Z Image Turbo Controlnet | High-Quality Text-to-Image API

Z-Image Turbo ControlNet

Z-Image Turbo ControlNet is a powerful image generation model that gives you precise control over composition through structural guidance signals. Unlike standard text-to-image models that interpret prompts freely, ControlNet lets you define the exact structure, edges, depth, or pose of your output by analyzing a reference image.

Think of it as a blueprint system: you provide a reference image, choose how to analyze it (depth map, edge detection, or pose estimation), and the model generates a new image that follows that structural blueprint while matching your text prompt.

Why Choose This?

Precise composition control Define exact layouts, poses, and spatial relationships instead of hoping the model interprets your prompt correctly.
Multiple control modes Choose depth mapping for 3D structure, canny edge detection for outlines, pose estimation for human figures, or none for standard generation.
Reference-guided generation Use existing images as structural templates while completely changing style, content, and appearance.
Flexible strength control Adjust how strictly the model follows the control signal — from loose inspiration to exact replication.
Fast and affordable Turbo-optimized for quick generation at just $0.05 per image.

Understanding Control Modes

The mode parameter determines how the model analyzes your reference image:

Mode	What It Extracts	Best For
depth	3D depth information (near/far relationships)	Architectural scenes, landscapes, maintaining spatial depth
canny	Edge outlines and contours	Line art, sketches, preserving shapes and boundaries
pose	Human body keypoints and skeleton	Character poses, figure drawing, action scenes
none	No control signal (standard generation)	When you don't need structural guidance

Parameters

Parameter	Required	Description
prompt	Yes	Text description of the image you want to generate
image	Yes	Reference image URL for ControlNet to analyze
mode	No	Control mode: depth, canny, pose, or none (default: depth)
size	No	Output size in pixels as widthheight (default: 10241024)
strength	No	Control signal strength 0-1 (default: 0.6)
seed	No	Random seed for reproducibility (-1 for random)
output_format	No	Output format: jpeg, png, or webp (default: jpeg)

How to Use

Upload reference image — The image that defines your composition structure.
Choose control mode — Select depth, canny, pose, or none based on what you want to preserve.
Write your prompt — Describe the style, content, and appearance you want.
Adjust strength — Higher values follow the control signal more strictly.
Set output size — Define your target dimensions.
Run — Submit and download your controlled generation.

Pricing

$0.012 Per image. Simple flat-rate pricing regardless of control mode or image size.

Best Use Cases

Architectural Visualization (depth mode) — Maintain spatial relationships while changing style or materials.
Character Posing (pose mode) — Generate characters in specific poses from reference photos.
Style Transfer with Structure (canny mode) — Apply new styles while preserving exact outlines and shapes.
Product Photography (depth mode) — Generate product images with consistent composition across variations.
Comic and Illustration (canny/pose modes) — Convert sketches or poses into fully rendered artwork.

Pro Tips

Depth mode works best with images that have clear foreground/background separation.
Canny mode is ideal when you have line art, sketches, or want to preserve exact shapes.
Pose mode requires images with visible human figures — it won't work on landscapes or objects.
Start with strength 0.6 and adjust: lower for loose interpretation, higher for strict adherence.
The prompt matters more at lower strength values; at high strength, structure dominates.
Use the same seed to compare different control modes on the same reference image.

Notes

Reference image quality affects control accuracy — clear, well-lit images work best.
Pose mode only detects human poses; it won't extract structure from other subjects.
At strength 0, the control signal has minimal effect (similar to standard generation).
At strength 1, output will closely match the reference structure regardless of prompt.

Related Models

Z-Image Turbo Text-to-Image — Standard text-to-image without structural control.
Z-Image Turbo Image-to-Image — Transform existing images with variable strength.
Z-Image Turbo Inpaint — Edit specific regions of images with masks.

Z Image Turbo Controlnet API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/z-image-turbo/controlnet with your input as JSON. The endpoint returns a prediction id; poll the prediction endpoint until status flips to completed, then read the output URL from data.outputs[0]. Examples for Z Image Turbo Controlnet below.

HTTP example

# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/z-image-turbo/controlnet" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d '{
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "image": "https://example.com/your-input.jpg",
    "mode": "depth",
    "size": "1024*1024",
    "strength": 1,
    "seed": -1,
    "output_format": "jpeg",
    "enable_sync_mode": false,
    "enable_base64_output": false
}'

# Response includes a prediction id. Poll for the result:
curl -X GET "https://api.wavespeed.ai/api/v3/predictions/{request_id}/result" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY"

# When status is "completed", read the output from data.outputs[0].

Node.js example

// npm install wavespeed
const WaveSpeed = require('wavespeed');

const client = new WaveSpeed(); // reads WAVESPEED_API_KEY from env

const result = await client.run("wavespeed-ai/z-image-turbo/controlnet", {
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "image": "https://example.com/your-input.jpg",
        "mode": "depth",
        "size": "1024*1024",
        "strength": 1,
        "seed": -1,
        "output_format": "jpeg",
        "enable_sync_mode": false,
        "enable_base64_output": false
});

console.log(result.outputs[0]); // → URL of the generated output

Python example

# pip install wavespeed
import wavespeed

output = wavespeed.run(
    "wavespeed-ai/z-image-turbo/controlnet",
    {
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "image": "https://example.com/your-input.jpg",
    "mode": "depth",
    "size": "1024*1024",
    "strength": 1,
    "seed": -1,
    "output_format": "jpeg",
    "enable_sync_mode": false,
    "enable_base64_output": false
}
)

print(output["outputs"][0])  # → URL of the generated output

Z Image Turbo Controlnet API — Frequently asked questions

What is the Z Image Turbo Controlnet API?

Z Image Turbo Controlnet is a WaveSpeedAI model for image generation, exposed as a REST API on WaveSpeedAI. Z-Image-Turbo ControlNet generates images guided by structural control signals (depth, canny edge, pose) for precise composition control. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Z Image Turbo Controlnet API?

POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID; poll the prediction endpoint until status flips to "completed", then read the output URL from the result. The playground generates a ready-to-paste code sample in Python, JavaScript, or cURL for whatever inputs you've set. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/wavespeed-ai/z-image-turbo-controlnet.

How much does Z Image Turbo Controlnet cost per run?

Z Image Turbo Controlnet starts at $0.012 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.

What inputs does Z Image Turbo Controlnet accept?

Key inputs: `prompt`, `image`, `size`, `seed`, `enable_base64_output`, `enable_sync_mode`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/wavespeed-ai/z-image-turbo-controlnet.

How long does Z Image Turbo Controlnet take to generate?

Average end-to-end generation time on WaveSpeedAI is around 28 seconds per request — measured across recent runs. Queue time scales with global demand; live status is visible in the prediction record.

Can I use Z Image Turbo Controlnet outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (WaveSpeedAI). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.

ExamplesView all

Related Models

README

Z-Image Turbo ControlNet

Why Choose This?

Understanding Control Modes

Parameters

How to Use

Pricing

Best Use Cases

Pro Tips

Notes

Related Models

Z Image Turbo Controlnet API — Quick start

Z Image Turbo Controlnet API — Frequently asked questions