Scenario Gemini Image

Overview

Gemini, Google's image family on Scenario (the Nano Banana line), generates and edits through one required prompt: edits are instructions against the references, never mask painting. Discover members with search and treat model_schema_get as the contract: the creative fields are shared and nearly everything else is per member.

Connection and the core loop: see the scenario skill in this repo; model-agnostic image work: the scenario-image skill. Gemini video belongs to the scenario-gemini-omni skill; Gemini speech models are the audio domain. If a sibling skill named here is missing from your available skills, ask the user to install it (npx skills add scenario-labs/skills --skill <name>); unattended, proceed from tool schemas and flag the gap.

Quick reference

Three members at authoring time (per-member facts move, so read the schema):

Member	Resolution	Sets it apart
3.1 Flash	512 to 4K, default 1K	video input, four thinkingLevel steps, Search grounding
3.0 Pro	1K to 4K, default 2K	built for complex instruction edits and multi-image fusion
3.1 Lite	fixed 1K, no resolution	fastest and cheapest; thinkingLevel is MINIMAL or HIGH

Shared fields: referenceImages (up to 14, an array even for one), numOutputs (1 to 4 variations of one prompt), aspectRatio (21:9 through 9:16 plus the default auto; pin the ratio when a placement demands one), and a prompt cap near 250000 characters, so a full brief fits verbatim. Flash alone takes video (one clip, about 15 MB, sampled at videoFps, default 1 fps) to pull stills from footage; video and referenceImages are mutually exclusive. useGoogleSearch (Flash and Pro) grounds the run in live web context and moves the price, like every field marked cost_impact. No mask, seed, or negative-prompt field exists: regional edits are sentences, and reruns give variations, not reproductions.

Write instructions, not tags

Describe subject, setting, and style in plain sentences. For edits, state the change and what must survive it: "keep the shoe's shape, colors, and branding unchanged; place it on a sunlit deck". With several references, assign roles by position ("image 1 is the character's face, image 2 the outfit, image 3 the background style"); unassigned references blur together. Lock identity explicitly: say the features must be preserved while pose, lighting, or scene changes. Quote in-image copy exactly and say where each block sits ("the title reads 'LUMEN', top third"); unquoted wording is treated as a theme to depict, not copy to render. When one word keeps mangling, do not rerun the design: pass the keeper in referenceImages and spell the word letter by letter ("the title reads 'LUMEN', spelled L, U, M, E, N"), changing nothing else. On Flash and Lite, thinkingLevel defaults to HIGH, the careful setting; drop to MINIMAL for speed on simple runs.

Price the member before the batch

The cost cliff sits between members more than between resolutions: at authoring time the same 1K edit cost about double on Flash what Lite charged, Pro's 2K default roughly doubled it again, and Lite returned in under half the time. dry_run=true the same parameters on two members before any batch, and re-estimate whenever resolution, numOutputs, references, or useGoogleSearch change.

Worked example: product restyle with locked identity

1. search with target="models", query="gemini", public=true. Video and speech members surface too; pick an image member, e.g. model_google-gemini-3-1-flash (a live hit at authoring time: re-discover each session).
2. model_schema_get with that id: fields, caps, and defaults.
3. upload_asset the product photo and the style reference (see the scenario skill) to get asset ids.
4. model_run with that model_id, dry_run=true, and parameters={"prompt": "Image 1 is the product: keep its shape, label, and colors exactly. Image 2 sets the mood: warm sunset palette, soft shadows. Place the product on a marble counter in natural window light.", "referenceImages": ["asset_a", "asset_b"], "aspectRatio": "4:5", "resolution": "2K", "numOutputs": 2} for the cost estimate.
5. Repeat model_run with wait=false, then jobs_wait with the returned job id, re-called with pending_job_ids on timeout, never a second model_run.
6. asset_display to review both variations, asset_download to save the pick.

Common mistakes

• Passing video and referenceImages together on Flash: they are mutually exclusive.
• Reaching for seed or mask: neither exists on any member; describe the edit and batch numOutputs to pick from.
• Carrying fields across members: resolution fails on Lite, thinkingLevel on Pro, video everywhere but Flash.
• Keyword-tag prompts: comma lists underperform; write the sentence you would give a designer.
• Several references with no roles: outputs blend them; say which image is which by position.
• Enabling useGoogleSearch for stylistic work: it raises cost and earns it only on factual, real-world subjects.