Victoria Pedretti, actress from The Haunting of Hill House, Bly Manor, You, etc.

Lower CFG gives best results, I hang out around 4.5.

TIP - DON'T USE MANY (if any) DESCRIPTIVE WORDS ABOUT THE SUBJECT IN YOUR PROMPT WHEN USING A SUBJECT BASED TI.
Textual Inversions are trained on the appearance of the subject, therefore every time you add a descriptive element (hair color, body type, etc) to your prompt, you are fighting the embedding and results will be less accurate. Stuff like hairstyles (hair in a ponytail) usually will not fight an embedding and can help if they describe actual traits in the embedding, but if they aren't true to the character it will only fight it. And too many of them definitely will fight it.

When using textual inversions for people/characters, use a formula like:
<embedding> + scene + pose/outfit + environment/lighting/quality triggers

Lower CFG also gives more strength to the embedding vs your overall prompt. I’ve found a lower CFG works better for any embedding on this site.

TEST PROMPT
(I use a system the uses <> for TI triggers, remove those if your UI doesn't utilize that.)

<fenn_victoria>, hyper realistic photograph, photo of a beautiful girl, long straight hair, full body, wearing leggings and a t-shirt, magazine cover, poster art, highly detailed, large eyes, photorealism, sharp focus, best quality, 4k, vibrant colors, backlit, rim light, key light, low key, (looking at viewer), shot on Canon, detailed skin, studio lighting, (solid color background), synthwave,

Negative -
far away, ugly, low-res, graphic tee, blurry, wrinkles, bad anatomy, anime, cartoon, 3d render, illustration, disfigured, poorly drawn face, (text, watermark, signature), mutation, mutated, extra limb, ugly, poorly drawn hands, missing limb, floating limbs, disconnected limbs, malformed hands, out of focus, long neck, long body, disgusting, poorly drawn, mutilated, mangled, old, surreal, far away shot, monochrome,


Using mostly Realistic Vision v1.4 in these, but it works well in most models trained on diverse datasets. Big fan of Epi_Noiseoffset as well.

Trained at 2000 steps on 40 images. Before anyone asks, it's a .bin file because it was trained using the Google Colab, and that's what it gives you.