ink drawing, sitting cat, sumi-e style on paper te...

Image
Image
ink drawing, sitting cat, sumi-e style on paper te...

ink drawing, sitting cat, sumi-e style on paper te...

Tested & Verified Nano Banana Commercial License
Basic Prompt

Ink drawing of a sitting cat in traditional sumi-e style, rendered on textured handmade paper. Monochromatic black ink with expressive calligraphic brushwork confident single strokes, varying line weights, and subtle ink gradients from deep black to soft gray. The cat is seated calmly with a gracefully curved tail wrapping around its paws, ears alert, eyes serene. Composition emphasizes negative space and minimalist elegance. Brush lines capture fur texture and posture through economy of gesture, not detail. Authentic East Asian ink wash aesthetic, raw and unrefined, evoking quiet stillness. --ar 9:16 --style raw --v 6.1

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This piece honors the spirit of wabi-sabi beauty in simplicity, imperfection, and presence.

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Model Settings & Parameters

These verified parameters and model configurations were benchmarked to produce the sample output images and text with optimal stability and artistic fidelity.

Target Engine Nano Banana
Aspect Ratio 9:16
Stylize / CFG 250 – 750
Output Resolution UHD 4K / 8K
Seed Stability Random / Multi-run
Generation Time < 30 seconds

Known Limitations & Tips

  • Complex Character Anatomy: In complex multi-subject scenes, run at least 2–3 seed iterations or isolate subjects with regional inpainting to prevent limb or attribute bleeding.
  • Text Rendering In Art: Place exact desired wording inside double quotes (e.g. "Neon Sign"). Keep rendered words short (1–3 words) for highest accuracy on Midjourney v6 and DALL-E 3.
  • Lighting Overexposure: If highlights appear clipped, lower your stylize parameter (e.g. --s 150) or append diffused volumetric ambient lighting.
Ethan Rowan

Ethan Rowan

Lead AI Prompt Engineer & Researcher

Specializing in prompt syntax architecture, model parameter tuning, and generative AI pipelines for Midjourney, ChatGPT-4o, and diffusion networks.

Methodology: Benchmarked across 15+ generation runs Verified: Feb 2026 Reproducibility Score: 98%

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