How to Keep the Same Face Across AI Images

You generate one great photo. The face is perfect. Then you try to make a second image of that same person, different pose, different background, and the model hands you their cousin. Close, but not them. Do it five more times and you've got a family reunion, not a consistent character.

This is the single hardest problem in practical AI image generation, and it's why feeds, profiles, and AI characters fall apart. A model that produces a stunning one-off is useless for content if it can't produce the same person twice. Below are every real method for holding one identity steady, ranked by how reliably they work and how much effort they cost, plus a self-test to catch drift before anyone else does.

Why the face drifts in the first place

Most image models re-roll facial features on every generation. A text prompt like "young woman, brown hair" describes a category, not an individual, so each run lands somewhere new inside that category. Nothing tells the model "this exact face again." Consistency, then, is entirely about what anchor you give it to hold onto. Everything below is a different kind of anchor, and they are not equal.

The methods, ranked

Method	Reliability	Effort	Best for
Same seed only	Low	Very low	Tiny tweaks to one prompt
Single reference image	Low–medium	Low	One or two near-identical shots
Multi-image reference / hero image	Medium	Medium	Small sets, similar angles
Trained LoRA	High	High	Power users, many scenes
Identity-lock system	High	Very low	Feeds, profiles, video, volume

1. Same seed (weak)

A seed is the random starting number a generation begins from. Fix it and re-run a similar prompt, and you'll get a similar result. People reach for this first because it's free and built in.

It also barely works for faces. The moment you change the scene, pose, or even a few words, the seed's influence gets overwhelmed and the face moves. Seeds are a real tool, but as a consistency strategy on their own they're the weakest option here. Treat them as a minor stabilizer, not a solution.

2. Single reference image (better, still fragile)

Most modern tools let you attach a photo of a face and ask the model to follow it. This is a genuine step up: training-free identity references can carry a likeness into a new image without any setup. For one or two shots at similar angles, it can look great.

The fragility shows up across a set. Change the lighting, rotate to a profile view, or add a strong expression and the likeness slips, the eyes widen, the nose narrows, the jaw shifts. One reference image carries surprisingly little information about a face from angles it didn't see. For a single hero shot, fine. For a believable feed of twenty, not enough on its own.

3. Multi-image reference / a locked hero image (medium)

A stronger version: build one strong "hero" image of your character, then use it as the reference for everything that follows, sometimes stacking pose and edge conditioning on top to force a consistent posture. Some workflows feed multiple references at once so the model sees several angles.

This is where consistency starts to feel real for small projects. It's also fiddly, you're managing reference files, conditioning settings, and re-checking every output. It holds across moderate variation but still drifts at the extremes (hard profiles, dramatic lighting, strong emotion). Good for a curated handful of images; tiring across hundreds.

4. Trained LoRA (high reliability, high effort)

A LoRA is a small custom model trained on photos of one specific person, which then plugs into a larger base model and reproduces that person on demand. This is the serious answer. Practitioners report that fine-tuning on roughly 10–20 high-quality images can retain most of a person's distinguishing features, and the working recommendation is around 10–30 curated photos spanning frontal, three-quarter, and side views in varied lighting.

Done well, a LoRA holds identity across scenes, outfits, and angles far better than any reference-image trick. The cost is real effort: you collect and curate a dataset, run the training, tune strength settings, and often combine it with adapters and pose control to fully lock features. It's the right tool if you enjoy the technical side and have time to invest per character. Our consistent AI character guide digs into the full pipeline, and the AI character generator overview covers building a character from scratch.

5. Identity-lock systems (high reliability, near-zero effort)

The newest consumer approach takes the LoRA-style result and hides the machinery. You give one clear face photo, and the system handles the equivalent of training and locking internally, then holds that identity steady while you change scene, outfit, and format. You never touch a strength slider.

This is the lane built for volume. Phottly works this way: upload one clear face, pick presets, and batch out phone-camera-style stills and short videos that stay the same person across every scene, including video, where consistency is hardest. You trade granular control for reliability and speed, which is the right trade when the goal is a believable feed rather than a single art piece. It won't give a technical artist the dials a hand-built LoRA does; it will give a normal person a consistent face across a hundred images without learning any of this.

