Vladartym Vsb Skills Nano Banana

Prompting Nano Banana

Read image-prompting first. It covers the four universal rules, the full prompt-anatomy slot list, the reference-image keep/ignore pattern, and the vocabulary banks — those apply to every image model. This skill only adds Nano Banana–specific behavior that diverges from the trunk.

Nano Banana is built on Gemini 3 — it reasons over the prompt before drawing, so the natural-language scene-description approach in image-prompting works especially well here. The same endpoint handles text-to-image AND edits: pass image_input (an array) to switch into edit mode.

Verify the live schema first: vsb schema image/nano-banana --json.

Picking a variant

All three share the same image_input array, aspect_ratio enum, and output_format enum. Confirm with vsb schema before assuming.

Image edits (image_input set)

When image_input is non-empty, the model is editing — the prompt should lead with the change and be explicit about what stays the same. The universal subject-anchor pattern (in image-prompting) is the floor; Nano Banana takes especially well to plain-English edit instructions on top.

Good: "Change the man's tie to dark green. Keep the rest of the photo, lighting, and composition exactly as they are."

Bad: "man with green tie" (loses every other detail).

Edit patterns Nano Banana handles cleanly:

• Add / remove / swap: "Remove the car in the background." "Replace the coffee mug with a vintage typewriter."
• Restyle: "Restyle as a 1990s film photograph — kodak gold, slight grain, warm cast." "Convert to ink-on-paper line art."
• Reframe / re-angle: "Re-render this scene from a low angle, keeping the same subject and lighting."
• Weather / time-of-day: "Same scene but at golden hour with backlit rim light."
• Product extraction: "Extract the mug and place it on a clean white studio backdrop with a soft shadow."

For the aspect_ratio field, match_input_image only makes sense in edit mode — it keeps the first reference's dimensions.

Text rendering (Nano Banana's standout strength)

Nano Banana is one of the few image models that renders legible text. Pro is the sharpest of the three.

• Quote the exact words. "The headline 'URBAN EXPLORER' rendered in bold, white, sans-serif font at the top of the poster."
• Describe the font. Style words work (bold, cursive, serif, monospace, neon signage), and named fonts work surprisingly often (Century Gothic, Helvetica-style).
• Localize. Write the prompt in any language and pass the target text in quotes. For exotic scripts give the model the literal characters — don't ask it to translate.
• Text-first hack for tricky layouts. Two-step: first ask Nano Banana (chat-style) for the literal text content, then run a generation request that quotes that text. Improves accuracy on long strings.

Multi-reference character consistency

The universal subject-anchor pattern (in image-prompting) tells you how to phrase "keep the subject, ignore the rest". Nano Banana adds two specifics on top:

• Name each character / object in the prompt. The model uses the name as an anchor across edits. "Marigold the calico cat" survives across follow-up edits better than "the cat".
• Use a reference pack. Multiple angles of the same subject (front, profile, three-quarter) help the model triangulate identity. Pro takes up to 14 references per prompt; standard nano-banana is best with 1–3. For more than 3 references, switch to Pro.

Multi-subject anatomy lock

Multi-person scenes hallucinate extra hands/arms when limb count is left implicit. Budget every visible limb: state the exact total ("visible hands: 2"), name whose and where each is, then declare the remaining arms out of frame or hidden behind a torso. Same rule applies to feet on full-body shots.

What this family gets wrong (known limits)

Adds to the model-agnostic failure modes in image-prompting:

• Tiny / dense text. Long paragraphs at small size will hallucinate glyphs. Pro is best; even Pro isn't perfect.
• Factual diagrams. Anatomy, scientific cross-sections, and infographics may be confidently wrong. Verify any data-driven visual yourself.
• Multilingual grammar. Non-English text may have small grammatical slips or cultural mismatches. Provide the literal target string in quotes when accuracy matters.

Worked examples

Photoreal portrait (text-to-image)

vsb run image/nano-banana \
  --prompt "A 28-year-old florist standing in her shop doorway at golden hour. She wears a denim apron over a cream linen shirt. Shot on Fujifilm X-T5, 35mm, f/1.8 shallow depth of field. Warm rim light from the right, soft fill from the open window. Photoreal, cinematic muted teal-and-orange grading, slight sensor grain." \
  --aspect_ratio 4:5 \
  --output_format jpg \
  --download "./out/{request_id}.jpg" \
  --json

Edit with reference (image_input)

URL=$(vsb upload ./product.jpg --json | jq -r '.url')
vsb run image/nano-banana \
  --prompt "Use the reference image as the PRODUCT source only. Keep the mug's pattern, colour, handle shape, and reflections exactly as they are. Ignore everything else from the reference — including any watermarks, text overlays, original background, and original lighting.

Now place this mug on a sunny tropical beach with sand and ocean waves in the background. Photoreal, natural midday daylight, soft shadow on the sand. No logos, no captions, no watermarks." \
  --image_input "[\"$URL\"]" \
  --aspect_ratio match_input_image \
  --download "./out/" \
  --json

Poster with crisp typography (Pro)

vsb run image/nano-banana-pro \
  --prompt "A minimal travel poster. Composition: 9:16 vertical, full-bleed. Background: silhouette of a mountain range at dusk, indigo-to-magenta sky. Foreground typography: the word 'PATAGONIA' rendered in bold, white, condensed sans-serif font, baseline aligned at the bottom third. Subline reads 'Edge of the World' in a smaller serif italic directly underneath. Cinematic, slight film grain." \
  --aspect_ratio 9:16 \
  --output_format png \
  --download "./out/poster.png" \
  --json

Multi-reference character blend (Pro shines here)

A=$(vsb upload ./pose.jpg --json | jq -r '.url')
B=$(vsb upload ./style.jpg --json | jq -r '.url')
C=$(vsb upload ./background.jpg --json | jq -r '.url')
vsb run image/nano-banana-pro \
  --prompt "Render a single image. Use Image A as the IDENTITY source — keep the subject's face, hair, skin tone, and body proportions exactly. Use Image B as the STYLE source — match its color grade, grain, and lighting feel, but ignore its subject. Use Image C as the BACKGROUND source — match the environment, but ignore any people or text in it. Cinematic, high-detail, no watermarks, no captions." \
  --image_input "[\"$A\",\"$B\",\"$C\"]" \
  --aspect_ratio match_input_image \
  --json

Sources

• Google DeepMind — Gemini Image prompt guide: https://deepmind.google/models/gemini-image/prompt-guide/
• Google Cloud — Ultimate prompting guide for Nano Banana (2026-03): https://cloud.google.com/blog/products/ai-machine-learning/ultimate-prompting-guide-for-nano-banana
• Google blog — 7 tips for Nano Banana Pro: https://blog.google/products-and-platforms/products/gemini/prompting-tips-nano-banana-pro/

Source: vladartym/vsb-skills — distributed by TomeVault.