Nano Banana 2 Multi-Character Scenes: Prompts That Actually Keep Everyone Looking Like Themselves

Multi-character scenes are where most AI image generators fall apart. You ask for two friends at a café, and by frame three one of them has swapped faces, gained a new haircut, and appears to be a completely different person. Nano Banana 2 — Google’s Gemini 3.1 Flash Image generator — ships with subject consistency for up to five characters, which means you can actually build a cast and keep them across multiple images without watching them slowly morph into strangers. That’s the promise. This tutorial is about making that promise hold up.

Whether you’re building a graphic novel, a social media series, a product campaign with recurring models, or just trying to generate a consistent scene without spending forty minutes rerolling — the prompts and workflow below will get you there. Available in the Gemini app, AI Studio, the Gemini API, and Vertex AI, Nano Banana 2 gives you several entry points depending on whether you want a quick visual or programmatic control over every parameter.

The short version: subject consistency in Nano Banana 2 works through careful prompt architecture, not magic. The model needs clear, distinct, anchored descriptions for every character. Here’s exactly how to write them.

Why Multi-Character Prompts Fail (And Why Nano Banana 2 Is Different)

The root problem with multi-character generation in most models is identity bleed — the model averages out physical features across characters, especially when they share demographic similarities. Two women with dark hair? The model starts borrowing features between them. Three men in suits? By scene two, they’ve effectively merged into a composite.

Nano Banana 2’s subject consistency system treats each character as a distinct anchor point. The key is that you have to give the model enough differentiation data to maintain those anchors. Vague descriptions collapse. Specific, contrasting descriptions hold. The prompts below are built around this principle: every character gets a unique visual fingerprint — not just a name.

Setting Up Your Character Roster

Before writing a single scene prompt, build what you can think of as a character card for each person in your cast. This card becomes the consistent block of descriptive text you paste into every prompt. The model doesn’t remember previous generations between sessions, so the card does the memory work for you.

A character card needs four elements: a distinct name tag (used as an anchor in the prompt), physical markers that won’t change (height relative to others, face shape, eye color), a signature style element (a specific jacket, a hairstyle that’s genuinely distinctive), and a contrast note against the other characters. That last element is the one most people skip — and it’s the one that prevents identity bleed.

Pro tip ✅

Build your character cards in a text file before you open Nano Banana 2. Copy-paste the card block into every scene prompt. The thirty seconds this saves per generation adds up fast across a multi-scene project.

The Core Multi-Character Prompt Structure

The structure that works most reliably in Nano Banana 2 for two-to-five character scenes follows this pattern: scene setup → character one description → character two description → (repeat per character) → interaction/action → lighting and camera → style directive. Don’t bury characters mid-sentence. Give each one their own clause or sentence. The model parses characters better when they’re syntactically isolated.

Here’s a two-character portrait that demonstrates the structure:

Two women standing in a Tokyo convenience store at night, fluorescent lighting. MAYA: tall, angular jawline, short platinum pixie cut, wearing an oversized olive military jacket, narrow brown eyes. KEIKO: shorter by a head, round face, long black hair in a low ponytail, wearing a red turtleneck, light freckles across her nose. Maya is reaching for a shelf item, Keiko is looking at her phone. Editorial photography style, 4K, shallow depth of field, cool tones.

The name tags in caps (MAYA, KEIKO) function as anchors. The height differential and the contrasting hair descriptions give the model clear separation criteria. The action clause keeps them spatially distinct within the frame.

Three men at a rooftop bar during golden hour. DAVID: 6'2", broad-shouldered, shaved head, full dark beard, wearing a navy linen blazer, no tie. CARLO: 5'9", lean, curly auburn hair to his collar, clean-shaven, wearing a white shirt with rolled sleeves, thin silver chain visible. THEO: 5'11", glasses with thick black frames, cropped natural hair, wearing a burgundy bomber jacket, hands wrapped around a glass. David is laughing, Carlo is leaning against the railing, Theo is watching the skyline. 4K, warm cinematic lighting, medium shot, slight film grain.

Notice the height specifics — they give the model a stacking order it can use to arrange figures without guessing. The individual objects (silver chain, specific jackets, glasses) act as persistent visual markers that resist blending between characters.

Scene Variants: Keeping Identity Across Multiple Frames

The real test of subject consistency is whether your characters look like themselves across three or four different scenes. The workflow here is to keep the character card blocks identical between prompts and change only the scene setup, action, and camera variables. Any edit to a character’s core description — even a small one — risks drift.

Here’s how you’d take the rooftop trio above into a second scene:

Three men inside a dimly lit jazz bar, late evening. DAVID: 6'2", broad-shouldered, shaved head, full dark beard, wearing a navy linen blazer, no tie. CARLO: 5'9", lean, curly auburn hair to his collar, clean-shaven, wearing a white shirt with rolled sleeves, thin silver chain visible. THEO: 5'11", glasses with thick black frames, cropped natural hair, wearing a burgundy bomber jacket, hands wrapped around a glass. David is sitting at the bar, Carlo is talking to a bartender, Theo is reading a menu. 4K, warm amber lighting, intimate atmosphere, bokeh background, cinematic color grade.

The character blocks are word-for-word identical to the rooftop version. Only the venue, lighting, and actions changed. This is the discipline that keeps a multi-scene project coherent.

Pro tip ✅

If you’re running a campaign or narrative project with recurring characters in Nano Banana 2, generate a “reference sheet” first — all five characters in a single neutral frame (white background, facing forward, good lighting). Save that prompt. Use it to verify your character cards are producing consistent results before committing to scene work.

