Reference Boards & Consistency Grids — Which Model for What

Nodaro’s Generate Image node ships a family of factory presets for identity consistency — keeping the same person, pet, product, or place recognizable across every generation in a project. This guide explains the two families, which model each one uses, and the head-to-head experiment behind those defaults so you can make your own trade-offs.

The two families

	Reference Sheet boards	Cast & Consistency grids
Presets	Character, Pose, Location, Product, Outfit, Scene, Creature, Vehicle, Food, Mascot, Pet Board	Character Reference Grid, Cast Mega Grid, Cast Scene
What they make	A dense, editorial production sheet: hero shot, metadata block, labeled panels (views, expressions, details, lighting), HEX color palette	A sterile, neutral-background grid of canonical angles — no text, no decoration
Made for	Humans — art direction, briefing, a project’s visual bible	Models — feeding back into later generations as an identity anchor
Default model	nano-banana-pro @ 2K, 16:9	nano-banana-2 @ 4K, 3:4

Both follow the same workflow: connect one sharp, well-lit, front-facing photo → generate → reuse the result as a reference image in every later generation (image or video) featuring that subject. For multi-character work, build one Cast Mega Grid (2–4 characters as labeled strips in a single image), then stage scenes that reference cast members by the names on the grid without re-describing them — less re-description means less identity drift.

Which model should you use?

Job	Use	Why
Any Reference Sheet board	nano-banana-pro (default)	Best identity fidelity across panels and best text rendering for the metadata block, panel labels, and HEX swatches
Identity grids to feed back as references	nano-banana-2 (default)	Nearly Pro-level identity at lower cost and higher speed — consistency work is iteration-heavy, so cost-per-attempt matters; 4K keeps panel faces sharp when reused
Layout-critical sheets where likeness is secondary	gpt-image-2	In our tests it followed multi-panel layout instructions the most completely and produced very uniform panel sizing — but the face drifts (see below)
Label/edit workflows (Edit by Name, annotations)	gpt-image-2	Strong instruction-following for overlay/labeling tasks

The experiment behind the defaults

We ran the same prompts with the same source photos head-to-head (June 2026, one generation per cell — treat as directional, not statistical):

• Test 1 — Character Board: nano-banana-pro vs gpt-image-2, both 2K / 16:9
• Test 2 — Clean Reference Grid: nano-banana-2 vs gpt-image-2, both 4K / 3:4
• Test 3 — Cast Mega Grid (2 people): nano-banana-2 vs gpt-image-2, both 4K / 3:4

What we found

1. Identity fidelity is the differentiator — the Nano Banana family wins it. Across all three tests, nano-banana-pro / nano-banana-2 reproduced the same person from the source photo (face shape, stubble pattern, freckles, fabric texture). gpt-image-2 consistently produced a convincing casting double — similar, attractive, clearly inspired by the source, but visibly not the same face. For a preset whose entire job is identity anchoring, that decides it.

2. Both render text cleanly now. No garbled labels, no misspelled headings on either model — the old “use GPT for anything with text” rule no longer holds at these tiers. nano-banana-pro went further: its color palette had named swatches with HEX values that matched the actual outfit and skin tones.

3. gpt-image-2 follows layout instructions most completely. It rendered every requested panel heading and the most uniform panel sizing; nano-banana-pro merged one panel heading into a neighbor on its run. The factory board prompts now include an explicit “render ALL panel headings, never merging or omitting a panel” clause to close that gap.

4. Resolution and speed are not equal at the same setting. At “2K”, nano-banana-pro returned 2752×1536 while gpt-image-2 returned 2048×1152 — and GPT took ~1.8× longer on the dense board (210s vs 118s). The cheaper-per-image model is not cheaper per usable pixel.

5. No identity blending on multi-character grids. Both models kept the two cast members cleanly separated in their labeled strips with correctly spelled names — the Cast Mega Grid technique is robust across providers.

Practical tips

• The source photo is the #1 quality lever. Sharp, evenly lit, front-facing, one subject. A soft or filtered photo is the most common cause of distorted or drifting faces.
• Regenerate, don’t settle. If a face drifts on a board, regenerate the board — don’t carry a drifted board forward as a reference. Two or three takes usually lands a clean one.
• Feed the board back. A board only pays off when you attach it as a reference image to later generations — including video (the Scene Recipes presets in Generate Video are built to consume these boards).
• Boards for people, grids for machines. The editorial styling on a board (dark background, neon accents, labels) is noise when a model consumes it as a reference; that’s exactly why the clean grids exist. When in doubt: show the board to your team, feed the grid to the model.

See the factory preset catalog for the full list of boards, grids, and the Scene Recipes that consume them.