How to Use Nano Banana Pro for Photo Editing [2026]

Google retired the original Nano Banana image model on October 2, 2026, and that single date forced a lot of workflows to change overnight. If you built prompts, presets, or an entire editing pipeline around Gemini 2.5’s Nano Banana, those prompts now point at a dead end. The replacement, Nano Banana Pro, runs on Gemini 3 and behaves differently enough that copy-pasting your old prompts will give you worse results, not the same ones. This tutorial walks through the current, correct way to edit real photos with Nano Banana Pro in October 2026: object removal, masked local edits, in-image text changes, identity-safe multi-edit workflows, batch processing, and a small Python project that automates the whole thing through the Gemini API.

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Why Nano Banana Pro Is Reshaping AI Photo Editing in 2026

Nano Banana Pro is Google’s current image model, built on Gemini 3 rather than the Gemini 2.5 generation that powered the first Nano Banana release. The jump matters because Gemini 3’s image stack was built specifically for editing existing photos, not just generating new ones from scratch. Google describes the goal as studio-quality precision and control, and the feature set backs that up: object-level selection, text editing inside images, format-aware cropping, and native upscaling to 2K or 4K all ship in the same model.

The timing is not incidental. Google began deprecating Gemini 2.5 with Nano Banana starting October 2, 2026, which means any tutorial, prompt library, or automation script still targeting the old model is already out of date. Nano Banana Pro and a closely related update, Nano Banana 2.1, are now the versions rolling out across the Gemini app, Google Search’s AI Mode, Google AI Studio, Google Flow, Stitch, Google Ads, and the Gemini Enterprise Platform. That is a wide surface area, and it is also why the setup steps below spend time on picking the right entry point before touching a single prompt.

What actually changed for editing, specifically, breaks into five capabilities worth knowing before you start: selecting and modifying individual objects, editing or translating text elements without disturbing the rest of the frame, reformatting one image for multiple aspect ratios in a single pass, upscaling finished edits to 2K or 4K, and processing several edits in one batch. Google Pics, the editing-focused surface sitting on top of the model, also keeps a revert history so you can back out of an edit chain instead of starting over from the original file. None of this requires design software experience. Most of it requires a clear prompt and, for anything precision-dependent, a mask.

The rest of this guide treats Nano Banana Pro as a tool with real constraints, not a magic wand. It will redraw more of your image than you want if your prompt is vague, it will drift a person’s face slightly across repeated edits unless you tell it not to, and it will happily degrade quality if you re-feed it an already-edited JPEG five times in a row. Knowing those failure modes up front saves more time than any single prompting trick.

Here’s the full capability set at a glance, with where each one actually lives across Google’s current product lineup:

Capability	How you trigger it	Where it’s available
Object selection and removal	Click an object in Google Pics, or describe it by name and position in a prompt	Google Pics, Gemini app
Mask-based local edits	Draw a freehand or rectangular mask, then prompt only the change	Google Pics, AI Studio image tools (Nano Banana 2.1)
Text editing and translation	Select the text element and describe the replacement or target language	Google Pics
Format-aware cropping	Name the destination format (social, print, web, digital)	Google Pics
2K/4K upscaling	Apply as the final step after content edits are finished	Google Pics
Batch processing	Apply one instruction across a selected set of images	Google Pics
Programmatic editing	Call the Gemini API with an image and a text instruction	Google AI Studio, Vertex AI
Photoshop/Firefly integration	Select Gemini 3 with Nano Banana Pro in the Generative Fill model picker	Adobe Photoshop, Adobe Firefly

Prerequisites: Accounts, Versions, and Tools You’ll Need

You do not need a paid subscription to follow the UI-based steps in this tutorial, but a couple of them (identity-consistent multi-edit chains, 4K upscaling, and the API project near the end) go faster or further with one. Here is what to have ready before Step 1.

