description: <h1 id="heading-1855"><code>facialized</code>: a new LoRA for facials on women</h1><p>LoRA to generate photo-realistic images of women with cum on their face.</p><p>Include in your prompt <code>&lt;lora:facialized:1&gt;, cum, facial</code>.You might want to include in your negative prompt <code>cum on breasts, cum on body</code>.</p><p>The model works best with low steps and CFG, I get good results with 10 steps and a CFG of 3 or 4.</p><p><strong><u>I am open to advice/help to improve this LoRA.</u></strong> If you are willing to help, you can send me a mail at the mail address at the end of this description.</p><p>Training methodology below.</p><p></p><h2 id="heading-1856">Know issues with the model</h2><p>As rightly noted by some comments, the model is still imperfect. Here is a list of a few issues I found, along with ways to circumvent them when possible. <br />I will share tags present in the training dataset that might help you circumvent some of these issues in the format <code>the tag in question [number of images out of 2676 that were associated with this tag]</code>. For example <code>smiling at camera [486]</code> means that 486 images in the dataset (composed of 2676 images in total) had the tag <code>smiling at camera</code>.</p><h3 id="heading-1857">Cum on the body / torso</h3><p>Most of the generated images depict women with cum on their body / torso / breasts. It is currently hard to remove it and ensure that cum only appears on the face.</p><p>Here are some tags present in the training dataset that might help you with this issue:</p><ul><li><p><code>cum on breasts [195]</code></p></li><li><p><code>cum on body [472]</code></p></li><li><p><code>cum on clothes [10]</code></p></li></ul><h3 id="heading-1858">Bad eyes</h3><p>The model might generate faces with non-symmetrical eyes or ill-formed eyes. You can try to alleviate this by using tags such as:</p><ul><li><p><code>symmetric eyes</code></p></li><li><p><code>same color eyes</code></p></li><li><p><code>bad eyes</code> (negative prompt)</p></li><li><p><code>strange eyes</code> (negative prompt)</p></li></ul><p>These tags do not appear in the training data but help the underlying stable diffusion checkpoint generating correct faces.</p><p></p><h2 id="heading-1859">Training methodology explained</h2><p><strong>Disclaimer</strong>: this is my first time making a LoRA and I am more than open to advice to improve it! I am detailing my methodology below, if you have any idea on how to improve it please feel free to comment.</p><h3 id="heading-1860">Dataset</h3><p>The full dataset is composed of 2676 images, hand-picked from one source. Their quality varies greatly, but nearly all of them show a unique women with cum on her face. Some outliers show 2 women (~10 images).</p><p>The image sizes are very disparate, I am reproducing here a count of the number of images for each resolution that has 10 or more images (there are 1454 different resolutions in the whole dataset).</p><pre><code>    154 3024x4032
       98 1536x2048
       96 2316x3088
       51 960x1280
       46 750x1000
       36 768x1024
       27 853x1280
       25 510x680
       25 1920x1080
       22 1080x1920
       20 1000x1333
       19 1280x960
       17 1024x768
       14 1280x1707
       14 1000x750
       13 854x1280
       13 2268x4032
       13 1200x1600
       12 2448x3264
       11 1280x1920
       11 1067x1600
       11 1024x1365
       10 600x800
       10 2000x2666</code></pre><h3 id="heading-1861">Filtering and tagging</h3><p>I used <a target="_blank" rel="ugc" href="https://colab.research.google.com/github/hollowstrawberry/kohya-colab/blob/main/Dataset_Maker.ipynb">https://colab.research.google.com/github/hollowstrawberry/kohya-colab/blob/main/Dataset_Maker.ipynb</a> to filter the dataset. Duplicates have been found with FiftyOne AI and a similarity threshold of 0.985.</p><p>Image tagging has been performed locally in stable-diffusion-ui <a target="_blank" rel="ugc" href="https://github.com/toriato/stable-diffusion-webui-wd14-tagger">stable-diffusion-webui-wd14-tagger</a> extension. Duplicate tags are removed, I used the <code>wd14-vit-v2-git</code> interrogator with a threshold of 0.35. I also added the additional tags <code>"facial", "cum", "1girl", "1women", "face", "sperm"</code>.</p><p>Below is a list of all the tags appearing on more than 20 images in the dataset. Note that these tags are mostly obtained from an auto-tagging procedure (as described above).</p><pre><code>                    1girl  2676
                     1women  2676
                        cum  2676
                       face  2676
                     facial  2676
                      sperm  2676
                  realistic  2631
                       lips  2192
                       solo  2160
          looking at viewer  1459
                    breasts  1262
                  long hair  1085
                 brown hair  1031
                       nude  1011
                 black hair   997
                      smile   946
                    nipples   885
                    jewelry   883
                closed eyes   804
                blonde hair   718
                 brown eyes   706
                 open mouth   663
                   freckles   592
                       1boy   554
                      teeth   539
                     hetero   513
                 solo focus   496
             medium breasts   496
                     tongue   494
                   earrings   486
                      penis   479
                cum on body   472
                   necklace   449
                 upper body   334
                 tongue out   328
                 uncensored   328
              small breasts   325
                       nose   298
                    indoors   290
              large breasts   279
                  blue eyes   262
                       grin   255
                       mole   253
                     blurry   253
                 short hair   246
                cum on hair   242
                   piercing   234
               cum in mouth   228
                 from above   227
                   cleavage   224
               closed mouth   212
                     tattoo   209
                     makeup   201
