Nano Banana — The Creative Professional's Guide

Timon 4 months ago

Executive Summary:

- Nano-Banana represents a paradigm shift from traditional AI art tools to intelligent creative partners, enabling context-aware image editing and multi-modal fusion for unprecedented creative efficiency[^1][^2]

- Professional applications span industries — from product packaging and virtual try-ons to architectural visualization and brand marketing, offering creative teams new workflow optimization opportunities[^2][^1]

- Accessible yet powerful — combines intuitive prompt-based controls with sophisticated multi-image synthesis capabilities, democratizing professional-grade creative tools for designers and product managers[^3][^1][^2]

What is Nano-Banana?

Nano-Banana is Google's breakthrough AI image editing model that transforms how creative professionals approach visual content creation. Unlike traditional AI art generators that work from scratch, Nano-Banana excels at understanding existing images' context, lighting, and spatial relationships to make intelligent modifications. Whether you're a product designer needing quick mockups, a content creator building marketing assets, or a brand manager visualizing campaigns, this tool bridges the gap between creative vision and technical execution with remarkable precision.[^1][^2][^3]

Curated Case Collection

Case \#1 — Illustration to Product Figure

A. Case Introduction

This application showcases Nano-Banana's ability to transform 2D artwork into realistic 3D product visualizations. The model doesn't just create a figure — it constructs an entire commercial presentation including packaging, display base, and even shows the 3D modeling process on screen. For product designers and toy manufacturers, this represents a revolution in concept-to-prototype workflows.

B. Case Prompt

turn this photo into a character figure. Behind it, place a box with the character's image printed on it, and a computer showing the Blender modeling process on its screen. In front of the box, add a round plastic base with the character figure standing on it. set the scene indoors if possible

Prompt Variants:

Create figure with LED light base and premium packaging (enhances presentation quality)
Generate limited edition figure with metallic finish base (premium product positioning)

C. Case Effect

!Portrait art featuring flowing hair interwoven with clouds and stars in a dreamy, painterly style, illustrating creative AI image generation effects.

Portrait art featuring flowing hair interwoven with clouds and stars in a dreamy, painterly style, illustrating creative AI image generation effects.

- Visual Results Expected:

- High-quality 3D figure rendering with precise detail work

- Professional packaging design incorporating brand elements

- Natural indoor lighting with realistic shadows

- Authentic-looking Blender interface display

- Commercial-grade presentation suitable for marketing

- Business Takeaway: Enables rapid product concept validation, reducing time-to-market for collectibles and branded merchandise.

Case \#2 — Map Arrow to Street View

A. Case Introduction

This innovative application converts Google Maps overhead views into realistic ground-level perspectives. By understanding directional arrows and geographic context, Nano-Banana can generate what a person would actually see standing at that location. This has immediate applications for real estate marketing, urban planning, and navigation systems.

B. Case Prompt

draw what the red arrow sees 
/ 
draw the real world view from the red circle in the direction of the arrow.

Prompt Variants:

Generate street view photo with sunny weather and clear visibility (weather control)
Create dusk street scene with warm lighting and traffic (atmospheric enhancement)

C. Case Effect

AI-generated reproduction prompt (reproducible):

Street level view of urban intersection with buildings, showing realistic perspective from aerial map reference point with directional arrow

AI-generated reproduction — expected differences in architectural details and traffic patterns

- Visual Results Expected:

- Accurate street-level perspective matching map geometry

- Realistic building proportions and architectural styles

- Appropriate environmental details (signage, vehicles, pedestrians)

- Natural lighting conditions and atmospheric perspective

- Geographic accuracy within reasonable interpretation limits

- Business Takeaway: Real estate developers can provide instant location previews, while logistics companies can improve route planning visualization.

Case \#3 — AR Information Overlay

A. Case Introduction

Transform ordinary photographs into AR-enhanced information displays by intelligently identifying points of interest and adding contextual annotations. This technique bridges the gap between static imagery and interactive experiences, offering valuable applications for tourism, education, and smart city initiatives.

B. Case Prompt

you are a location-based AR experience generator. highlight [point of interest] in this image and annotate relevant information about it.

