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Z-Image Turbo API: Fast Text to Image

Call Z-Image Turbo, the distilled six-billion-parameter text-to-image model from Alibaba Tongyi, over a hosted API: a prompt in, up to four images out, no GPU of your own. Run the preset directly on this page or call the same agent-ready endpoint from your product.

Run this tool View API recipe
LIVE TOOL
Run z image turbo
Describe the image, pick its shape and how many to render, and download the results here.
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Describe the subject, the light and the framing. Z-Image Turbo fast and cheap, 1k only, ignores negative prompts.

1024 × 1024 px

Z-Image Turbo is trained at 1024 px, so it renders at 1k only.

Up to 4. Each one is billed.

Z-Image Turbo runs without a guidance pass, so a negative prompt would have nothing to steer away from. Put what you want in the prompt itself, or switch to Qwen-Image when you need one.

1 image at 1k, Z-Image Turbo (Apache-2.0). Billed per image, reserved when the job is queued and refunded if the render fails.

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BEFORE / AFTER

See what the tool changes.

A real example: the after side is the unedited output of a KinoGenie job. Run the live form above to generate the same result from your own media.

BeforeSource video
WebM · VP9
mountain.webm
AfterCompatible MP4
Ready
MP4 · H.264
mountain.mp4

Demo footage: public-domain timelapses (Bureau of Land Management, Oregon · NASA SVS). Processed by the same pipeline the API and MCP tools call.

Useful defaults, typed options.

The tool slug stays stable while your agent supplies named media inputs and a narrow set of documented options.

  • Eight sampling steps, so batches of four stay cheap
  • Apache-2.0 weights, hosted for you
  • Same API, MCP server and account as the rest of the catalog
What this preset does
  1. 01Send a prompt, an aspect ratio and how many images you want
  2. 02Render them with Z-Image Turbo on a GPU worker
  3. 03Mark each file as AI-generated in its metadata
  4. 04Return one downloadable file per image in result.outputs
Stable endpointPOST /api/v1/tools/z-image-turbo
Live API request
{
  "inputs": [],
  "options": { "prompt": "A neon-lit ramen stall in the rain at night, steam rising off the bowl, shallow depth of field, cinematic.", "aspect_ratio": "16:9", "images": 4 }
}
Successful output example
{
  "result": {
    "output": {
      "filename": "z-image-turbo-output.mp4",
      "contentType": "video/mp4",
      "byteSize": 437021,
      "downloadUrl": "https://cdn.kinopipe.com/…"
    }
  }
}

Know the boundaries before you run.

Renders at the 1k tier only: the model is trained at 1024 px and a 2k request is refused rather than returned off-distribution
Negative prompts have no effect on this model, and are accepted then dropped (see the FAQ)
One to four images per call, each one billed
Weights: Tongyi-MAI/Z-Image-Turbo, Apache-2.0
Every image carries a synthetic-content marker in its metadata (EU AI Act art. 50)

About z-image turbo api

What is Z-Image Turbo?

A six-billion-parameter text-to-image model from Alibaba Tongyi, distilled so that it needs eight forward passes instead of the usual twenty or more. That is what makes it fast, and cheap enough to render four images at a time.

Why do negative prompts do nothing here?

The model is guidance-distilled and runs without a separate unconditional pass, so there is no second branch to steer away from. The field is accepted and dropped rather than silently pretending to work. Put what you want in the prompt, or use Qwen-Image when you need a negative prompt.

Can I render at 2k?

Not on this model. It is trained at 1024 pixels, and larger renders drift off-distribution, so a 2k request returns an error naming the limit instead of a disappointing image. Qwen-Image covers the 2k tier.

What is it good and bad at?

Good at fast iteration, batches and general photographic or illustrative prompts. Not the one to pick when the image has to contain dense, legible text.

What licence are the weights under?

Apache-2.0, on the Tongyi-MAI/Z-Image-Turbo repository. We host the model; the images you generate are yours to use, subject to our terms and to the AI-generated marking we write into every file.

Do I need my own GPU?

No. The weights are open, so you can run them yourself, but this endpoint hosts the model: one HTTP call or one MCP tool call and the files come back. You pay per image rather than per hour of GPU.

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