Imagegenskill

When to Use

• You need scientific-looking diagrams/posters (laboratory poster aesthetic) generated from a short natural-language brief.
• The user requests SVG output specifically (e.g., “output SVG”, “vector graphic”, “embeddable in a web page”).
• You want language-to-image results without using diffusion/LLM image models, prioritizing interpretable structure over photorealism.
• You need repeatable, parameter-controlled visuals (seed/palette/structure) for research notes, slides, or documentation.
• You want a structured visualization (grids, networks, waveforms, symbol rings) rather than an illustrative drawing.

Key Features

• Converts a natural-language brief into a renderable SVG with a scientific, restrained visual style.
• Multiple built-in styles via STYLE:
  • lab-atlas (default): calm, stable, laboratory map feel
  • signal-loom: denser spectral waveforms, stronger texture
  • lattice-field: prominent lattice grids, denser nodes
• Produces SVG + JSON metadata (e.g., prompt, seed, palette) for traceability.
• Writes a convenience preview file: output/svggen/latest.svg.
• Tunable density and composition controls (e.g., nodes, noise, bands, rings).

Dependencies

• Python 3.8+

Note: No third-party Python packages are specified in the provided documentation. If scripts/svg_gen.py imports external libraries, add them here with exact versions.

Example Usage

# 1) Create the brief (UTF-8)
mkdir -p input
cat > input/brief.txt << 'EOF'
Scientific poster-style SVG: "Graph topology in latent space".
Include a calm lab-atlas aesthetic, visible grid + network + waveform layers,
and a few symbol rings. Use restrained colors, high text readability.
Keywords: latent space, manifold, spectral bands, topology.
EOF

# 2) (Optional) Edit configuration at the top of the generator script
#    - STYLE (lab-atlas | signal-loom | lattice-field)
#    - canvas width/height
#    - density parameters (node_count, noise_points, band_count, ring_density)
# Example:
# sed -i 's/^STYLE = .*/STYLE = "lab-atlas"/' scripts/svg_gen.py

# 3) Run generation
python scripts/svg_gen.py

# 4) View output
# Primary output directory:
ls -la output/svggen/
# Quick preview file:
# open output/svggen/latest.svg   (macOS)
# xdg-open output/svggen/latest.svg (Linux)
# start output/svggen/latest.svg  (Windows)

Expected outputs:

• output/svggen/latest.svg (latest render for quick preview)
• output/svggen/<name>.svg (generated SVG)
• output/svggen/<name>.json (metadata: includes prompt, seed, palette)

Implementation Details

Workflow

1. Write requirements to input/brief.txt (UTF-8).
2. Adjust the configuration section at the top of scripts/svg_gen.py (e.g., STYLE, canvas dimensions, density parameters).
3. Run python scripts/svg_gen.py.
4. Open output/svggen/latest.svg to inspect the result.

Prompt / Brief Guidelines

• Use clear research semantics: field, object, structure, atmosphere, keywords.
• English technical terms are allowed (e.g., latent space, graph topology) and should remain unchanged.
• Keep the brief concise; the script maps text into structural elements and symbols.

Composition & Quality Criteria

• Text readability: ensure key labels (e.g., prompt/mode text if present) are not obscured.
• Structural hierarchy: at least three layers should be simultaneously visible, chosen from:
  • grid
  • waveform / spectral bands
  • network / nodes
  • symbol rings
• Style consistency: avoid overly saturated colors; maintain scientific visual restraint.

Tuning / Troubleshooting Parameters

• Output too dense: decrease node_count or noise_points.
• Output too empty: increase band_count or ring_density.
• Style mismatch: switch STYLE and regenerate.

Primary Entry Point

• Generator script: scripts/svg_gen.py