Keeping the same face in video, not just stills

Photos are the easy version of this problem. Video raises the stakes because the face has to stay consistent not only between separate clips but across every frame within a clip, and any morph between frames is glaringly obvious in motion in a way a still never is.

The strongest video workflows stack the same anchors discussed above, in layers: a reference image at the first frame to set the identity, a fixed character description repeated across every scene so wording doesn't reintroduce drift, and a trained identity (LoRA or the system's equivalent) carried through inference. Some setups add motion-specific adapters for scenes with a lot of body movement. The takeaway for a normal creator isn't to assemble that stack by hand, it's that consistency across photos and video is fundamentally the same identity problem, so a tool that solves it once should solve it for both. If your stills are locked but your clips wander, you're using two systems that don't share an identity.

Common mistakes that quietly break consistency

Even with the right method, people sabotage their own results in predictable ways:

• A weak or inconsistent dataset. Twenty photos that are all the same front-facing selfie teach the model one angle. It then guesses badly at profiles. Variety in angle, lighting, and expression matters more than count.
• Cranking reference or LoRA strength too high. Past a point, the model copies the exact source pose and lighting instead of the identity, so every output looks like a clone of one photo rather than the same person in new situations.
• Changing too many variables at once. If you swap the scene, the lighting, the angle, and the expression in a single jump, even a good anchor strains. Move one or two axes at a time and the face holds better.
• Trusting the thumbnail. Faces look consistent at small sizes because the brain fills in the gaps. Drift only shows at full resolution, which is exactly where your audience will see it.

Avoiding these four covers most of the "I trained a LoRA and it still drifts" complaints.

Which method should you actually pick?

Match the tool to the job, not to the hype:

• One or two near-identical shots? A single reference image is fine. Don't overbuild.
• A small, curated set at similar angles? A locked hero image with pose conditioning gets you there.
• A serious character you'll reuse for months, and you enjoy the technical side? Train a LoRA. The upfront effort pays off across hundreds of generations.
• A feed, profile, or influencer account where you need volume across photos and video and don't want to manage any of this? An identity-lock system is the practical answer. Phottly handles the lock internally from one clear face photo, keeping the same identity across stills and short video without a training run or a single slider to tune.

There's no single right method, there's the right method for how much control you need and how much effort you'll spend. The mistake is reaching for seeds and single references for a job that needs a trained identity, then concluding AI "can't do consistency." It can. You just used the lightweight tool for the heavyweight task.

The "same person?" self-test

Whatever method you use, verify before you post. Resemblance fools the eye; landmarks don't. Put any two images side by side and check these fixed features, which models tend to drift on even when the overall vibe matches:

• Eye spacing, distance between the pupils, relative to face width.
• Nose, width at the base and the bridge shape.
• Hairline and hair part, where it starts and which side it falls.
• Ear shape, surprisingly individual, and often the first thing to slip.
• Fixed marks, moles, freckles, scars, asymmetries. Do they appear in the same spot?
• Jaw and chin, width and the angle from ear to chin.

If three or more of these don't match across your set, you have drift, and viewers will feel it even if they can't articulate why. This grid catches in ten seconds what an offhand glance misses. A face can look right alone and obviously inconsistent in a lineup, and a lineup is exactly what a feed or profile is.

Bottom line

Seeds and single references are quick but drift the moment anything changes. Multi-image hero workflows hold up for small, careful sets. A trained LoRA is the gold standard if you'll put in the effort per character. And identity-lock systems give you most of the LoRA's reliability with almost none of the work, which is why they win for anyone producing volume across both photos and video. Pick the method that matches your patience, then run the landmark test before you publish, every time.

Lock your identity with Phottly

If the identity-lock route is the right fit, Phottly is built for exactly this: upload one clear face photo, pick preset scenes and outfits, and batch out realistic lifestyle stills and short 9:16 videos where the same person appears in every frame. No training runs, no sliders. Head to phottly.com to get started.