JSON Prompting for Image Generation

A Practical Guide for Sora and Gemini Nano-Banana

Most users still prompt image models using plain English paragraphs. This approach is fast, but fundamentally weak. Plain text prompts suffer from ambiguity, inconsistency, and a lack of modularity. If your goal is control, reproducibility, and character continuity, plain text is insufficient.

This is where JSON prompting becomes relevant.

1. What a JSON Prompt Is

A JSON prompt is not magic syntax. It is simply a structured instruction object that forces the model to reason in components. Instead of saying “A realistic portrait of a woman in cinematic lighting”, you explicitly define who the subject is, what traits are stable, and how the image should be rendered.

JSON = constraint + hierarchy + intent clarity

Why JSON Works on Sora and Nano-Banana

Both models share three behaviors that make this effective:

They parse structured fields more deterministically.
They preserve identity descriptors better when repeated.
They weight stable attributes higher when grouped logically.

2. From Image to JSON: Reverse-Engineering Characters

When you upload an image, your objective is not realism; it is identity compression. You want a JSON prompt that reproduces the same character while allowing pose and scene changes.

Core Identity vs Variable Attributes

You must separate attributes into two categories. If you mix these layers, the model will drift.

Core Identity (Never Changes):

Gender expression, Age range, Ethnicity
Facial structure (jawline, nose, eyes)
Skin texture, Hair type, Body proportions

Variable Attributes (Allowed to Change):

Clothing, Pose, Environment
Expression, Camera angle, Lighting style

Example: Character Seed JSON
{
  “meta”: {
    “prompt_type”: “character_seed”,
    “source”: “image_reference”
  },
  “identity”: {
    “gender_expression”: “female”,
    “age_range”: “late 20s”,
    “ethnicity”: “southern european”,
    “face_structure”: {
      “jawline”: “soft angular”,
      “nose”: “straight, narrow bridge”,
      “eyes”: “almond shaped, medium spacing”
    },
    “hair”: {
      “color”: “dark brown”,
      “texture”: “slightly wavy”,
      “length”: “shoulder length”
    }
  },
  “consistency_rules”: {
    “identity_lock”: true,
    “face_morphing”: “disabled”
  }
}
        

This JSON should not describe beauty or mood. Those come later.

3. Building Production-Grade JSON Prompts

Once you have a locked character seed, you can build scene prompts on top. The scene layer references the identity without redefining it.

Example: Scene Render JSON
{
  “meta”: { “prompt_type”: “scene_render” },
  “scene”: {
    “environment”: “empty urban street at dawn”,
    “weather”: “light fog”
  },
  “action”: {
    “pose”: “walking forward”,
    “expression”: “neutral, introspective”
  },
  “camera”: {
    “framing”: “medium shot”,
    “angle”: “eye level”
  },
  “lighting”: {
    “type”: “soft natural light”,
    “contrast”: “low”
  },
  “negative_constraints”: {
    “beauty_filters”: “disabled”,
    “skin_smoothing”: “forbidden”
  }
}
        

            Critical Note: Notice what is NOT present in the scene JSON: No face description. No age. No ethnicity. That is intentional to prevent conflicting instructions.
        

4. Identity Persistence & Drift Control

Identity persistence means the model must reuse the same latent identity vector across different scenes without facial or age drift.

The Identity Anchor Pattern: Every serious JSON prompt should contain an identity anchor, explicitly declared and never modified.

“identity_anchor”: {
  “id”: “character_001_v1”,
  “lock_priority”: “absolute”,
  “mutation_allowed”: false
}
        

Drift Prevention Rules

You must explicitly encode what cannot change to avoid morphological or temporal drift.

“drift_control”: {
  “facial_structure”: “locked”,
  “age_progression”: “disabled”,
  “symmetry_enhancement”: “forbidden”
}
        

5. Multi-Scene Continuity

When generating sequences, you should tell the model that time is progressing but identity is not.

“narrative_context”: {
  “sequence_type”: “continuous”,
  “time_progression”: “linear”,
  “character_identity”: “persistent”,
  “physical_changes”: “none”
}
        

6. Prompt Hygiene

Never Rewrite Identity Fields: If you change wording, the model interprets it as a new instruction. “Straight nose” vs “narrow straight nose” is not the same. Pick one. Freeze it. Reuse it.

Versioning Discipline: Version identity like software (e.g., character_001_v1). If you casually tweak descriptors, you lose control.

Explore the JSON Library

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