de GoogleImagen
Nano Banana
11 modelos · desde 76 créditos
Modelos de la familia Nano Banana
| Modelo | Modo | Precio | Relaciones de aspecto | |
|---|---|---|---|---|
| Nano Banana 2 Edit | Edición de imagen | desde 100 créditos | — | Abrir → |
| Nano Banana 2 Reference to Image | Imagen | desde 100 créditos | 1:1 · 3:2 · 2:3 · 3:4 · 4:3 · 4:5 | Abrir → |
| Nano Banana 2 Text-to-Image | Texto a imagen | desde 100 créditos | — | Abrir → |
| Nano Banana 2.1 EditNuevo | Edición de imagen | desde 83 créditos | 1:1 · 3:2 · 2:3 · 3:4 · 4:3 · 4:5 | Abrir → |
| Nano Banana 2.1 Reference-to-ImageNuevo | Imagen | desde 400 créditos | 1:1 · 3:2 · 2:3 · 3:4 · 4:3 · 4:5 | Abrir → |
| Nano Banana 2.1 Text-to-ImageNuevo | Texto a imagen | desde 80 créditos | 1:1 · 3:2 · 2:3 · 3:4 · 4:3 · 4:5 | Abrir → |
| Nano Banana Edit | Edición de imagen | desde 76 créditos | 1:1 · 3:2 · 2:3 · 3:4 · 4:3 · 4:5 | Abrir → |
| Nano Banana Pro Edit | Edición de imagen | desde 175 créditos | — | Abrir → |
| Nano Banana Pro Edit Ultra | Edición de imagen | desde 188 créditos | — | Abrir → |
| Nano Banana Pro Text-to-image | Texto a imagen | desde 175 créditos | — | Abrir → |
| Nano Banana Pro Text-to-image Ultra | Texto a imagen | desde 188 créditos | — | Abrir → |
Reglas de LUVI Prompt Engine para Nano Banana 2.1
TARGET MODEL: Nano Banana 2.1 (Google, gemini-nano-banana-2.1). It reads a brief the way a language model reads one: positively, in prose. There is no negative_prompt, no weighting and no style dial.
Reglas que deben cumplirse
- Narrative prose, not tag stacks. Describe a scene a person could photograph or design: register → subject and action → setting → light → camera → in-image text → finish.
- Exclusions become presences that do NOT name the excluded thing: "no people, no cars" → "a quiet, deserted street", never "no cars" and never "empty of people and cars". Naming the unwanted thing can summon it.
- Aspect ratio and resolution are parameters. Never write "16:9", "widescreen", "4K" or "8k" into the prose — on a conflict the parameter wins and the words are wasted.
- In-image text: put every visible string in double quotes, exactly as it must read, and describe the type (weight, style, colour) and its placement in plain words. Never write the words "in quotes" or "quoted" — the model draws the quotation marks. Every other visible surface (signs, apparel, packaging, screens) is either given quoted copy or described as plain/blank; unquoted incidental lettering comes back as gibberish.
- Keep quoted copy verbatim. If it contains an em dash, the model draws it as a separate rule that splits the line: add OUTSIDE the quotes that the text stays on a single line with a literal em dash between the phrases. Example: a sign reading "Taze simit — İçeride çay var" on a single line, with a literal em dash between the two phrases.
- Non-English copy (e.g. Turkish İ ı ğ ş ç) is kept character-exact and LARGE in the frame; small lettering drops diacritics at any resolution.
- People: keep every physical detail the user gave (age, build, hair, wardrobe, expression) — never flatten a described person to "a man"; vague unnamed people drift toward real public-figure lookalikes. Keep distinct on-screen identities few; give extras varied faces or defocus them.
- Never ask for a transparent background — the model has no alpha. Ask for a plain seamless white background instead.
Lo que mejor funciona
- Open with the operation and the deliverable: "Create an image: a photorealistic product photograph of …", "… an infographic poster …".
- State purpose and audience when known ("for an e-commerce product page", "suitable for a 4th grader").
- Many elements → order them in words: "First … Then … Finally …".
- When the user names light, lens or mood, phrase it concretely ("soft window light from the left" beats "cinematic lighting"). Do not add light directions, lenses, camera specs, scene elements (buildings, props, background figures) or wardrobe the user did not ask for — expand only what the user wrote.
- Prose over JSON: when the user supplies JSON, rewrite its content as prose — JSON loses counted details and soft details.
De las reglas de LUVI Prompt Engine para esta familia: las mismas que aplican el reescritor del espacio de trabajo y el conector MCP de LUVI.
Reglas de LUVI Prompt Engine para Nano Banana
Notas
- Nano Banana has no negative_prompt — describe positively (“an empty, deserted street”, not “no cars”). Naming what you don’t want can summon it into the frame.
- Weight syntax such as (word:1.3) is ignored; it only wastes prompt space.
- Keep to at most 5 distinct characters in one prompt.
De las reglas verificadas de LUVI para esta familia.