Image generation and editing model for rapid creation and subject consistency
nano-banana:official corresponds to Google Gemini 2.5 Flash Image, suitable for generating images from text concepts and for using reference images to change scenes, adjust colors, and blend assets. Its practical focus is continuously creating visual variations around the same product or character, enabling a coherent workflow for creative validation, product presentation, and marketing asset revisions without having to rethink the entire image each time.
Input parameters and result formats vary by service. Use the public API for this model and follow its guide for generation, task retrieval and editing operations.
Specifications and API features
Clarify capacity, inputs and outputs, and invocation methods before selecting a model.
Model positioning
Gemini 2.5 Flash Image; image generation and editing
Creation modes
generate text-to-image, edit image editing
Input methods
Text prompts; image_urls image reference array
Aspect ratio options
1:1、3:2、2:3、16:9、9:16、4:3、3:4
Generation count parameter
count is 1—4, default is 1
Delivery format
Image URL, task ID, and tracking ID; supports asynchronous processing and callbacks
API endpoint
POST /nano-banana/images; model is nano-banana:official
The model positioning describes its image creation capabilities; aspect ratio, quantity, and task processing options describe the scope of this platform's endpoint and do not represent native pixel specifications.
Core Capabilities
Learn what nano-banana:official can bring to your work.
Create visual variations around a subject
Use product photos or character images to establish a visual baseline, then describe new scenes, lighting, and compositions. This is ideal for creating a series of assets around the same subject. Clearly specify the appearance, color scheme, and identifying features that need to be retained so that changes focus on the background and atmosphere rather than rewriting both the subject and scene at once.
Perform targeted edits with natural language
Editing does not need to start over with a description of the entire image. After providing the original image, you can request a background replacement, lighting adjustments, changes to object colors, or the addition of new elements. Describe the areas to preserve separately from the intended changes, and iterate gradually according to local needs, making it easier to assess whether each edit aligns with the creative direction.
Blend reference assets into a scene
By combining product, background, or style references, the model can attempt to organize different assets into a unified image. The key is to explain the role of each image: which determines the subject, which provides the environment, and which is only a color reference. This prevents multiple assets from competing for compositional focus and is suitable for brand visuals and product scene exploration.
Use Cases
Start with specific tasks to find where the model can be effective.
Product scene swaps and color studies
Input clear product photos and describe target scenes such as a wooden table, bathroom, or office environment to generate contextual images suitable for product presentation. You can also experiment with different backgrounds and color schemes around the same product, delivering a set of visual options for operations teams to review; before formal use, check packaging text, outlines, and key structural details.
Event posters and social covers
Provide the event theme, brand references, and an accurate short title. First determine the key visual, then create landscape or portrait assets separately. This is suitable for exploring holiday themes, advertising compositions, and cover directions. Keep text brief, specify title hierarchy and whitespace placement, and check typography and layout after generation.
Character storyboards and continuous creative work
Use character reference images together with storyboard descriptions to try generating images across different scenes, poses, or camera shots for storyboards, event mascots, and content planning. Reuse the same identity-feature descriptions each time, use selected images as subsequent references, and deliver continuous visual drafts for discussion.
How to choose this model
Choose based on task complexity, input materials, and expected results.
Prioritize fast iteration tasks
If the task focuses on product background changes, visual variants, and creative drafts, the fast generation and editing positioning of nano-banana:official is better suited to this type of workflow. Use it first to establish the subject, composition, and atmosphere, then decide whether to proceed to detailed production. Do not interpret fast positioning as a fixed number of seconds per image, and do not skip checking product details and text within images.
Differentiate it from Pro and second-generation names
Nano Banana refers to Gemini 2.5 Flash Image and is not the same as Nano Banana Pro or Nano Banana 2. If requirements focus on high-precision final outputs or specific high-resolution delivery, compare the detailed specifications of the corresponding models; the :official suffix in this entry is used to select the invocation ID, does not indicate switching to Pro, and does not automatically add new-generation features.
Get started
From a small-scale task to formal integration.
01
Prepare the task and materials
Define the objective, required inputs, and output requirements, using real business examples as a starting point.
02
Try it in the API debugging area
Open the trial page, confirm the parameters supported by this entry, then submit a small-scale task to review the results.
03
Integrate according to the API documentation
Retain the complete model ID, use the request format specified in the documentation, and confirm billing rules on the Pricing page.
Usage limitations
Before formal use, understand the scope of output quality and capabilities.
Subject consistency is suitable for assisting with series creation, but does not mean the original image can be fully replicated every time. Product logos, small components, human poses, and occlusion relationships should be checked image by image; for key product displays, first define the features that must not change, then edit around a small number of variables.
Adding text to images is better suited to short titles, labels, and simple layouts. Long paragraphs, dense small text, or complex multilingual layouts should not be used directly as final deliverables; when precise wording is needed, provide the complete original text and verify that spelling, line breaks, and characters are not missing.
For sequential edits, explicitly provide the image that needs to be retained and describe the changes for the current round; do not assume a new image request will automatically remember previous results. Image generation is also not a structural verification tool, and product exploded views or internal construction images cannot be used directly as engineering references.
Frequently Asked Questions
Answers to common questions about using nano-banana:official.
Is nano-banana:official a standalone Google model?
It is the specific ID used to invoke Nano Banana, whose public model name is Gemini 2.5 Flash Image. Nano Banana is the model's nickname; :official does not constitute a new Google model name, nor is it equivalent to Nano Banana Pro. Use the full ID for integration.
Can I generate images using only a prompt?
Yes. Select generate and provide a prompt describing the subject, environment, lighting, composition, and style to start text-to-image generation. If you want to match an existing product or brand visual, you can also add image references; choose the aspect ratio based on the actual use case, such as a cover, banner, or vertical content.
How do I replace backgrounds or blend multiple images?
Use edit, provide the images to process through image_urls, and explain the relationship between the assets in the prompt. For example, keep the product's shape and replace the environment with a morning-lit wooden table scene. When blending multiple images, clearly specify the roles of the main subject image and background image rather than writing only vague compositing requirements.
Can it automatically continue the previous editing round?
When creating consecutive variants, it is recommended to use the image selected in the previous round again as a reference and clearly state what should be retained and changed in the current round. This makes the editing baseline explicit without relying on implicit history; for character or product series, continue using stable descriptions of identity traits to reduce unnecessary changes.
How can generated results be integrated into business workflows?
The endpoint returns an image URL along with task_id and trace_id for linking tasks and results. For batch asset workflows, use async and callback_url to handle completion notifications, then send images through selection, review, and archiving steps; before official release, you should still verify subject details and text.
Model information · Updated: 2026-10-01. For invocation parameters and billing rules, see the API and pricing sections.