Prompt Variants:

Add tourist information with multilingual labels and ratings (tourism focus)
Create business directory overlay with hours and contact info (commercial application)

C. Case Effect

AI-generated reproduction prompt (reproducible):

Urban street scene with AR-style information overlays highlighting buildings and landmarks with text annotations and interface elements

AI-generated reproduction — expected differences in specific text content and UI styling

- Visual Results Expected:

- Clean, professional AR-style interface elements

- Well-positioned information callouts that don't obstruct key visuals

- Consistent typography and color scheme throughout

- Realistic integration of digital elements with photographic content

- Modern UI design language matching current AR applications

- Business Takeaway: Tourism boards and educational institutions can create engaging location-based content without expensive AR development costs.

Case \#4 — Isometric 3D Building Extraction

A. Case Introduction

Convert real architecture photographs into clean isometric 3D models suitable for game development, architectural visualization, and urban planning. This process extracts essential structural elements while transforming perspective to create assets that would typically require specialized 3D modeling software.

B. Case Prompt

Make Image Daytime and Isometric [Building Only]

Prompt Variants:

Create night version with illuminated windows and street lighting (time variation)
Generate pixel art style isometric building for game assets (stylistic adaptation)

C. Case Effect

!AI-generated isometric 3D model of a hospital building showcasing detailed architectural design and urban context.

AI-generated isometric 3D model of a hospital building showcasing detailed architectural design and urban context.

- Visual Results Expected:

- Precise isometric perspective with correct proportional relationships

- Clean architectural lines and structural accuracy

- Simplified but recognizable building features

- Uniform lighting that enhances dimensional clarity

- Professional architectural visualization quality

- Business Takeaway: Architects and game developers can rapidly generate isometric assets for presentations and prototypes without specialized 3D modeling expertise.

Case \#5 — Era-Specific Portrait Transformation

A. Case Introduction

Transport subjects through time by adapting their appearance to different historical periods while preserving facial identity. This technique opens creative possibilities for period drama casting, historical education, and personalized entertainment applications.

B. Case Prompt

Change the characer's style to 's classical [male] style
Add [long curly] hair, 
[long mustache], 
change the background to the iconic [californian summer landscape]
Don't change the character's face

Prompt Variants:

Transform to 1920s Shanghai style with qipao dress and vintage setting (cultural period variation)
Create 1980s disco look with neon lighting and club atmosphere (different era focus)

C. Case Effect

!AI-generated vintage-style portraits showcasing hairstyles and attire from the mid-20th century, illustrating AI transformation effects on modern photos.

AI-generated vintage-style portraits showcasing hairstyles and attire from the mid-20th century, illustrating AI transformation effects on modern photos.

- Visual Results Expected:

- Preserved facial features with high identity consistency

- Authentic period-appropriate styling and clothing details

- Historically accurate environmental backgrounds

- Natural integration of vintage photographic characteristics

- Believable temporal transformation while maintaining subject recognition

- Business Takeaway: Entertainment companies can rapidly test period costume concepts, while marketing teams can create nostalgic campaign imagery.

Case \#6 — Multi-Image Composition

A. Case Introduction

Demonstrate Nano-Banana's sophisticated multi-image synthesis by combining elements from multiple reference photos into a cohesive scene. This workflow revolutionizes advertising photography by eliminating the need for complex physical setups and multiple models.

B. Case Prompt

A model is posing and leaning against a pink bmw. She is wearing the following items, the scene is against a light grey background. The green alien is a keychain and it's attached to the pink handbag. The model also has a pink parrot on her shoulder. There is a pug sitting next to her wearing a pink collar and gold headphones.

Prompt Variants:

Shoot at golden hour with warm atmospheric lighting (lighting enhancement)
Use fashion magazine photography style with professional studio setup (style elevation)

C. Case Effect

AI-generated reproduction prompt (reproducible):

Fashion photography scene with model leaning against pink BMW, multiple colorful accessories, pets with headphones, professional lighting and composition

AI-generated reproduction — expected differences in model appearance and specific prop arrangements

- Visual Results Expected:

- Seamless integration of all specified elements without obvious compositing artifacts

- Consistent lighting and shadow relationships across all subjects

- Coordinated color palette maintaining pink theme throughout

- Professional photography quality with sharp focus and proper exposure

- Natural poses and interactions between model, vehicle, and animal subjects

- Business Takeaway: Advertising agencies can create complex product shots without expensive location shoots and multiple talent coordination.