Requirement	Minimum tier	Why you need it
Google account	Free	Access to the Gemini app and Nano Banana Pro’s “Thinking” model mode
Gemini app or gemini.google.com	Current web/mobile release, October 2026	Primary editing surface for object edits, masks, and text changes
Google AI Pro or AI Ultra (optional)	Paid	Higher generation quotas and faster access to Nano Banana Pro during peak load
Google AI Studio account	Free tier available	Generates an API key for the automation project in Step 9
Python	3.10 or newer	Running the batch-edit script and the google-genai SDK
google-genai SDK	Latest published release	Python client for calling the Gemini image model programmatically
Adobe Photoshop or Firefly (optional)	Current Creative Cloud release	Using Nano Banana Pro as a partner model inside Generative Fill
Source photos	JPEG, PNG, or WEBP	The images you’re actually going to edit

One note on versioning: do not hardcode a model ID string from an old tutorial, including this one six months from now. Google’s preview and production model names shift as new versions ship, and AI Studio’s model picker always lists the current, callable ID. Copy it from there rather than from memory.

Step 1: Pick Your Editing Surface and Select the Model

Nano Banana Pro is reachable from at least four places: the Gemini app, Google AI Studio, Google Search’s AI Mode, and, if you have Creative Cloud, Photoshop’s Generative Fill. For a first editing session, start in the Gemini app or at gemini.google.com, since that surface has the fastest path from upload to edited output.

Once you’re in, the model selection step is easy to miss. Google’s own guidance on this is blunt: “To use Nano Banana Pro, select ‘Thinking’ or ‘Pro’ from the model menu.” That menu sits near the prompt box, usually as a dropdown labeled with the active model name. If it still shows a plain “Nano Banana” or a Gemini 2.5 label, you’re on the deprecated path and your edits will use the older, less capable model without any warning banner telling you so.

Google also frames the entry point as close to a one-click action. In its own words, “You can simply select ‘Create images’ and choose the ‘Thinking’ model to use it.” In practice that means: open a new chat, look for the image-creation toggle or the model dropdown, and pick the Thinking or Pro variant depending on what your account tier exposes. Free accounts typically see “Thinking” as the label for Nano Banana Pro access, while Pro and Ultra subscribers may see an explicit “Pro” designation with higher-resolution output and faster queue priority.

If you plan to use Photoshop or Firefly instead, skip ahead to Step 10, since the model selection step there works through Adobe’s partner-model picker rather than Google’s own interface. If you’re building the automation project later in this guide, open AI Studio now in a second tab so you can generate an API key while the model name is fresh in front of you.

Step 2: Upload a Source Photo and Write Your First Edit Prompt

With the Thinking or Pro model active, upload the photo you want to edit directly into the chat. Nano Banana Pro treats an uploaded image as context for the next prompt, so the order matters: image first, instruction second. Uploading without a clear instruction just gets you a description of the photo, not an edit.

The single biggest quality lever at this stage is prompt specificity. Google’s own prompting guidance for this model states it plainly: “Use specific details in your prompts: subject, composition, action, location, and style.” That advice applies just as much to edits as it does to fresh generations. A prompt like “make this photo better” gives the model enormous latitude to change things you never wanted changed. A prompt like “keep the subject, framing, and lighting identical, remove the coffee cup on the left side of the table, and fill that space with matching wood grain” gives it almost none.

Here’s a template worth keeping on hand for your first few edits, before you develop your own shorthand:

Edit this photo. Keep everything else in the frame unchanged:
composition, lighting, color grade, and all other objects.
Change only: [the one thing you want changed].
Do not alter the subject's face, pose, or clothing unless
explicitly instructed above.

That last constraint line is doing more work than it looks like. Without it, Nano Banana Pro will sometimes “helpfully” smooth skin, adjust lighting, or nudge a pose when it regenerates the edited region, because its training pushes it toward aesthetically pleasing output by default. Telling it explicitly what to leave alone cuts down on that drift substantially.

Step 3: Remove or Replace Objects With Prompt-Based Edits

Object removal and replacement are the most common first edit people try, and they’re also where Nano Banana Pro’s object-level selection pays off. Google Pics, the editing layer built on top of the model, supports selecting and editing individual objects within an image directly, rather than requiring you to describe spatial coordinates in text.

