description: <h3 id="heading-156"><u><span style="color:rgb(250, 176, 5)">About</span></u><span style="color:rgb(250, 176, 5)">:</span></h3><p>Hello!</p><p>Have you ever been embarrassed or insecure about taking a picture of your 🐥 and sending it to someone?</p><p>Your problems are over! </p><p>Now you can just generate as many super realistic dongs as you want with this LoRA and spread them around. 😂</p><p><span style="color:rgb(193, 194, 197)">It was trained in 768x768, using captions and 166 (for now) images.</span></p><p>As usual, I make a test version without enhancing the photos, to assess viability. This one seems promising, so I'm going to upscale the dataset and add more content (God only knows when, because I have ADHD).</p><p>DO NOT FORGET THE DASHES IN PROMPT if you want to replicate the images I did.</p><p><strong><span style="color:rgb(250, 82, 82)">The demo images aren't edited, but they are cherry-picked and generated in A1111 WebUI (v1.8 RC), using hires-fix (0.25 ~ 0.50 denoising), 4x_NMKD-Siax_200k (or 4x-UltraSharp) upscaler.</span></strong></p><h3 id="heading-157"><u><span style="color:rgb(250, 176, 5)">Suggested Settings for Inference</span></u><span style="color:rgb(250, 176, 5)">:</span></h3><p><strong><span style="color:rgb(130, 201, 30)">Model:</span></strong> <a target="_blank" rel="ugc" href="https://civitai.com/models/241415/picxreal">PicX_real</a></p><p><strong><span style="color:rgb(130, 201, 30)">Positive:</span></strong> dick-pic, large penis, holding-a-thick-staff, first-person-pov, every-day, (((Ultra-HD-quality-details))) &lt;lora:<strong><span style="color:rgb(21, 170, 191)">Dick_Pic_by_IsnAI</span></strong>:<strong><span style="color:rgb(76, 110, 245)">1</span></strong>:<strong><span style="color:rgb(250, 82, 82)">0.7</span></strong>)</p><p><strong><span style="color:rgb(130, 201, 30)">Negative:</span></strong> morbide-body-parts, snap-fingers:2, apoplexy, wrong-placed-limbs</p><p><strong><span style="color:rgb(130, 201, 30)">Sampler:</span></strong> DPM++ 2M Karras</p><p><strong><span style="color:rgb(130, 201, 30)">Steps:</span></strong> 28</p><p><strong><span style="color:rgb(130, 201, 30)">CFG:</span></strong> 6~7 got best results, but also works with 5~6</p><p><strong><span style="color:rgb(130, 201, 30)">Resolution:</span></strong> 512x768</p><p><strong><span style="color:rgb(130, 201, 30)">LoRA Weights:</span></strong> <strong><span style="color:rgb(76, 110, 245)">1</span></strong>:<strong><span style="color:rgb(250, 82, 82)">0.7</span></strong></p><p><strong><span style="color:rgb(250, 82, 82)">Note¹:</span></strong> <span style="color:rgb(209, 213, 219)">As you can see, the LoRA tag has two weights: </span>&lt;lora:<strong><span style="color:rgb(21, 170, 191)">Dick_Pic_by_IsnAI</span></strong>:<strong><span style="color:rgb(76, 110, 245)">1</span></strong>:<strong><span style="color:rgb(250, 82, 82)">0.7</span></strong>)<br /><strong><span style="color:rgb(250, 82, 82)">This is a feature of the A1111</span></strong><span style="color:rgb(209, 213, 219)"> (</span><em><span style="color:rgb(209, 213, 219)">which many people aren't aware of</span></em><span style="color:rgb(209, 213, 219)">), but you can set separate weights for the UNet and the Text Encoder. The TE is always a pain in the ass to get the training right, so it's usually better to reduce its weight in the inferences rather than having it undertrained.</span></p><p><strong><span style="color:rgb(250, 82, 82)">Note²:</span></strong><span style="color:rgb(209, 213, 219)"> You will see big variations in style and images depending on the base model used and minor adjustments in resolution.</span></p><p><strong><span style="color:rgb(250, 82, 82)">Note³: </span></strong>If your images are getting weird and bizarre, you probably didn't reduce the TE weight. If you aren't using A1111 WebUI, you can do the common weight reduction: &lt;lora:<strong><span style="color:rgb(21, 170, 191)">Dick_Pic_by_IsnAI</span></strong>:<strong><span style="color:rgb(250, 82, 82)">0.7</span></strong>), but you will need to test which weight will suit better.</p><h3 id="heading-158"><u><span style="color:rgb(250, 176, 5)">Triggers</span></u><span style="color:rgb(250, 176, 5)">:</span></h3><p><strong><span style="color:rgb(130, 201, 30)">Main Tags Used on Training: </span></strong>dick-pic, penis, first-person-pov, holding-a-thick-staff</p><p><strong><span style="color:rgb(130, 201, 30)">Secondary Tags Used on Training:</span></strong> hairy, shaved, uncut, view-from-the-side, legs, hand, fingers, feet, toes</p>
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