vulca

VULCA — AI-native cultural art creation & evaluation SDK. L1-L5 scoring, layer editing, self-evolution. 1104 tests, 21 MCP tools, 13 traditions.

VULCA

AI-native cultural art creation SDK. Generate, evaluate, decompose, and evolve visual art across 13 cultural traditions — runs locally (ComfyUI + Ollama) or in the cloud (Gemini). L1-L5 scoring, structured layer generation, self-evolving weights.

<img src="assets/demo/v3/gallery/chinese_xieyi.png" alt="Chinese Xieyi ink wash landscape" width="260"> <img src="assets/demo/v3/gallery/japanese_traditional.png" alt="Japanese traditional snow temple" width="260"> <img src="assets/demo/v3/gallery/brand_design.png" alt="Brand design tea packaging" width="260"> Three traditions, one SDK — generated locally via ComfyUI/SDXL, zero cloud API cost

$ vulca evaluate mona_lisa.jpg -t western_academic

  Score:     100%   Tradition: western_academic   Risk: low

    L1 Visual Perception         ████████████████████ 100%  ✓
    L2 Technical Execution       ████████████████████ 100%  ✓
    L3 Cultural Context          ████████████████████ 100%  ✓
    L4 Critical Interpretation   ████████████████████ 100%  ✓
    L5 Philosophical Aesthetics  ████████████████████ 100%  ✓

Based on peer-reviewed research: VULCA Framework (EMNLP 2025 Findings) and VULCA-Bench (7,410 samples, L1-L5 definitions).

Install

pip install vulca

Local stack (free, no API key)

Set up ComfyUI + Ollama for zero-cost local generation:

export VULCA_IMAGE_BASE_URL=http://localhost:8188   # ComfyUI
export VULCA_VLM_MODEL=ollama_chat/gemma4            # Ollama VLM
vulca create "Misty mountains after spring rain" -t chinese_xieyi --provider comfyui -o art.png

See Local Provider Setup for full ComfyUI + Ollama installation guide.

Cloud (Gemini)

export GOOGLE_API_KEY=your-key
vulca create "Misty mountains" -t chinese_xieyi -o art.png

No GPU? Try mock mode

vulca create "Misty mountains" -t chinese_xieyi --provider mock -o art.png

<details> <summary>Optional extras</summary>

pip install vulca[mcp]             # MCP server for Claude Code / Cursor
pip install vulca[layers-full]     # rembg + SAM2 for layer extraction
pip install vulca[tools]           # OpenCV algorithmic analysis tools
pip install vulca[all]             # everything

</details>

What You Can Do

Generate — 13 cultural traditions, structured layers

vulca create "水墨山水" -t chinese_xieyi --layered --provider comfyui

Evaluate — L1-L5 cultural scoring, three modes

vulca evaluate artwork.png -t chinese_xieyi --mode reference

Edit — layer redraw + region inpaint

vulca layers redraw ./layers/ --layer sky -i "warm golden sunset"
vulca inpaint art.png --region "the sky" --instruction "stormy clouds"

Decompose — split any image into transparent layers

vulca layers split painting.jpg -o ./layers/ --mode extract

Studio — brief-driven creative sessions

vulca studio "Cyberpunk ink wash" --provider comfyui --auto

Analyze — 5 algorithmic tools, zero API cost

vulca tools run brushstroke_analyze --image art.png -t chinese_xieyi

Create

<details> <summary>See create + evaluate workflow (GIF, 3.8 MB)</summary>  <img src="assets/demo/v2/vhs-create.gif" alt="Create and evaluate workflow" width="800"> </details>

vulca create "水墨山水，雨后春山" -t chinese_xieyi --provider comfyui -o landscape.png
vulca create "Tea packaging, Eastern aesthetics" -t brand_design --provider gemini --colors "#C87F4A,#5F8A50"
vulca create "Zen garden at dawn" -t japanese_traditional --provider comfyui --hitl

CJK-aware prompts: VULCA automatically translates CJK prompts to English for CLIP-based providers (ComfyUI/SDXL) while preserving CJK natively for multilingual providers (Gemini).

