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---
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license: other
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tags:
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- blowjob
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- cock lick
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- cock worship
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- concept
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- handjob
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- oral
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---
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# Worship It - I2V - Low - 2982264
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**Model Type**: LORA
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**Base Model**: Wan Video 2.2 I2V-A14B
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**Trigger Words**: None
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**Tags**: blowjob, cock lick, cock worship, concept, handjob, oral
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## Gallery
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<table>
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<tr>
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<td><video src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/1e8a84c4-3c84-4a43-bd6d-5603a1cb31f0/original=true/132021576.mp4" width="200" controls muted autoplay loop></video></td>
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<td><video src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/841355ef-eed3-43e9-b651-4d71b2fd73bb/original=true/132021575.mp4" width="200" controls muted autoplay loop></video></td>
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</tr>
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</table>
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## Description
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An attempt to make a lora focused on worshiping big cocks. It was trained on 69 clips (not on purpose, the 70th clip had a hard cut so it was cut from the dataset last minute). Never trained a motion Lora before.
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**T2V is trained on Wan 2.2 T2V fp16 model
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I2V is trained on Wan 2.2 I2V fp16model**
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I started training the model as an I2V because those lora's always gave me better results even with T2V. Due to difficulties renting a GPU, I decided to try a different service to compare speed and price so I opted to train the T2V on the second service. I figured it it worked well enough I'd throw them up on Civitai.
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I believe the workflow used to generate the samples was created by a former user - playtime\_ai
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It utilizes the MoE ksampler.
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I get best results using the following settings:
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Sampler - er\_sde
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Scheduler - beta57
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Shift - 8
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Steps - 8
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Lightning - [i2v\_lightx2v\_4step\_1022](https://huggingface.co/lightx2v/Wan2.2-Distill-Loras/tree/main)
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Str - 1 on both high and low.
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(Yes, I use the i2v lightning lora for T2V generations. I don't know why, but it always gives more more dynamic and realistic results).
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Trained with the following trigger words:
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cock\_worship
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cock\_lick
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cock\_slap
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blowjob
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deepthroat
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titfuck
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handjob
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Does somethings better than others. Slapping and titfuck it struggles with. Occasionally does some fun body horror.
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**Follow up edit**: I've added some clarity to the description and will attach a zip file to the T2V Low noise model for all the captions used for Lora training. They all follow the same structure - start with the trigger word(s) featured in the video trained followed by solely focusing on the movement during the video. All videos were 320x320, 81 frames, 16 FPS.
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Generate responsibly.
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---
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Author: [alternative_penguin](https://civitai.com/user/alternative_penguin)
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Model: [CivitAI Model Page](https://civitai.com/models/2655839?modelVersionId=2982264)
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Archive: [CivArchive Page](https://civarchive.com/models/2655839?modelVersionId=2982264)
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