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# Introduction
The Google Gemini 2.5 Flash Image model, affectionately known as Nano Banana, represents a significant leap in AI-powered image manipulation, moving beyond the scope of traditional editors. Nano Banana excels at complex tasks such as multi-image composition, conversational refinement, and semantic understanding, allowing it to perform edits that seamlessly integrate new elements and preserve photorealistic consistency across lighting and texture. This article will serve as your practical guide to leveraging this powerful tool.
Here, we will dive into what Nano Banana is truly capable of, from its core strengths in visual analysis to its advanced composition techniques. We’ll provide essential tips and tricks to optimize your workflow and, most importantly, lay out a series of example prompts and prompting strategies designed to help you unlock the model’s full creative and technical potential for your image editing and generation needs.
# What Nano Banana Can Do
The Google Gemini 2.5 Flash Image model is able to perform complex image manipulations that rival or exceed the capabilities of traditional image editors. These capabilities often rely on deep semantic understanding, multi-turn conversation, and multi-image synthesis.
Here are five things Nano Banana can do that typically go beyond the scope of conventional image editing tools.
// 1. Multi-Image Composition and Seamless Virtual Try-On
The model can use multiple input images as context to generate a single, realistic composite scene. This is exemplified by its ability to perform advanced composition, such as taking a blue floral dress from one image and having a person from a second image realistically wear it, adjusting the lighting and shadows to match a new environment. Similarly, it can take a logo from one image and place it onto a t-shirt in another image, ensuring the logo appears naturally printed on the fabric, following the folds of the shirt.
// 2. Iterative and Conversational Refinement of Edits
Unlike standard editors where changes are finalized one step at a time, Nano Banana supports multi-turn conversational editing. You can engage in a chat to progressively refine an image, providing a sequence of commands to make small adjustments until the result is perfect. For example, a user can instruct the AI to upload an image of a red car, then in a follow-up prompt, ask to “Turn this car into a convertible,” and subsequently ask, “Now change the color to yellow,” all conversationally.
// 3. Complex Conceptual Synthesis and Meta-Narrative Creation
The AI can transform subjects into elaborate conceptual artworks that include multiple synthetic elements and a narrative layer. An example of this is the popular trend of transforming character photos into a 1/7 scale commercialized figurine set within a desktop workspace, including generating a professional packaging design and visualizing the 3D modeling process on a computer screen within the same image. This involves synthesizing a complete, highly detailed fictional environment and product ecosystem.
// 4. Semantic Inpainting and Contextually Appropriate Scene Filling
Nano Banana allows for highly selective, semantic editing — aka inpainting — through natural language prompts. A user can instruct the model to change only a specific element within a picture (e.g. changing only a blue sofa to a vintage, brown leather chesterfield sofa) while preserving everything else in the room, including the pillows and the original lighting. Furthermore, when removing an unwanted object (like a telephone pole), the AI intelligently fills the vacated space with contextually appropriate scenery that matches the environment, ensuring the final landscape looks natural and seamlessly cleaned up.
// 5. Visual Analysis and Optimization Suggestions
The model can function as a visual consultant rather than just an editor. It can analyze an image, such as a photo of a face, and provide visual feedback with annotations (using a simulated “red pen”) to denote areas where makeup technique, color choices, or application methods could be improved, offering constructive suggestions for enhancement.
# Nano Banana Tips & Tricks
Here are five interesting tips and tricks that go beyond beyond basic prompting for editing and creation for optimizing your workflow and results when using Nano Banana.
// 1. Start with High-Quality Source Images
The quality of the final edited or generated photo is significantly influenced by the original photo you provide. For the best outcomes, always begin with well-lit, clear images. When making complex edits involving specific details, such as clothing pleats or character features, the original photos need to be clear and detailed.
// 2. Manage Complex Edits Step-by-Step
For intricate or complex image editing needs, it is recommended to process the task in stages rather than attempting everything in a single prompt. A recommended workflow involves breaking down the process:
- Step 1: Complete basic adjustments (brightness, contrast, color balance)
- Step 2: Apply stylization processing (filters, effects)
- Step 3: Perform detail optimization (sharpening, noise reduction, local adjustments)
// 3. Practice Iterative Refinement
Do not expect to achieve a perfect image result on the very first attempt. The best practice is to engage in multi-turn conversational editing and iteratively refine your edits. You can use subsequent prompts to make small, specific changes, such as instructing the model to “make the effect more subtle” or “add warm tones to the highlights”.
