Scenario Consistency

Overview

"Make variant two look exactly like variant one except for X" is the most repeated creative ask, and agents reach for seeds, which do not solve it. Consistency comes from what you feed the model, in rising order of durability: a prompt baseline, reference images, a style reference, a control map, a trained model. The same references carry a character into video. Connection and the core loop: see the scenario skill; training: see scenario-model-training; creating and filing a named character or prop library, with its sheets: see scenario-identity-library. 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

Technique	Holds	Effort	Reach for it when
Baseline-plus-delta prompt	identity, framing, palette	low	always, it is the floor
Reference image input	identity and world	low	a set of scenes or angles
Style reference input	rendering style, not a subject	low	icon sets, UI, an illustration series
asset_detect control map	pose, geometry, composition	medium	the layout must not move
Reference-to-video	identity through motion	medium	the character or product has to move
Seed reuse	one image's exact roll	low	re-rolling a single generation
Trained model	a house style, or a cast at scale	high	references stop scaling (see below)

Scenario's published pipeline guidance: generate one strong reference image, pass it as an image input to every scene generation, and prefer models with the most reference-image slots.

The baseline-plus-delta prompt

Write out everything that must not change, then put the single change in a final clause. Keep the prompt byte-identical between runs and edit only that clause. A change every item must carry (new wardrobe, a haircut) is baseline, not delta: run it once as the hero's own delta, approve the result as the new hero, then anchor the set to it, so the reference and the enumeration describe the same look.

Enumerate specifics: subject geometry, camera height and angle, subject size and position in frame, lighting direction, each named sub-element and where it sits, palette by name or hex, and embedded text. Look at the baseline first (asset_display): you cannot enumerate a shade you have not seen, and vague anchors drift.

Attach the approved baseline as a reference image alongside it. Image models converge on referenceImages, but cap, requiredness, and cardinality come from model_schema_get, and on some the field is a single scalar file. A scalar image plus strength is img2img, not a reference slot: it anchors composition along with identity, and at the default strength a plain-background anchor overrides a whole-scene delta, so a scene set needs a true reference field. Wrap an array only where the schema says array: true, even a lone asset as ["asset_..."]: a bare string is silently dropped and the run succeeds ignoring it. State each reference's role in the prompt. Approved on-model shots fill the remaining slots: curate them as a collection (catalog tools, write lane) and retrieve them with search filters={"collection_ids": [...]}. Some style-reference members mint a reusable style on the first run: the id sits in the finished job's output metadata (job_get with verbose=true), not in the run response or on the asset, and later runs pass it in the schema's id field in place of the images. The two fields are exclusive and only the image field is flagged cost_impact, so the mint is paid once: read the field text before passing the same reference set on every run. The first run must still carry the images even where the schema marks both fields optional: the member refuses a run with neither.

The anchor can be an upload: the user's character art, sketch, or product photo goes up with upload_asset plus upload_asset_complete (see scenario) and rides the reference field like any approved hero. asset_describe (see scenario-asset-analysis) returns a style synthesis (medium, palette, lighting, composition, mood) that seeds the palette and lighting lines of the enumeration; the sub-element inventory comes from looking, or from asset_analyze with the enumeration headings as its instruction (both are catalog tools; asset_describe prices with dry_run).

A set takes one model_run per item: a batch-count field repeats one prompt and cannot carry a per-item delta clause.

Locking a style without a subject

A style reference is a different slot from a subject reference, and a few image models expose it: at authoring time one family took one to ten images as styleReferenceImages, built a private style from them for a small extra charge, and returned a reusable style id in the job output that later runs pass as styleId instead of the images (the two are mutually exclusive), with styleMatch choosing precise or flexible adherence. It holds rendering, palette, and line, not a character: pair it with a subject reference or the baseline enumeration for identity. Find such members with recommend and the style need in the user's own words, then confirm the field on model_schema_get. Palette fields taking RGB triples (colors) are preferences, not constraints: keep the hex values in the prompt too.

Locking structure with a control map

Generate the map and pass it as a conditioning input. Never extract it for the attribute that is the delta: a pose map from the approved image locks the pose you were asked to change; a pose set needs its map from a target-pose image, or none.

asset_detect takes an asset_id and a modality from canny, depth, grayscale, lineart_anime, mlsd, normal, pose, scribble, segmentation, sketch (remove_background defaults true). Catalog-only and write lane, despite the docs page grouping it under Analysis: run it via scenario_tool_execute_write.

The control block (controlImage, controlModality, controlStrength, controlStart, controlEnd) exists only on models listing controlnet in capabilities: check before planning around it; models without it take reference images. controlModality allows canny, tile, depth, blur, pose, gray and low-quality: only canny, depth and pose map across, grayscale becoming gray. controlStrength defaults to 0.7 with a recommended 0.3 to 0.8 band: near 0.7 for canny, depth and tile, 0.8 to 0.9 for pose, gray and blur, rigid above 0.9. Strength is how much, controlStart and controlEnd are when: controlEnd near 0.65 locks composition early, then releases so the prompt refines detail.

