Definition — Fixing a persona's facial identity at training time so that every subsequent generation shares the same face and build. The alternative — re-describing a character in each prompt — produces visible identity drift between images.
There are two places an identity can live: in the prompt or in the model. Prompt-level identity ('a woman in her late 20s with auburn hair...') is re-interpreted from scratch on every generation, so each image lands somewhere slightly different in face-space. Identity lock moves the face into the model itself at training time, so generations vary in everything except who is depicted.
Locking the identity does not lock the content. Scene, wardrobe, lighting, mood and framing all remain prompt-controlled — and on InfluencerForge.app they are typically set per shoot with Forge Styles, so the persona stays constant while the look changes. The first photoshoot guide shows the split in practice.
It is a strong guarantee, not an absolute one. Extreme stylization prompts or contradictory descriptors can still pull a likeness off-center. When that happens the fixes are procedural — tighter situational prompts, seed reuse, and in stubborn cases a retrain with a broader reference set. The persona drift guide and consistency guide cover the toolkit.
Frequently asked questions
The one-time process of teaching an image-generation model a specific persona's identity from a set of reference photos. After training, the persona can be generated in unlimited new scenes without re-describing its appearance in each prompt.
The gradual or sudden change of a generated character's face, build or style between images. Drift is the main failure mode of prompt-only character workflows and the problem trained, identity-locked models exist to solve.
The curated collection of photos (typically 8–20) used to train a persona. Reference quality and variety — angles, lighting, expressions — directly determine how consistent and robust the trained identity is.
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