Nano Banana 2 Image Editing: How It Works and How to Edit Images

Nano Banana 2 image editing means using the Nano Banana 2 model inside a multi-model workspace to change an existing image with a text instruction — for example, swapping a background, adjusting lighting, or restyling a subject — instead of generating a brand-new image from scratch. On HeyMarmot, a third-party AI creative workspace (not a Google official product), you do this by uploading or generating a base image, describing the change you want, and generating the result. It fits tasks where you already have an image you mostly like and want to modify one or two things while keeping the rest intact.

Where Nano Banana 2 editing sits in a workspace like HeyMarmot

HeyMarmot is an all-in-one AI creative assistant that bundles text-to-video, image-to-video, AI image creation, and text-to-music in one place. Its image tools let you either describe an idea and generate a visual, or upload a reference image to guide the output. Nano Banana 2 editing is the reference-guided path: you bring an image in, and the model edits it according to your prompt.

Two things follow from that setup:

• You are not locked into one model. The workspace integrates multiple top-tier models, so image editing is one capability among several rather than a standalone app.
• Editing and generation share the same interface. The same upload-and-describe flow that generates an image also edits one, which is why the steps below look similar to plain image generation.

The edit flow, step by step

The general pattern for an edit is: provide a base image, state the change, generate, then check the result.

1. Start with a base image. Either upload an existing image or generate one first with a text prompt. If you generated it, keep that image as your working base so later edits stay anchored to it.
2. Describe only the change. Write a prompt that names what should change and, where it matters, what should stay the same. "Replace the gray background with a sunset beach, keep the subject's pose and clothing unchanged" is more controllable than "make it nicer."
3. Generate and inspect. The model returns an edited image. Check the specific region you asked to change and the regions you asked to preserve.
4. Iterate on the same base. If something drifted, re-run from the original base image with a corrected prompt rather than editing the edited output. Repeatedly editing an edit compounds unwanted changes.

Expected result: an image that matches the base in the areas you protected and reflects your instruction in the area you targeted. If it doesn't, the prompt — not the workflow — is usually the thing to fix.

Writing edit prompts: separate "keep" from "change"

The single most useful habit for Nano Banana 2 editing is to split your prompt into two explicit parts.

A combined prompt reads like: "Keep the subject's face, hair, and clothing exactly as in the reference. Change only the background to a rainy city street at night, matching the original lighting direction."

Why this works: the model has to decide what your instruction implies for every pixel. Naming what to preserve removes that guesswork. Vague prompts like "improve this" or "make it professional" leave both the target and the protected areas undefined, which is where unwanted changes come from.

Keeping a character consistent across edits

Character consistency is the main reason people use reference images. The workspace supports uploading multiple reference images so the model captures color tone, composition style, and scene details, and weaves them into the output. For a character, that means:

• Reuse the same base or reference set for every edit. Consistency comes from a fixed anchor, not from the prompt alone.
• Add references when the character appears in new contexts. If you move a character to a new scene, include both the character reference and a scene reference so the model has context for each.
• Describe the character's fixed traits in the keep section. Hair color, face shape, and signature clothing are the traits most likely to drift, so state them explicitly.
• Expect drift to accumulate. Each generation is a fresh interpretation. If a character looks slightly off after several edits, go back to the earliest good reference and re-edit from there.

How credits are consumed

Nano Banana 2 on this workspace uses a credit model: you get starter credits on sign-up, and continued use is paid. Editing is a generation, so each edit you run consumes credits the same way a fresh image generation does. Practical implications:

• Iterating costs credits. Re-running an edit to fix drift is another generation, so a tight prompt saves both time and credits.
• Check current pricing before scaling up. The site links to a pricing page; exact rates and plan details are not fixed here, so confirm them there rather than assuming.
• Starter credits are for trying the flow, not for volume work. Treat them as a way to test whether the edit quality meets your needs before committing to paid usage.

Common failure cases and how to correct them

Unwanted changes outside the edit area. The model altered the face, hands, or background you didn't mention. Fix: add an explicit keep clause naming those elements, and re-run from the original base.

Identity drift across a series. The character looks like a cousin rather than the same person. Fix: re-anchor to the first strong reference, include multiple reference images, and list the character's fixed traits in the prompt.

The edit is too subtle or too aggressive. "Change the lighting" may barely register, while "make it dramatic" may overhaul everything. Fix: name the specific attribute and its target state — "increase the key light from the left, keep shadows soft."

Prompt ignored entirely. Usually a sign the instruction conflicts with the base image or is buried in unrelated detail. Fix: shorten the prompt to one keep clause and one change clause, then expand only if needed.

When this workflow is the right choice

Use Nano Banana 2 editing when you have a base image you want to modify in a controlled way — especially when a character or product must stay recognizable across multiple versions. It is a good fit for iterating on a design prototype, restyling a photo, or placing a consistent character into new scenes.

It is a weaker fit when you want a fully new image with no anchor, or when you need pixel-exact, deterministic edits — text-guided editing is interpretive, so treat each result as a strong draft rather than a guaranteed output. If your goal is a brand-new visual, plain text-to-image generation in the same workspace is the more direct path.