Reference

Style Dataset Lab v2.1 ships the sdlab CLI, 12 shared library modules, and pipeline scripts. All commands accept --project <name> to target a project under projects/. The default is star-freight. The deprecated --game flag still works with a warning.

CLI Commands

sdlab init

Scaffold a new project from a domain template.

sdlab init <project-name> [--domain <domain>]
sdlab init my-project --domain character-design
sdlab init                                       # list available domains

sdlab project doctor

Validate project config completeness and correctness.

sdlab project doctor --project <name>

sdlab generate

Drive ComfyUI to produce candidate images from a prompt pack.

sdlab generate <prompt-pack-path> --project <name> [--dry-run]

Flag	Default	Description
--game <name>	star-freight	Target game directory under games/
--dry-run	—	Print what would be generated without calling ComfyUI
--subject <name>	all	Only generate for one subject
--seeds <n>	3	Number of random seeds per subject-variation pair

Prompt pack format (inputs/prompts/*.json):

{
  "defaults": {
    "checkpoint": "dreamshaperXL_v21TurboDPMSDE.safetensors",
    "loras": [{ "name": "classipeintxl_v21.safetensors", "weight": 1.0 }],
    "steps": 8, "cfg": 2.0,
    "sampler": "dpmpp_sde", "scheduler": "karras",
    "width": 1024, "height": 1024,
    "negative_prompt": "photorealistic, photograph, 3d render..."
  },
  "subjects": [
    {
      "id": "compact_officer",
      "prompt": "concept art of a Compact military officer...",
      "variations": [
        { "suffix": "bridge", "prompt_append": "on the bridge..." }
      ]
    }
  ]
}

Outputs: Images to games/<name>/outputs/candidates/, records to games/<name>/records/.

Environment variables:

Variable	Default	Description
COMFY_URL	http://127.0.0.1:8188	ComfyUI server URL

---

curate.js

Move a candidate to approved/rejected/borderline and record the judgment.

node scripts/curate.js --game <name> <asset_id> <status> <explanation> [options]
node scripts/curate.js --game <name> --list

Argument	Required	Description
asset_id	yes	Record ID (filename without extension)
status	yes	approved, rejected, or borderline
explanation	yes	Free-text rationale for the judgment

Flag	Default	Description
--game <name>	star-freight	Target game directory under games/
--list	—	Show uncurated candidates (no other args needed)
--scores <k:v,...>	—	Per-dimension scores, e.g. silhouette:0.9,palette:0.8
--failures <f1,f2>	—	Named failure modes, e.g. too_clean,wrong_material
--notes <text>	—	Improvement notes for borderline or rejected images

Behavior: Updates the record’s judgment block, moves the image file from outputs/candidates/ to the status directory within the game folder. The record is written before the file move to prevent orphaned images.

---

compare.js

Record a pairwise A-vs-B style comparison.

node scripts/compare.js --game <name> <asset_a_id> <asset_b_id> <winner> <reasoning>

Argument	Required	Description
asset_a_id	yes	Record ID of the first image
asset_b_id	yes	Record ID of the second image
winner	yes	a, b, or tie
reasoning	yes	Why the winner is better

Flag	Default	Description
--game <name>	star-freight	Target game directory under games/
--scores <k:v/v,...>	—	Per-dimension comparison, e.g. silhouette:0.9/0.6

Outputs: Comparison record to games/<name>/comparisons/. Used by repo-dataset to produce preference training pairs.

---

canon-bind.js

Populate canon assertions in all records based on judgment scores and failure modes.

node scripts/canon-bind.js --game <name> [options]

Flag	Default	Description
--game <name>	star-freight	Target game directory under games/
--dry-run	—	Preview bindings without writing to records
--stats	—	Print coverage statistics

Behavior: For each curated record, maps judgment scores and failure modes to constitution rules (e.g., RND-001, MAT-002) and writes canon.assertions with pass/fail/partial verdicts. Each assertion includes the rule ID, category, description, and a one-line rationale derived from the scores.

---

painterly.js

Post-process images through an img2img painterly pass via ComfyUI.

node scripts/painterly.js --game <name> [options]

Flag	Default	Description
--game <name>	star-freight	Target game directory under games/
--source <dir>	outputs/approved	Source directory for images (relative to game dir)
--limit <n>	all	Maximum number of images to process
--offset <n>	0	Skip the first n images
--dry-run	—	Preview without processing

Defaults:

Parameter	Value
Denoise	0.50
Steps	10
CFG	2.5
Seed	42 (fixed for reproducibility)

Outputs: Processed images to games/<name>/outputs/painterly/.

---

generate-identity.js

Generate named-subject identity images with lineage tracking.

node scripts/generate-identity.js --game <name> <identity-packet-path> [options]

Flag	Default	Description
--game <name>	star-freight	Target game directory under games/
--dry-run	—	Preview without generating
--subject <name>	all	Only generate for one subject
--seeds <n>	3	Discovery seeds per shot
--phase <mode>	discovery	Generation phase: discovery or follow_on
--anchor <path>	—	Anchor source image (required for follow_on)
--denoise <n>	0.38	Denoise strength for follow_on phase

Phases:

1. Discovery — txt2img from prompt, multiple seeds. No prior image input.
2. Follow-on — img2img from anchor image. Requires --anchor and --denoise. Anchor curation happens between phases (manual step).

