Z Image Turbo Inpaint
Playground
Try it on WavespeedAI!Z-Image Turbo Inpaint delivers ultra-fast image inpainting with natural-language instructions—seamlessly fill, fix, or replace regions in your images with production-quality results. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing.
Features
Z-Image Turbo Inpaint
Z-Image Turbo Inpaint is a fast, production-ready inpainting model for removing, repairing, or replacing specific regions in an image using simple natural-language instructions. Provide an image plus a mask, describe what you want to happen, and the model blends the edited region naturally into the original scene—optimized for low latency and clean results.
Where Z-Image Turbo Inpaint shines
- Product photo cleanup (remove dust, labels, reflections, minor defects)
- Object removal and background repair
- Photo restoration and localized fixes
- High-throughput pipelines that need consistent, quick turnaround
Key benefits
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Natural, seamless blending Reconstructs masked regions with realistic texture, lighting, and structure to minimize visible seams.
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Prompt-driven control Use plain English to specify what to remove or what should replace the masked area.
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Fast iteration at scale Built for rapid reruns—ideal for design iteration, batch processing, and automation.
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Simple API integration Straightforward inputs (image, mask, prompt) with stable behavior and no cold starts.
How it works
- Provide an input image.
- Provide a mask that marks the region to modify (the masked area is what the model will change).
- Provide a prompt describing the intended edit.
- The model fills or replaces the masked region while keeping the rest of the image stable.
Pricing
- $0.02 per inpainted image
Tips for best results
- Mask only what you need, then expand slightly if artifacts remain.
- Use clean mask edges for structured objects; use softer/feathered edges for textures like hair, smoke, or fabric.
- Be specific in the prompt when replacing content (object type, material, lighting, style).
More Image Tools on WaveSpeedAI
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FLUX.2 [max] Edit — Production-grade AI image editing with natural-language instructions and precise control for high-fidelity commercial creative workflows.
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FLUX.2 [flex] Edit — Fast, controllable AI image editing for clean inpainting, color-accurate adjustments, and consistent style preservation at scale.
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Qwen Edit Plus — Prompt-driven AI inpainting and local image edits with strong composition preservation, ideal for background cleanup and targeted refinements.
Authentication
For authentication details, please refer to the Authentication Guide.
API Endpoints
Submit Task & Query Result
# Submit the task
curl --location --request POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/z-image/turbo-inpaint" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{}'
# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"
Parameters
Task Submission Parameters
Request Parameters
| Parameter | Type | Required | Default | Range | Description |
|---|---|---|---|---|---|
| prompt | string | Yes | - | The text description for the inpainting task. | |
| image | string | Yes | - | URL of the input image to be inpainted. | |
| mask_image | string | Yes | - | URL of the mask image. White areas will be inpainted, black areas will be preserved. | |
| size | string | No | - | 256 ~ 1536 per dimension | The size of the generated media in pixels (width*height). |
Response Parameters
| Parameter | Type | Description |
|---|---|---|
| code | integer | HTTP status code (e.g., 200 for success) |
| message | string | Status message (e.g., “success”) |
| data.id | string | Unique identifier for the prediction, Task Id |
| data.model | string | Model ID used for the prediction |
| data.outputs | array | Array of URLs to the generated content (empty when status is not completed) |
| data.urls | object | Object containing related API endpoints |
| data.urls.get | string | URL to retrieve the prediction result |
| data.has_nsfw_contents | array | Array of boolean values indicating NSFW detection for each output |
| data.status | string | Status of the task: created, processing, completed, or failed |
| data.created_at | string | ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”) |
| data.error | string | Error message (empty if no error occurred) |
| data.timings | object | Object containing timing details |
| data.timings.inference | integer | Inference time in milliseconds |
Result Request Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| id | string | Yes | - | Task ID |
Result Response Parameters
| Parameter | Type | Description |
|---|---|---|
| code | integer | HTTP status code (e.g., 200 for success) |
| message | string | Status message (e.g., “success”) |
| data | object | The prediction data object containing all details |
| data.id | string | Unique identifier for the prediction, the ID of the prediction to get |
| data.model | string | Model ID used for the prediction |
| data.outputs | string | Array of URLs to the generated content (empty when status is not completed). |
| data.urls | object | Object containing related API endpoints |
| data.urls.get | string | URL to retrieve the prediction result |
| data.status | string | Status of the task: created, processing, completed, or failed |
| data.created_at | string | ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”) |
| data.error | string | Error message (empty if no error occurred) |
| data.timings | object | Object containing timing details |
| data.timings.inference | integer | Inference time in milliseconds |