How to Build an Image Workflow with the Nano Banana Images API

How to Build an Image Workflow with the Nano Banana Images API

When you add AI image features to a product, the hard part is rarely the first prompt. The real work is building a dependable workflow: accept a prompt, decide whether the user is creating or editing, keep track of task identifiers, show image results, and handle partial failures without losing the whole request.

What you can do

The Nano Banana Images API gives builders one endpoint for two jobs: generating new images and editing existing images. The base URL is https://api.acedata.cloud, and the endpoint is POST /nano-banana/images. The request body is JSON.

The key switch is action. Use generate when the image should come from a text prompt. Use edit when the request should work from one or more existing images passed through image_urls. This makes the API easy to wire into product flows where users may start from either a blank prompt box or an uploaded/reference image.

  • prompt: required for generation and editing.
  • image_urls: required for editing; each item should be a public HTTP or HTTPS image URL, and Base64 image data is also supported.
  • count: optional, supports 1 to 4 images, and defaults to 1.
  • model: optional; the default is nano-banana.

How it works

A successful call returns success, task_id, trace_id, and a data array. Each item in data includes the prompt used for the request and an image_url. In a real application, store task_id with the user job and keep trace_id in logs for support and debugging.

The documented model options include nano-banana, nano-banana-2-lite, nano-banana-2, nano-banana-pro, and the corresponding :official variants. The docs also note that nano-banana-2-lite supports only 1K resolution. Optional layout fields include aspect_ratio, such as 1:1 or 16:9, and resolution, such as 1K, 2K, or 4K.

Generate an image from text

For a text-to-image feature, the minimum useful body contains action and prompt. You can add model and count when you want more control.

{
  "action": "generate",
  "model": "nano-banana-pro",
  "prompt": "A photorealistic close-up portrait of an elderly Japanese ceramicist inspecting a freshly glazed tea bowl in a rustic sun-drenched workshop, golden hour light, 85mm portrait lens, soft bokeh, vertical portrait orientation.",
  "count": 1
}

This pattern fits product illustration tools, thumbnail generators, internal creative assistants, and visual prototyping dashboards. The important product detail is to store the returned identifiers before showing the image. That gives users a way to retry, report, or revisit an image later.

Comments

Post a Comment

Popular posts from this blog

Artistic QR Code API Integration Guidance

Image
Artistic QR code is an innovative technological product that combines QR codes with beautiful background images, creating practical and visually appealing works. They not only possess the functionality of traditional QR codes, allowing quick scanning and recognition by smart devices, but also incorporate artistic elements to enhance visual appeal and brand recognition. Some artistic QR codes are even generated by artificial intelligence, fully leveraging modern technology to demonstrate unparalleled innovation and uniqueness. This makes artistic QR codes widely applicable in areas such as brand marketing and advertising. In simple terms, artistic QR codes are the perfect combination of QR code scanning and artistic aesthetics. They not only provide the function of information transmission, but also enhance the user's visual experience, making each scan a delightful artistic enjoyment. Overview of Artworks Let's take a look at a few QR code works: How are they? These are ar...
Read more

How to Configure Claude Code with CC Switch and Ace Data Cloud

Image
If you use Claude Code across a terminal, VS Code, or remote development setup, the awkward part is often not the model itself but the configuration: where should requests go, where should the token live, and how do you switch providers without editing files by hand? This guide walks through a practical setup using CC Switch as the graphical configuration manager and Ace Data Cloud as the Anthropic-compatible base URL. The goal is simple: configure Claude Code once, verify the active endpoint, and keep the setup easy to change later. What you can do With the setup described in the Ace Data Cloud documentation, you can: Manage Claude Code provider configuration through a desktop UI instead of manually editing settings.json . Store an Ace Data Cloud provider with ANTHROPIC_BASE_URL and ANTHROPIC_AUTH_TOKEN . Switch the active Claude Code provider from CC Switch without rewriting your configuration every time. Use the same provider configuration from the terminal an...
Read more

How to Build a Server-Side Image Editing Workflow with GPT-Image-2

Image
If your product needs image editing, the hard part is rarely the model call itself. The real challenge is building a reliable workflow around user images, reference assets, predictable output sizes, and slow-running jobs without forcing every file through a manual upload step. This guide walks through a practical server-side workflow using the OpenAI Images Edits API on Ace Data Cloud. The focus is gpt-image-2 , because the documented editing flow supports direct image URLs, base64 input, multiple reference images, high-resolution output through size , and asynchronous callbacks. What you can do The edits endpoint is designed for turning one or more input images plus an instruction into a modified output image. In practical terms, you can use it to: Convert an existing infographic or poster into a different visual theme while preserving its layout. Change a product, shelf, room, or object style while keeping the original arrangement stable. Pass image URLs directly f...
Read more