Mache dies für {argument name="structure name"default="Todesstern"}: <instruction>
1. Die Semantische Inferenz-Engine (Das Meisterwerk):
Eingabe A ist ein berühmtes Gebäude, eine Stadt oder eine fiktive Struktur (z. B. das Kolosseum, Hogwarts, der Todesstern).
Analysieren Sie das Thema, um die Modellästhetik zu bestimmen:
Das Material: Bestimmen Sie das primäre Modellierungsmedium.
(z. B. antike Ruinen -> Gips/Ton. Moderne Stadt -> weiße Schaumstoffplatte/Acryl. Fantasy -> Holz/Moos).
Die Baupläne: Identifizieren Sie die Referenzdokumente auf dem Tisch.
(z. B. alte Pergamentkarten vs. blaue Cyanotypien vs. digitale CAD-Ausdrucke).
2. Die Werkstatt:
Ziel: „Kinoreifes Handwerker“-Porträt.
Der Raum: Ein unordentliches Modellbauer-Atelier mit hohen Decken. Holzböden säumen die Wände, gefüllt mit „früheren Projekten“ (anderen Modellen desselben Genres).
Der Charakter: Ein von der Eingabe inspirierter Handwerker-Charakter, der eine passende Schürze und ein geeignetes Visier oder Schutzwerkzeug trägt.
Der Tisch: Eine vernarbte Holzwerkbank, bedeckt mit Modellbauwerkzeugen: X-Acto-Messer, Klebstoffflaschen, Schleifklötze und Sägemehl.
3. Die Erzählung:
Die Pose: Der Handwerker beugt sich nah über das „Modell“ (Eingabe A), das zentral auf dem Tisch steht.
Die Interaktion: Er platziert filigran ein winziges Detailteil mit einer Pinzette oder seinen Fingern auf der Struktur.
(z. B. einen Miniaturbaum pflanzen, eine winzige Flagge platzieren oder eine mikroskopische Figur kleben).
Der Maßstab: Das Modell wirkt großartig, aber winzig; die menschlichen Hände wirken im Vergleich massiv und verwittert.
4. Beleuchtung & Atmosphäre:
Beleuchtung: Volumetrische Lichtstrahlen (Tyndall-Effekt). Ein starker, einzelner warmer Sonnenstrahl fällt von einem hohen Fenster (oben links/Mitte) herein, beleuchtet die schwebenden Staubpartikel und setzt das Modell ins Rampenlicht.
Kontrast: Die Ecken der Werkstatt liegen im tiefen Schatten; die Arbeitsfläche ist hell.
Palette: Warme, staubige Erdtöne (Braun, Gold, Beige).
Ausgabe: EIN Bild, 16:9 Seitenverhältnis, Kinematische Fotografie, „National Geographic“-Ästhetik, Volumetrische Beleuchtung.
</instruction>
Produktmarketing - Kinoreifes Künstlerporträt-Prompt für den Modellbau-Workshop
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Dieser detaillierte Prompt ist darauf ausgelegt, ein filmisches Porträt eines Handwerkers im Stil von National Geographic zu erstellen, der an einem Miniaturmodell einer berühmten Struktur, wie dem Todesstern, arbeitet. Er spezifiziert die Ästhetik, Materialien, Beleuchtung und Komposition, um ein äußerst atmosphärisches Bild einer unaufgeräumten Werkstatt zu erzeugen.
A complex, structured JSON prompt for generating a hyper-realistic medium format portrait of a subject (Dakota Johnson likeness implied) enveloped in swirling saffron yellow chiffon fabric within a cluttered artisan textile dye workshop. The prompt focuses heavily on texture contrast, volumetric lighting, and surreal fabric physics, specifying Hasselblad camera details and Kodak Portra film simulation.
A highly detailed JSON prompt for GPT Image 1.5 and Google Nano Banana Pro to generate an ultra-cinematic, hyper-realistic commercial render of a luxury artisan soap bar. The prompt specifies the product details (ivory base, gold swirls, engraved crest), composition (floating bar, suspended water droplets), lighting (soft spa-inspired golden glow), and extreme macro-level detail and material physics.
A structured prompt template for generating an artisan's method board, designed to visually document the creation process of a handmade object. It divides the visual into three sections: raw materials, the making process (with hands and tools visible), and the finished object in context, emphasizing a transition in visual style from earth tones to warm workshop light and natural light.
A highly detailed product photography prompt for generating an image of a fictional artisan toy, the 'Kál-Húf Lantern.' It specifies materials (dark Nordic spruce, bioluminescent moss), complex PBR layering, macro lens camera settings, and a dual-lighting setup (harsh flash and internal glow) to create a fusion of Frutiger Aero and Dreamcore aesthetics.
A detailed, multi-image prompt for generating ultra-realistic food photography of artisan chocolate products in a luxury editorial style. It specifies composition, lighting (dramatic side lighting, rim light), ingredients (hazelnut, raspberry, passionfruit), and background details for four different chocolate items.
A highly complex, multi-image prompt structure for generating ultra-realistic food photography of artisan chocolates. It details four distinct compositions (praline tower, ruby chocolate tablet, white chocolate bonbons, dark chocolate bar), specifying fillings, lighting, textures, and background for a luxury editorial aesthetic.
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Data collected and processed: IP addresses, Device identifiers, Probabilistic identifiers, Browsing and interaction data, Non-precise location data, Users’ profiles, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Probabilistic identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Probabilistic identifiers, Browsing and interaction data, Non-precise location data
Data collected and processed: IP addresses, Device characteristics, Probabilistic identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Authentication-derived identifiers, Browsing and interaction data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Authentication-derived identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, User-provided data, Non-precise location data, Precise location data
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Authentication-derived identifiers, Browsing and interaction data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, Users’ profiles, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, User-provided data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, Users’ profiles, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
Data collected and processed: IP addresses, Device identifiers, Probabilistic identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
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