Consistent Characters in AI Art: The Prompting System I Use (and Why It Works)

You can get a gorgeous AI portrait in one shot. Then you ask for "the same character, different pose," and suddenly your character has a new face, a new haircut, and (somehow) a different ethnicity. That's not you being "bad at prompts." That's you asking a probabilistic image model to preserve identity without giving it a stable identity anchor.

Here's the thing I learned the hard way: character consistency isn't a single prompt. It's a system. You're not just describing a person-you're defining a character spec and then reusing it with minimal drift.

In this post I'll show you how I write consistent character prompts for AI art in a way that's repeatable, debuggable, and friendly to both UI workflows (ComfyUI, etc.) and API workflows.

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What "consistency" actually means (and why it breaks)

Most people think "consistent character" means "same face." In practice, it's a bundle of constraints: silhouette, hair shape, palette, clothing language, materials, and the camera/lens + lighting choices that make a face feel like the same person.

Diffusion-style image generation also tends to lock in different aspects of the image at different "stages" of denoising. Research on multimodal diffusion points out that different modes/eigenfeatures stabilize at different rates during the reverse process, which helps explain why small changes can cause "desynchronization artifacts" (the vibe stays, but the identity slips) [3]. That's why you can keep the cyberpunk lighting but lose the freckles and jawline.

So we compensate by doing two things:

We reduce randomness where it matters (identity tokens), and we allow flexibility where it doesn't (pose, background, action). Then we test changes incrementally.

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The character spec: a reusable prompt block

I treat the prompt as two parts: a stable "identity core" and a swappable "shot directive."

The identity core is what you reuse every time. It should be short enough that you'll actually keep it unchanged, and specific enough that the model has something to latch onto.

Here's my template.

CHARACTER CORE (keep constant)
[Name/handle], [age range], [gender presentation]
Face: [2-3 distinctive facial markers]
Hair: [color + cut + texture]
Skin: [tone + 1 detail like freckles/scar]
Body: [build + height vibe]
Signature: [one iconic accessory or feature]
Wardrobe anchor: [recurring outfit elements + palette]
Style anchor: [medium + aesthetic + quality level]

SHOT DIRECTIVE (change per image)
Scene: [location + time]
Action/pose: [pose, gesture, framing]
Camera: [lens, distance, angle]
Light: [key light description]
Mood: [2 adjectives]
Composition: [rule of thirds/centered, background depth]
Constraints: [avoid X, no makeup change, keep hairstyle, etc.]

The important move is that the character core is not a paragraph. If you write a whole biography, you'll start changing it unconsciously, and the character will mutate with it.

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Use perceptual anchors, not just nouns

Diffusion models aren't "reading" your prompt like a human. They're mapping text to visual features. Papers like PixelGen argue that a lot of the challenge in image generation is about focusing learning (and guidance) on perceptually meaningful structure-global semantics and local texture-rather than every pixel detail [2]. You can borrow that idea at prompting time.

So in your character core, include both:

A global semantic anchor (e.g., "sharp triangular jawline, slightly wide-set eyes") and a local texture anchor (e.g., "light freckles across nose bridge," "small scar through left eyebrow"). Those tiny features do work because they constrain where details land.

If you only describe "brown hair, green eyes," you're basically asking the model to sample a new person from a massive cluster.

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Keep the generation knobs consistent (seed, denoise, guidance)

Prompting is only half the story. Your settings are part of the "prompt," even if they're not written in text.

ComfyUI's KSampler parameters are a nice plain-English reminder of what matters: seed controls repeatability, CFG scale controls how hard the model follows text, and denoise controls how much of an input image is preserved in image-to-image workflows [4]. If you're trying to keep a character consistent, changing these values wildly between runs is like changing your prompt.

What I do in practice is keep a "character baseline" run with a fixed seed and stable CFG. Then I only change one variable at a time.

Also, if you're doing pose changes, image-to-image with a lower denoise value is your friend, because it preserves more of the original structure while still allowing the new pose to form [4]. That's often more reliable than pure text-to-image for "same character, new shot."

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My favorite trick: write a negative prompt that targets identity drift

Negative prompts get abused ("bad anatomy, low quality, blah blah"). For consistent characters, I use negatives like guardrails:

"No different hairstyle, no hair length change, no bangs, no hat, no heavy makeup, no age change, no different eye color."

