Student Hangout Room Transformation

Student Hangout Room Transformation

Document original

GPT Image 2 · en

Using REFERENCE_0 as the room base, create a natural realistic photo as if this room has become a casual hangout for {argument name="number of students" default="three"} {argument name="student type" default="20-year-old female college students"}. Keep the room layout, camera angle, furniture, curtains, bed, table, storage drawers, hanging clothes, lighting, and lived-in atmosphere from the uploaded room photo unchanged as much as possible.

Add exactly 3 people: one sitting on the left edge of the bed holding a canned drink, one sitting cross-legged on the bed near the center holding a smartphone, and one in the foreground on the right sitting near the table/bed edge holding a canned drink and turned toward the others. Dress them in coordinated {argument name="outfit style" default="Japanese school-uniform-inspired white shirts with dark plaid skirts and ribbon ties"}. Make them look relaxed, chatting, and comfortable in the room; vary their hairstyles and poses naturally.

Add casual hangout clutter on the table: exactly 7 visible snack/drink items — 2 canned drinks, 1 large open snack bag, 1 cup noodle container, 1 plate or small dish, 1 colorful snack package, and 1 small boxed item — while preserving existing objects such as the remote, blue box, and earbuds where plausible. Keep the result photorealistic, unposed, and like a spontaneous phone snapshot. Faces may be anonymized/blurred for privacy. Do not make it look like a studio shoot, illustration, cosplay poster, or heavily edited composite.

Mode d’emploi

  1. Copiez le prompt original complet, paramètres compris.
  2. Adaptez le sujet, le texte et la composition à vos besoins.
  3. Utilisez un outil externe compatible ; vérifiez l’accès et le résultat.

Avant de commencer

  • Vérifiez si une image de référence est nécessaire. La compatibilité ne prouve pas le modèle utilisé pour cet exemple.
  • Vérifiez noms, chiffres et textes ; les versions du modèle peuvent produire des résultats différents.

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