ImageDNA
Your visual style, structured for AI.
Upload a reference image to see the structured visual data AI needs to reproduce your brand’s style. Camera, lighting, grading, mood, composition, subject treatment extracted automatically and ready to deploy across every tool in your stack.
AI doesn't see images the way you do.
When you upload a reference image to Midjourney's --sref or Firefly's style reference, the model pattern-matches against what it sees. It infers warmth, depth of field, mood. But inference is not instruction. The model is approximating your style from a visual impression rather than executing against defined parameters.
That's why style references drift. The first generation looks right. The fiftieth doesn't. Each model interprets the same reference slightly differently, and those differences compound across sessions, tools, and team members.
The fix isn't a better reference image. It's extracting the rules behind the image structured, explicit, and queryable, so every tool receives the same instructions every time.
What Image DNA extracts
Upload any reference image. The AI analyzes it and returns six structured fields:
Camera : Lens range, depth of field character, technical approach. “85mm, shallow DoF, mirrorless” tells a generator exactly how to frame and compress the shot.
Lighting : Source, quality, color temperature. “Natural, warm 5500K, soft directional” produces far more consistent results than “good lighting.”
Grading : Color treatment, shadow, and highlight handling, palette character. The difference between “muted earth tones, lifted shadows” and a flat, oversaturated output.
Mood : Emotional register and visual personality. “Candid, documentary-adjacent, never posed” gives the model a behavioural constraint, not just a feeling.
Composition : Framing approach, negative space, crop logic. “Off-center, generous negative space, environmental context” is instruction. “Balanced” is not.
Subject Treatment : How subjects are presented: candid vs posed, isolated vs environmental, eye contact or none.
Mulitple outputs from one extraction
Image DNA produces three ready-to-deploy outputs:
LLM prompt : A 3–5 sentence creative direction paragraph for Claude, GPT, and Gemini. Written the way a photographer briefs a team.
Midjourney / Flux prompt : A terse, comma-separated cinematic prompt ending with
--ar 3:4 --stylize 200 --q 2. Copy directly into the prompt box.Negative prompt : 8–12 visual attributes your style should never include. Paste directly into any generator. Often more impactful than the positive prompt.
Putting Image DNA to work
Generating images in Midjourney or Flux: Copy the Midjourney prompt directly into the prompt box. Add the negative prompt. Consistent style, one paste.
Generating with context-aware tools (Claude, GPT, Firefly): Paste the LLM creative direction paragraph as system context before generating.
Building a queryable brand system: Add your Image DNA JSON to your brand system's MCP endpoint. Every tool in your stack queries the same visual rules automatically, always current.
Briefing agencies or freelancers: Share the LLM prompt as a creative direction brief. Structured, specific, and immediately actionable.
Documenting visual identity: Use the extracted fields as the foundation of your imagery guidelines. Structured data alongside your reference images, not just mood boards.
This is one image. Your brand has more.
Image DNA extracts the visual rules from a single reference. A complete brand imagery system covers multiple content types: portraits, environments, product-in-context, campaign imagery. Each can have its own DNA block.
Every block outputs structured JSON that lives in your brand system alongside your color semantics, voice rules, and design tokens. Together they form a complete, queryable brand system, one source of truth that every AI tool in your stack can access.