Digipop Coloring Book Creation

Source Strategy

Search broadly across free/open illustration sources. Be creative:

• Stock illustration sites (Unsplash, Pexels, Pixabay — illustration filters)
• Open clipart repositories (OpenClipart, SVG Repo, Wikimedia Commons)
• Creative Commons illustration archives
• Public domain art collections
• DeviantArt (CC-licensed), Behance (reference only)
• Pinterest (reference/discovery only — do not scrape)
• Theme-specific fan art communities (with licensing check)

Future: Source list may be locked to proven reliable sites after iteration.

Candidate Scoring

Rate each image 1-10 on these axes:

CandidateScore = (compat * 3 + flair * 2 + clarity * 2 + lines * 2 + print * 1) / 10

Threshold: Accept candidates scoring ≥ 6.0. Target 20+ candidates so ~10 survive through QA.

Output

Save to {bundle_dir}/research/:

• candidates.json — array of {url, source, license, scores, total_score, notes}
• thumbnails/ — downloaded preview of each candidate
• research-report.md — summary with top picks and rationale

Phase 2: Vectorization (Tracing Agent)

Goal: Convert accepted raster candidates to clean SVG.

fal.ai Vectorization Endpoints (pick best per image)

Process

1. Download full-res source image
2. Run through vectorization endpoint
3. Save SVG output to {bundle_dir}/vectorized/
4. Save {filename}.meta.json with: source_url, endpoint_used, parameters, timestamp
5. Visual diff check: does the SVG faithfully represent the source?

Selection Heuristic

• Source is already SVG/vector → skip this phase, use directly
• Source has clean solid colors → recraft/vectorize
• Source is complex illustration → star-vector
• Source is simple graphic → image2svg

Phase 3: Line Art Extraction

Goal: Extract clean black outlines from vectorized illustrations.

Primary Tool: fal.ai Line Art Preprocessor

Endpoint: fal-ai/image-preprocessors/lineart

This extracts line art from any image — feed it the vectorized SVG rendered as PNG, or the original raster if vectorization wasn't needed.

Alternative: Local Processing

If fal.ai lineart doesn't produce clean enough output, fall back to local processing with Python/Pillow:

# Edge detection + threshold approach
from PIL import Image, ImageFilter
img = Image.open(path).convert('L')
edges = img.filter(ImageFilter.FIND_EDGES)
bw = edges.point(lambda x: 0 if x > threshold else 255)

Output Spec

• Pure black lines on white background
• Target stroke weight: visible at 8.5x11 print (minimum ~2px at 300dpi)
• No gray, no gradients, no fills
• Save as PNG (3300x2550 for landscape, 2550x3300 for portrait)
• Save to {bundle_dir}/lineart/

Phase 4: Cleanup

Goal: Production-ready coloring pages. Should look amazing/perfect.

Cleanup Steps

1. Binarize: Strict black/white threshold (no gray pixels)
2. Stroke normalization: Ensure consistent line weight across the page
3. Artifact removal: Remove stray dots, broken lines, noise
4. Border cleanup: Clean margins, no edge artifacts
5. Resolution normalize: Scale to exactly 3300x2550 (landscape) or 2550x3300 (portrait) at 300dpi
6. Format: Save as PNG + companion SVG if available

QA Metrics (automated pre-check before Phase 5)

Save to {bundle_dir}/cleaned/

Phase 5: QA (Independent Agent)

Goal: Every page independently inspected by a visual model agent.

Critical: The QA agent must be a SEPARATE agent/session from the one that produced the art. Fresh eyes, no confirmation bias.

QA Checklist (visual inspection via Gemini/GLM)

1. Is this a good coloring page? Would a person enjoy coloring this?
2. Line quality: Are lines clean, consistent, unbroken?
3. Complexity balance: Not too sparse (boring), not too dense (frustrating)?
4. Subject recognizability: Can you tell what the image depicts?
5. Colorable regions: Are there clear, bounded regions to color in?
6. Print artifacts: Any visual glitches, moiré, or rendering errors?
7. Age appropriateness: Suitable for target audience?

Scoring

Each criterion scored 1-5. Page passes if:

• No criterion scores below 3
• Average score ≥ 3.5
• "Is this a good coloring page?" scores ≥ 4

Output

• {bundle_dir}/qa/qa-report.json — per-page scores and notes
• {bundle_dir}/qa/qa-summary.md — human-readable summary
• Pages that fail are moved to {bundle_dir}/qa/rejected/ with rejection reason

Phase 6: Upload to Gallery

Goal: Upload passing pages to Piwigo for Carrie's review.

Upload Target

• Gallery: https://media.delo.sh
• Album: "Dumply's Daily Dump" (ID: 1) or create sub-album per bundle
• API: Piwigo pwg.images.addSimple via ws.php

Upload Requirements

• Use User-Agent: Dumply/1.0 and Referer: https://media.delo.sh/
• Authenticate via pwg.session.login (creds from env: PIWIGO_USER, PIWIGO_PASSWORD)
• Upload helper: ~/workspace-dumpling/bin/piwigo-upload
• Or direct API calls per references/piwigo-api.md

Post-Upload

• Set album description with bundle name, page count, theme
• Notify Cack via sessions_send that bundle is ready for Carrie review
• Pipeline pauses here — contingent on human-in-the-loop approval from Carrie

Directory Structure

Each bundle lives under ~/workspace-dumpling/assets/{bundle-name}/:

{bundle-name}/
├── research/
│   ├── candidates.json
│   ├── thumbnails/
│   └── research-report.md
├── vectorized/
│   ├── {page}.svg
│   └── {page}.meta.json
├── lineart/
│   └── {page}.png
├── cleaned/
│   ├── {page}.png
│   └── {page}.meta.json
├── qa/
│   ├── qa-report.json
│   ├── qa-summary.md
│   └── rejected/
└── bundle-manifest.json

bundle-manifest.json

{
  "name": "K-pop Demon Hunters",
  "theme": "K-pop fantasy coloring",
  "page_count": 10,
  "target_format": "8.5x11 PDF bundle",
  "created": "2026-02-21T15:00:00-05:00",
  "status": "pending_review",
  "phases_completed": ["research", "vectorize", "lineart", "cleanup", "qa", "upload"],
  "gallery_url": "https://media.delo.sh/index.php?/category/1",
  "plane_ticket": "DIGI-1"
}

Plane Integration

Every bundle MUST have a Plane ticket in the DIGI project (workspace: lasertoast). Create the ticket before starting Phase 1. Update status as phases complete.

Agent Delegation Guide