             cum on breasts   195
                   forehead   185
                  underwear   177
                      shirt   171
                   portrait   171
                    sitting   165
                   erection   163
                    glasses   141
                      navel   138
                 looking up   136
                 black eyes   127
                      lying   126
                parted lips   124
                        bra   119
                        pov   119
                       oral   118
                   fellatio   111
                   kneeling   110
                        bed   106
                 male focus   103
          blurry background    96
              multiple boys    95
             mole on breast    89
                    handjob    86
                    on back    84
                    topless    84
                       ring    83
                    bukkake    82
                nail polish    81
                 green eyes    78
                 pubic hair    75
                     choker    73
             one eye closed    73
                     collar    73
            nipple piercing    73
           photo background    68
                      2boys    67
                  eyelashes    64
              hoop earrings    63
            male pubic hair    62
                  dark skin    59
              cum on tongue    57
           multiple penises    56
                 downblouse    56
                   close-up    55
                 thighhighs    55
                       what    55
                  twintails    53
                    panties    52
                   outdoors    52
                  testicles    52
          multicolored hair    52
                   censored    52
                 flat chest    49
                   red hair    48
                   barefoot    48
                     pillow    48
                   tank top    47
                   bracelet    46
                  group sex    44
                     selfie    44
                        wet    43
             bare shoulders    43
                      tears    43
               clothes lift    42
                 collarbone    41
                  watermark    41
            completely nude    41
                       bdsm    39
                   lingerie    38
           half-closed eyes    37
                      bangs    37
                 shirt lift    36
                      bound    34
                      braid    34
              hair ornament    33
                black shirt    32
                      veins    32
                    cosplay    31
             depth of field    31
                   swimsuit    31
                white shirt    31
                      pants    30
            oral invitation    30
                      denim    29
                    bondage    29
                     saliva    28
                   fishnets    28
                      leash    28
                  black bra    27
                   ponytail    27
            tongue piercing    27
                        tan    27
        dark-skinned female    27
                  head tilt    27
               open clothes    27
                     window    27
               ear piercing    27
             multiple girls    26
                   lipstick    26
              breasts apart    26
                artist name    26
               body writing    26
             after fellatio    25
                   tanlines    25
                      couch    25
                      skirt    25
          simple background    24
                 curly hair    24
                  eyeshadow    24
              nose piercing    24
                     bikini    24
                ejaculation    24
                      heart    23
                   bathroom    23
                   sleeping    23
                breasts out    22
                  grey eyes    22
                      plant    22
                twin braids    22
             navel piercing    22
       black-framed eyewear    21
                    sweater    21
                     2girls    21
                      watch    21</code></pre><h3 id="heading-1862">Training</h3><p>I used <a target="_blank" rel="ugc" href="https://colab.research.google.com/github/hollowstrawberry/kohya-colab/blob/main/Lora_Trainer.ipynb">https://colab.research.google.com/github/hollowstrawberry/kohya-colab/blob/main/Lora_Trainer.ipynb</a> to train the LoRA. The training model was <code>sd-v1-5-pruned-noema-fp16.safetensors</code>. Tags are automatically shuffled and there is no activation tags (<code>activation_tags</code> set to 0).</p><p>Due to the size of the dataset, each image is only repeated once (i.e., no repetition). I used 5 epochs, with a batch size of 2. The UNet learning rate was set to <code>5e-4</code>. The LoRA network dimension was set to 32, and the network alpha to 16.</p><h2 id="heading-1863">Further notes on other tentatives</h2><p>I tried to manually filter images to only include "nice" images:</p><ul><li><p>high enough resolution and quality</p></li><li><p>no cum on body</p></li><li><p>"pretty" women (according to my taste)</p></li></ul><p>I filtered down the dataset to 1170 images, re-trained a LoRA, but the resulting model is clearly under-performing. I even have hard times trying make cum appear on the generated images with this model.</p><p>I tried different LoRA network dimension and alpha dimension (<code>(16, 8)</code>, <code>(32, 16)</code> and <code>(64, 32)</code>), but the best performing one seems to be the <code>(32, 16)</code>.</p><p></p><h2 id="heading-2326">Contact for collaboration</h2><p>If you want to help me generate a <code>v0.2</code> or even <code>v1</code> of this model, please contact me at the address below by presenting what you think you can bring to the project. </p><p>For the address, everything enclosed in square braces should be replaced, spaces should be removed.</p><p><code>[my pseudo here in civitai] [dot] dev [at] protonmail.com</code></p>
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name: facialized
tags: photorealistic female facial cumshot style woman cum realistic nsfw