Structured Creation (`--layered`)

VULCA plans the layer structure from tradition knowledge. The first layer generates serially as a style anchor — its raw RGB output becomes the visual reference for all subsequent layers, which generate in parallel (Defense 3, v0.14).

vulca create "水墨山水，松间茅屋" -t chinese_xieyi --layered --provider comfyui
# → 5 layers: paper, distant_mountains, mountains_pines, hut_figure, calligraphy

Works across traditions — photography produces depth layers, gongbi produces line art + wash layers, brand design produces logo + background + typography layers.

Layer-Driven Design Transfer

Extract elements from one artwork, transform into a new design while preserving cultural context:

<img src="assets/demo/v2/hero-xieyi.png" alt="Original ink wash landscape" width="220"> →  <img src="assets/demo/v2/display-workflow-mountains.png" alt="Extracted mountain layer" width="220"> →  <img src="assets/demo/v2/workflow-brand-output.png" alt="Tea packaging using mountain reference" width="220"> Ink wash painting → extract mountain layer → tea packaging (92% brand consistency)

vulca layers split landscape.png -o ./layers/ --mode extract
vulca create "Premium tea packaging, mountain watermark" \
  -t brand_design --reference ./layers/distant_mountains.png --provider comfyui

Evaluate — Three Modes

Dimension	What It Measures
L1 Visual Perception	Composition, color harmony, spatial arrangement
L2 Technical Execution	Rendering quality, technique fidelity, craftsmanship
L3 Cultural Context	Tradition-specific motifs, canonical conventions
L4 Critical Interpretation	Cultural sensitivity, contextual framing
L5 Philosophical Aesthetics	Artistic depth, emotional resonance, spiritual qualities

Strict Mode (Judge)

Binary cultural scoring — does the art conform to the tradition?

$ vulca evaluate artwork.png -t chinese_xieyi

  Score:     90%    Tradition: chinese_xieyi    Risk: low

    L1 Visual Perception         ██████████████████░░ 90%  ✓
    L2 Technical Execution       █████████████████░░░ 85%  ✓
    L3 Cultural Context          ██████████████████░░ 90%  ✓
    L4 Critical Interpretation   ████████████████████ 100%  ✓
    L5 Philosophical Aesthetics  ██████████████████░░ 90%  ✓

Reference Mode (Mentor)

Cultural guidance with professional terminology — not a judge, a mentor:

$ vulca evaluate artwork.png -t chinese_xieyi --mode reference

  L1 Visual Perception         ██████████████████░░ 90%  (traditional)
     To push further: varying the density of the mist (留白) more dramatically,
     using a darker, more diffused wash to suggest deeper valleys.

  L2 Technical Execution       █████████████████░░░ 85%  (traditional)
     To push further: exploring texture strokes — axe-cut (斧劈皴)
     for sharper rocks, rain-drop (雨点皴) for rounded forms.

  L3 Cultural Context          ███████████████████░ 95%  (traditional)
     To push further: adding a poem (题画诗) for poetry-calligraphy-
     painting-seal (诗书画印) harmony.

Fusion Mode (Cross-Cultural Comparison)

Evaluate the same artwork against multiple traditions simultaneously:

$ vulca evaluate artwork.png -t chinese_xieyi,japanese_traditional,western_academic --mode fusion

  Dimension                   Chinese Xieyi Japanese Tradit Western Academi
  Visual Perception                   90%             90%             10%
  Technical Execution                 90%             90%             10%
  Cultural Context                    95%             80%              0%
  Critical Interpretation            100%            100%             10%
  Philosophical Aesthetics            90%             90%             10%
  Overall Alignment                    93%             90%              8%

  Closest tradition: chinese_xieyi (93%)

Edit + Inpaint

Layer-Based Editing

"The sky doesn't feel right, but the mountains are perfect."

<img src="assets/demo/v2/scenario1-comparison.png" alt="Before vs after: sky redrawn as sunset, mountains untouched" width="800">

Only the sky layer was redrawn — mountains, pavilion, pine trees, and calligraphy are pixel-identical. Now provider-agnostic (v0.15) — works with ComfyUI, Gemini, or any provider.

vulca layers split artwork.png -o ./layers/ --mode regenerate --provider comfyui
vulca layers lock ./layers/ --layer calligraphy_and_seals
vulca layers redraw ./layers/ --layer background_sky \
  -i "warm golden sunset with orange and purple gradients"
vulca layers composite ./layers/ -o final.png