Identity packet format (inputs/identity-packets/*.json): Defines subjects with identity locks, shot intents, and lineage metadata per canon/identity-gates.md.

Record extensions: Adds identity block (subject name, faction, role, view type, shot type) and lineage block (generation phase, anchor references, persistence scores).

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Dataset Commands

sdlab snapshot

Create, list, show, and diff frozen dataset snapshots.

sdlab snapshot create [--profile <name>] [--project <name>]
sdlab snapshot list [--project <name>]
sdlab snapshot show <snapshot-id> [--project <name>]
sdlab snapshot diff <id-a> <id-b> [--project <name>]

sdlab eligibility

Audit training eligibility for all project records.

sdlab eligibility audit [--profile <name>] [--project <name>]

sdlab split

Build, list, show, and audit dataset splits.

sdlab split build [--snapshot <id>] [--profile <name>] [--project <name>]
sdlab split list [--project <name>]
sdlab split show <split-id> [--project <name>]
sdlab split audit <split-id> [--project <name>]

sdlab card

Generate dataset cards (markdown + JSON twin).

sdlab card generate [--snapshot <id>] [--split <id>] [--project <name>]

sdlab export

Build and list versioned export packages.

sdlab export build [--snapshot <id>] [--split <id>] [--profile <name>] [--copy] [--project <name>]
sdlab export list [--project <name>]

Use --copy to copy images instead of symlinking them.

sdlab eval-pack

Build and inspect canon-aware eval packs.

sdlab eval-pack build [--project <name>]
sdlab eval-pack list [--project <name>]
sdlab eval-pack show <eval-id> [--project <name>]

See Dataset Workflow for the full end-to-end walkthrough.

---

Training Commands

sdlab training-profile

List and inspect training profiles.

sdlab training-profile list [--project <name>]
sdlab training-profile show <profile-id> [--project <name>]

sdlab training-manifest

Create, validate, and inspect frozen training contracts.

sdlab training-manifest create --export <id> --profile <id> [--adapter <target>] [--base-model <name>] [--project <name>]
sdlab training-manifest validate <manifest-id> [--project <name>]
sdlab training-manifest show <manifest-id> [--project <name>]
sdlab training-manifest list [--project <name>]

sdlab training-package

Build trainer-ready packages from manifests.

sdlab training-package build --manifest <id> [--adapter <target>] [--copy] [--project <name>]
sdlab training-package show <package-id> [--project <name>]
sdlab training-package list [--project <name>]

Available adapters: generic-image-caption, diffusers-lora.

sdlab eval-run

Score generated outputs against eval packs.

sdlab eval-run create --manifest <id> --eval-pack <id> [--project <name>]
sdlab eval-run score <eval-run-id> --outputs <path> [--project <name>]
sdlab eval-run show <eval-run-id> [--project <name>]
sdlab eval-run list [--project <name>]

sdlab implementation-pack

Build implementation example packs.

sdlab implementation-pack build --manifest <id> [--project <name>]
sdlab implementation-pack show <impl-id> [--project <name>]
sdlab implementation-pack list [--project <name>]

sdlab reingest

Re-ingest generated outputs as new project records.

sdlab reingest generated --source <dir> --manifest <id> [--dry-run] [--project <name>]
sdlab reingest audit [--project <name>]

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Export (via repo-dataset)

Export is handled by the separate @mcptoolshop/repo-dataset CLI. Point it at the specific game directory:

# Generate training data
repo-dataset visual generate ./games/star-freight --format trl --output games/star-freight/exports

# With embedded images (base64 in JSONL)
repo-dataset visual generate ./games/star-freight --format trl --embed

# Inspect scanner results
repo-dataset visual inspect ./games/star-freight

# Validate output
repo-dataset visual validate games/star-freight/exports/dataset.jsonl

Supported formats: TRL, LLaVA, Qwen2-VL, Axolotl, LLaMA-Factory, ShareGPT, OpenAI, DPO, ORPO, KTO.

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Additional scripts

generate-controlnet.js

Generate candidates using ControlNet (pose/depth-guided generation).

node scripts/generate-controlnet.js --game <name> <prompt-pack-path> [options]

generate-ipadapter.js

Generate candidates using IP-Adapter (reference-image-driven generation).

node scripts/generate-ipadapter.js --game <name> <prompt-pack-path> [options]

bulk-curate-wave2-5.js / bulk-curate-waves11-18.js

Batch curation scripts for processing multiple assets from specific waves.

curate-wave25.js

Specialized curation for wave 25 (alien species) with species-specific scoring.

migrate-records.js

Migrate records from older schema versions to the current format.

node scripts/migrate-records.js --game <name> [--dry-run]

painterly-test.js

Test the painterly pipeline on a single image before running a full batch.