If you’re working through the Gemini app rather than Google Pics, you describe the object instead of clicking it. Name it by what it is and where it sits: “the red umbrella in the background, upper right,” not just “the umbrella,” especially if more than one similar object appears in frame. Ambiguous references are the top cause of the model editing the wrong thing.

For replacement rather than removal, describe the replacement with the same level of detail you’d give for the original object:

Replace the plain white mug on the desk with a matte black
ceramic mug of the same size and position. Match the existing
light source direction and shadow length. Leave the desk,
laptop, and background completely unchanged.

Run the edit, then compare the output against the original side by side before accepting it. If the model changed something outside the object you targeted, go back to the revert history, restore the pre-edit version, and tighten the prompt rather than layering a correction on top of a flawed result. Chaining fixes on top of an already-wrong edit compounds errors fast.

Step 4: Use Mask-Based Local Edits for Pixel-Precise Control

Prompt-only edits work well for isolated, clearly describable objects. They work less well when the region you want to change sits close to detail you want untouched, like a logo near a face or text overlapping a textured background. For that level of precision, Nano Banana 2.1 adds mask-based editing, which lets you select a specific region of an existing image and limit changes to that area only.

Drawing a freehand mask

Inside the editing surface that exposes masking (currently Google Pics and AI Studio’s image tools), select the brush or lasso option and paint over the exact area you want changed. The model then treats everything outside that mask as locked. This matters most for edits like swapping a shirt color without touching skin tone, or removing a background object that sits one pixel away from a subject’s outline.

Using a rectangular region for simple crops of attention

For less fiddly edits, a rectangular region mask is faster to draw and still constrains the model enough to prevent unwanted bleed into surrounding pixels. Use this for edits like changing a sign’s color or cleaning up a corner of frame, where pixel-perfect boundaries matter less than general confinement.

Once a mask is active, your prompt only needs to describe the change, not the boundary, since the mask already handles that: “change this shirt from blue to forest green, keep the fabric texture and folds” is enough when a mask is already isolating the shirt.

Step 5: Edit Text Inside a Photo Without Touching Anything Else

Text-in-image editing is one of the features that separates Nano Banana Pro from older generative editors, which typically mangled text or required a separate OCR-and-replace pipeline. Google Pics can edit, reformat, or translate individual text elements inside an image, which covers three distinct use cases: fixing a typo on a sign, changing a price on a flyer, or translating a label into another language while preserving the original font style.

The reliability of text edits depends heavily on how legible the original text is and how much it’s integrated with the background. A clean sans-serif sign on a flat background edits cleanly almost every time. Stylized script text wrapped around a curved object, or text partially obscured by another object, is far more likely to come back distorted.

For straightforward text swaps, combine a mask on the text region with a prompt like:

Change the text "SALE 20% OFF" to "SALE 40% OFF".
Match the original font, size, color, and placement exactly.
Do not change anything outside the masked text region.

For translation rather than substitution, name the target language explicitly and ask the model to preserve layout: “Translate this sign’s text to Spanish, keep the same line breaks and font style.” Always re-check the output character by character. Even a strong text-editing model occasionally drops a character or mis-renders an accent mark, and that’s an easy miss if you’re skimming instead of reading the actual output text.

Step 6: Keep a Subject’s Identity Consistent Across Multiple Edits

Identity drift is the quiet failure mode nobody notices until they put the first and fifth edited versions of a photo side by side. Each edit pass gives the model another opportunity to subtly reinterpret a face, a logo shape, or a product’s exact proportions, and those small shifts accumulate.

Three habits keep identity stable across an editing session. First, edit from the original source file each time rather than chaining edits onto an already-edited output, since every regeneration pass introduces a small amount of reinterpretation. Second, when the surface you’re using supports reference images, attach the unedited original alongside your edit request so the model has a ground truth to match against rather than relying purely on its own prior output. Third, keep your “preserve” instructions explicit in every single prompt rather than assuming the model remembers your intent from an earlier message in the same chat.