// 4. Prioritize Lighting Consistency During Edits
When applying major transformations, such as changing backgrounds or replacing garments, it is crucial to ensure that the lighting remains consistent throughout the image to maintain realism and avoid an obviously “fake” look. The model must be guided to preserve the original subject shadows and lighting direction so that the subject fits believably into the new environment.
// 5. Observe Input and Output Limitations
Keep practical limitations in mind to streamline your workflow:
- Input Limit: The nano banana model works best when using up to 3 images as input for tasks like advanced composition or editing.
- Watermarks: All generated images created by this model include a SynthID watermark
- Clothing compatibility: Clothing replacement works most effectively when the reference image shows a new garment that has a similar coverage and structure to the original clothing on the subject
# Prompting Nano Banana
Nano Banana offers advanced image generation and editing capabilities, including text-to-image generation, conversational editing (image + text-to-image), and combining multiple images (multi-image to image). The key to unlocking its functionality is using clear, descriptive prompts that adhere to a structure, such as specifying the subject, action, environment, art style, lighting, and details.
Below are 5 prompts designed to explore and demonstrate the advanced functionality and creativity of the Nano Banana model.
// 1. Hyper-Realistic Surrealism with Focused Inpainting
This prompt tests the model’s ability to execute hyper-realistic surreal art and perform precise semantic masking (inpainting) while maintaining the integrity of key details.
- Prompt type: Image + text-to-image
- Input required: High-resolution portrait photo (face clearly visible)
- Functionality tested: Inpainting, hyper-realism, detail preservation
The prompt:
Using the provided portrait photo of a person’s head and shoulders, perform a hyper-realistic edit. Change only the subject’s neck and shoulders, replacing them with intricate, mechanical clockwork gears made of antique brass and polished copper. The person’s face (eyes, nose, and neutral expression) must remain completely untouched and photorealistic. Ensure the new mechanical elements cast realistic shadows consistent with the original photo’s key light source (e.g. top-right studio lighting). Highly detailed, 8K ultra-realistic rendering of the metal textures.
This prompt forces the model to treat the subject as two separate entities: the unchanged face (testing high-fidelity detail preservation) and the hyper-realistic new element (testing the ability to seamlessly add complex textures and realistic physics/lighting, as seen in the liquid physics simulation example). The requirement to change only the neck/shoulders specifically targets the model’s precise inpainting capability.
Example input (left) and output (right):
Example output image: Hyper-realistic surrealism with focused inpainting
// 2. Multi-Modal Product Mockup with High-Fidelity Text
This prompt demonstrates the ability to execute advanced composition by combining multiple input images with the model’s core strength in rendering accurate and legible text in images.
- Prompt type: Multi-image to image
- Input required: Image of a glass jar of honey; image of a minimalist circular logo
- Functionality tested: Multi-image composition, high-fidelity text rendering, product photography
The prompt:
Using image 1 (a glass jar of amber honey) and image 2 (a minimalist circular logo), create a high-resolution, studio-lit product photograph. The jar should be placed precariously on the edge of a frozen waterfall cliff at sunset (photorealistic environment). The jar’s label must cleanly display the text ‘Golden Cascade Honey Co.’ in a bold, elegant sans-serif font. Use soft, golden hour lighting (8500K color temperature) to highlight the smooth texture of the glass and the complex structure of the ice. The camera angle should be a low-angle perspective to emphasize the cliff height. Square aspect ratio.
The model must successfully merge the logo onto the jar, place the resulting product into a dramatic, new environment, and execute specific lighting conditions (softbox setup, golden hour). Crucially, the demand for specific, branded text ensures the AI demonstrates its text rendering proficiency.
Example input:
Glass jar of amber honey (created with ChatGPT)
Minimalist circular logo (created with ChatGPT)
Example output:
Example output image: Multi-modal product mockup with high-fidelity text
// 3. Iterative Atmospheric and Mood Refinement (Chat-based Editing)
This task simulates a two-step conversational editing session, focusing on using color grading and atmospheric effects to change the entire emotional mood of an existing image.
- Prompt type: Multi-turn image editing (chat)
- Input required: A photo of a sunny, brightly lit suburban street scene
- Functionality tested: Iterative refinement, color grading, atmospheric effects
The first prompt:
Using the provided photo of the sunny suburban street, dramatically replace the background sky (the upper 65% of the frame) with layered, deep dark-cumulonimbus clouds. Shift the overall color grading to a cool, desaturated midnight blue palette (shifting white-balance to 3000K) to create an immediate sense of impending danger and a cinematic, noir mood.
