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scenario-model-training

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Use when generated assets must keep a consistent style, character, or product look and references stop scaling, or when a user asks to train a custom model through the Scenario MCP, fine-tune a LoRA, clone a voice, curate a training dataset, choose a base model, set epochs and sample prompts, estimate training cost, diagnose a trained model that lost its identity or has one epoch, or generate with a trained model. Keywords: custom model, LoRA, dataset curation, epoch previews.

skills/scenario-model-training/SKILL.md

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# Scenario Model Training

## Overview

Train a custom model when one look must hold across many assets: an icon set, a recurring character,
a product line. Prompts, references, and control maps are cheaper first steps: see
`scenario-consistency`. Before quoting a training run, run the reference test `scenario-consistency`
teaches for the subject at hand: the approved art as style references (with the prompt saying they
set the style only) for a look, the hero as a subject reference for a character or product. At
authoring time a style test matched the look of a custom LoRA on the same brief at a comparable
per-image price, for the cost of one generation. Train when that test drifts across the set, not
before; the worked example below starts after it.

The judgment calls live in two references:
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