Marshmallow Michelin Man Attacks New York

A cinematic recreation of the Ghostbusters final battle, replacing the Stay Puft Marshmallow Man with a skyscraper-sized Michelin Bibendum made of giant white marshmallows. The scene shows panicked New Yorkers fleeing, crushed yellow taxis, explosions and smoke, shot from a dramatic low angle with fiery sunset lighting in a practical-effects style.

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Déborah @dvorahfr ·
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Prompt

Epic recreation of the final battle scene from Ghostbusters (1984), but instead of the Stay Puft Marshmallow Man, it's a colossal Michelin Bibendum made entirely of giant white marshmallows. He is skyscraper-sized, standing in the middle of New York City streets, looking down with a big innocent smile. His soft puffy marshmallow body has the classic stacked Michelin Man shape. Panicked New Yorkers fleeing, yellow taxis crushed under his feet, explosions and smoke around him, dramatic low-angle perspective, cinematic lighting with fiery sunset, practical special effects style, ultra detailed, masterpiece
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  1. Copy the prompt using the green "Copy prompt" button above.
  2. Open your model — launch Grok Imagine on its official interface.
  3. Paste & generate — keep the verbatim prompt text for best adherence. Set aspect ratio to 16:9 and clip duration to 6s.

What makes this prompt work

Every insight below is verified against benchmark generation parameters, not asserted. How our prompt engineering benchmarks work →

CAMERA
"low-angle" is a core cinematic camera technique for establishing spatial immersion and subject framing.
LIGHTING
"cinematic lighting, golden hour" establishes luminance depth, volumetric shadow falloff, and tonal contrast across the generation.
MOOD
"epic, dramatic" — Mood and aesthetic tone are explicitly directed to maintain emotional consistency throughout the scene.
DURATION
At 6s, this clip is optimized for high temporal coherence and fluid, realistic motion physics.
MODEL
Grok Imagine (xAI) provides the underlying diffusion and motion architecture. Camera, lighting and duration cues transfer across models — what differs is how literally each model interprets motion.
PROMPT LANGUAGE
The prompt is written in English. This library keeps every prompt in its original language rather than translating it — the exact words are what was conditioned during generation.
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