<details> <summary>Scenario 2: Event Poster Design (Designers)</summary>  <img src="assets/demo/v2/hero-xieyi.png" alt="Source artwork" width="300"> →  <img src="assets/demo/v2/scenario2-poster.png" alt="Cultural festival poster" width="300"> Ink wash painting → event poster with typography (92% brand consistency)

vulca layers split artwork.png -o ./layers/ --mode extract
vulca layers merge ./layers/ --layers mountains,pavilion,mist --name "poster_bg"
vulca create "Cultural festival poster, modern typography overlay" \
  -t brand_design --reference ./layers/poster_bg.png

</details> <details> <summary>7 layer editing operations</summary>

Operation	What It Does
`add`	Create new transparent layer
`remove`	Delete layer (blocked if locked)
`reorder`	Move layer z-index
`toggle`	Show/hide in composite
`lock`	Prevent deletion/merge
`merge`	Combine selected layers
`duplicate`	Copy for experimentation

</details>

Region-Based Inpainting

Pixel-level preservation outside the target region. PIL local blend, not full-image regeneration. Provider parameter added in v0.15 — no longer Gemini-only.

vulca inpaint artwork.png --region "the sky in the upper portion" \
  --instruction "replace with dramatic stormy clouds" -t chinese_xieyi --provider comfyui
vulca inpaint artwork.png --region "0,0,100,40" \
  --instruction "golden sunset gradient" --count 4 --select 1 --provider gemini

Decompose

Split any image into semantically meaningful layers with real transparency.

<details> <summary>See layer decomposition in action (GIF, 3.3 MB)</summary>  <img src="assets/demo/v2/vhs-layers.gif" alt="Layer decomposition demo" width="800"> </details>  <img src="assets/demo/v2/masters/qi_baishi_shrimp.jpg" alt="Qi Baishi shrimp" height="250"> → <img src="assets/demo/v2/masters/qi_baishi_layers/ink_shrimp.png" alt="Shrimp layer" height="250"> <img src="assets/demo/v2/masters/qi_baishi_layers/ink_calligraphy.png" alt="Calligraphy layer" height="250"> <img src="assets/demo/v2/masters/qi_baishi_layers/red_seals.png" alt="Seals layer" height="250"> Qi Baishi's Shrimp → shrimp / calligraphy / seals — each on transparent canvas  <img src="assets/demo/v2/masters/mona_lisa.jpg" alt="Mona Lisa" height="220"> → <img src="assets/demo/v2/masters/mona_lisa_layers/mona_lisa_face_and_hair.png" alt="Face and hair" height="220"> <img src="assets/demo/v2/masters/mona_lisa_layers/mona_lisa_body_and_dress.png" alt="Body and dress" height="220"> Mona Lisa → face & hair / body & dress — clean semantic separation

vulca layers split qi_baishi.jpg -o ./layers/ --mode regenerate --provider comfyui
vulca layers split mona_lisa.jpg -o ./layers/ --mode extract    # free, no API
vulca layers split photo.jpg -o ./layers/ --mode sam            # SAM2 segmentation

Three split modes — all produce full-canvas RGBA with real transparency:

Mode	How It Works	Cost
extract	Color-range masking from original pixels	Free
regenerate	Redraws each layer (content + alpha mask)	~$0.05/layer
sam	SAM2 pixel-precise segmentation	Free (local)

Studio — Brief-Driven Creative Session

<details> <summary>See studio workflow (GIF, 1.6 MB)</summary>  <img src="assets/demo/v2/vhs-studio.gif" alt="Studio session" width="800"> </details>  <img src="assets/demo/v2/studio-c1.jpg" alt="Concept 1" width="170"> <img src="assets/demo/v2/studio-c2.jpg" alt="Concept 2" width="170"> <img src="assets/demo/v2/studio-c3.jpg" alt="Concept 3" width="170"> <img src="assets/demo/v2/studio-c4.jpg" alt="Concept 4" width="170"> → <img src="assets/demo/v2/studio-output.jpg" alt="Final output" width="170"> Brief: "Cyberpunk ink wash, neon pavilions" → 4 concepts → select #2 → final output (93%)

vulca studio "Cyberpunk ink wash" --provider comfyui              # interactive (local)
vulca studio "Zen garden at dawn" --provider gemini --auto        # non-interactive (cloud)
vulca brief ./project -i "Cyberpunk shanshui" -m "epic-futuristic"  # step by step

Tools — Algorithmic Analysis (No API)

5 tools that run locally with zero API cost. Feed results into VLM evaluation for hybrid scoring.

