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: references/base-model-selection.md (the user interview that feeds recommend_training) and references/dataset-curation.md (dataset size, image rules, captions, and review per training type).