Google · Photo

Nano Banana 2.1 Text-to-Image

Images à partir de texte4K

Google's Nano Banana 2.1 is the latest Nano Banana text-to-image model, with gains in visual design, subject consistency and prompt adherence, 1K to 4K output and optional web search grounding.

dès0,08 €par rendu
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Exemples

Nano Banana 2.1 Text-to-Image: Candid portrait of an elderly fisherman mending a net on a wooden dock at golden hour, weathered hands and fac
Prompt

Candid portrait of an elderly fisherman mending a net on a wooden dock at golden hour, weathered hands and face, warm side light, shallow depth of field, 85mm photo, realistic skin texture

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Nano Banana 2.1 Text-to-Image: A mid-century modern travel poster for a fictional seaside town. Bold flat shapes: turquoise sea, a white ligh
Prompt

A mid-century modern travel poster for a fictional seaside town. Bold flat shapes: turquoise sea, a white lighthouse, orange sun, two sailboats. Large headline at the top reading "SOLMARE", smaller line at the bottom reading "Summer Sea Festival · July 12-20". Clean grid layout, subtle paper grain, limited warm palette.

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Nano Banana 2.1 Text-to-Image: Street photo of a woman with a clear umbrella walking down a narrow alley at dusk in the rain, warm glowing pa
Prompt

Street photo of a woman with a clear umbrella walking down a narrow alley at dusk in the rain, warm glowing paper lanterns, reflections on the wet pavement, 35mm film look, no readable signs

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Nano Banana 2.1 Text-to-Image: Overhead photo of a rustic breakfast table: sourdough toast with smashed avocado and poached eggs, a cup of bl
Prompt

Overhead photo of a rustic breakfast table: sourdough toast with smashed avocado and poached eggs, a cup of black coffee, fresh berries, linen napkin, soft morning window light, food photography

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Caractéristiques

Typetext-to-image
SortiePhoto
EntréeTexte
ID du modèlegoogle/nano-banana-2.1/text-to-image

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Les prix s'entendent par rendu et ne sont prélevés sur votre solde que si le rendu aboutit. Les rendus échoués sont remboursés automatiquement. Les exemples montrent ce dont le modèle est capable ; votre résultat dépend de votre prompt et de vos réglages.