> ## Documentation Index
> Fetch the complete documentation index at: https://docs.ai-stats.phaseo.app/llms.txt
> Use this file to discover all available pages before exploring further.

# Google: Nano Banana 1 to Nano Banana 2

> What actually changes when image workflows move from Nano Banana 1 to Nano Banana 2.

# Google: Nano Banana 1 to Nano Banana 2

Use this guide when you are replacing an older Nano Banana workflow with Nano Banana 2.

Nano Banana 2 is not only a quality upgrade. Google positions it as a production-ready image model with broader control over output format, resolution, and fidelity.

## What changed

* Nano Banana 2 adds explicit control over aspect ratios
* output resolutions now range from `512px` to `4K`
* subject consistency is improved across generations and edits
* instruction following is stronger
* text rendering and translation inside images are better
* visual fidelity is higher while generation remains fast

## What to change in your integration

### 1. Re-check output assumptions

If downstream code assumes a fixed image size, aspect ratio, or crop behavior, update it before rollout. Nano Banana 2 is more flexible, which means older assumptions become easier to violate.

### 2. Update product presets

Revisit:

* social aspect-ratio presets
* default resolution choices
* edit and inpaint workflows
* UI labels that describe available image sizes

### 3. Re-tune prompts for image editing

Because instruction following is stronger, prompts that used to over-specify composition, text, or style may now be unnecessarily rigid. Re-test with shorter prompts and compare results.

## What to test

### Output quality

* vertical, square, and widescreen aspect ratios
* low versus high resolution outputs
* subject consistency across iterative edits
* text rendering, signage, captions, and translated text inside images

### Workflow compatibility

* file handling for larger resolutions
* timeout behavior on higher-fidelity generations
* moderation or human-review pass rate
* downstream resizing and asset-processing assumptions

### Product metrics

* end-to-end generation latency
* usable-output rate
* retry rate
* cost per approved asset

## Rollout advice

1. Split testing by aspect ratio and resolution rather than treating this as a single migration.
2. Validate your asset pipeline on the largest output size you plan to support.
3. Sample real production prompts, especially edits and text-in-image requests.
4. Roll out default preset changes separately from the model swap if your UI exposes image options to users.

## Sources

* [Nano Banana 2](https://blog.google/innovation-and-ai/technology/ai/nano-banana-2/)