organvm-iv-taxis/generative-art-deployment

Generative Art Deployment

Move generative artworks from development to exhibition, print, and archive.

Deployment Targets

| Target | Format | Resolution | Considerations | |--------|--------|-----------|----------------| | Web gallery | HTML/JS, WebGL | Screen (72-96 DPI) | Performance, loading time | | Physical print | PNG/TIFF | 300 DPI minimum | Color profiles, bleed | | LED installation | Video/WebGL | Panel-specific | Brightness, refresh rate | | NFT/on-chain | PNG/SVG/HTML | Variable | File size, determinism | | Social media | PNG/MP4 | Platform-specific | Compression, aspect ratio | | Archive | Source + renders | Maximum | Reproducibility |

Web Gallery Deployment

Static Gallery Structure

gallery/
├── index.html          # Gallery grid/navigation
├── works/
│   ├── piece-001/
│   │   ├── index.html  # Full-screen viewer
│   │   ├── sketch.js   # Live generative code
│   │   ├── thumbnail.png
│   │   └── metadata.json
│   └── piece-002/
│       └── ...
├── assets/
│   ├── style.css
│   └── gallery.js
└── catalog.json        # Machine-readable catalog

Work Metadata

{
  "title": "Recursive Bloom #47",
  "artist": "Artist Name",
  "date": "2026-03-20",
  "medium": "Generative, p5.js",
  "dimensions": "3840 × 2160",
  "seed": 1742518400,
  "parameters": {
    "complexity": 0.7,
    "palette": "autumn",
    "iterations": 5000
  },
  "description": "An exploration of recursive growth patterns...",
  "series": "Recursive Bloom",
  "edition": "1/1",
  "tags": ["recursion", "organic", "growth"]
}

Performance Optimization

// Render once, display static
function setup() {
  const canvas = createCanvas(3840, 2160);
  noLoop(); // Don't animate
}

function draw() {
  randomSeed(SEED);
  // ... generate artwork
  saveCanvas('output', 'png');
}

// For interactive pieces: use requestAnimationFrame
// with quality degradation on low-end devices
function draw() {
  if (frameRate() < 30) {
    reduceComplexity();
  }
}

Print Production

Resolution Pipeline

def render_for_print(sketch_path: str, width_inches: float, height_inches: float, dpi: int = 300):
    pixel_width = int(width_inches * dpi)
    pixel_height = int(height_inches * dpi)

    # Add bleed (0.125 inches on each side)
    bleed = int(0.125 * dpi)
    total_width = pixel_width + 2 * bleed
    total_height = pixel_height + 2 * bleed

    return {
        "canvas_width": total_width,
        "canvas_height": total_height,
        "safe_area": {
            "x": bleed, "y": bleed,
            "width": pixel_width, "height": pixel_height,
        },
        "dpi": dpi,
        "format": "TIFF",  # Lossless for print
        "color_profile": "sRGB",  # Or Adobe RGB for wide gamut
    }

Color Management

from PIL import Image, ImageCms

def convert_for_print(input_path: str, output_path: str):
    img = Image.open(input_path)
    srgb_profile = ImageCms.createProfile("sRGB")
    # For fine art printing, embed the ICC profile
    img.save(output_path, "TIFF", dpi=(300, 300), icc_profile=ImageCms.ImageCmsProfile(srgb_profile).tobytes())

Print Sizes

| Size | Inches | Pixels (300 DPI) | |------|--------|-------------------| | A4 | 8.3 × 11.7 | 2490 × 3510 | | A3 | 11.7 × 16.5 | 3510 × 4950 | | A2 | 16.5 × 23.4 | 4950 × 7020 | | 24×36 poster | 24 × 36 | 7200 × 10800 |

Rendering Pipeline

Batch Rendering

import subprocess
from pathlib import Path

def batch_render(sketch: str, seeds: list[int], output_dir: str, width: int, height: int):
    Path(output_dir).mkdir(parents=True, exist_ok=True)
    for seed in seeds:
        output = f"{output_dir}/render_{seed:08d}.png"
        subprocess.run([
            "node", sketch,
            "--seed", str(seed),
            "--width", str(width),
            "--height", str(height),
            "--output", output,
        ], check=True)

# Render a series
batch_render("sketch.js", seeds=range(1, 101), output_dir="renders/series-01", width=3840, height=2160)

Deterministic Rendering

// Ensure reproducibility: same seed = same output
function setup() {
  const seed = parseInt(getURLParam('seed') || '42');
  randomSeed(seed);
  noiseSeed(seed);
  // Record seed in metadata
  document.title = `Piece #${seed}`;
}

Exhibition Patterns

Installation Checklist

• [ ] Hardware specs confirmed (GPU, resolution, orientation)
• [ ] Autostart on boot configured
• [ ] Crash recovery (watchdog/supervisor process)
• [ ] No UI chrome (cursor hidden, fullscreen)
• [ ] Network not required (all assets local)
• [ ] Power failure recovery tested

Kiosk Mode Setup

# Linux kiosk mode
#!/usr/bin/env bash
xset -dpms          # Disable power management
xset s off           # Disable screen saver
unclutter -idle 0.5 & # Hide cursor

chromium-browser \
  --kiosk \
  --disable-infobars \
  --disable-session-crashed-bubble \
  --noerrdialogs \
  file:///home/gallery/piece/index.html

Archival Strategy

Archive Package

archive/
├── README.md           # How to run this piece
├── source/             # Original source code
│   ├── sketch.js
│   └── package.json
├── renders/            # High-res rendered outputs
│   ├── render_001.tiff
│   └── render_001.png
├── metadata.json       # Full metadata including parameters
├── dependencies/       # Vendored dependencies
│   └── p5.min.js
└── documentation/
    ├── process.md      # Artist statement, process notes
    └── screenshots/    # Exhibition documentation

Reproducibility Contract

{
  "runtime": "node 20.x + p5.js 1.9.x",
  "seed": 42,
  "canvas": "3840x2160",
  "parameters": {},
  "checksum": "sha256:abc123...",
  "rendered": "2026-03-20T10:00:00Z"
}

Anti-Patterns

• No seed recorded — Every generative piece must have a reproducible seed
• Web-only renders — Always produce high-res static renders for print/archive
• Missing metadata — Parameters, dimensions, and creation date are essential
• No offline fallback — Exhibition pieces must work without network
• Lossy-only archives — Keep lossless TIFF/PNG alongside compressed versions
• Undocumented dependencies — Vendor or lockfile all runtime dependencies