Question 1

How many inference steps should I use with Z-Image Turbo?

Accepted Answer

The model is distilled for 8 function evaluations, and the chute defaults to 9 steps. Stick close to the default; unlike standard diffusion models, raising the step count substantially does not buy proportional quality because the distillation targets a fixed short schedule.

Question 2

Why is guidance_scale 0, and can I use a negative prompt?

Accepted Answer

Turbo is distilled with classifier-free guidance baked out, so the chute defaults guidance_scale to 0 and exposes no negative prompt field. Express exclusions inside the positive prompt. If you need CFG and negative prompting, the non-distilled base Z-Image model supports both.

Question 3

What license is Z-Image Turbo under? Can I use it commercially?

Accepted Answer

Apache-2.0, per the Hugging Face repo Tongyi-MAI/Z-Image-Turbo. Apache-2.0 permits commercial use, modification, and redistribution with attribution and license notice.

Question 4

How do I call this chute from code?

Accepted Answer

POST a flat JSON body to https://vonkaiser-z-image-turbo.chutes.ai/generate with an Authorization: Bearer header carrying your Chutes API key. The response body is the PNG image itself, not JSON, so write it straight to a file or blob.

Question 5

Can it render text inside images?

Accepted Answer

Yes. Bilingual text rendering (English and Chinese) is one of the model card's headline capabilities. Quote the exact text you want in the prompt and keep it short for best fidelity.

Question 6

What image sizes does it support?

Accepted Answer

The chute defaults to 1024x1024 and accepts width and height fields. Keep dimensions moderate while iterating; larger canvases cost more compute and take longer.

Question 7

Is Z-Image Turbo an editing model?

Accepted Answer

No, it is text-to-image only. The Z-Image family includes a separate Z-Image-Edit variant for instruction-based editing, and Chutes hosts dedicated edit models such as Qwen-Image-Edit-2511.

Z image turbo

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