If you’re producing a set of product photos that all need to show the exact same item in different settings, treat the original hero shot as your fixed reference for every subsequent edit rather than editing the previous output again and again. It’s slightly more manual work per edit, but it stops the slow accumulation of small inconsistencies that become obvious once you lay six images in a grid.

Step 7: Reformat, Crop, and Upscale Your Finished Edit

Once the edit itself is finished, the output still needs to fit wherever it’s going, and Nano Banana Pro handles both the aspect-ratio problem and the resolution problem in the same surface.

Reformatting for different placements

Google Pics supports cropping images for web, social, print, and digital formats, which means you can take one finished edit and generate a square version for a social feed, a tall version for a story format, and a wide version for a banner, all from the same source without re-running the actual edit. Ask for the crop by name of destination rather than raw ratio when possible: “reformat this for an Instagram post” tends to produce better framing decisions than “crop to 1:1,” since the model has context about what that format typically looks like.

Upscaling to 2K or 4K

Google Pics can upscale images to 2K or 4K, and the order of operations here matters: finish every content edit first, then upscale last. Upscaling early and then making further content edits forces the model to work with an already-interpolated image, which tends to introduce soft artifacts in fine detail like hair, fabric weave, and small text. Treat upscaling as the final step in your pipeline, not an early convenience.

Step 8: Batch-Edit an Entire Folder of Photos

Google Pics supports processing multiple edits at once, which is the difference between editing a wedding gallery one photo at a time and running the same correction across the whole set in one pass. Batch mode is most useful for edits that apply identically across many images: a consistent color grade, the same watermark removal, or a uniform crop for a product catalog.

Batch editing works best when every image in the set shares a similar composition and the edit instruction doesn’t depend on image-specific details. “Remove the background clutter and replace with a neutral gray” applies cleanly across fifty similar product shots. “Fix whatever looks off in this photo” does not, because the model has nothing consistent to apply across the batch and will produce wildly inconsistent results from image to image.

Before committing a full batch run, test the exact prompt on three or four representative images from the set first. If the test batch comes back consistent, scale up to the full folder. If it comes back uneven, the prompt needs tightening before you burn a full batch’s worth of generation quota on a flawed instruction.

Step 9: Automate Edits With the Gemini API — A Working Mini Project

For anything beyond a few dozen images, or for edits that need to run unattended on a schedule, the Gemini API gives you programmatic access to the same underlying model. This section builds a small, complete script that walks a folder of source photos, applies the same edit instruction to each one, and saves the results to an output directory.

Install the SDK and authenticate

Generate an API key from Google AI Studio first, then install the official Python SDK:

pip install google-genai

export GEMINI_API_KEY="your-api-key-here"

Confirm the exact model ID for the image-capable Gemini 3 model from the “Models” page inside AI Studio before running anything, since preview model names change between releases and a stale ID will fail with a clear not-found error rather than silently falling back to an older model.

The batch-edit script

This script reads every image in an input/ folder, sends each one with the same edit prompt, and writes the result to output/:

import os
import time
from google import genai
from google.genai import types

client = genai.Client()  # reads GEMINI_API_KEY from env

MODEL_ID = "REPLACE-WITH-CURRENT-MODEL-ID-FROM-AI-STUDIO"
EDIT_PROMPT = (
    "Remove the watermark in the bottom right corner. "
    "Keep everything else in the image completely unchanged."
)

input_dir = "input"
output_dir = "output"
os.makedirs(output_dir, exist_ok=True)

for filename in os.listdir(input_dir):
    if not filename.lower().endswith((".jpg", ".jpeg", ".png", ".webp")):
        continue

    path = os.path.join(input_dir, filename)
    with open(path, "rb") as f:
        image_bytes = f.read()

    try:
        response = client.models.generate_content(
            model=MODEL_ID,
            contents=[
                types.Part.from_bytes(data=image_bytes, mime_type="image/jpeg"),
                EDIT_PROMPT,
            ],
        )
    except Exception as e:
        print(f"Failed on {filename}: {e}")
        time.sleep(2)
        continue

