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How to Build Image Generation and Editing Workflows with the Nano Banana Images API

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If your product needs both prompt-based image generation and practical image editing, the awkward part is often not the model call itself—it is designing a request shape that works for both fresh images and edits based on existing assets. What you can do The Nano Banana Images API exposes a single image endpoint for two related workflows: generating images from text and editing images from supplied image URLs. The base URL is https://api.acedata.cloud , and the endpoint is POST /nano-banana/images . Requests use JSON and authenticate with an authorization: Bearer {token} header. The key switch is action : generate : create an image from a text prompt . edit : transform one or more existing images using image_urls plus a prompt . That makes the API useful for builder workflows such as product mockups, image variations, campaign assets, visual QA tools, internal creative dashboards, or any app where users move between “make something” and “change this specific thing.” ...
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How to Build a Voice-Cloned Music Workflow with the Suno Voices API

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If you are building a music tool, the hard part is rarely just generating a track; it is keeping a recognizable vocal identity across generations without turning your app into a manual production workflow. The Suno voice cloning flow in Ace Data Cloud gives you a simple two-step pattern: upload a clean vocal sample as a private voice persona, then pass the returned persona_id into a music generation request. This guide walks through that workflow as an implementation pattern, with the practical details that matter when you wire it into a real product. What you can do The Voice Cloning API lets you create custom voice personas from your own audio file. Unlike persona flows based on a Suno-generated audio_id , this endpoint accepts a publicly accessible audio_url pointing to your own vocal recording. Create a private voice persona from an MP3 or WAV file. Reference that persona later with the returned persona_id . Generate a new song using the cloned voice by calling the...
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Getting Started with Nano Banana MCP for Image Generation in Your AI Client

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If your image workflow keeps bouncing between a chat client, a design tool, and a separate API console, an MCP server gives you a cleaner path: keep the conversation in your AI client and expose image-generation tools as callable actions. What you can do The Nano Banana MCP server described in the Ace Data Cloud documentation is built for AI clients such as Claude Desktop, VS Code, and Cursor. Once configured, it lets your assistant call image tools through the Model Context Protocol instead of asking you to copy prompts into a separate UI. The documented tool surface is intentionally small: nanobanana_generate_image for generating images from text prompts. nanobanana_edit_image for editing or combining existing images. nanobanana_get_task for checking the status of one generation task. nanobanana_get_tasks_batch for checking multiple task statuses together. The same guide also lists practical scenarios: image generation, image editing, virtual try-on, prod...
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Три API-контракта, один агент: как надёжно подключать пользовательские модели

Интеграция собственной модели в агентную платформу редко сводится к замене одного URL. Когда в проекте доступны несколько API-стандартов, инженер отвечает не только за успешный первый запрос, но и за предсказуемое поведение потокового вывода, вызовов инструментов, обработки ошибок и учёта расходов. Практичный способ уменьшить риск — считать каждый протокол отдельным контрактом и проверять его коротким воспроизводимым набором тестов. В документации Ace Data Cloud для подключения пользовательских моделей выделены три экспериментальные ветки: OpenAI Chat Completions, OpenAI Responses и Anthropic Messages. Они похожи по назначению, но различаются базовым адресом, правилами формирования пути, заголовками, форматом событий streaming и представлением вызовов инструментов. Успех в одной ветке не доказывает готовность другой: конфигурацию и тесты следует вести раздельно. Сначала зафиксируйте контракт интеграции Перед настройкой полезно завести небольшой файл интеграции в репозитории. В нё...
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A Practical Guide to MiniMax H3 Video Generation with the Ace Data Cloud API

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Video generation becomes much easier to build around when you stop treating every workflow as a separate endpoint and instead model the request as structured multi-modal input. This guide walks through the MiniMax H3 video generation API exposed through Ace Data Cloud. The goal is practical: send text, optional reference media, and a few production parameters to create a video task that can either complete synchronously or be polled asynchronously. What you can do The POST /minimax/videos endpoint is designed around a unified content array. That means the same API surface can cover several common builder workflows: Text-to-video : provide one non-empty text item and a fixed ratio . First-frame animation : combine text with an image_url item whose role is first_frame . Last-frame or start/end control : use last_frame , or combine first_frame and last_frame to guide the opening and closing frames. Reference-driven generation : use reference_image , reference_video...
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How to Build a Seedance Video Generation Workflow with Ace Data Cloud

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Generating short videos from prompts is easy to demo, but harder to integrate cleanly: you need predictable request fields, model-specific limits, reference media handling, and a safe async path for production jobs. What you can do The Seedance video endpoint on Ace Data Cloud lets you create video tasks with text prompts, image references, audio references, and video references through one HTTP API: POST https://api.acedata.cloud/seedance/videos The same endpoint supports text-to-video with content.type set to text , image-to-video with image_url roles such as first_frame or last_frame , and Seedance 2.x reference workflows using reference_image , reference_audio , or reference_video . How it works Every request is a JSON body sent with authorization , accept , and content-type headers. A minimal request provides model , content , resolution , ratio , and duration . Optional fields include seed , camerafixed , watermark , generate_audio , return_last_frame , callback_url , ...
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A Practical Guide to Using Ace Data Cloud MCP in Claude Code

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When your coding agent understands the repository but still needs to search the web, generate a README image, shorten release links, or prepare media assets, the bottleneck is usually not reasoning—it is leaving the terminal and stitching tools together by hand. What you can do Ace Data Cloud exposes several managed remote MCP servers that Claude Code can call from the same terminal session where it reads files, edits code, runs commands, and explains errors. The useful mental model is simple: Claude Code remains your programming agent, while MCP adds callable tool arms for tasks that normally live outside the editor. According to the public Claude Code MCP overview, one Ace Data Cloud API token can be used with multiple MCP servers, including: Google Search for real-time web, image, news, maps, and video search. ShortURL for turning long links into short links and organizing shareable references. Flux , Seedream , and NanoBanana for image generation or image editin...
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