<details> <summary>See all 5 tools in action (GIF, 1.4 MB)</summary>  <img src="assets/demo/v2/vhs-tools.gif" alt="Tools demo" width="800"> </details>  <img src="assets/demo/v2/tools-viz.png" alt="Brushstroke energy, whitespace distribution, composition analysis" width="800">

$ vulca tools run brushstroke_analyze --image artwork.png -t chinese_xieyi
  Energy: 0.87 — aligns with xieyi's expressive style. Confidence: 0.90

$ vulca tools run whitespace_analyze --image artwork.png -t chinese_xieyi
  Whitespace: 32.8% — in ideal range (30%-55%). Distribution: top_heavy.

$ vulca tools run composition_analyze --image artwork.png -t chinese_xieyi
  Thirds alignment: 0.75 — asymmetric, dynamic arrangement. Confidence: 0.90

$ vulca tools run color_gamut_check --image artwork.png -t chinese_xieyi
  In-gamut: 98.2% — 1.8% pixels over-saturated. Fix mode: auto-desaturate.

$ vulca tools run color_correct --image artwork.png -t chinese_xieyi
  Suggestion: reduce saturation 5% for ink wash feel. Channel bias: R+2, G+1, B-3.

Architecture

┌──────────────────────────────────────────────────────────────┐
│                         User Intent                          │
└──────┬───────────┬──────────────┬──────────────┬─────────────┘
       │           │              │              │
  ┌────▼──┐  ┌─────▼───┐  ┌──────▼─────┐  ┌─────▼─────┐
  │  CLI  │  │  Python  │  │    MCP     │  │  ComfyUI  │
  │       │  │   SDK    │  │  21 tools  │  │  11 nodes │
  └───┬───┘  └────┬────┘  └──────┬─────┘  └─────┬─────┘
      └───────────┴───────┬──────┴───────────────┘
                          │
                 vulca.pipeline.execute()
                          │
      ┌───────────────────┼───────────────────┐
      │                   │                   │
 ┌────▼────┐       ┌──────▼─────┐      ┌──────▼──────┐
 │ DEFAULT │       │  LAYERED   │      │  CULTURAL   │
 │ Gen→Eval│       │ Plan→Layer │      │  Tools+VLM  │
 │ →Decide │       │ →Artifact  │      │  Hybrid     │
 └────┬────┘       └──────┬─────┘      └──────┬──────┘
      └───────────────────┼───────────────────┘
                          │
              ┌───────────▼───────────┐
              │    Image Providers    │
              │  ComfyUI │ Gemini    │
              │  OpenAI  │ Mock      │
              └───────────┬───────────┘
                          │
              ┌───────────▼───────────┐
              │    VLM Evaluation     │
              │  Ollama + Gemma 4    │
              │  (or cloud Gemini)   │
              └───────────┬───────────┘
                          │
              ┌───────────▼───────────┐
              │    13 Traditions      │
              │  Weights + Taboos +   │
              │  Terminology + L1-L5  │
              └───────────────────────┘

4 image providers: ComfyUI (local SDXL), Mock (no GPU), Gemini (cloud), OpenAI (cloud).

Provider	Generate	Inpaint	Layered	Multilingual
ComfyUI	✓	✓	✓	English-only
Gemini	✓	✓	✓	CJK native
OpenAI	✓	—	—	English-only
Mock	✓	✓	✓	—

All 8 E2E phases validated on local stack (ComfyUI + Ollama, Apple Silicon MPS). See MPS Compatibility Guide.

<details> <summary>Self-Evolution</summary>

The system learns from every session. Evolved weights, few-shot references, and cultural insights feed back into evaluation and generation prompts automatically.

$ vulca evolution chinese_xieyi

  Dim     Original    Evolved     Change
  L1        10.0%     10.0% +    0.0%
  L2        15.0%     20.0% +    5.0%    ← Technical Execution strengthened
  L3        25.0%     35.0% +   10.0%    ← Cultural Context most evolved
  L4        20.0%     15.0%    -5.0%
  L5        30.0%     20.0%   -10.0%
  Sessions: 71

Create / Evaluate ──► Session Store ──► LocalEvolver (per tradition)
       ▲                                        │
       └──────── Evolved Weights ◄──────────────┘

Evolution is automatic — every session contributes. strict mode strengthens tradition conformance, reference mode tracks exploration trends. Intentional departures are not penalized. Gating: minimum 5 sessions + 3 feedback sessions before weights shift.

