Character Consistency in AI Images: A Free Technique That Actually Helps
You generate a character you like. You generate them again in a different pose, or a different outfit, or just a different angle — and it's a different person. Same prompt structure, same style keywords, completely different face. If you've hit this wall in Midjourney, Flux, ChatGPT's image tool, or Leonardo, you're not doing anything wrong. Character consistency is a real, unsolved-by-default problem with how these models work, and there's a specific, free technique that materially improves it. Here's what actually causes the drift, and how to fix most of it without paying for anything.
Why the Face Changes Every Time
Text-to-image models don't have a persistent idea of "your character" between generations. Every time you hit generate, the model is starting fresh from your text prompt and a new random seed. If your prompt describes a person loosely — "a woman with brown hair, blue eyes, athletic build" — the model has enormous freedom to interpret that description differently each time, because "brown hair" and "blue eyes" describe millions of different faces.
This is the core issue: vague, short descriptions leave room for drift, and every generation is an independent roll. Some tools offer features that fight this at the model level — Midjourney's character reference, seed locking, image-to-image with a reference photo — and those help when they're available. But the technique that works everywhere, regardless of which model you're using or whether it has a character-reference feature, is a better prompt structure. That's what the rest of this post covers.
It's worth being honest about the ceiling here up front: no prompt technique, on any model available today, guarantees pixel-perfect identity across every generation. What a good technique does is raise your hit rate — more of your generations look like the same person, so you throw away fewer, and the ones that don't match are recognizable as an outlier instead of the norm.
The Four Things That Actually Drive Consistency
1. A specific, structured character description you reuse verbatim.
This is the single biggest lever. Not "a woman with brown hair" — a full block covering face shape, skin tone and texture, specific eye color and shape, brow shape, nose, lips, hair color/length/texture/style, body type, and one or two signature details (a specific earring, a beauty mark, a particular haircut). The more specific and the more consistently you paste that exact block first in every prompt, the less room the model has to reinterpret the face.
2. Locking the geometry language, not just the description.
Beyond describing features, explicitly instruct the model to keep facial geometry, bone structure, eye spacing, and hairline unchanged across generations. This sounds redundant with the description, but it's a different kind of instruction — it tells the model "this is a hard constraint," not just "this is a suggestion," and in practice it noticeably reduces subtle drift on later generations in a session.
3. Varying only the things you mean to vary.
Every time you change wardrobe, location, pose, lighting, camera angle, and expression *in the same prompt* as the character description, you're giving the model more places to also drift the face without meaning to. Batch your generations: lock the identity, generate several images of one scene/outfit combination before changing anything about the character block itself. If the face is holding well across a batch, that batch becomes your reference set.
4. Reference images and seed-locking, when your tool has them.
Text alone gets you most of the way, but if your model offers character reference, image-to-image from your best generation, or seed locking, use them on top of the text technique rather than instead of it. Generate 3–5 outputs from your locked description, pick the one that looks most "right," and feed that back in as a reference for future generations. Tools differ in exactly how this works — more on that below by model.
A Worked Example: One Reusable Character Block
Here's what a consistency-focused character description actually looks like in practice, in plain words rather than a finished prompt string:
Before (vague — high drift risk): "A young woman, brown hair, casual style, friendly."
That description could match an enormous range of faces. Every generation is a fresh guess at what "young woman with brown hair" means.
After (specific, structured — the technique above applied): "Early-30s woman, fair skin with cool undertone, heart-shaped face, clear skin with natural pores, dark brown almond eyes, softly arched brows, refined nose, defined cupid's-bow lips, sleek straight black hair in a low ponytail, tall lean build, minimal monochrome wardrobe, small pearl studs. Maintain identical facial geometry, bone structure, eye spacing, and hairline across all generations. Photorealistic human."
Notice what changed: every vague noun got a specific modifier, one signature detail got added (the pearl studs — small, recognizable, easy to keep consistent), and the geometry-locking instruction got added as its own sentence rather than folded into the description. Paste that exact block first, every time, before whatever scene you're actually generating that day (a coffee shop portrait, an outdoor shot, a product-in-hand photo) — the scene changes, the block doesn't.
In practice, this doesn't produce identical output on every generation — it produces a noticeably higher rate of "yes, that's recognizably the same person" outputs, with fewer total re-rolls needed to get a usable batch.
Where It Still Fails, Honestly
This technique reduces drift; it doesn't eliminate it. A few honest limitations worth knowing before you rely on it:
- Extreme angle or lighting changes stress the system. A face held consistently in front-facing portraits can still drift more on a dramatic side profile or unusual lighting setup, because those are further from anything the model has strong reference points for.
- Long sessions drift more than short ones. The further you get from your original reference generations, the more chances there've been for small variations to compound. Re-pasting the full lock block (not an abbreviated version) periodically helps reset this.
- Ethnicity and age cues need to be restated, not implied. If you drop a specific cue after a few prompts assuming the model "remembers" it from earlier in the session, it doesn't — every generation reads only what's in that prompt.
- Hands, product interactions, and complex poses are a separate problem from face consistency, governed more by general anatomy handling than by your character lock. Don't expect an identity block to fix broken hands; that's a different fix (shorter prompts, negative prompts, tighter crops).
If a source is telling you a prompt technique guarantees perfect consistency, or promising specific percentage improvements without showing their work, be skeptical — that's not how any of these models currently behave.
Model-by-Model Notes
The technique above is model-agnostic — it works as a prompting discipline regardless of tool. A few notes on how to apply it to the four generators it's been used with directly:
Midjourney. Text-based locking works as described above. If your version supports character reference (`--cref`) or a seed, layer it on top of the text block rather than replacing it — generate your best reference image first, then reuse it as a character reference for subsequent prompts in the same series.
Flux. Responds well to detailed, structured text descriptions, and tolerates a longer character block without losing coherence. If you're running it through an interface that exposes seed locking, fixing the seed across a scene batch (while varying only the scene portion of the prompt) is worth testing alongside the text technique.
ChatGPT's image tool. Works with the same structured-description approach; because it's conversational, you can also explicitly reference "the same person as before" in follow-up turns within one conversation, which behaves differently (often better, within a session) than a fresh prompt in a new chat.
Leonardo. The text technique applies the same way; Leonardo's PhotoReal and Alchemy modes are worth pairing with a consistently reused character block for portrait-style work specifically.
Other text-to-image tools not named here can generally use the same structured-description discipline — the technique is about prompt engineering, not a feature exclusive to any one platform — but it hasn't been tested and documented against every generator on the market, so treat results on any tool not listed above as unverified until you try it yourself.
If You Want This Done for You
Building a character block from scratch, testing it, and then writing 250+ scene prompts that all correctly lead with it is a few hours of work the technique above will get you most of the way toward on your own. If you'd rather start from something already built, the Consistent AI Influencer Prompt System packages this exact Identity Lock approach — a fill-in-the-blanks character bible, a ready-to-paste lock block, 15 starter character examples, and 257 numbered scene prompts across portraits, lifestyle, UGC product shots, fashion, fitness, and more — all written to lead with the lock block first, for Midjourney, Flux, ChatGPT's image tool, and Leonardo specifically. It's a one-time $7, no subscription, and it carries the same honest limitation as this post: no prompt system gets perfect consistency every time, this one just gives you a tested structure and a much higher hit rate than starting from a blank page.