man wearing jeans, two-tone striped polo (navy and...

Image
Image
man wearing jeans, two-tone striped polo (navy and...

man wearing jeans, two-tone striped polo (navy and...

Tested & Verified FLUX Commercial License
Basic Prompt

Rough wax crayon illustration of a man in jeans and two-tone navy/cream striped polo, navy cap, mid-swing hitting tennis ball with racket toward a white long-haired Labrador. Dynamic awkward motion pose, distorted proportions, flat shapes with no volume. Dog simplified to rough outline with exaggerated lunge toward ball. Minimal tennis court: only 3–4 loose chalk lines on ground. Dry pastel texture, grainy broken strokes, scribbled color application with visible gaps and uneven fill. Loose expressive black sketch lines, limited palette (navy, cream, dirty green, dull yellow, black). Intentionally crude naive style like a rushed 10-second sketch on textured paper, no refinement, no fur detail, no realistic anatomy, messy action lines, raw hand-drawn quality.

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This isn't a mistake. It's a rebellion against perfection.

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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 FLUX
Aspect Ratio 16:9 / 1:1
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: Mar 2026 Reproducibility Score: 98%

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