Five-Character Scenes: The Crowded Frame Problem

Five characters in one image is where composition starts doing as much work as description. The model needs to place five distinct people without overlapping features or pushing anyone to an indistinct background blur. The prompt has to handle spatial arrangement explicitly.

Five colleagues in a modern open-plan office, midday. Front left: SARA, short and compact, natural curly red hair, freckled, wearing yellow-framed glasses and a teal blouse. Front right: JAMES, tall and lanky, light brown undercut, wearing a grey hoodie, small stud earring in left ear. Center background: PRIYA, medium height, long straight black hair, wearing a crisp white button-down, reading a document. Back left: MARCUS, heavyset, shaved sides with a fade, thick dark eyebrows, wearing a black polo shirt. Back right: YUKI, petite, straight dark hair with blunt fringe, wearing a mustard cardigan, holding a coffee cup. Natural window light from the left, wide-angle shot, 4K, editorial style, warm neutral palette.

The spatial labels (front left, center background, back right) are doing critical composition work here. Without them, the model makes arbitrary placement decisions that can push characters together or stack similar-looking people adjacent to each other — the fast lane to identity bleed.

Warning ⚠️

Avoid giving two characters the same hair color in a five-person scene unless you’re compensating with very strong contrasting markers elsewhere (height differential, distinct clothing colors, different hair lengths). The model will treat them as visually similar anchors and drift them toward each other over repeated generations.

Social Media and Product Formats

Multi-character consistency matters most in formats where you’re generating a series — a brand campaign, a story arc, a recurring content format. Here’s a prompt built for a vertical social media format with two characters in a lifestyle product context:

Two friends at a farmers market on a sunny morning, vertical 9:16 format. ELENA: tall, olive skin, dark wavy hair to her shoulders, wearing white linen wide-leg trousers and a blue striped crop top, large woven tote bag. NINA: shorter, pale skin, blonde hair in a messy bun, wearing a floral midi dress in peach and green, small leather crossbody bag. Both are holding reusable coffee cups and examining produce at a stall. Bright natural sunlight, lifestyle photography, warm tones, shallow depth of field. 4K.

The 9:16 format instruction sends a clear signal about cropping and composition. Combined with the action (examining produce at a stall), it keeps both characters active and present in the frame rather than one drifting into background filler.

Same two women at a brunch table outdoors, bright afternoon light, vertical 9:16 format. ELENA: tall, olive skin, dark wavy hair to her shoulders, wearing white linen wide-leg trousers and a blue striped crop top, large woven tote bag on the chair beside her. NINA: shorter, pale skin, blonde hair in a messy bun, wearing a floral midi dress in peach and green, small leather crossbody bag on the table. They are laughing, glasses of orange juice on the table. Lifestyle photography, warm tones, golden hour light. 4K.

Note 💡

Nano Banana 2 images carry SynthID watermarks by default. If you’re using output in a professional context, check the platform’s export settings — AI Studio and Vertex AI give you more granular control over output handling than the consumer Gemini app.

Editorial and Narrative Prompts

For editorial photography-style scenes or graphic novel panels, the prompt needs a stronger style directive and often benefits from specifying a specific focal character:

Editorial fashion photograph, urban alley, overcast light. Primary focus: ALEX, non-binary, tall and angular, defined cheekbones, bleached eyebrows, wearing a structured black coat with exaggerated shoulders and wide-leg leather trousers, combat boots. Secondary figure out of focus in background: RIVER, shorter, braided dark hair, wearing a khaki jacket, leaning against a wall looking away. Vogue editorial style, muted desaturated palette with deep shadows, 4K, high-contrast black and white conversion with subtle brown tone.

Labeling one character as “primary focus” and one as “secondary figure out of focus” gives the model explicit permission to treat them differently — one sharp and detailed, one atmospheric. This actually helps consistency because the secondary character doesn’t need to carry full detail, reducing the rendering load that causes feature drift.

Pro tip ✅

When working with Nano Banana 2 through the Gemini API or AI Studio, you can run character card generation and scene generation as separate structured calls and batch your scenes programmatically. This is especially useful for content teams generating multiple campaign variants — you write the character cards once and loop the scene variables.

Avoid 🚫

Don’t use generic descriptors like “beautiful woman” or “handsome man” as core character identifiers. They tell the model nothing distinctive and actively contribute to characters looking like the same generic face in different outfits. Specific beats generic every time: “wide-set green eyes, strong brow, prominent nose” is a face. “Beautiful” is a placeholder.

Accessing Nano Banana 2: Where to Go

The Gemini app gives you the fastest access — you’re generating images within seconds of opening it, with no setup. AI Studio sits one step up in terms of control, letting you adjust parameters and see what the API is actually receiving. For production work or programmatic generation, the Gemini API and Vertex AI are the right tools — they let you build character card templates, batch scene variants, and handle output programmatically. The right entry point depends entirely on whether you’re exploring or building.

Pro tip ✅

AI Studio is genuinely useful as a testing environment before you commit to API calls. Run your multi-character prompt there, check character consistency, then take the working prompt to the API. It saves you debugging abstract generation errors in code.

The Prompt That Ties It All Together

Multi-character consistency in Nano Banana 2 comes down to three disciplines: give every character a distinct visual fingerprint with no shared ambiguous features, keep your character card blocks identical across scenes, and use spatial language to manage composition in crowded frames. The model has the subject consistency architecture to support a five-character cast across a full project — your prompts just need to give it enough separation data to use it properly.

Build your character cards before you start generating. Keep them in a text file. Paste them into every scene. Change only what changes between scenes. That workflow — unglamorous as it sounds — is the difference between a coherent visual series and a set of images where everyone slowly becomes the same person.

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