Nano Banana Pro | AI Photo Editing and Image to Image

About this model

Google Nano Banana Pro is a higher-end image model for premium text-to-image and image-to-image work when you need sharper detail, more reliable text rendering, multi-image composition, and resolution options up to 4K.

When is this model useful?

Nano Banana Pro is the better fit when image quality, readable in-image text, and more controlled edits matter more than keeping the workflow as cheap or lightweight as possible.

Best fit tasks

Generating posters, banners, packaging, UI mockups, diagrams, and infographics where the image needs readable text, not just decorative lettering.
Premium text-to-image work for ad creatives, product visuals, presentations, landing-page hero images, and branded marketing assets.
Image-to-image edits where you need to preserve the main subject while changing lighting, mood, framing, depth of field, style, or scene details.
Multi-reference compositions that blend several product shots, sketches, characters, or style references into one polished result.
Localization workflows where you want to replace text in an image with another language while keeping the rest of the visual close to the original.

Main advantages

It is stronger than lighter image models when the output includes visible text, structured layouts, diagrams, or branded graphic elements.
It supports both generation and editing, so the same model can handle first-draft ideation, product mockups, and controlled revisions.
You can upload up to 14 reference images, which helps when the final image needs to combine several visual inputs in one scene.
The integration exposes 1K, 2K, and 4K output options, making it easier to choose between faster tests and more presentation-ready detail.
It can make more sophisticated visual changes such as changing time of day, lighting feel, composition, or selective scene elements through plain-language prompting.

Limitations to know

It still generates static images, not video, animation, or interactive design files.
Readable text is better than on many image models, but you should still proofread spelling, grammar, facts, and small typography before publishing.
Complex edits such as heavy relighting, many merged references, or major scene reconstruction can still produce artifacts or less natural details.
Higher resolution costs more credits, especially 4K, so this model is better for quality-focused work than for the cheapest bulk ideation.
Consistency across multiple runs is improved with good references and clear prompts, but it is not a locked production pipeline.

How to use this model

A practical workflow is to start with a clear plain-language prompt, then add only the reference images and controls that materially improve the result.

Simple workflow

Write the prompt in plain language. Describe the subject, setting, style, lighting, camera feel, mood, and any exact words that must appear in the image.
If the image should include text, keep the wording short, put it in quotation marks, and mention what kind of asset it is, for example poster, package front, infographic, label, or slide.
Choose the aspect ratio that fits the channel. 1:1 works for square posts, 16:9 for banners and slides, 9:16 for stories, and 21:9 for extra-wide hero visuals.
Choose resolution based on the stage of work. 1K is fine for quick tests, 2K is a balanced default, and 4K is best when you want maximum detail for polished delivery.
For editing or remixing, upload one or more reference images and explain what each one should contribute, for example subject from image one, lighting from image two, and layout from image three.

Supported inputs

Required: a text prompt.
Optional: up to 14 reference images for image editing, compositing, style transfer, product mockups, or multi-image blends.
The safest image upload formats in AISVIT are JPG, PNG, and WEBP.
In AISVIT, there is no separate layer-based editing workflow for this model, so precise changes should be described directly in the prompt.

What you get

One generated or edited image per run.
JPG or PNG output.
Resolution choices: 1K, 2K, or 4K.
Supported frame shapes: match input image, 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, and 21:9.