I'm pleased to present my latest motion concept LoRA, a kind of successor to my <a target="_blank" rel="ugc" href="https://civarchive.com/models/1122330/riding-dildo-for-hunyuan-video">Riding Dildo Video LoRA</a>. I've done a lot of experimenting since then and you should find this LoRA far more flexible and higher quality.<h3 id="v2.0-z3h3ks674">V2.0</h3>New version is trained for 8400 steps vs 3600 steps of v1.0, an extra 25 videos in the dataset and the faces have been censored so it should work well now with character LoRAs. You should also be able to grab the workflow from the new videos I've uploaded, which is a slightly modified version of Flexbility's workflow.<h3 id="prompting-52hi5bwyq">Prompting</h3>The key phrase is She is using a sex machine with a dildo attached to a metal rod. The dildo thrusts back and forth into her {asshole. anal object insertion|vagina. vaginal object insertion}. Some of the dataset had squirting or ejaculate which you can try prompting for.This is the prompt I used for the sample videos (using impact wildcard processor):<pre><code>A video of a nude woman with __haircolors__ hair in the style of __hairstyles__, {lying on her back|squatting|standing|on all fours facing away from the camera looking behind} on a {black|white} {couch|wood table|metal table|kitchen counter}. She has {medium|large} breasts. She is using a sex machine with a {huge|large|medium|small} {orange|pink|flesh colored|black} dildo attached to a metal rod. The dildo thrusts back and forth into her {asshole. anal object insertion|vagina. vaginal object insertion}. Her legs are spread wide. The background is a {dimly lit|brightly lit}. Camera is slightly to the side.</code></pre>Nothing fancy and lots of room for experimentation. The videos were generated using Flexability's excellent upscale workflow found <a target="_blank" rel="ugc" href="https://civarchive.com/models/1184655/a-flexible-hunyuan-workflow?modelVersionId=1352771">here</a>.Videos were generated in 10 steps with FastVideo lora, FP8, guidance and shift both 10 and a single upscale v2v pass. Around 3 minutes per gen on my 3090Ti.<h3 id="training-details-jx5ncn8oj">Training Details</h3>This LoRA was trained using difusion-pipe on a dataset of around 50 videos at 300px res and 60 frames on a 3090Ti. Learning rate 2e-5, repeats 4. I'll upload the configs soon for more details if anyone is curious.<h3 id="future-plans-aqh0p4fjr">Future Plans</h3>There is headroom for further training and I have some ideas for improvements, depending on how this is received so keep an eye out of for new versions.

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