Using the uploaded reference photo, create a stand...

Image
Image
Using the uploaded reference photo, create a stand...

Using the uploaded reference photo, create a stand...

Tested & Verified Nano Banana Commercial License
Basic Prompt

A poorly framed, low-effort iPhone selfie taken at night with flash: a young woman sits on a dark sofa in complete darkness, illuminated only by the harsh, direct phone flash. The photo is slightly overexposed her face washed out, shadows clipped with subtle motion blur from shaky hands. The angle is awkward (phone held too high, tilted left), cutting off the top of her head and showing too much ceiling. She wears casual clothes (e.g., hoodie, jeans) and simple accessories (stud earrings, no makeup). No intentional composition: background is a messy living room (blurred TV, laundry pile). Skin texture is realistic but flattened by flash; eyes slightly red-eyed. Overall mood: boring, forgettable, like a pocket-dial photo.

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This isn’t a photo. It’s a mistake you didn’t delete.

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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 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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