# Consistent AI Character Images: 4 Ways to Keep the Same Face

> Four ways to keep an AI character's face the same: reference images in an editing model, identity adapters, a trained LoRA, and a fixed identity line.

- Published: 2026-10-14
- Updated: 2026-10-14
- Author: SecretCrush AI Editorial
- Category: Guides
- Canonical URL: https://secretcrush.ai/blog/consistent-ai-character-images

## The short answer

A character's face drifts because every image is generated from fresh random noise, and your prompt only describes part of her. There are four ways to hold the face steady, and they can be combined:

1. **Reference images in an editing model.** Show the model a picture of the character and ask for a new scene. No training.
2. **Identity adapters.** A small add-on turns a face picture into extra guidance for an existing model. Fast, mostly face-only.
3. **Fine-tuning (a LoRA).** Train a small add-on on 10 to 30 images of the character. Strongest likeness, most work.
4. **Prompt discipline.** One identity line, copied word for word, with one change at a time. Free, works everywhere, weakest on its own.

This guide explains each, with the trade-offs as they stand in October 2026, then shows the prompt method with three worked examples you can paste into any generator.

## Why faces drift

An image model does not store your character anywhere. Each request starts from random noise and is steered by what you give it. "A woman with brown hair in a café" is true of millions of different faces, and the model will happily give you a new one each time.

Three things make drift worse:

- **A thin description.** Every detail you leave out is decided by chance. Hair colour alone pins down very little.
- **Changing several things at once.** New outfit, new place, new pose and new lighting in one prompt, and the face moves with them.
- **Changing style.** A photo-realistic face and an anime face are drawn on different rules. A character does not carry across them unchanged.

You cannot remove the randomness. You can give the model something fixed to hold on to: a picture, a trained add-on, or at least a precise description.

## The four approaches compared

| | Reference images | Identity adapter | Fine-tune (LoRA) | Prompt discipline |
|---|---|---|---|---|
| Setup | None | Minutes, if your tool has one | Training run per character | None |
| What you need | 1 to 10 pictures, depending on the model | One clear face picture | 10 to 30 images of the character | A written identity line |
| Likeness | Good; can drift over many generations | Strong on the face, weaker on hair, body and outfit | Strongest, face and style | Weakest on its own |
| Flexibility | High: change scene by instruction | Can reduce how closely the prompt is followed | High once trained | Full |
| Cost | Per image, e.g. $0.045 for a FLUX.2 [pro] edit, $0.0336 for a Nano Banana 2.1 image | No training cost | Training compute and time | Free |
| Best for | A handful to dozens of images; putting two characters together | Portraits | A character you will use hundreds of times | Always, on top of any of the others |

*General trade-offs, from the papers and the makers' own pages. Prices are list prices at the time of writing.*

## 1. Reference images in an editing model

You give the model one or more pictures and an instruction such as "the same woman, now on a rooftop at dusk".

Black Forest Labs' **FLUX.1 Kontext** (29 May 2025) is the best-documented early example. Its paper describes placing the reference image's tokens in the same sequence as the image being generated, so the model looks at the reference while it draws. BFL reported that Kontext scored best for character preservation on its own benchmark, and that identity drifted little across repeated edits.

Since then the feature has become standard:

- **FLUX.2** (November 2025) builds references into every tier: up to 8 through the [pro] API and up to 4 for the small, fast klein models. Our [FLUX.2 explainer](https://secretcrush.ai/blog/flux-2-explained) covers the tiers and licences.
- **Qwen-Image-Edit-2511** (23 December 2025), an open model, was released with improved character and multi-person consistency. **Qwen-Image-2.1** (September 2026) takes up to 10 references and lists identity preservation, under a non-commercial licence.
- **Nano Banana 2.1** from Google (generally available 6 October 2026) lists better subject consistency among its changes.
- **Midjourney** replaced its Omni Reference feature with a V8.2 edit model on 27 August 2026.

**Pros:** no training, works with a single good picture, and handles several people at once on models that take multiple references. **Cons:** identity can wander over a long series, so compare each result against the original rather than against the last one. Results depend heavily on the model, and hosted models apply their provider's content rules.

## 2. Identity adapters

An identity adapter is a small network bolted onto an existing image model. It turns a reference picture into extra conditioning, so the model steers towards that face while still following the text prompt. The best-known research examples are **IP-Adapter** (August 2023), which works from any reference image, and **InstantID** (January 2024) and **PhotoMaker** (December 2023), which focus on faces.

