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经典跑车侧剖信息图:letterpress 语义切面公式

2×2 / 16:9,把车身拆成机械词/材质词/气动词贴在真实结构位;`$ car_scene` 变量可换四台冷门经典跑车。汽车科普海报别只会摆一张侧拍——先让术语长在骨架上。

经典跑车侧剖信息图:letterpress 语义切面公式 — 1

Prompt

2x2 grid, 16:9, do this for 4 unusual but real classic cars: Create one image of a world famous classic sports car in side cutaway view.  Variable: $ car_scene = A technical automotive cutaway where the entire vehicle is reconstructed from mechanical terminology, material vocabulary, aerodynamic language, and performance descriptors placed in true structural position.  Function Draw($ car_scene){ <composition> Single centered vehicle, side profile with slight cutaway exposure. No background clutter, precise negative space, poster-like authority. The silhouette must be iconic and readable at first glance. </composition>  <semantic_inference> Infer chassis, frame, suspension, drivetrain, engine block, pistons, valves, intake, exhaust, wheel hub, steering column, brake assembly, upholstery, windshield, body panel. Use aerodynamic and driving terms where relevant: torque, grip, drag, speed, acceleration, cornering. </semantic_inference>  <letterpress_spec> MEDIUM: Precision lead type on smooth archival vellum. INK: Graphite black, gunmetal gray, soft steel undertones. IMPRESSION: Deep controlled embossing with strongest bite in chassis and engine, lighter in glass and airflow zones. STYLE: Mid-century automotive blueprint transformed into letterpress. </letterpress_spec>  <transformation> Chassis and engine use crankshaft, piston, axle, block, manifold, gearbox, differential in heavy industrial slab-serif. Body panels and interior use fender, hood, leather, dash, seat, trim, coupe in refined serif. Glass, airflow, and motion zones use windshield, glare, slipstream, drag, velocity in airy technical sans-serif. Wheel and suspension regions use hub, spoke, caliper, spring, traction, alignment in circular micro-type. The result should feel engineered, fast, and luxurious. </transformation>  Output: one image }

Attribution

Author
Gdgtify
Published
2026-09-25
Original post
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Collected from
xianyu110/awesome-gpt-image2.5 · 2103281580085035495 · MIT