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A Practical Guide to Image Generation and Editing with the Nano Banana Images API

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If your app needs to generate product visuals, transform existing images, or combine multiple reference images into one result, you need an image API that is simple enough to call from a backend job but explicit enough to debug in production. This guide walks through the Nano Banana Images API on Ace Data Cloud as a practical builder workflow: one endpoint, two actions, optional async callbacks, and response fields you can store for troubleshooting. What you can do The API exposes a single image endpoint, POST /nano-banana/images , under the base URL https://api.acedata.cloud . The same endpoint supports two main modes through the action field: generate : create images from a text prompt . edit : edit one or more existing images by passing image_urls plus a prompt that describes the target change. That makes it useful for common application flows such as generating campaign mockups, creating concept art from a brief, editing a reference image, or combining a person...
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How to Use Ace Data Cloud as an OpenAI-Compatible Provider in Cline for VS Code

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When you are building with an AI coding agent inside VS Code, the first practical problem is not the prompt. It is making sure the agent is connected to the right model endpoint, using the right key, and still able to call tools against your local workspace. This guide walks through a focused setup: configuring Cline in VS Code to use Ace Data Cloud through Cline’s OpenAI Compatible provider mode. The goal is simple: get from a blank provider screen to a working coding-agent task, then verify that chat and file-oriented tool use both behave as expected. What you can do With this configuration, Cline can route its model requests through Ace Data Cloud while staying inside the workflow developers already use in VS Code. The documented setup supports: Using OpenAI Compatible as the Cline API provider. Setting the Ace Data Cloud API base URL to https://api.acedata.cloud/v1 . Authenticating with an API key copied from an Ace Data Cloud application. Selecting the...
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A Practical Guide to Editing Images with GPT Image 2 on Ace Data Cloud

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Image editing APIs are most useful when they let you keep the parts of an image that already work, change only what needs to change, and fit naturally into an existing build pipeline. What you can do The GPT Image 2 / 2.5 image editing API on Ace Data Cloud gives builders a direct endpoint for transforming an existing image with a text instruction. The core endpoint is https://api.acedata.cloud/openai/images/edits . You can call it with a JSON body when your source image is already hosted at a URL, or with multipart/form-data when you need to upload local files. At a practical level, this covers workflows such as: Changing a product color while preserving composition, camera angle, and shadows. Replacing a background without rebuilding the subject from scratch. Editing a constrained region with a PNG alpha mask . Sending asynchronous jobs to a callback_url when the request may take longer. The documented model choices include gpt-image-2 , gpt-image-2:reverse ,...
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Единый API для инженерного цикла: поиск, модели и контроль расходов

В инженерной работе полезность модели определяется не количеством демонстраций, а тем, насколько быстро она попадает в существующий цикл: локальная диагностика, поиск первоисточника, изменение кода, проверка гипотезы и фиксация результата в задаче. Когда для текста, поиска, изображений и других операций приходится поддерживать разные способы аутентификации и разные клиентские библиотеки, усложняется не только код, но и эксплуатация. Практичный подход — отделить прикладной сценарий от конкретного поставщика и работать через единый API. Ace Data Cloud объединяет доступ к нескольким моделям и инструментам через платформу . Это удобно для разработчиков, работающих с несколькими моделями: один набор переменных окружения, единые правила учёта расходов и предсказуемое подключение к проектам. В этой статье разберём базовый контур интеграции, запрос к чат-модели, поиск в процессе отладки и практики, которые помогают не превратить интеграцию в набор хрупких скриптов. Сначала опишите рабочий ...
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How to Use Cline in VS Code with Ace Data Cloud

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If you already use Cline inside VS Code, the most useful setup is often the least dramatic one: point Cline at a reliable OpenAI-compatible provider, verify that plain chat works, then verify that file-oriented tool use works before trusting it on a real repository. This guide walks through that practical path using Ace Data Cloud as the provider behind Cline. The goal is not to introduce a new workflow for its own sake; it is to make your coding agent predictable enough that you can use it on everyday tasks like reading a file, explaining a module, or making a small change with confidence. What you can do With the configuration described in the Ace Data Cloud Cline guide, you can connect Cline's OpenAI Compatible provider setting to Ace Data Cloud, supply an API key, choose the model ID provided by your Coding Plan, and then run a minimal verification task inside VS Code. Use https://api.acedata.cloud/v1 as the Cline Base URL . Keep Custom Headers blank unless you h...
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How to Add NanoBanana Image Editing to Claude Code with MCP

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If your workflow already lives in the terminal, switching to a browser just to edit a product screenshot or compose two reference images can break your focus. This guide shows how to connect NanoBanana MCP to Claude Code so image generation and image editing can sit next to code review, docs work, and release prep. The goal is not to replace a design tool. It is to make small, useful visual tasks callable from the same Claude Code session where you are already reading files, writing copy, or preparing an implementation note. What you can do The NanoBanana MCP document describes two callable tools: nanobanana_generate_image for text-to-image generation. nanobanana_edit_image for image editing, including multiple image inputs. The most interesting part is composition. The docs explain that NanoBanana can understand relationships between images: for example, you can provide two image URLs and ask it to place an object from one image into the scene of another. That makes it...
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A Practical Guide to Building Image Generation and Editing with Seedream

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When you add image generation to a product, the hard part is usually not the prompt itself. It is choosing the right request shape, deciding when to run synchronously or asynchronously, and handling image edits without turning your backend into a pile of special cases. This guide walks through the Seedream Images API as documented by Ace Data Cloud, using the real endpoint and fields from the integration reference. The goal is a practical builder-oriented starting point: generate a clean image from a prompt, edit an existing image, and move longer jobs into an async workflow. What you can do The Seedream Images API uses a single endpoint, POST https://api.acedata.cloud/seedream/images , for both generation and image editing. The basic request can include fields such as prompt , model , image , size , watermark , response_format , output_format , callback_url , and async . From the documented examples, you can use it for: Text-to-image generation with action: "generate"...
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