</details>

13 Cultural Traditions

<img src="assets/demo/v3/gallery/chinese_xieyi.png" alt="Chinese Xieyi" width="150"> <img src="assets/demo/v3/gallery/japanese_traditional.png" alt="Japanese Traditional" width="150"> <img src="assets/demo/v3/gallery/western_academic.png" alt="Western Academic" width="150"> <img src="assets/demo/v3/gallery/brand_design.png" alt="Brand Design" width="150"> <img src="assets/demo/v3/gallery/ui_ux_design.png" alt="UI/UX Design" width="150"> Cultural traditions / Design disciplines / Media types — each with its own L1-L5 weights, terminology, and taboos <details> <summary>All 13 traditions</summary> <img src="assets/demo/v3/readme/tradition_grid.png" alt="13 cultural traditions" width="900"> </details>

chinese_xieyi chinese_gongbi japanese_traditional western_academic islamic_geometric watercolor african_traditional south_asian contemporary_art photography brand_design ui_ux_design default

Custom traditions via YAML — vulca evaluate painting.jpg --tradition ./my_style.yaml:

# my_style.yaml
name: pixel_art
display_name: { en: Pixel Art, zh: 像素艺术 }
weights: { L1: 0.25, L2: 0.30, L3: 0.15, L4: 0.15, L5: 0.15 }
terminology:
  - term: sprite
    definition: A small raster graphic used as a visual unit
    l_levels: [L1, L2]
taboos:
  - rule: Do not apply anti-aliasing judgments
    severity: high

Entry Points, Research + Citation

CLI

vulca create "intent" -t tradition --provider comfyui -o art.png
vulca create "intent" -t tradition --layered                  # structured layers
vulca evaluate art.png -t tradition --mode reference           # mentor mode
vulca layers split art.png -o ./layers/ --mode regenerate      # decompose
vulca layers redraw ./layers/ --layer sky -i "add sunset"      # edit
vulca layers composite ./layers/ -o final.png                  # composite
vulca inpaint art.png --region "sky" --instruction "storm"     # inpaint
vulca studio "concept" --provider comfyui --auto               # brief session
vulca tools run brushstroke_analyze --image art.png            # algorithmic
vulca evolution tradition_name                                 # check evolution

<details> <summary>Full CLI reference (all commands + flags)</summary>

# Create
vulca create "intent" -t tradition --provider mock|gemini|openai|comfyui
  --layered                    # structured layer generation
  --hitl                       # pause for human review
  --weights "L1=0.3,L2=0.2"   # custom dimension weights
  --reference ref.png          # reference image
  --colors "#hex1,#hex2"       # color palette constraint
  --residuals                  # attention residuals
  --sparse-eval                # evaluate only relevant dimensions
  -o output.png                # output path

# Evaluate
vulca evaluate image.png -t tradition
  --mode strict|reference|fusion
  --skills brand,audience,trend  # extra commercial scoring skills
  --sparse-eval                # auto-select relevant dimensions

# Layers (14 subcommands)
vulca layers analyze image.png
vulca layers split image.png -o dir --mode extract|regenerate|sam
vulca layers redraw dir --layer name -i "instruction"
vulca layers add dir --name name --content-type type
vulca layers toggle dir --layer name --visible true|false
vulca layers lock dir --layer name
vulca layers merge dir --layers a,b --name merged
vulca layers duplicate dir --layer name
vulca layers composite dir -o output.png
vulca layers export dir -o output.psd
vulca layers evaluate dir -t tradition
vulca layers regenerate dir --provider gemini

# Inpainting
vulca inpaint image.png --region "description or x,y,w,h"
  --instruction "what to change" -t tradition --count 4 --select 1

# Studio
vulca studio "concept" --provider gemini [--auto] [--max-rounds 3]
vulca brief ./project -i "intent" -m "mood"
vulca brief-update ./project "add more contrast"
vulca concept ./project --count 4 --provider gemini [--select 2]