    for part in response.candidates[0].content.parts:
        if part.inline_data:
            out_path = os.path.join(output_dir, f"edited_{filename}")
            with open(out_path, "wb") as out_f:
                out_f.write(part.inline_data.data)
            print(f"Saved {out_path}")

    time.sleep(1)  # basic rate-limit spacing between requests

For a single REST call without the SDK, the same edit looks like this with curl, assuming the current endpoint and model ID from AI Studio:

curl -X POST \
  "https://generativelanguage.googleapis.com/v1beta/models/REPLACE-MODEL-ID:generateContent" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [{
      "parts": [
        {"inline_data": {"mime_type": "image/jpeg", "data": "BASE64_IMAGE_DATA"}},
        {"text": "Remove the watermark in the bottom right corner. Keep everything else unchanged."}
      ]
    }]
  }'

Two practical notes on running this at scale. First, add real rate-limit handling, not just a flat sleep call, once you move past a handful of test images, since API quotas vary by account tier and a burst of requests can get throttled. Second, log every filename that fails alongside the error message rather than letting the script silently skip it, so a partial batch run doesn’t quietly lose a chunk of your folder without you noticing until much later.

Step 10: Bring Nano Banana Pro Into Photoshop and Firefly

If your workflow already lives in Adobe’s tools, you don’t need to leave them. Adobe’s own documentation on partner models in Creative Cloud apps confirms that Gemini 3 with Nano Banana Pro is available as a selectable model for prompt-based image editing inside Firefly, and that the same model also powers Generative Fill in Photoshop, letting you alter parts of an existing image through Adobe’s own selection tools instead of Google’s.

Practically, this means you can keep using Photoshop’s selection, masking, and layer tools exactly as before, and just point the Generative Fill model picker at Nano Banana Pro instead of Adobe’s own Firefly model when you want that model’s specific strengths, like text editing or object-level understanding. The workflow mechanics don’t change: make a selection, open Generative Fill, write a prompt, generate, and keep or discard the result on its own layer the way Photoshop has handled generative edits since Firefly first launched there.

One advantage of routing through Photoshop specifically is non-destructive layering. Because Generative Fill results land on their own layer, you can compare a Nano Banana Pro attempt against an Adobe Firefly attempt for the same selection without redoing any work, just by toggling layer visibility. For teams that need to pick a final model per project based on results rather than committing up front, that comparison workflow alone can be worth staying inside Photoshop rather than moving the whole job into the Gemini app.

Common Pitfalls When Editing Photos With Nano Banana Pro

Vague prompts that trigger a full repaint. Instructions like “improve this photo” give the model permission to change far more than you intended. Always name the specific change and what should stay untouched.
Skipping masks on detail-adjacent edits. Editing an object that sits close to a face, logo, or textured background without a mask often bleeds into the area you wanted preserved.
Chaining edits onto already-edited output. Re-feeding an edited image back in for another edit compounds quality loss and identity drift. Edit from the original source whenever possible.
Upscaling before finishing content edits. Upscaling early and then editing further forces the model to work from an interpolated image, which shows up as soft artifacts in fine detail.
Assuming unlimited free generation. Free-tier accounts hit quota limits faster than expected during a long editing session, especially with 4K upscales, which cost more generation budget than standard edits.
Running a full batch before testing a sample. Committing fifty images to a batch edit without testing the prompt on three or four first wastes quota when the prompt needs adjustment.

