AceDataCloud

If your product needs both text-to-image generation and image-based editing, the hard part is usually not sending one request; it is designing a workflow that can handle prompts, reference images, callbacks, partial failures, and traceable results. What you can do The Nano Banana Images API exposes one image endpoint for two practical jobs: generating images from text and editing one or more existing images. The base URL is https://api.acedata.cloud , and the endpoint is POST /nano-banana/images . Authentication is done with an HTTP header: authorization: Bearer {token} . The core switch is the action field: generate : create an image from a text prompt . edit : edit existing image material supplied through image_urls . You can also choose a model. The default is nano-banana . Other documented options include nano-banana-2-lite , nano-banana-2 , nano-banana-pro , and their corresponding :official variants. The request can ask for multiple outputs with count , from ...

If you have ever tried to generate a poster with Chinese text inside an image, you have probably seen the failure mode: broken strokes, unreadable characters, or text that looks close but not quite right. This guide shows how to use Seedream MCP inside VS Code so a builder can stay in the editor, ask GitHub Copilot Agent for an image task, and route that work through a Seedream MCP server configured with an Ace Data Cloud API key. What you can do The documented VS Code workflow is intentionally narrow and practical: install the Seedream MCP extension, store your Ace Data Cloud API key in VS Code SecretStorage / the system keychain, then ask Copilot Agent to use seedream for image generation tasks. Seedream is useful when your output needs Chinese text or Chinese-market visual design. The source documentation gives three concrete scenarios: A tech salon poster with a title such as AI Engineering Practice Sharing Session , a subtitle such as January 18, 2025 · Beijing , and sp...

Расширение LMLocal позволяет добавить в Visual Studio собственного провайдера с совместимым API. Для команды это удобный способ отделить конфигурацию среды разработки от конкретной модели: один базовый адрес, один формат запросов и явный выбор модели в настройках или коде. В этой статье разберём воспроизводимую настройку через Ace Data Cloud, проверку первого запроса и несколько инженерных правил, которые помогают избежать труднообъяснимых ошибок. Платформа Ace Data Cloud объединяет доступ к нескольким моделям через единый интерфейс. Каталог, приложения и документация доступны соответственно на русской версии платформы , в разделе Applications и в документации . Для IDE особенно важно не подменять интеграционные детали догадками: модель, путь API и способ передачи ключа должны быть зафиксированы явно. Что подготовить до настройки Нужны Visual Studio 2022 или 2026, установленное расширение LMLocal и API-токен Ace Data Cloud. В консоли создайте или выберите приложение, затем выпус...

If you use Claude Desktop for real work, the useful question is not only which model you can chat with, but how reliably you can route requests, tools, and usage records through an infrastructure layer you control. What you can do The Claude Desktop Third-Party Inference Gateway configuration lets you point Claude Desktop at Ace Data Cloud as a gateway provider. In practice, that means Claude Desktop can use a gateway base URL, a static API key, and bearer authentication instead of relying only on the default built-in provider path. This is useful when you want a desktop workflow that still has operational visibility. After configuration, you can verify a normal conversation, run a simple tool task, and inspect invocation records in Usage History. The documented gateway support includes /v1/models , bearer-authenticated streaming /v1/messages , tool calling, and tool_result continuation used by Claude Desktop. How it works Claude Desktop has a Third-Party Inference configurati...

Shipping voice features is often less about picking a model and more about the details around audio format, latency, retries, and voice reuse. This guide walks through a practical Fish TTS API integration using the documented Ace Data Cloud endpoint for text-to-speech, saved voices, and one-time instant voice cloning. What you can do The Fish TTS API exposes one main endpoint: POST https://api.acedata.cloud/fish/tts With that endpoint, you can build several common product flows: Generate an mp3 voiceover from plain text. Return wav or pcm when later processing needs a WAV container. Use a reusable cloned or public voice through reference_id . Use one-time instant voice cloning through references . Adjust speech with prosody.speed and prosody.volume . Move long-running synthesis behind a webhook using callback_url . How it works Authentication uses an authorization header with Bearer {token} , and the request body is JSON. The required body field is text , a non-empt...

When you are building an assistant into a product, the hard part is rarely the first prompt. The hard part is keeping useful context, streaming partial output to the UI, accepting images or files, and giving the model enough room to call tools without turning your backend into a pile of one-off integrations. The Ace Data Cloud AI Chat v2 API is designed for that middle layer: a single conversation endpoint that can behave like a simple chat API, but also supports stateful conversations, structured streaming events, multimodal input, tool-use events, async tasks, and conversation management actions. This guide walks through the practical integration path using only the fields and behavior documented for /aichat2/conversations . What you can do At its simplest, AI Chat v2 accepts a model and a question , then returns a JSON object with answer and id . That makes it easy to migrate from a basic chat flow without changing your entire client. Stateful conversations by passing state...

Moving image work into a terminal sounds small until you are building docs, release notes, product mockups, or UI examples and keep losing context to browser tabs. NanoBanana MCP gives Claude Code a practical way to generate and edit images from the same project session where you already inspect files, write copy, and run commands. This guide walks through the setup described in the Ace Data Cloud documentation and shows how to think about NanoBanana as a developer workflow tool rather than a standalone image app. What you can do NanoBanana is useful when the image task depends on understanding existing visual content. The documentation highlights three common jobs: Image composition: pass multiple image URLs and ask the model to combine objects and scenes naturally. Image cleanup: remove unwanted text, such as a watermark or annotation, and fill the area with the surrounding background. Page illustration: generate small product or documentation illustrations directly...