You're not banning creativity. You're banning identity edits.

ComfyUI examples tend to keep negatives simple ("low quality, blurry…") [4], which is fine for quality, but identity is its own failure mode. Call it out.

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Practical examples: three prompts you can steal

Let's build a consistent character named Mara and generate three different shots.

CHARACTER CORE
Mara Venn, late 20s, woman
Face: sharp triangular jawline, light freckles across nose bridge, scar through left eyebrow
Hair: deep auburn, chin-length blunt bob, slight wave
Skin: warm olive
Body: lean athletic build
Signature: small silver hoop nose ring (left nostril)
Wardrobe anchor: charcoal cropped jacket over cream turtleneck, muted teal accents
Style anchor: cinematic portrait photography, high detail, natural skin texture

Now the "shot directive" changes.

SHOT 1
Scene: rainy night street, neon reflections
Action/pose: standing, shoulders turned 30 degrees, looking at camera
Camera: 85mm lens, shallow depth of field, head-and-shoulders portrait
Light: soft key light from storefront window, rim light from neon sign
Mood: calm, intense
Constraints: keep hairstyle and nose ring, keep freckles and eyebrow scar
Negative: different hairstyle, bangs, hat, heavy makeup, different eye color, different age

SHOT 2
Scene: sunlit kitchen, morning
Action/pose: leaning on counter, candid smile, looking slightly off-camera
Camera: 50mm lens, medium shot
Light: soft natural window lighting
Mood: warm, approachable
Constraints: same face markers, same outfit palette (charcoal + cream + teal)
Negative: different hairstyle, glossy doll skin, heavy makeup, different nose ring, different eye color

SHOT 3
Scene: sci-fi hangar interior
Action/pose: walking toward camera, coat moving, dynamic
Camera: 35mm lens, low angle, motion implied
Light: volumetric overhead beams, cool fill, subtle fog
Mood: determined, cinematic
Constraints: preserve freckles/scar, keep auburn bob, keep nose ring
Negative: different hairstyle, long hair, no nose ring, different facial structure, different ethnicity

That "soft natural window lighting" phrasing isn't random-people consistently report that lighting and expression words change portrait realism a lot, even when the rest of the prompt is unchanged [5]. I've noticed the same: realism is often "camera words + light words," not more adjectives.

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The workflow that keeps you sane: version your character like code

If you want consistency across a project, treat your character core like a locked file.

I keep:

A single canonical "Mara core" prompt string, and then shot directives as separate snippets. If I edit the core, I bump a version number (Mara v1.1), regenerate a baseline portrait, and accept that I've effectively forked the character.

This is boring. It's also how you stop losing hours.

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Closing thought: stop trying to "describe harder"

When character consistency fails, the instinct is to add more words. That can help, but it can also increase the surface area for drift.

Instead, make the identity small, crisp, and repeatable. Then enforce it with settings (seed/denoise/CFG) and with explicit "no changes" constraints. Your prompt becomes a spec, not a wish.

Try it with one character: write a 6-8 line character core, generate one baseline portrait you love, and then do three shots by changing only the shot directive. You'll feel the difference immediately.

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References

Documentation & Research

1. SemBind: Binding Diffusion Watermarks to Semantics Against Black-Box Forgery Attacks - arXiv cs.LG (discusses semantic invariance vs drift; prompt-conditioned views and stability) - https://arxiv.org/abs/2601.20310
2. PixelGen: Pixel Diffusion Beats Latent Diffusion with Perceptual Loss - arXiv (perceptual manifolds: global semantics + local texture; why perceptual anchors matter) - http://arxiv.org/abs/2602.02493v1
3. Dynamical Regimes of Multimodal Diffusion Models - arXiv (explains desynchronization and stabilization at different rates during generation) - http://arxiv.org/abs/2602.04780v1

Community Examples

4. The KDnuggets ComfyUI Crash Course - KDnuggets (seed/CFG/denoise explanations; practical image-to-image guidance) - https://www.kdnuggets.com/the-kdnuggets-comfyui-crash-course
5. Prompts I used to improve my ai portraits results - r/ChatGPTPromptGenius (practical portrait phrasing: lighting, expression, depth of field) - https://www.reddit.com/r/ChatGPTPromptGenius/comments/1rfervs/prompts_i_used_to_improve_my_ai_portraits_results/