AceDataCloud

Building an image feature is not only about sending a prompt to a model. The harder part is designing a workflow that can generate, edit, stream, or poll results when a job takes longer than a single HTTP request. What you can do The Seedream Images API gives builders one endpoint for prompt-to-image generation, image editing with one or more input images, asynchronous jobs, streaming output, callback delivery, and Seedream 5.0 Pro layer decomposition. The core endpoint is POST https://api.acedata.cloud/seedream/images . In a typical request you pass a model , a prompt , and optionally fields such as image , size , watermark , response_format , output_format , stream , async , callback_url , tools , background , or layer_decomposition . How it works Send JSON with accept: application/json , authorization: Bearer YOUR_API_TOKEN , and content-type: application/json . The basic generation action uses action: generate , a full model string, and a prompt. The model must be the full stri...

Most image editing APIs are easy to demo once, but harder to turn into a reliable workflow: you need predictable inputs, constrained edits, clear error handling, and a way to avoid blocking your app while a large edit runs. This guide walks through a practical image editing flow using the Ace Data Cloud OpenAI Images Edits endpoint. The goal is not to generate random pictures from text. The goal is to take one or more existing images, describe a controlled edit, and receive a usable image result while keeping enough structure around the request to debug failures and scale the workflow later. What you can do The Images Edits API supports two main input styles: Edit from an image URL by sending JSON to https://api.acedata.cloud/openai/images/edits . Edit local images with multipart/form-data , including optional mask-based local edits for supported :official models. The core fields are intentionally small: model , image , prompt , and optional controls such as size , ...

If your product needs generated visuals, image variations, or simple multi-image edits, the hard part is usually not the model call itself—it is designing a request shape your app can track, retry, and debug later. What you can do The Nano Banana Images API provides one endpoint for two common image workflows: text-to-image generation and prompt-guided image editing. Both use the same base URL, https://api.acedata.cloud , and the same endpoint, POST /nano-banana/images . The core switch is the action field: generate : create an image from a text prompt . edit : edit one or more existing images using image_urls plus a prompt . The API returns a success flag, a task_id , a trace_id , and a data array containing successful image results. Each result includes the echoed prompt and an image_url . How it works Every request is authenticated with an HTTP header: authorization: Bearer {token} . The recommended request headers are accept: application/json and content-type: applica...

Audio transcription looks simple until you need it in a production workflow: large files, subtitles, timestamps, proper nouns, streaming output, and client timeouts all show up sooner than expected. This guide walks through a practical way to use Ace Data Cloud's OpenAI-compatible speech recognition endpoint so you can move from a local audio file to plain text, subtitles, or incremental transcription without changing much of an existing OpenAI-style integration. What you can do The transcription API accepts an audio file as multipart/form-data and returns text or subtitle-oriented output depending on the model and response_format you choose. The request URL is: POST https://api.acedata.cloud/v1/audio/transcriptions There is also an alias endpoint, POST /openai/audio/transcriptions . Requests use an Authorization: Bearer {token} header. The supported file formats are flac , mp3 , mp4 , mpeg , mpga , m4a , ogg , wav , and webm . A single file can be up to 25 MB , and a sin...

This practical guide shows how builders can call the Seedance video generation API with structured JSON instead of relying on prompt text alone. What you can do The API endpoint is POST https://api.acedata.cloud/seedance/videos . Requests use model and content . Content items can be text , image_url , audio_url , or video_url . How it works Send accept: application/json , content-type: application/json , and an authorization bearer token. Useful body fields include resolution , ratio , duration , seed , camerafixed , watermark , and generate_audio . curl -X POST 'https://api.acedata.cloud/seedance/videos' -H 'authorization: Bearer YOUR_TOKEN' -H 'accept: application/json' -H 'content-type: application/json' -d '{"content":[{"type":"text","text":"A white ceramic coffee mug on a glossy marble countertop with soft morning window light. The camera slowly orbits 360 degrees around the mug, steam gently rising....

Инструментальный агент в терминале полезен не потому, что умеет отвечать на вопросы, а потому, что становится частью повторяемого процесса разработки. Документация, описание pull request, релизная заметка и внутренний отчёт часто требуют одних и тех же действий: найти актуальные сведения, подготовить ссылку, проверить ответ API и оставить понятный след в репозитории. MCP позволяет вынести такие действия из набора ручных переключений между окнами в управляемый поток внутри Claude Code. Хорошая отправная точка — не пытаться подключить всё сразу. Сначала выберите одну узкую операцию с очевидным результатом. Для работы с документацией это может быть создание короткой ссылки; для исследовательской задачи — поиск; для визуальных материалов — генерация изображения. После одного успешного вызова легче понять, где хранить настройку, как передавать секреты и какие границы нужны команде. Что именно добавляет MCP к терминальному циклу Claude Code остаётся средой для чтения исходников, изменен...

If you are building a tool that needs to create images from prompts or modify existing assets, the hard part is usually not the model itself. It is designing a reliable request shape, tracking outputs, handling partial failures, and keeping enough metadata to debug what happened later. The Nano Banana Images API on Ace Data Cloud gives you one endpoint for both text-to-image generation and image editing: POST /nano-banana/images . This guide walks through the practical pieces a builder needs: request structure, edit workflows, callbacks, response handling, and error cases. What you can do The API supports two image workflows through the same endpoint: action: "generate" creates an image from a text prompt . action: "edit" edits one or more existing images passed through image_urls . The base URL is https://api.acedata.cloud , and the endpoint is POST /nano-banana/images . Requests use JSON, and authentication is sent with authorization: Bearer {token...