**Pros:** zero-shot, meaning no training per character, and fast. **Cons:** they are mostly about the face, so hair, body and clothing can still change; and a strong adapter can make the model follow the rest of your prompt less closely. Whether you can use one depends on whether your tool offers it.

## 3. Fine-tuning: LoRA and DreamBooth

Fine-tuning teaches the model the character itself. **DreamBooth** (August 2022) showed that a model can learn a specific subject from a handful of photos and reproduce it in new scenes. **LoRA** (June 2021, first written for language models) is now the usual lightweight way to do it: the big model stays frozen and a small add-on file, often tens to hundreds of megabytes, shifts it towards the character.

**Pros:** the strongest lock on likeness and style, and once trained it costs nothing extra per image. **Cons:** you need 10 to 30 good, varied images of the character first, plus training time, and one add-on per character. A LoRA also inherits the licence of its base model: one trained on a non-commercial model, such as FLUX.2 [dev], cannot be used commercially either. Model makers now plan for this; Black Forest Labs released undistilled "Base" versions of FLUX.2 klein specifically for fine-tuning.

## 4. Prompt discipline

Whatever else you use, the prompt still matters, and if you only have a text box it is all you have. The rules are simple.

**Write the identity line once and paste it, unchanged, at the start of every prompt.** Keep fixed:

- Age, as a number
- Hair colour, length and style
- Eye colour
- Skin tone and build
- One or two distinctive features: freckles, a gap between her front teeth, a small scar through one eyebrow
- The style, realistic or anime

**Vary one thing at a time:** setting, outfit, light, or framing and pose.

**Keep the same order every time:** subject (identity line, then clothing and action), setting, light, framing.

Distinctive features do more than you would expect. "Freckles across her nose and a slightly crooked smile" gives the model something specific to reproduce, where "pretty" gives it nothing. And reword nothing: "auburn hair" in one prompt and "red hair" in the next are two different instructions.

## Three worked examples with the prompts

The character: Mara, 28. Her identity line is the first sentence of each prompt and never changes.

**1. The baseline.** Plain setting, soft light, close framing. This is the picture you compare the others against.

```
A 28-year-old woman with shoulder-length wavy auburn hair, green eyes, fair freckled skin and a slim build, a small scar through her left eyebrow. Wearing a cream knit sweater, standing by a window in a quiet apartment, soft daylight, head-and-shoulders portrait, looking at the camera.
```

**2. Change the setting and light only.** Same outfit, same framing.

```
A 28-year-old woman with shoulder-length wavy auburn hair, green eyes, fair freckled skin and a slim build, a small scar through her left eyebrow. Wearing a cream knit sweater, sitting at a table on a rooftop bar at dusk, warm string lights behind her, head-and-shoulders portrait, looking at the camera.
```

**3. Change the outfit and framing.** Back to simple light, so only two things move.

```
A 28-year-old woman with shoulder-length wavy auburn hair, green eyes, fair freckled skin and a slim build, a small scar through her left eyebrow. Wearing a dark green raincoat with the hood down, walking along a harbour wall on an overcast morning, soft even light, waist-up shot from slightly to one side.
```

For each one, generate a small batch and keep the closest match to the baseline. Close framing holds a face better than a full-length shot, where the face is only a small part of the picture, so get the identity right in close-up before you pull back. If your tool accepts a reference image, give it the baseline picture alongside prompts 2 and 3; the identity line and the reference then work together.

We have not included output images in this post. The prompts are written to be pasted in as they are; your results will differ from ours, and from each other, which is the point of generating several.

## Fixing common failures

- **The hair colour shifts.** Name it once, plainly, and remove other colour words near it. A "red dress" next to "auburn hair" invites bleed. Move the outfit colour away or change it.
- **She looks older or younger.** State the age as a number in the identity line, and keep it there.
- **The face changes when the scene gets busy.** Simplify. One place, one action, closer framing.
- **Something unwanted keeps appearing.** If your tool has a negative prompt, add that one thing, such as "glasses" or "hat", and nothing else. A long list copied from elsewhere rarely helps.
- **The style looks different.** Check you have not switched between realistic and anime. Treat them as two separate characters.
- **A long series has wandered.** Go back to the baseline picture, not the latest one, as your reference and comparison.

