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How to Keep Character Consistency in AI-Generated Images

Why does the same character come out with a different face every one of ten times you generate it? This post lays out three techniques that actually work — reference images, fixed seeds, and character description anchoring — with concrete steps. A must-read if you're producing a series of images.

Why is it that generating five images of the same character with AI, one after another, gives you five different faces? This gets asked in the comments constantly, so let's settle it once and for all.

The conclusion up front: character consistency isn't something "a few more adjectives" can fix. It takes three concrete mechanisms working together — any one alone isn't stable enough.

Why the inconsistency happens

Every time a model generates, it's essentially resampling from a probability distribution. Even if your text description is word-for-word identical, without an extra locking mechanism, the specific facial details the model "draws" each time are a fresh random outcome. It's the same as telling ten different illustrators "draw a girl with a heart-shaped face, almond eyes, blunt bangs" — ten different artists produce ten different faces. No matter how specific the description gets, there's still a huge amount of room left for interpretation.

Method one: reference image locking

This is currently the most direct and effective approach. Upload a reference image of the character, and use image-to-image or the model's built-in "character reference" feature so all subsequent generations are based on that image's facial features. For content like avatars that require a face to recur precisely, reference image locking is basically standard practice — without it, it's very hard to guarantee "the same person" across a series of images.

For more on how image-to-image actually works and how it differs from text-to-image, see the difference between image-to-image and text-to-image, which covers it in more detail.

Method two: fixed seed

The seed determines the starting point of that particular random sample. The same prompt plus the same seed should, in theory, produce highly similar results. The limitation here is that this only stays stable if the prompt itself also stays completely unchanged — the moment you alter the wording of the prompt, even just the scene description, the seed's ability to hold the facial features steady drops noticeably. So a fixed seed suits scenarios where "the prompt framework barely changes, only the background is tweaked slightly," not series work with major variations.

Method three: character description anchoring

This is the most commonly overlooked method, but also the cheapest — write the character description with extreme specificity, detailed enough to function as a "character reference sheet" rather than a vague summary.

Compare the two:

Vague version: "a cute girl, long hair, big eyes"
Anchored version: "black chin-length bob with inward curl, loose strands tucked behind the right ear, deep brown round eyes, a small mole on the left side of the bridge of the nose, wearing a light blue shirt"

The second approach, carried along with the full anchored description in every generation, doesn't lock things down as tightly as a reference image, but it noticeably narrows the range of randomness, so the character across different images at least resembles the same person in broad strokes. This method works especially well at a stage where no reference image has been settled on yet and the series is being generated purely from text.

How to combine all three

In practice, these three are never an either-or choice — they stack:

Start by locking the base features in with character description anchoring, as a fixed module in the prompt
If possible, generate or find a reference image you're satisfied with as early as possible, then run all subsequent images as image-to-image based on that reference
If the scene doesn't change much (say, the same person in a few different poses), stack a fixed seed on top to narrow the randomness further

Stack all three layers together and consistency can be pushed to a fairly stable level; use only one layer and there will always be some drift.

Which scenarios demand the highest consistency

Avatar series, sequential story illustrations, and multi-angle portrait series demand the highest consistency — it's worth using all three methods together for these. If it's just a single image with no "same character recurring repeatedly" involved, this isn't really something to worry about — a normal prompt is enough. To try the reference-image locking workflow directly, the homepage has prompt tools to play with.