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scenario-reve

scenario-reve

github.com/scenario-labsCreative & media1.2k397 installsUpdated 1d ago
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About scenario-reve

A Scenario skill for working with the Reve image models. It explains how to choose between Reve v2.1 and Reve Remix and configure each model’s distinct inputs for image generation, editing, and compositing.

Key features

  1. 01Generate and edit images with Reve v2.1
  2. 02Composite multiple images with Reve Remix
  3. 03Configure references and aspect ratios per model
  4. 04Address v2.1 references with numbered tags
  5. 05Estimate, run, and inspect image jobs

Use cases

  1. 01Generate images from prompts or edit existing ones
  2. 02Combine photos, illustrations, or design assets
  3. 03Replace poster text while preserving its visual style

MCP support

The skill uses Scenario MCP tools to discover models, retrieve their schemas, upload reference assets, and run image jobs.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/scenario-labs/skills --skill scenario-reve

After installing, type/scenario-revein Claude Code to confirm it loaded.

Details

From the project README

Scenario Reve Image

Overview

Reve, Reve AI's image family on Scenario, splits into two members that share a brand and little else: v2.1 is a reasoning-driven generator and instruction editor (a top-2 text-to-image arena entry at authoring time), Remix a fast, cheap compositor that blends several images into one. Discover them with search and treat model_schema_get as the contract: they disagree on field names, prompt caps, and ratio lists.

Connection and the core loop: see the scenario skill in this repo; model-agnostic image work: the scenario-image skill. 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

At authoring time (the live schema wins):

MemberCapabilitiesReferencesPrompt capAspect ratios
Reve v2.1txt2img, img2imgreferences, optional400018 values, 4:1 through 1:4, default auto
Reve Remiximg2imgreferenceImages, required, 1 to 625607 values, 16:9 through 9:16, default 3:2

Three parameters each: the prompt, the reference array, aspectRatio. Neither member has a mask, seed, width and height, negative prompt, or batch-count field, so an edit is scoped by wording and size comes only as a ratio. auto (v2.1 only) picks a fitting shape; pass an explicit ratio when the deliverable demands one.

Pick by job. v2.1 covers generation from a prompt alone and precise edits: swap the text on a poster, recolor a product, change a material, relight or restyle, merge references while every subject stays consistent. Remix blends photos, illustrations, or design assets into one cohesive composite. Where either would do, dry_run both: at authoring time a v2.1 run cost several times a Remix run and took over a minute at p50 against Remix's twenty-odd seconds.

Frame tags wire v2.1 references

In a v2.1 prompt, a reference is addressed with a <frame>N</frame> tag, numbered from 0 in the order the assets appear in references: the first is <frame>0</frame>, the second <frame>1</frame>. Use the tags whenever more than one reference is in play; without them the model guesses which image you mean. Remix documents no tag syntax: name subjects in plain words ("the bottle from the first image, the lighting from the second").

Edits are instructions, not masks

Neither member takes a mask, so the prompt carries the whole edit: state what changes and what must stay untouched. v2.1's reasoning rewards specific plain-language instructions over keyword lists, and its 4000-character budget leaves room for constraints. For masked inpainting on an exact region, use a model that exposes a mask field (see scenario-image).

Worked example: swapping the text on a poster

  1. search with target="models", query="reve", public=true. At authoring time the hits were model_reve-v2-1 (generation and instruction edits) and model_reve-remix (multi-image blends); re-discover each session.
  2. model_schema_get on the pick: field names, caps, and the ratio list before anything else.
  3. upload_asset the poster (see the scenario skill) to get an asset id.
  4. model_run with that model_id, dry_run=true, and parameters={"prompt": "Replace the headline on the poster in <frame>0</frame> with 'SUMMER SALE', matching the original font, perspective, and lighting. Change nothing else.", "references": ["asset_x"], "aspectRatio": "auto"} 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 check the swap, then asset_download to save.

Common mistakes

  • Counting frames from 1: <frame>1</frame> is the second reference; the first is <frame>0</frame>.
  • Carrying a field name across members: v2.1 takes references, Remix takes referenceImages; each schema lists only its own.
  • Running Remix without references: referenceImages is required (1 to 6 at authoring time); prompt-only generation belongs to v2.1.
  • Reusing a v2.1 prompt on Remix without checking length: 4000 characters overflow its 2560 cap.
  • Sending width, height, a mask, or a seed: none exists on either member; aspectRatio is the only size control.
  • Treating a v2.1 jobs_wait timeout as an error: p50 latency topped a minute at authoring time; re-call with pending_job_ids.

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