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.
Submit requests to the public API at api.acedata.cloud using the documented parameters, then use the results in your application.
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.
Clarify capacity, inputs and outputs, and invocation methods before selecting a model.
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.
Learn what nano-banana:official can bring to your work.
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.
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.
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.
Start with specific tasks to find where the model can be effective.
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.
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.
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.
Choose based on task complexity, input materials, and expected results.
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.
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.
From a small-scale task to formal integration.
Define the objective, required inputs, and output requirements, using real business examples as a starting point.
Open the trial page, confirm the parameters supported by this entry, then submit a small-scale task to review the results.
Retain the complete model ID, use the request format specified in the documentation, and confirm billing rules on the Pricing page.
Before formal use, understand the scope of output quality and capabilities.
Answers to common questions about using nano-banana:official.
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.
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.
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.
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.
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.
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