The Nano Banana Effect: How Google’s Viral AI is Reshaping Architectural Visualization

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Lately, one name has broken the internet: Nano Banana Pro.

While the name might sound playful, the technology behind it is anything but. Officially known as Gemini 3 Pro Image, “Nano Banana” is the viral internal codename for Google’s latest image generation and editing model. Originally a hidden gem within the Gemini 2.5 Flash infrastructure, it has recently been superseded by this “Pro” iteration.

The release has triggered a polarized response. On one side, there is the very thrill of a new creative frontier; on the other, a real fear. The realism capable of being generated raises serious ethical questions regarding deepfakes, non-consensual identity usage, and misinformation. However, beyond the broader societal panic, a specific and highly technical industry is currently undergoing an existential debate, guess, yes, it’s us, architecture.

Can Nano Banana Pro change the way we design buildings? Is it a tool for liberation, or the announcer of the end for the ArchViz profession?

The Community Divide: Excitement vs. Anxiety

The discourse is perhaps most heated on Reddit. In the r/GeminiAI and r/ArchViz communities, threads dedicated to Nano Banana Pro are a mix of excitement and defensiveness. Users are flocking to these channels to share workflows, but they are also asking the uncomfortable question: Does this make the human artist ancient?

One user on the r/ArchViz subreddit offered a nuanced take that caught my attention. They argued that while AI is powerful, it lacks the accuracy required for high-stakes professional work:

“I really don’t think there is any AI out there that can outright replace 3D artists […] The only place where AI is being used heavily is to enhance already done renders […] When you really need to present renders at council hall approvals, stakeholder meetings, or to the municipality, you need to accurately depict the surroundings as is. That level of detail and control is really not possible with AI; it will mess up something in multiple angles.”

So, AI “hallucinations” (where a model invents details that don’t exist) are acceptable in concept art but a fatal problem in construction documents. If an interior designer specifies a particular fabric from a manufacturer, or a specific joinery detail, an AI that “vibes” the answer is insufficient. As the user said, “If you’re an expert, you should be leveraging your skill to create actual renders […] AI is not going to get everything right.”.

The Expert View: Reading the Blueprints

Conceptual Diagram, image generated by the author using Gemini 3 Pro Image

Despite concerns about accuracy, the model’s ability to understand technical input is quite good. Ismail Seleit, an architect and AI advocate, recently shared his experiments on LinkedIn:

“First of all, I am super impressed by the graphical quality […] This is not a vector-based model, (so I have) no idea how it does that.” He pointed out that Nano Banana Pro didn’t just generate pretty pictures; it interpreted plans in a way that created genuine architectural ideation. “Plans also start to give you some interesting ideas; I found this exercise really refreshing in that sense.”

This view also shows some parallels to some reviews I came across on X (Twitter), user @ai_for_success tweeted:

“Nano Banana Pro / Gemini 3 Pro Image is crazy. It turned this blueprint into a realistic 3D image. It did not just create the image, it first read the blueprint properly and then created the final output with every small detail.”

This ability to “read” rather than just “dream” is what sets this generation of AI apart.

The Experiment: Testing the Formula

To truly understand the tool’s capabilities, I decided to run my own experiment. The goal was to move from a raw concept to a render using Nano Banana Pro.

Many users recommend a specific prompt formula to maximize the model’s output: Subject + Action + Environment + Style + Lighting + Details.

Step 1: The Concept

The Main Render (Front View), image generated by the author using Gemini 3 Pro Image

I began by asking Gemini to generate a text-based conceptual floor plan for a museum. The AI proposed a two-story structure featuring:

Ground Floor: A central atrium lobby, a grand staircase, a large exhibition hall, a café, and a gift shop.

Second Floor: A secondary exhibition hall, classrooms, and staff offices.

Exterior: A garden with organic, winding paths.

Step 2: The “Engineer” Pivot

The Side View (Right View), image generated by the author using Gemini 3 Pro Image

When I initially asked for a “front view” based on this description, the model struggled to maintain coherence. I switched tactics, uploading the plan and asking for a render. Interestingly, Nano Banana Pro pushed back. It stated, “I cannot directly generate a render file… but I can act as your prompt engineer.”

This was an interesting moment for me because I thought I had done something wrong, checked the process, re-requested the action, received a similar reply, and then built a prompt again to get a response, which highlighted that the “human in the loop” is still essential. The AI needed me to guide it, to approve the translation of visual data into a descriptive prompt.

Step 3: The Execution

The Aerial View (Bird’s Eye), image generated by the author using Gemini 3 Pro Image

Using the “Prompt Engineer’s” suggestion and the community formula, I constructed the final prompt:

Subject: A contemporary, two-story art museum with a flat white roof and limestone cladding.
Action: Cultural landmark.

Environment: A garden with winding paths and abstract metal sculptures.

Style: Photorealistic architectural render, 8k resolution, cinematic wide-angle.

Lighting: Golden hour, with warm artificial light spilling from the windows.

Details: Weathering on the stone, HVAC units on the roof, and silhouetted figures for scale.

The Result

Interior, image generated by the author using Gemini 3 Pro Image

The output was good.

However, it wasn’t perfect (and I wasn’t expecting it to be perfect). When I requested different angles or specific architectural diagrams (like an isometric cutaway), the model often drifted. It required constant “re-prompting” to ensure the sculpture garden stayed in the same place or that the window mullions remained consistent.

Nano Banana vs. Midjourney vs. DALL-E

Model Comparison – Image generated using the same architectural prompt (at DALL·E) to evaluate consistency in instruction-following performance across models.

With the arrival of Nano Banana Pro, the “Big Three” of AI-generated visuals have finally settled into separate roles.

For years, Midjourney and DALL-E 3 have been the industry standards, but they serve different masters. Midjourney is the “dreamer,” with mostly cinematic lighting, artistic details and mood. DALL-E 3, on the other hand, is the “communicator,” easy to use and mostly loyal to your prompt instructions, but often lacking that final layer of realism.

Nano Banana Pro (Gemini) has carved out a third, more technical niche: the “engineer.”

While Midjourney and DALL-E excel at conceptualizing a project, Nano Banana Pro could excel at visualizing it. Its potential for realistic visualization and detailed edits is significantly higher. It can pull real-world textures (like specific limestone weathering or accurate glass reflections) that feel less like a painting and more like a photograph.

But the true game-changer is blueprint literacy.

Midjourney often treats a floor plan as a collection of abstract lines, creating “artistic” interpretations that don’t make structural sense. Nano Banana Pro’s ability to “read” the blueprint is a massive plus. It interprets the lines as architectural instructions, creating a result that respects the intended spatial logic.

The Verdict: Inspiration, Not Replacement

So, where does this leave us?

For now, designers are safe. The consensus (and my experiment confirms this) is that while Nano Banana Pro is a good engine for ideation, it is not yet a replacement for documentation.

We are likely moving toward a hybrid workflow. In the next five years, AI literacy in construction and architecture may become as standard as knowing AutoCAD or Revit. We will use tools like this to iterate through “moods” and “atmospheres” in the early stages, before moving to traditional BIM software for the precision required to actually build.

There are, of course, the critical questions we chose to set aside for this experiment: the environmental cost of training these massive models and the energy consumption required to generate every “banana” output.

Nano Banana Pro is not an architect or designer. It is a mirror. It reflects our ideas back to us, sharper and brighter than we imagined, but it still requires a human hand to hold it stable.

Architects: Want to have your project featured? Showcase your work by uploading projects to Architizer and sign up for our inspirational newsletters.

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