--- name: wavespeed description: Generate or edit AI media (image, video, audio, 3D) on WaveSpeed, through the WaveSpeed MCP tools when they are connected or the wavespeed CLI on the user's machine. Use whenever the user asks to create, edit, animate, extend, upscale, or transform an image or video, generate audio/TTS/music or 3D, or produce marketing creatives. --- # WaveSpeed WaveSpeed runs every model on its platform. There are two ways in, and they do the same things: - **MCP tools** (`generate_image`, `generate_video`, …): use them when the WaveSpeed MCP server is connected. The hosted server cannot read local files: pass public URLs, or use the CLI for files on disk. - **The `wavespeed` CLI**: use it when it is installed, for local files (`@./path`), batches and scripts. For plain image and video work, use the shortcuts. For anything else (audio, 3D, upscaling, a specific endpoint, exact parameters), use the generic run. ## Images and videos: the shortcuts | Task | CLI | MCP tool | |---|---|---| | Image from a prompt (optionally with reference pictures) | `wavespeed generate image "" [--image @./ref.jpg]` | `generate_image` | | Edit, restyle, combine images | `wavespeed edit image "" --image @./photo.jpg` | `edit_image` | | Video from a prompt, a first/last frame, or references | `wavespeed generate video "" [--first-frame …] [--ref-image …]` | `generate_video` | | Edit a video | `wavespeed edit video "" --video @./clip.mp4` | `edit_video` | | Continue a video | `wavespeed extend video "" --video @./clip.mp4` | `extend_video` | They use the same models as the WaveSpeed /chat page and pick the endpoint (text, image, reference, edit) from the inputs you give. In the prompt, refer to media as `@image 1`, `@video 1`, `@audio 1`, in the order given ("put the product in @image 1 into the kitchen in @image 2"). - **Price first for videos:** CLI `--estimate --json`, MCP `estimate_only: true`. It returns the endpoint, the exact request and the price, and submits nothing. - **Models:** pass `-m` / `model` only when the user names one; a named model is never switched. Without it the default is used, or another model only when the default cannot take the inputs. - **Options:** `--aspect-ratio`, `--resolution`, `--duration` (CLI) or the same fields (MCP). Set them only when the user asks; values are snapped to what the model supports, and every change is reported. - CLI: add `--json` to read the result, `--download` to save files. ### Default models | | Default | Alternatives (`-m` / `model`) | |---|---|---| | Image | `seedream-5-pro` (Seedream 5 Pro) | `gpt-image-2.5` (GPT Image 2.5, the model behind ChatGPT Images; strong at text and photorealism), `nano-banana-2.1`, `nano-banana-pro`, `nano-banana-2`, `gpt-image-2` | | Video | `seedance-2.5` (Seedance 2.5) | `seedance-2.5-turbo`, `kling-4.0`, `wan-3.0`, `wan-3.0-prime`, `minimax-h3` (cheap and fast, native stereo audio; defaults to 768p, its counterpart of 720p), older Seedance 2.0 tiers | Video resolution is 720p unless the user asks for another (MiniMax H3: 768p). Offer 480p for a cheaper draft. ## Everything else: the generic run Every model on the platform is one `wavespeed run ` (MCP: `run_model`). The model id is always explicit. ```bash # 1. FIND a model in the live catalog (MCP: list_models) wavespeed models "upscale" wavespeed models --type text-to-audio --popular # 2. INSPECT its inputs (MCP: get_model_schema) wavespeed run -h # 3. RUN it, with --json so you can read the result (MCP: run_model) wavespeed run -p "..." -i key=value --json ``` `run --json` returns `{ id, model, prompt, outputs: [url, ...], saved: [path, ...], elapsed_ms, raw }`. Keep `id`: it is the handle for `wavespeed show ` (MCP: `get_prediction`) if anything is interrupted. Use the URL when the user wants a link; add `--download` when they need the file on disk. ```bash # Local files: @path uploads the file and passes its URL wavespeed run -i image=@./photo.jpg --json wavespeed run -i images='["@./a.jpg","@./b.jpg"]' --json # Or upload separately when you need the URL itself (MCP: upload_file / create_upload) URL=$(wavespeed upload ./photo.jpg --json | jq -r .url) # Save outputs with a template wavespeed run -p "..." --download "./out/{index}.{ext}" ``` The generic run never changes the user's prompt or inputs. The single exception is explicit: an `@path` value uploads that file and substitutes its hosted URL. Bare paths are never uploaded. ## Project config and aliases If `wavespeed.json` exists (created by `wavespeed init`): - **`defaultModel`** lets `wavespeed run -p "…"` (no model arg) work. - **Aliases** are named shortcuts that bundle model + default inputs. Run `wavespeed aliases` to see them; `wavespeed run -h` shows the resolved schema. CLI `-i k=v` overrides alias defaults. ## Auth - CLI: `wavespeed status` shows whether the user is signed in. If not, ask them to run `wavespeed login` (opens https://wavespeed.ai/accesskey). - MCP: if the tools ask for authorization, the user approves the connection once (in Claude Code: `/mcp`, pick `wavespeed`). - **Never** ask the user to paste an API key into the chat. ## Cost and credits - `wavespeed price -i k=v --json` (MCP: `get_price`) estimates a run without generating or charging. The shortcuts have `--estimate` / `estimate_only`. - `wavespeed balance` (MCP: `get_balance`) shows the remaining credit; `wavespeed usage` and `wavespeed billings` show what was spent. - If a run fails with `Insufficient credits` (or says a top-up is required), the balance is too low: do not retry. Tell the user and give them https://wavespeed.ai/top-up. You cannot pay for them. ## Pitfalls - Don't invent model IDs. Confirm with `wavespeed models` or `wavespeed schema ` (MCP: `list_models`, `get_model_schema`) before a generic run. - Local files: use `@./file.jpg`. Bare paths are NOT uploaded and the model will reject them. The hosted MCP server cannot read local files at all. - `wavespeed delete` requires `--yes` when run non-interactively (that includes you). - A run that fails with a gateway or network error (502/503/504, connection reset, timeout) does NOT mean the task failed, and the submit request is never retried automatically, so re-running can create a duplicate paid task. Do not re-run immediately. If the error contains a task id (`task_id` / prediction id), keep checking it with `wavespeed show --json` (MCP: `get_prediction`) until its status is terminal (`completed`, `failed`, `cancelled`, `timeout`, `deleted`). If no id was returned, check recent predictions with `wavespeed history --json` (MCP: `list_predictions`; add the model; default window is the last 24h) and re-run once only if none matches. A client-side wait timeout or an interrupted run does not mean the task failed: tasks can run for a long time, so do not give up after a fixed time.