# Utilities
vulca traditions                        # list all traditions
vulca tradition tradition_name          # detailed guide
vulca tradition --init my_style         # generate template YAML
vulca evolution tradition_name          # evolution status
vulca sync [--push-only|--pull-only]    # cloud sync

</details>

Python SDK

import vulca

# Evaluate
result = vulca.evaluate("artwork.png", tradition="chinese_xieyi")
print(result.score, result.suggestions, result.L3)

# Create
result = vulca.create("Tea packaging", provider="comfyui", tradition="brand_design")
print(result.weighted_total, result.best_image_b64[:20])

# Structured creation
result = vulca.create("水墨山水", provider="comfyui", tradition="chinese_xieyi", layered=True)

# Decompose
from vulca.layers import analyze_layers, split_extract, composite_layers
import asyncio
layers = asyncio.run(analyze_layers("artwork.png"))
results = split_extract("artwork.png", layers, output_dir="./layers")
composite_layers(results, width=1024, height=1024, output_path="composite.png")

# Self-evolution weights
weights = vulca.get_weights("chinese_xieyi")
# → {"L1": 0.10, "L2": 0.20, "L3": 0.35, "L4": 0.15, "L5": 0.20}

MCP Server (Claude Code / Cursor)

pip install vulca[mcp]
claude plugin install vulca-org/vulca-plugin    # 21 tools + 10 skills

21 tools: create_artwork, evaluate_artwork, list_traditions, get_tradition_guide, resume_artwork, get_evolution_status, studio_create_brief, studio_update_brief, studio_generate_concepts, studio_select_concept, studio_accept, inpaint_artwork, analyze_layers, layers_split, layers_redraw, layers_composite, layers_edit, layers_evaluate, layers_export, layers_regenerate, sync_data.

ComfyUI

git clone https://github.com/vulca-org/comfyui-vulca  # in custom_nodes/
pip install vulca>=0.11.0

11 nodes: Brief, Concept, Generate, Evaluate, Update, Inpaint, Layers Analyze/Composite/Export, Evolution, Traditions.

Research

VULCA builds on peer-reviewed research on culturally-aware visual understanding:

Paper	Venue	Contribution
VULCA Framework	EMNLP 2025 Findings	5-dimension evaluation framework
VULCA-Bench	arXiv	L1-L5 definitions, 7,410 samples, 9 traditions
Fire Imagery	WiNLP 2025	Cultural symbol reasoning
Art Critique	arXiv	Cross-cultural critique evaluation

Citation

@inproceedings{yu2025vulca,
  title     = {VULCA: A Framework for Culturally-Aware Visual Understanding},
  author    = {Yu, Haorui},
  booktitle = {Findings of EMNLP 2025},
  year      = {2025}
}

@article{yu2026vulcabench,
  title   = {VULCA-Bench: A Benchmark for Culturally-Aware Visual Understanding at Five Levels},
  author  = {Yu, Haorui},
  journal = {arXiv preprint arXiv:2601.07986},
  year    = {2026}
}

License

Apache 2.0

Issues and PRs welcome. Development happens in a private monorepo and is synced here via git subtree.

Repository

vulca-org

vulca-org/vulca

Created

March 16, 2026

Updated

April 13, 2026

Language

Python

vulca

VULCA

Install

Local stack (free, no API key)

Cloud (Gemini)

No GPU? Try mock mode

What You Can Do

Generate — 13 cultural traditions, structured layers

Evaluate — L1-L5 cultural scoring, three modes

Edit — layer redraw + region inpaint

Decompose — split any image into transparent layers

Studio — brief-driven creative sessions

Analyze — 5 algorithmic tools, zero API cost

Create

Structured Creation (--layered)

Layer-Driven Design Transfer

Evaluate — Three Modes

Strict Mode (Judge)

Reference Mode (Mentor)

Fusion Mode (Cross-Cultural Comparison)

Edit + Inpaint

Layer-Based Editing

Region-Based Inpainting

Decompose

Studio — Brief-Driven Creative Session

Tools — Algorithmic Analysis (No API)

Architecture

13 Cultural Traditions

Entry Points, Research + Citation

CLI

Python SDK

MCP Server (Claude Code / Cursor)

ComfyUI

Research

Citation

License

Repository

Created

Updated

Language

Category

Structured Creation (`--layered`)