Case \#7 — Intelligent Photo Enhancement

A. Case Introduction

Transform mundane photographs into visually compelling images through AI-driven enhancement. Rather than applying generic filters, Nano-Banana analyzes image content to make targeted improvements in contrast, color, and composition while maintaining photographic authenticity.

B. Case Prompt

This photo is very boring and plain. Enhance it! Increase the contrast, boost the colors, and improve the lighting to make it richer,You can crop and delete details that affect the composition.

Prompt Variants:

Optimize for portrait photography with skin softening and eye enhancement (portrait specialization)
Enhance landscape drama with sky dynamics and foreground detail (landscape focus)

C. Case Effect

AI-generated reproduction prompt (reproducible):

Before and after comparison showing enhanced photo with improved contrast, vibrant colors, and optimized composition

AI-generated reproduction — expected differences in original image content and specific enhancement choices

- Visual Results Expected:

- Significantly improved color saturation without oversaturation artifacts

- Enhanced contrast creating better depth and dimensionality

- Optimized composition through intelligent cropping decisions

- Preserved natural image characteristics while elevating visual impact

- Professional-grade enhancement suitable for publication

- Business Takeaway: Content creators and social media managers can achieve professional photo quality without expensive post-production resources.

Case \#8 — Sketch-Controlled Character Poses

A. Case Introduction

Use hand-drawn sketches as precise control mechanisms for character positioning and interaction. This workflow bridges traditional art direction with AI generation, allowing creators to maintain artistic control while leveraging AI capabilities for execution.

B. Case Prompt

Have these two characters fight using the pose from Figure 3. Add appropriate visual backgrounds and scene interactions,Generated image ratio is 16:9

Prompt Variants:

Add dynamic motion lines and special effects for comic book style (visual enhancement)
Use cinematic lighting with dramatic shadows and high contrast (mood intensification)

C. Case Effect

AI-generated reproduction prompt (reproducible):

Two characters in fighting poses based on sketch reference, dramatic background with dynamic composition in 16:9 aspect ratio

AI-generated reproduction — expected differences in character appearance and background details

- Visual Results Expected:

- Precise adherence to sketch-specified character positions and poses

- Natural anatomical proportions and realistic movement dynamics

- Cohesive background environment supporting the action

- Proper 16:9 aspect ratio suitable for video production

- High-energy composition with clear visual narrative

- Business Takeaway: Animation studios and game developers can rapidly prototype character interactions and action sequences from concept sketches.

Case \#9 — Perspective Transformation

A. Case Introduction

Transform ground-level photographs into aerial perspectives while intelligently marking the original photographer's position. This spatial reasoning capability has applications in urban planning, security analysis, and architectural documentation.

B. Case Prompt

Convert the photo to a top-down view and mark the location of the photographer.

Prompt Variants:

Generate architectural floor plan view with dimension annotations (technical drawing)
Create traffic flow analysis view with vehicle path markings (planning application)

C. Case Effect

AI-generated reproduction prompt (reproducible):

Aerial top-down view of urban area with photographer position marked, accurate spatial relationships and geographic features

AI-generated reproduction — expected differences in specific location details and marking styles

- Visual Results Expected:

- Accurate perspective transformation maintaining spatial relationships

- Clear photographer position indicator with appropriate visual treatment

- Preserved important environmental details and landmarks

- Logical geographic layout consistent with ground-level reference

- Professional surveying or mapping visualization quality

- Business Takeaway: Urban planners and security professionals can quickly generate overhead analysis views without expensive aerial photography.

Case \#10 — Custom Sticker Creation

A. Case Introduction

Convert photographs into stylized sticker designs with custom typography integration. This application demonstrates Nano-Banana's ability to maintain subject recognition while adapting to specific graphic design requirements and branding needs.

B. Case Prompt

Help me turn the character into a white outline sticker similar to Figure 2. The character needs to be transformed into a web illustration style, and add a playful white outline short phrase describing Figure 1.

