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How to keep an AI influencer's face consistent

A consistent face is not a prompt trick. It comes from generating one anchor set once, then referencing those exact files in everything you make afterwards.

Why the face drifts

Every generation is a fresh roll of the dice. Ask for “a 24-year-old woman with dark hair” twice and you get two different people, because nothing ties the second image to the first. Text alone cannot carry identity — a description narrow enough to fix a face would have to specify measurements no model reads reliably.

Rule 1 — build the anchor first

Generate 4–6 variants with no reference at all, then pick one or two where the face reads the same from different angles. Those become the anchor: the permanent identity of the character. Everything you make from then on references them. Spend real time here, because every later photo inherits whatever you settle for.

Rule 2 — never send more than two references

Three or more reference photos make the model average them, and the average of three faces is a fourth face. Two is the practical limit: one front, one at an angle. Past that the drift starts again, only slower and harder to notice.

Rule 3 — a fixed angle set, generated once

Front and over-the-shoulder, generated once from the anchor, cover most scenes. Reuse those files rather than regenerating the angle each time — a regenerated angle is a new roll of the dice, and it will not match the previous one exactly.

Rule 4 — keep the identity block identical

Write the identity part of the prompt once, word for word, and change only the scene around it: “Same woman as in the reference image. Keep her exact face: identical facial proportions, same eyes, nose, lips, jawline and eyebrows. Do not idealize, beautify or alter any of her features.” Rewriting that block between generations is the most common cause of a face that slowly becomes someone else.

Common questions

Why does my AI model look different in every photo?

Because nothing links one generation to the next. Text descriptions cannot carry identity. You need reference images from a fixed anchor set, sent with every generation.

How many reference photos should I use?

One or two. Three or more makes the model average them together, which produces a face that belongs to none of the originals.

Do I need to train a LoRA model?

No. Reference-based generation with a fixed anchor set gets you consistency without training, and without the hours and cost that training requires.

Everything above, in one place

Syntfluence does the anchor set, the prompts, the voice and the clip — with the consistency rules built in. The guided run is free and needs no card.

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