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style-dataset-lab

Style Dataset Lab Handbook

Style Dataset Lab turns approved visual work into versioned, review-backed datasets, splits, export packages, and eval packs — then puts trained models to work in a full production workflow and feeds the best outputs back into the corpus. Teams define what they are making (visual canon), curate outputs against project-specific rubrics, bind accepted work to constitution rules, produce reproducible dataset packages, and run a closed production loop through brief compilation, generation, selection, and re-ingest.

The pipeline ships as an npm package (@mcptoolshop/style-dataset-lab) with the sdlab CLI, shared library modules, and domain-specific starter templates.

The pipeline

Section titled “The pipeline”
canon → generate → curate → bind → snapshot → split → export → eval
  |        |          |        |        |         |        |       |
rules   ComfyUI   judgment  rules   frozen    subject  package  verify
                                    selection isolation
         ↓
    brief → run → critique → batch → select → re-ingest
              ↑                                    |
              └────────────────────────────────────┘

Each stage writes structured JSON records to projects/<name>/records/. All commands accept --project <name> (defaults to star-freight). Records accumulate provenance, judgment, and canon binding over time. Nothing is lost – a curated record still carries its original generation provenance.

The four dataset artifacts

Section titled “The four dataset artifacts”

The dataset spine produces four artifacts. These are the product.

Artifact What it is Command
Snapshot Frozen, fingerprinted selection of eligible records with explicit reason traces sdlab snapshot create
Split Subject-isolated, lane-balanced train/val/test partition (seeded PRNG, zero leakage) sdlab split build
Export package Self-contained dataset: manifest, metadata.jsonl, images, splits, dataset card, checksums sdlab export build
Eval pack Canon-aware test instruments: lane coverage, forbidden drift, anchor/gold, subject continuity sdlab eval-pack build

sdlab defines and owns the dataset. Downstream format conversion (TRL, LLaVA, Parquet) is handled by repo-dataset.

The training triangle

Section titled “The training triangle”

Every record in the dataset carries three things:

  1. Provenance – generated by ComfyUI with full history (checkpoint, LoRA, seed, sampler, cfg scale, resolution, timing).
  2. Canon binding – which constitution rules this asset passes, fails, or partially meets, with rationale strings.
  3. Quality judgment – approved, rejected, or borderline, with per-dimension scores and cited failure modes.

This triple – provenance, canon, judgment – is what makes the dataset useful beyond simple image labeling. A model trained on images alone learns to generate. A model trained on images with grounded judgment learns to evaluate.

Domain templates

Section titled “Domain templates”

The pipeline is domain-agnostic. Five starter templates ship with real production worldviews:

Domain Focus Example lanes
game-art Game concept art with faction systems character, environment, prop, ship, interior
character-design Character production for film/animation/games portrait, full_body, turnaround, expression_sheet
creature-design Creature lineup and species development concept, orthographic, detail_study, action, habitat
architecture Architectural worldbuilding and pre-viz exterior, interior, streetscape, ruin, landscape
vehicle-mech Vehicle and mech design exterior, cockpit, component, schematic, damage_variant

Each template includes constitution rules, lane definitions, scoring rubrics, and group vocabulary designed for that production context.

Project model

Section titled “Project model”

Each project lives in projects/<name>/ with 5 JSON config files alongside its data:

  • project.json – identity, domain, generation defaults
  • constitution.json – rules array with rationale templates
  • lanes.json – subject lanes with regex detection patterns
  • rubric.json – scoring dimensions, thresholds, failure-to-rule mappings
  • terminology.json – group vocabulary with detection order

Use sdlab project doctor to validate any project’s config completeness.

Example: Star Freight

Section titled “Example: Star Freight”

The repo includes projects/star-freight/ as a complete working example – a gritty sci-fi RPG with 5 factions, 1,182 curated records, 28 prompt waves, 7 lanes, and 24 constitution rules. Clone the repo to explore it.

Next steps

Section titled “Next steps”