Troubleshooting: Fixing the Most Common Nano Banana Pro Errors

Output looks like a different photo entirely. Add an explicit preserve-everything-else clause to your prompt and resubmit from the original file, not the failed output.
Edited text changed font or style. Draw a mask around the text region specifically rather than relying on a text-only prompt description.
A person’s face looks slightly different after editing. Attach the original unedited photo as a reference image if your surface supports it, and keep your preserve-the-face instruction in every prompt.
Upscaled image shows blurry or doubled detail. Finish all content edits before upscaling. Upscaling mid-pipeline locks in interpolation artifacts.
Model menu still shows the old Nano Banana, not Pro. This usually means you’re on a path still routed to the deprecated Gemini 2.5 model. Switch the dropdown to “Thinking” or “Pro” explicitly.
Batch job times out partway through. Add delay between requests and check your account’s current quota in AI Studio. Large batches on a free tier are the most common cause.
Image generation rejected for a policy violation. Review the edit request for real public figures, restricted content, or anything that could read as deceptive imagery, and adjust the prompt or source image accordingly.
Output resolution caps lower than requested. Some account tiers cap maximum output resolution below 4K. Check your tier’s limits in AI Studio or account settings before assuming the request itself failed.
Photoshop doesn’t show Nano Banana Pro in the model picker. Update Photoshop and Creative Cloud to the current release, and confirm the partner model is available in your region, since Adobe rolls out new partner models progressively.
Revert history is missing or incomplete. Revert history lives inside Google Pics specifically, not every Gemini surface, so edits made directly in a Gemini app chat may not have the same rollback option.

Advanced Tips for Professional-Grade Output

Once the basics are solid, a few habits separate a quick edit from a production-ready one. Chain small, single-purpose edits instead of one large complex prompt. Asking for an object removal, a text change, and a crop all in one instruction gives the model three chances to misinterpret something at once. Running them as three sequential, narrow edits is slower but far more predictable.

Google’s own prompting guidance extends past basic subject and composition details into finer control: “Refine prompts with camera angles, lighting, text integration, and factual constraints for diagrams.” That last point matters more than it sounds. If you’re editing a technical image, chart, or diagram rather than a photo, explicitly constraining the model to preserve factual accuracy in labels and values stops it from “improving” a diagram in ways that quietly change its meaning.

For teams running this at real volume, separate your prompt templates from your automation code entirely, stored as plain text or a small config file rather than hardcoded strings inside the script. Prompts get iterated on far more often than the surrounding Python, and keeping them separate means a prompt tweak doesn’t require touching or re-testing the script logic.

If your batch volume grows past what AI Studio’s free tier comfortably handles, Vertex AI offers the same underlying model family with enterprise-tier quota increases and billing controls, which is the more appropriate path once you’re running thousands of edits a month rather than dozens.

Real-World Use Cases for Nano Banana Pro Editing

The step-by-step workflow above applies the same way across several practical jobs people are actually using Nano Banana Pro for right now. Seeing a few concrete scenarios makes it easier to decide which steps matter most for your own project.

E-commerce sellers use the object removal and replacement workflow from Step 3 constantly, mainly to clean up product photos shot in imperfect conditions: pulling a stray cable out of frame, swapping a distracting background prop, or replacing a scuffed table surface with a clean one. Because catalog photos usually share a similar composition, this is also one of the strongest fits for the batch editing covered in Step 8, since one well-tested prompt can clean up fifty listing photos in roughly the time it takes to clean one manually.

Real estate listings lean heavily on the text-editing and reformatting steps. Agents use text edits to update a “For Sale” sign to “Sold” or to swap a listing price across a batch of marketing photos, then use the format-aware cropping from Step 7 to generate a square version for Instagram, a wide version for a listing site banner, and a vertical version for a Stories post, all from one finished edit rather than three separate design passes.

Social media and marketing teams get the most value from the identity-consistency habits in Step 6, since a single campaign often needs the same product or spokesperson placed in five or six different settings without the subject’s face or the product’s shape drifting between versions. Teams running this at real volume are also the ones most likely to need the Gemini API project from Step 9, since generating variations for an entire campaign calendar by hand in the Gemini app chat interface doesn’t scale the way a scripted batch job does.

Personal photo restoration is a smaller but genuinely useful case: removing a scratch or crease artifact from a scanned family photo, mildly colorizing a faded print, or cleaning up a cluttered background in an old snapshot before printing it at a larger size with the 4K upscale from Step 7. These edits tend to be one-off rather than batch work, so the UI-based Gemini app workflow from Steps 1 through 7 covers the whole job without ever touching the API.