## Keeping a character consistent in video

Video models now take character references too: Google's Veo 3.1 has "Ingredients to video" (until its preview endpoints close on 22 October 2026), Gemini Omni has reference-to-video, and Kling and Seedance accept reference inputs. These keep a character steady within one clip. Keeping the same person across many separate clips is still hard. Every major video model accepts a starting image, so a practical route is to make a consistent still first and animate it. Our [AI video generator comparison](https://secretcrush.ai/blog/best-ai-video-generators) lists which models take references and what they cost.

## Consent and the law

All of these methods work on real faces as well as fictional ones, which is why the rules are strict. Use them only on fictional characters or on people who have agreed. Every major provider bans intimate images of real people made without consent, and it is increasingly a crime: in the US, the TAKE IT DOWN Act makes knowingly publishing such images a federal offence, and in England and Wales creating one has been an offence since 6 February 2026. Our explainer on [why AI image generators block adult content](https://secretcrush.ai/blog/why-ai-image-generators-block-adult-content) covers the rules in more detail.

## Further reading

If you have not written your character's personality yet, [how to write an AI character](https://secretcrush.ai/blog/how-to-write-an-ai-character) covers that side. To compare the image models themselves, see our [AI image generator comparison](https://secretcrush.ai/blog/best-ai-image-generators).

## How we checked

We read the papers behind each approach and the makers' own release notes, model cards and pricing pages on 14 October 2026. The trade-offs in the table are general ones from that reading, not results of our own tests.

## The easier way: SecretCrush AI

All four methods work, but each costs you set-up: reference pictures to manage, add-ons to install, training runs or a prompt routine to follow. On **SecretCrush AI** you skip all of it, with nothing for you to train or tune.

- **Fast.** High-quality character images in about 10 to 15 seconds, and videos in about 40 seconds per clip.
- **No set-up.** No installs, no GPU, no workflows, no prompts to tune.
- **Consistent.** The same character keeps the same look from one picture to the next.
- **Alive.** You chat with your character in immersive, realistic roleplay and ask for a picture or a video right inside the conversation.

A free account gets 10 messages a day, every day. Premium gives you unlimited messages with advanced memory and 500 bonus coins every month (600 on the yearly plan), from $8.99 a month on the yearly plan or $16.99 month to month. An image costs 6 coins, and coin packs ($10 for 300 coins) are open to free accounts too.

Create your first character image in seconds. 10 free messages every day, no card needed. [Start free on SecretCrush AI](https://secretcrush.ai/)

Unlimited messages, advanced memory and 500 bonus coins every month, from $8.99 a month. [See SecretCrush AI plans](https://secretcrush.ai/pricing)

## FAQ

**Q: Why does my AI character look different in every image?**

A: Each image starts from fresh random noise, and a short prompt leaves most of a face undecided. The generator fills the gaps differently every time. Reference images, adapters and fine-tuning give the model a fixed identity to work from; a precise, unchanging description narrows the gaps when you only have words.

**Q: What is the easiest way to keep a character consistent?**

A: Use a model that accepts reference images and ask it for a new scene with the same person. It needs no training. FLUX.2, Nano Banana 2.1 and Qwen-Image-Edit-2511 all advertise character or subject consistency from references. Identity can still drift over many generations, so keep comparing against your original picture.

**Q: Is a LoRA better than reference images?**

A: For likeness, usually yes: a LoRA trained on 10 to 30 images of a character gives the strongest lock on face and style. It costs training time, you need one per character, and it inherits the licence of the base model it was trained on. Reference images are quicker and need no training.

**Q: What should stay the same in every prompt?**

A: The identity line: age, hair colour and style, eye colour, skin tone, build and one or two distinctive features. Copy it word for word. Change only the setting, the outfit, the light or the framing, one at a time.

**Q: Does the order of words in a prompt matter?**

A: It helps to keep one order and stick to it: subject first, then setting, light and framing. A fixed order makes it easy to see what you changed, and keeps the identity at the front where it is least likely to be diluted.

**Q: Can I use these methods on a real person?**

A: Only with that person's consent. Every major provider bans intimate images of real people made without consent, and in many countries it is a crime: the US TAKE IT DOWN Act covers publishing them, and in England and Wales creating one has been an offence since 6 February 2026.