Prompt Variants:

Create colorful version maintaining cartoon aesthetic (color variation)
Add drop shadow effects for dimensional appearance (visual enhancement)

C. Case Effect

AI-generated reproduction prompt (reproducible):

Character transformed into white outline sticker design with web illustration style and integrated typography

AI-generated reproduction — expected differences in character details and text content

- Visual Results Expected:

- Clean white outline treatment with consistent line weight

- Simplified character features maintaining recognizability

- Integrated typography with appropriate playful styling

- Scalable vector-style appearance suitable for print and digital use

- Modern sticker aesthetic following current design trends

- Business Takeaway: Brands can rapidly create custom merchandise and marketing materials without specialized graphic design resources.

Tips \& Prompt-Writing Insights

Based on our curated case analysis, here are eight essential prompt-writing techniques for Nano-Banana:

1. Specify Subject Precisely — Use clear object references like "the character in Figure 1" or "the building in the center" to avoid ambiguous targeting (demonstrated in Case \#8 sketch control)[^1]

2. Define Environmental Context — Include specific scene settings like "indoor studio" or "californian summer landscape" to guide atmospheric generation (see Case \#5 era transformation)[^1]

3. Layer Style Keywords — Combine multiple aesthetic descriptors such as "1970s classical male style" + "long curly hair" for rich visual complexity (Case \#5 example)[^1]

4. State Technical Requirements — Specify output formats like "16:9 aspect ratio" or "isometric perspective" to ensure usability (Cases \#4 and \#8)[^2][^1]

5. Use Preservation Commands — Employ "don't change" or "maintain" statements to protect critical elements like facial features (Case \#5 identity preservation)[^1]

6. Structure Multi-Step Instructions — Break complex requests into sequential actions: "add base" → "place packaging" → "show modeling process" (Case \#1 methodology)[^1]

7. Reference Multi-Image Sources — Clearly indicate image relationships with "using color palette from Figure 2 for Figure 1" notation (preventing confusion in multi-input scenarios)[^1]

8. Include Enhancement Verbs — Use action words like "enhance," "boost," "improve" combined with specific targets to guide optimization (Case \#7 photo enhancement)[^1]

Ready-to-Use Prompt Templates

# Product Visualization Template
Transform [subject/character] into [display format]. Add [packaging elements], place in [environment setting], use [photography style]

Use for: Product concept visualization, figure design, merchandise mockups

# Perspective Conversion Template
Convert image from [current viewpoint] to [target perspective], and mark [key information points]

Use for: Architectural drawings, map conversions, spatial analysis

# Era Transformation Template
Change character's style to [time period]'s [gender] [style type], add [specific features], background [environment description], preserve [unchanging elements]

Use for: Historical content, vintage marketing, character design

# Multi-Element Fusion Template
Create scene: [main subject] positioned at [location], combined with [item list], environment [background description], overall style [aesthetic requirements]

Use for: Advertising photography, product displays, creative compositing

# Enhancement Template
This image [problem description]. Please [improvement actions]! Specifically [technical requirements list], you may [permitted operations]

Use for: Photo post-processing, content optimization, quality improvement

# Pose Control Template
Have [character description] use [reference figure]'s pose for [action], add [scene elements], output ratio [dimension requirements]

Use for: Animation production, game design, comic creation

# AR Information Template
You are [professional role]. Highlight [target objects] in image and annotate [information type]

Use for: Educational content, navigation apps, smart city applications

# Sticker Style Template
Convert [object] to [style type] sticker, using [visual characteristics], add [text content]

Use for: Brand marketing, social media content, personal customization

Quick Workflow \& Checklist

Nano-Banana Production-Ready Image Generation Workflow:

Step 1: Asset Preparation — Gather high-resolution reference images (>1024px), ensure clean, watermark-free sources, prepare detailed requirement specifications with clear success criteria

Step 2: Prompt Construction — Select appropriate template from library, populate with specific parameters, conduct iterative testing and refinement, document optimal configuration combinations

Step 3: Generation Testing — Execute initial generation runs, evaluate output against expectations, identify specific adjustment points, test variant approaches for comparison

Step 4: Quality Control — Verify image detail completeness and accuracy, confirm style consistency across elements, validate commercial usage standards, test scalability across different output sizes

Step 5: Delivery Optimization — Apply final adjustments based on usage context, prepare multiple format versions for different applications, establish standardized output procedures for team consistency