Z Image Turbo Controlnet

Z Image Turbo Controlnet

Playground

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.

Features

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:

Parameters

How to Use

1. Upload reference image — The image that defines your composition structure.
2. Choose control mode — Select depth, canny, pose, or none based on what you want to preserve.
3. Write your prompt — Describe the style, content, and appearance you want.
4. Adjust strength — Higher values follow the control signal more strictly.
5. Set output size — Define your target dimensions.
6. 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.

# Submit the task
curl --location --request POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/z-image-turbo/controlnet" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
    "mode": "depth",
    "size": "1024*1024",
    "strength": 1,
    "seed": -1,
    "output_format": "jpeg",
    "enable_sync_mode": false,
    "enable_base64_output": false
}'

# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"