfyUI to replicate them with their prompts, seeds, and settings.</h3>

Better Titfuck (WAN and HunYuan versions)

Includes better nipples, under clothing variation, and better adherence for side-view angles. Additionally, you won't have to be as overly descriptive with your prompts to keep the penis between her breasts. The LoRA now understands that Titfuck means "penis between breasts" usually.This version was trained using the following configuration and dataset settings:<ul><li>26 videos 4-5s long each. 256x256. Downscaled using bicubic to minimize pixelation</li><li>200 Epochs, each video is processed once per epoch.</li><li>Learning rate of 2e-5</li><li>Captions had reduced redundancy (removal of "penis between breasts" and other overly descriptive elements) to allow the LoRA to increase association with "titfuck" instead of other tokens.</li><li>Introduced more side-view videos, videos that included better nipple detail, and titfucks with clothing (bikinis, sports bras, etc)</li></ul>

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