A lora for<s> hiding your sword from the police</s>throatfuckingtrained on 97 images<h2 id="heading-2006"></h2><h2 id="heading-2007">Image info</h2><ul><li>Model used <a target="_blank" rel="ugc" href="https://huggingface.co/WarriorMama777/OrangeMixs/blob/main/Models/AbyssOrangeMix2/AbyssOrangeMix2_hard.safetensors">abyssorange2hard</a></li><li>Model used <a target="_blank" rel="ugc" href="https://huggingface.co/AnonymousM/Based-mixes/blob/main/Based64mix-V3.safetensors">based64</a></li><li><a target="_blank" rel="ugc" href="https://huggingface.co/yesyeahvh/bad-hands-5/blob/main/bad-hands-5.pt">bad hands embedding</a></li><li><a target="_blank" rel="ugc" href="https://huggingface.co/NiXXerHATTER59/bad-artist/blob/main/bad-artist.pt">bad artist embedding</a></li><li><a target="_blank" rel="ugc" href="https://huggingface.co/embed/bad_prompt/blob/main/bad_prompt_version2.pt">bad prompt embedding</a></li><li><a target="_blank" rel="ugc" href="https://civarchive.com/models/4629/deep-negative-v1x">deep negative embedding</a></li></ul><h2 id="heading-2008">Notes(Update)</h2><ul><li>v2.0 is a LORA NOT A LYCROIS</li><li>works much better and much more consistent</li><li>much more flexible( prompt for the girl to be kneeling/squatting/sitting etc)</li><li>Redid the entire dataset (available for download)</li></ul><h2 id="heading-2009">Training settings?</h2><h3 id="heading-2010">The most important part of training is getting a good dataset!</h3>Training settings can be found by installing the additional networks extension in auto1111Extensions -&gt; available -&gt; load from -&gt; additional networksRestart webuiplace lora into stable-diffusion-webui\extensions\sd-webui-additional-networks\models\loraTo view the training settingsGo to additional network tab -&gt; choose model -&gt; training paramters

Sword swallowing position (Throat fuck) | Concept

-Remade the entire dataset (available for download)-changed from lycrois to lora-MUCH MUCH more flexible-fixed spelling mistakes-fixed combability with other loras/models

Files with SHA256: 0befc1bf7d002f918cd6274ec9b57b3e8ebc9c83840ffd636917b9e43c2b7100