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| 1 |
+
---
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| 2 |
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license: other
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tags:
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- concept
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- cowgirl position
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- i2v
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- minimax
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- nsfw
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- ref2v
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---
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# [MinimaxH3] Cowgirl Position (NSFW) INT8 Convrot - v1.0 - 3258236
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**Model Type**: LORA
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**Base Model**: MiniMax H3
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**Trigger Words**: cowgirl position, thrusting her body
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**Tags**: concept, cowgirl position, i2v, minimax, nsfw, ref2v
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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/3ecd9f42-0b7c-4e35-8449-d21246bff535/original=true/140576092.mp4" width="200" controls muted autoplay loop></video></td>
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<td><video src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/7f0eee77-8320-4686-88f1-22dfbc3575ec/original=true/140576135.mp4" width="200" controls muted autoplay loop></video></td>
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<td><video src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/a0c95699-9526-4bab-af9e-b70b0f750a46/original=true/140576209.mp4" width="200" controls muted autoplay loop></video></td>
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</tr>
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<tr>
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<td><video src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/db2b4b3e-b4ca-450f-b937-12ae8a2f966d/original=true/140576279.mp4" width="200" controls muted autoplay loop></video></td>
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<td><video src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/51386d99-57b3-4535-b7c0-28b5c24574e0/original=true/140576354.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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This lora works on i2v and ref2v INT8 Convrot
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training time [2 hours 9 minutes]
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rank16
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resolution 512 (because 64GB of RAM and a 5090 isn't enough)
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[Guide]
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Captions were done using SDXL. Adding more words destroys the lora because many words can mean the same thing and different things in different contexts. This applies to all models. I found this out when using Krea2 by using the krusty krab restaurant and krusty the clown (New benchmark???). Minimax uses the same family so it shouldn't be different.
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If you train yourself eating an apple, you can just say, eating ohwx. Anything more causes conflicts within the lora. The model already knows what this eating apple action is, buts it's not trained in detail. You can now say a person eating an apple with and without using the trigger word.
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When you train a lora, you are training it as if your model was starting from zero. Reason being is the lora will weigh more than the model knowledge, your lora becomes the #1 priority over the model knowledge. For example, if you want a GTA style lora, you need images that depict it as the entire dataset of the model, meaning 1000 of thousands of images, even millions. While the model can guess it with around 20 images, it's better if the image exists of what you have in mind inside the dataset. Any guesswork will increase the chance to bleed concepts.
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I've had prodigy shit the bed after 1200steps when I did a krea2 lora, somehow the dataset I used didn't like it, but it works fantastic for most things I do.
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I use caveman talk since it works and is more efficient on tokens, saying "once apon a time" is just a waste of tokens.
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The model doesn't know what a cowgirl position is in great detail but if you feed it an existing image it knows what to do without this lora. What i'm doing is giving the model CPR to revive the cowgirl position meaning. By captioning the different angles, to not taint the knowledge of the other words, I keep it short and simple. This concept would be much better to do a full model training with longer descriptions.
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If you gut out words in your natural language prompt when your generating images, it will still work.
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If a pregnant woman says she is pregnant, does she describe in great detail how she got pregnant? Mostly likely not, you can assume that penis goes in vagina and that's that. If I were to break down what position it was, most likely doggie, missionary or cowgirl.
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When you caption, you use the words inside the model, for example if you say, a photorealistic image of an anthropomorphic mouse, when the image is actually 2D. It can turn that image into photorealistic, which is basically reverse psychology. Hence why words destroy the lora, because one can say hyper-realistic, surreal, photorealistic, realism, they all mean different things in context of the model since the model is trained on what the dataset was captioned with those words. Photorealistic isn't actually photorealistic and produces more of a 3d effect. But if you remove the photorealistic tag for it, it fixes it, but now you can't rely on the 2d part of the dataset because now the photorealistic tag contaminates the other tags that determines the style of the lora, the other styles will still work, not so well.
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Qwen is such a mess, I never had issues with Gemma4, so I could prompt it normally without much issue. They should have used Gemma4 instead. LTX2.3 prompt adherence was amazing but LTX 2.3 dataset is lack luster and not able to get the full advantage of Gemma4's abilities.
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BTW, captioning is also RNG, using prompts inside your AI generator also causes RNG combined with the lora captioning. So your captioning strategies can be RNG, however, the way I captioned it is consistent between runs and different concepts and models. However I haven't tested to see if every caption actually works, because that is also RNG, the main one does work and i've seen it get creative which actually makes the i2v portion more interesting.
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Sometimes you don't even need to say anything more than cowgirl position for it to work, thrusting her hips or thrusting her body is enough.
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**BE WARY OF THE MYSTERIOUS HAUNTED NIPPLE LIGHT AND THE EVIL LIGHT SWITCH**
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Don't worry about the dragon, she's just casting a curse on you in her native language
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cowgirl position
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cowgirl position from the front
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cowgirl position from behind
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cowgirl position from the front left
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cowgirl position from the left
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cowgirl position from the right
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moving hips while in the cowgirl position
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moving hips while thrusting in the cowgirl position
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inserting penis into vagina with her own hand
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cowgirl position while squatting
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thrusting slow
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male moaning
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male pov
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male pov low-angle
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male pov mid-angle
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male pov high-angle
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male pov low-angle to mid-angle
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male pov low-angle to high-angle
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male pov high-angle to low-angle
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male pov high-angle zoom-in
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closeup male pov low-angle
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female pov
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female moan
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female moaning
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female soft moaning
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mid-angle
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high-angle
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low-angle
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low angle focused
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wet pussy noise
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smacking noise
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moaning together
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bed creaking
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a woman is climbing onto the man laying on his back to get into the cowgirl position
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kissing
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after sex
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orgasm together
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female orgasm
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female muffled moaning
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wet nosie (I miss spelled it, should be the same as wet pussy noise)
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3rd-person
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wet sounds
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subtle female moaning
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male groaning
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closeup of breasts
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female quick moan
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female grunting
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male exhale
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thrusting her hips deeply
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female orgasm whimper noise
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sucking on breast
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+more
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---
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Author: [brand175](https://civitai.com/user/brand175)
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Model: [CivitAI Model Page](https://civitai.com/models/2882639?modelVersionId=3258236)
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Archive: [CivArchive Page](https://civarchive.com/models/2882639?modelVersionId=3258236)
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<!-- Version: 20260823_upload -->
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