Consistency
Keeping the same subject looking the same across every shot
- Consistency means keeping the same subject looking the same across many images or shots. It matters most in visual storytelling and video.
- Generate each one fresh, and small differences creep in. Eyeball the previous frame and copy it roughly, and the drift compounds until the last shot looks nothing like the first.
- The fix is one fixed reference, matched every single time. Locking the seed, feeding in a reference image, training on the subject alone, and supplying a pose all get mixed together.
- Results improve when you lock down only what has to match — face, outfit — and let pose and background stay loose.
- It pulls in the opposite direction from generating variation. The harder you push variation, the more consistency breaks.
Contents
1The analogy
Say you drop six photos off at a frame shop to hang together on one wall as a set. Every photo gets a mounting mat cut and fitted around it, and eyeballing each cut freehand leaves the width a little different every time. Look at any single one and it's fine, but hang all six side by side and the misaligned edges jump right out.
Worse is cutting each mat by eyeballing whichever one was cut right before it. Drift by even a single millimeter each time, and the sixth mat ends up noticeably different from the first. As long as each one is copied from the last, the drift only ever compounds.
The method the shop actually uses is simple. The first mat cut gets set aside as the template, and every new sheet gets cut against that same template every time. Because everyone is checking the same template rather than whatever was cut last, the six of them never drift apart.
2In detail
Why things drift on their own
Type one prompt and generate several images, and a different face comes out every time. All a prompt can really pin down is a short impression. Countless faces fit the description "a person with short hair and glasses," so every generation just draws one of them at random.
It might seem like feeding in the previous image and continuing from there would fix it, but this is exactly where the drift starts. Every time a new image references the last one, it shifts by just a little, and referencing that shifted result again lets the difference keep compounding. Ten shots in, the character has turned into a different person entirely.
So the order needs to change. What stops the drift from compounding is checking the same single template from start to finish, rather than whatever came right before.
Ways to lock down the template
The simplest method is fixing the seed that draws the starting noise. Keep the seed the same, and the overall impression comes out similar. But even a small edit to the prompt is enough to shake the face, so this alone isn't enough.
A step further is feeding in a reference image alongside the prompt — handing over a portrait as the template and asking the system to keep that face. Plenty of tools also work by pulling out just the facial features and transplanting them into the new image.
The most solid method is training on that one character specifically. Gather ten to twenty photos of the same person and train a small add-on module, and typing just a single name is enough to bring that person out. It takes more upfront work, but for a job with dozens of shots, this ends up faster in the end.
Specifying pose and composition with an image gets used alongside this too. Lock the pose down with a skeleton line or an outline, and only the face is left to worry about, which shrinks how much room there is for things to drift.
Deciding what to hold and what to let go
Lock everything down equally and the images go flat. Repeat the same pose, the same angle, the same expression, and there's no point in having made several shots at all. So it pays to split things into what has to be held and what gets left loose.
What has to be held is usually whatever people use to recognize the subject: face, hair style and color, outfit, any distinctive prop. What gets left loose is pose, angle, expression, background, and time of day.
Write down whatever has to be held in the exact same words every single time, and let the rest vary shot to shot. Even just rewording something that's supposed to be held constant is enough, on its own, to shake the character.
Video has two layers
In video, consistency splits into two layers. One is making sure nothing jumps between frames within a single shot. The other is making sure the same character comes out the same from one shot to the next.
Drift within a single shot shows up as clothing patterns changing frame to frame or background objects flickering in and out. This gets reined in by generating with the surrounding frames in view together. Drift between shots gets caught the same way it is in still images: by handing the same template down from shot to shot.
Keeping variation and interpolation separate
Variation means drawing several sibling-like results from the same prompt. Spreading out into diversity is the whole point, so it pulls in the opposite direction from consistency. The harder the similarity slider gets turned up, the more the face changes.
Interpolation strings a line between two results, so it's entirely natural for the middle to shift gradually. Consistency is about holding things still, so the two goals cross each other. When building a visual story, the usual split is to lock the character down with consistency while letting only the background or camera movement flow through interpolation.
3More precisely
Methods for keeping consistency split by what they anchor. The main ones are: fixing the starting noise, injecting features pulled from a reference image into the generation process, training a small add-on module on just that subject so a single name can call it up, and specifying pose and outline with an image. Real work usually layers two or three of these together. Checking how well it held up means either a person looking at the results side by side, or converting the face into numbers and measuring how close two images are.
The analogy breaks down in places. A mounting mat cut against a template comes out identical every time, but generation doesn't come out identical every time even with a reference supplied — the reference only points the direction, and everything else gets made fresh. And a mat only has one width to match, while a character has several things to match at once — face, hair, clothes, proportions — so locking one down tends to let another slip loose. A cut mat also stays fixed forever, while a locked-down character still leaves the system free to invent new detail whenever the template doesn't specify something.
4Try it yourself
5Common misconceptions
It's easy to think writing a detailed description of the character's looks in the prompt is enough to keep the same person, but actually there's a limit to how far words can narrow things down, so a different face shows up shot to shot.
It's easy to think feeding in the previous image and continuing from there keeps things consistent, but actually the small shifts compound, and the character turns into someone else by the later shots.
It's easy to think locking consistency down hard makes the images better, but actually pose and expression freeze up too, defeating the point of making several shots in the first place.
7One-line summary
In shortConsistency comes from matching every shot to one fixed template instead of copying whatever came right before, holding the same subject looking the same across every shot.
Spotted an error or have a better analogy? Suggest an edit · Last updated2026-09-02