Facialized

facialized: a new LoRA for facials on women LoRA to generate photo-realistic images of women with cum on their face. Include in your prompt , cum, facial.You might want to include in your negative prompt cum on breasts, cum on body. The model works best with low steps and CFG, I get good results with 10 steps and a CFG of 3 or 4. I am open to advice/help to improve this LoRA. If you are willing to help, you can send me a mail at the mail address at the end of this description. Training methodology below. Know issues with the model 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. I will share tags present in the training dataset that might help you circumvent some of these issues in the format the tag in question [number of images out of 2676 that were associated with this tag]. For example smiling at camera [486] means that 486 images in the dataset (composed of 2676 images in total) had the tag smiling at camera. Cum on the body / torso 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. Here are some tags present in the training dataset that might help you with this issue: - cum on breasts [195] - cum on body [472] - cum on clothes [10] Bad eyes The model might generate faces with non-symmetrical eyes or ill-formed eyes. You can try to alleviate this by using tags such as: - symmetric eyes - same color eyes - bad eyes (negative prompt) - strange eyes (negative prompt) These tags do not appear in the training data but help the underlying stable diffusion checkpoint generating correct faces. Training methodology explained Disclaimer: 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. Dataset 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). 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). 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 Filtering and tagging I used to filter the dataset. Duplicates have been found with FiftyOne AI and a similarity threshold of 0.985. Image tagging has been performed locally in stable-diffusion-ui extension. Duplicate tags are removed, I used the wd14-vit-v2-git interrogator with a threshold of 0.35. I also added the additional tags "facial", "cum", "1girl", "1women", "face", "sperm". 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). 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 Training I used to train the LoRA. The training model was sd-v1-5-pruned-noema-fp16.safetensors. Tags are automatically shuffled and there is no activation tags (activation_tags set to 0). 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