The second prompt:
That’s much better. Now, keep the new sky and color grade, but add a subtle, fine layer of rain and reflective wetness to the street pavement. Introduce a single, harsh, dramatic side lighting from camera left in a piercing yellow color to make the reflections glow and highlight the subject’s silhouette against the dark background. Maintain a 4K photoreal look.
This example showcases the power of iterative refinement, where the model builds upon a previous complex edit (sky replacement, color shift) with local adjustments (adding rain/reflections) and specific directional lighting. This demonstrates advanced control over the visual mood and consistency between turns.
Example input:
Photo of a sunny, brightly lit suburban street scene (created with ChatGPT)
Example output from the first prompt:
Example output image: Iterative atmospheric and mood refinement (chat-based editing), step 1
Example output from the second prompt:
Example output image: Iterative atmospheric and mood refinement (chat-based editing), step 2
// 4. Complex Character Construction and Pose Transfer
This prompt tests the model’s capability to execute multi-image to image composition for character creation combined with pose transfer. This is an advanced version of clothing/pose swap.
- Prompt type: Multi-image to image (composition)
- Input required: Portrait of a face/headshot; full-body photo showing a specific, dynamic fighting stance pose
- Functionality tested: Pose transfer, multi-image composition, high-detail costume generation (figurine style)
The prompt:
Create a 1/7 scale commercialized figurine of the person in image 1. The figure must adopt the dynamic fighting pose shown in image 2. Dress the figure in ornate, dieselpunk-style plate armor, etched with complex clockwork gears and pistons. The armor should be rendered in tarnished silver and black leather textures. Place the final figurine on a polished, dark obsidian pedestal against a misty, industrial city background. Ensure the face from image 1 is clearly preserved on the figure, maintaining the same expression. Ultra-realistic, focused depth of field.
This task layers three complex functions: 1) figurine creation (defining scale, base, and commercial aesthetic); 2) pose transfer from a separate reference image; and 3) multi-image composition, where the model pulls the subject’s identity (face) from one image and the body structure (pose) from another, integrating them into a newly generated costume and environment.
Example inputs:
Portrait of a face/headshot
Full-body photo showing a specific, dynamic fighting stance pose (generated with ChatGPT)
Example output:
Example output image: Complex character construction and pose transfer
// 5. Technical Analysis and Stylized Doodle Overlay
This prompt combines the ability of the AI to perform visual analysis and provide feedback/annotations with the creation of a stylized artistic overlay.
- Prompt type: Image + text-to-image
- Input required: Detailed technical drawing or blueprint of a machine
- Functionality tested: Analysis, doodle overlay, text integration
The prompt:
Analyze the provided technical drawing of a complicated factory machine. First, apply a bright neon-green doodle overlay style to add large, playful arrows and sparkle marks pointing out 5 distinct, complex mechanical components. Next, add fun, bold, hand-written text labels above each of the components, labeling them ‘HYPER-PISTON’, ‘JOHNSON ROD’, ‘ZAPPER COIL’, ‘POWER GLOW’, and ‘FLUX CAPACITOR’. The resulting image should look like a technical diagram crossed with a fun, brightly colored, instructional poster with a light and youthful vibe.
The model must first analyze the image content (the machine components) to accurately place the annotations. Then, it must execute a stylized overlay (doodle, neon-green color, playful text) without obscuring the core technical diagram, balancing the playful aesthetic with the necessity of clear, legible text integration.
Example input:
Technical drawing of a complicated factory machine (generate with ChatGPT)
Example output:
Example output image: Technical analysis and stylized doodle overlay
# Wrapping Up
This guide has showcased Nano Banana’s advanced capabilities, from complex multi-image composition and semantic inpainting to powerful iterative editing strategies. By combining a clear understanding of the model’s strengths with the specialized prompting techniques we covered, you can achieve visual results that were previously impossible with conventional tools. Embrace the conversational and creative power of Nano Banana, and you’ll find you can transform your visual ideas into stunning, photorealistic realities.
The sky’s the limit when it comes to creativity with this model.
Matthew Mayo (@mattmayo13) holds a master’s degree in computer science and a graduate diploma in data mining. As managing editor of KDnuggets & Statology, and contributing editor at Machine Learning Mastery, Matthew aims to make complex data science concepts accessible. His professional interests include natural language processing, language models, machine learning algorithms, and exploring emerging AI. He is driven by a mission to democratize knowledge in the data science community. Matthew has been coding since he was 6 years old.
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