Nano Banana Pro vs. Other AI Photo Editors in 2026

Nano Banana Pro isn’t the only serious option for AI-driven photo editing this year, and picking the right tool depends on what the edit actually requires. According to the Artificial Analysis image editing leaderboard, GPT Image 2.5 Sunburst held the top overall ranking as of September 2026, with Nano Banana 2.1 also placing among the leading current models in the October 2026 results. Grok Imagine Image 2.0, meanwhile, differentiates itself with magic-wand region edits, support for up to five reference images in a single request, and smart resizing across nine aspect ratios.

Model	Standout editing strength	Best suited for
Nano Banana Pro (Gemini 3)	Object selection, text editing, native 2K/4K upscale, batch edits	Everyday photo cleanup, product catalogs, social reformatting
Nano Banana 2.1	Mask-based local edits, wide cross-product rollout	Precision edits near faces, logos, or fine detail
GPT Image 2.5 Sunburst	Top overall leaderboard ranking as of September 2026	General-purpose generation and editing quality benchmarks
Grok Imagine Image 2.0	Magic-wand region edits, up to 5 reference images, 9 aspect ratios	Multi-reference consistency and rapid reformatting
Adobe Firefly (native model)	Deep Creative Cloud integration, commercial usage terms	Teams already standardized on Photoshop workflows

In practice, most people editing real photos rather than running formal benchmarks will choose based on where they already work rather than leaderboard position alone. If you live in Google’s ecosystem, Nano Banana Pro’s native integration across the Gemini app, Search AI Mode, and AI Studio makes it the path of least friction. If you live in Photoshop, routing through Adobe’s partner-model picker gets you the same model without changing your actual workflow.

Frequently Asked Questions

Is Nano Banana Pro free to use?
Yes, a free tier is accessible through the Gemini app and Google AI Studio, though generation quotas, output resolution, and queue priority are more limited than on Google AI Pro or Ultra subscriptions.

What happened to the original Nano Banana model?
Google began deprecating Gemini 2.5 with Nano Banana starting October 2, 2026. Workflows and prompts built for that version should move to Nano Banana Pro, built on Gemini 3.

Do I need to know how to code to edit photos with Nano Banana Pro?
No. The Gemini app, Google Pics, and Photoshop’s Generative Fill all expose the model through a prompt-and-click interface. Code is only needed for the automation project covered in Step 9, which is optional.

What’s the difference between Nano Banana Pro and Nano Banana 2.1?
Nano Banana Pro is the Gemini 3-based model focused on broad editing capability including object selection, text editing, and upscaling. Nano Banana 2.1 is the update that specifically adds mask-based local editing and has rolled out across more Google products, including Search AI Mode and Gemini Enterprise.

Can I use Nano Banana Pro inside Photoshop?
Yes. Adobe lists Gemini 3 with Nano Banana Pro as a selectable partner model inside Generative Fill, alongside Firefly’s native models.

What’s the maximum resolution Nano Banana Pro can output?
Google Pics supports upscaling finished edits to 2K or 4K, though actual maximum output resolution can depend on your account tier.

Can I batch-edit hundreds of photos with the Gemini API?
Yes, using the google-genai Python SDK or the REST endpoint directly. For volumes beyond a few hundred images a month, Vertex AI offers higher quota tiers than the standard AI Studio API key.

Is Nano Banana Pro better than GPT Image or Grok Imagine for editing?
It depends on the task. The Artificial Analysis editing leaderboard placed GPT Image 2.5 Sunburst at the top as of September 2026, with Nano Banana 2.1 also among the leading models. Grok Imagine Image 2.0 stands out specifically for multi-reference edits and rapid aspect-ratio resizing. Testing your specific edit type across two or three models is the most reliable way to pick a winner for your own workflow.

How to Use Nano Banana Pro: 12 Steps to 4K Edits [2026]