Carrying identity into video

Reference-to-video members animate the subject the references show, with no first frame locking composition. The catalog has no reference-to-video capability value: these members are tagged img2video, so recommend with capability="img2video" and the consistency need in the user's own words ("the same character as in these images"), then confirm on model_schema_get that the pick carries a multi-slot referenceImages array (7 to 9 slots on the leading members at authoring time, one of them billing slots past the first four) and, to copy a motion, a referenceVideo or referenceVideos slot (capped between 3 and 15 seconds, billed per second of upload on some). When nothing ranked carries the array, search a family name from the video model-family skills scenario-video lists. Slot count moves the price less than resolution and duration: read both enums off model_schema_get, where cost_impact marks them, and prove identity on the cheapest roll that still shows the whole action, the lowest resolution at the delivery duration (a shorter clip hides late drift), pricing that payload and the delivery payload with dry_run first (one proving roll priced 80 CU against 130 at the defaults). A passing proving roll validates the recipe, not the roll: the delivery render gets its own sweep before it ships. image, the first-frame anchor, and the reference arrays are mutually exclusive on several families: a reference run opens free, and pinning the opening frame is a different member. Fill the slots from the library: the hero and two or three approved on-model shots per scenario-identity-library, as individual shots, never the turnaround sheet. With no library yet, the approved hero alone in one slot is a valid first run (the published pipeline starts from one strong reference), and the next slots fill from approved stills out of the image reference loop above, once one reads as a clean character view (neutral pose, no scene props: a scene still carries its props and pose into the slot), never from cropping the sheet. Prompt the action, not the look: some schemas address uploads by order ("Image 1 turns toward the camera, moving as in Video 1"), others state that the subjects appear with or without a prompt, and on all of them re-describing the character fights the images. Per-family contracts and prompt syntax: the video model-family skills scenario-video lists. Judge identity across the whole clip: asset_get returns firstFrame and lastFrame as free asset ids, but a drift that fades in mid-shot passes both, so sweep the frames with model_scenario-video-to-image-seq (a fixed first-party id: Scenario's single deterministic frame extractor, so discovery would only re-derive it) before the clip ships: about two frames a second (frameInterval is an integer count of source frames between extractions, the clip's frameRate from asset_get halved and rounded, 12 at 24 fps; the clip goes in video), since a spot check of one frame per shot read clean at 10.5 seconds on a clip whose defect appeared at 11.0. Judge each frame against the baseline enumeration by sub-element: a named part gained, lost, recolored or reshaped is drift; a part that moves with the action, a cloak swinging or an antenna tilting, is animation and passes.

When references stop scaling

Reference-first is the published order and the cheaper one, so exhaust it before training. Training pays back when one character recurs across hundreds of images, when four or more identities must share scenes, or when every run needs the same rendering style whatever the subject. Train one model per recurring character and a separate one for the style: a single model carrying both drifts on whichever it saw less of. Multi-character scenes composite rather than co-generate, because reference slots split attention: generate each identity alone against its baseline, then place them together with the edit workflows in scenario-image-editing. Dataset size, base choice, and the quote-before-launch rule: scenario-model-training. A trained model never runs by its own id (runs_as and run_with, see scenario), and it still takes the baseline-plus-delta prompt: training lightens the enumeration, not the delta discipline.

Sprite sheets and animation frames are the hard case: adjacent frames from a general image model drift in proportion, scale, and silhouette, which an engine exposes at once. Frames of one character come from the video lanes in scenario-sprite-animation, where one render holds the identity instead of a generation per frame.

Worked example: five poses of one mascot

1. asset_display the approved hero (asset_hero) and write its baseline: the full must-not-change enumeration above.
2. recommend with the task's own words as prompt (search only for a named family), preferring models with reference-image slots, then model_schema_get: the reference field's name, cap, cardinality, requiredness. No reference field in the schema disqualifies the candidate: go back to the ranked list, and when that holds nothing reference-capable either (recommend ranks community fine-tunes and can miss first-party models), take a family name from a sibling model-family skill (scenario-gemini-image, scenario-seedream) and search for it.
3. One model_run per pose, five in all: the byte-identical baseline, the pose alone in the final clause, the hero in the reference field shaped as the schema says: ["asset_hero"] only under array: true. No seed. No control map: a pose map from the hero locks the pose being changed.
4. jobs_wait on the five jobs, re-calling with pending_job_ids until done. asset_display each against the hero; fix drift by tightening the enumeration, not by chaining outputs.

Common mistakes

• Reaching for a seed to make two prompts match: it reproduces one generation and transfers nothing. Leave it unset across a set; set one only to re-roll a single unchanged prompt.
• Writing "same as before": there is no memory between calls; restate the baseline in full each time.
• Chaining a set output to output: drift compounds. Anchor every item to the same approved baseline.
• Training for a one-off: one character in one scene is a reference job, and a recurring cast at scale is the training case above, one model per character plus one for the style.
• Assuming asset_detect modality names are valid controlModality values.
• Passing the turnaround sheet into a reference slot, video or image: a video member animates the grid, and on an image model the sheet is a filing and review artifact, not a reference. Slots take individual approved shots.
• Re-describing the character in a reference-to-video prompt: the images carry the look; the prompt carries action, camera, and timing.