Definition — 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.
Drift shows up as a persona that is recognizably 'the same kind of person' but not the same person: the jawline narrows between posts, apparent age slides a few years, the build changes with the outfit. Audiences register it even when they cannot name it — a feed with drift reads as stock photos of lookalikes rather than one creator's account.
The root cause is almost always identity living in the prompt. Every text description is re-interpreted per generation, and small wording changes ('portrait' vs 'candid photo') land in different corners of face-space. Secondary causes include contradictory descriptors stacked in one prompt and heavy style transfer bleeding into facial features.
The durable fix is structural: train the identity so it no longer depends on the prompt, keep prompts situational, and reuse seeds when iterating on a specific look. The drift-fixing guide diagnoses the common patterns, and the consistency guide covers preventing drift by design.
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.
A number that initializes the randomness of a generation. Reusing the same seed with the same prompt and settings reproduces the same image, letting you revisit and iterate on a look instead of re-rolling from scratch.
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.
See it live with 100 free credits
No card required. Generate with showcase models on the free tier, then train your own persona on any paid plan.