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.
Idle

$0.02per run·~50 / $1

A handsome anime-style man in his early 30s with medium-length wavy chestnut brown hair, slightly tousled with natural volume, and a well-groomed short beard. He sits in a warm-lit café interior, facing the camera with a gentle, relaxed smile and kind eyes. His eyes are large and expressive in classic anime style, with warm brown irises and soft highlights. He wears a dark olive-green casual jacket over a dark inner shirt, with the collar slightly open. The background shows a cozy café atmosphere with blurred warm wooden furniture, soft ambient lighting, and hints of windows casting golden light. The lighting is soft and warm, coming from the upper left, creating gentle shadows on the right side of his face. The art style is high-quality Japanese anime illustration with clean line art, smooth cel-shading, and warm color palette. The composition is a close-up portrait from chest up, with shallow depth of field. Makoto Shinkai cinematic style, detailed anime illustration, 4K quality.
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.
Natural, seamless blending Reconstructs masked regions with realistic texture, lighting, and structure to minimize visible seams.
Prompt-driven control Use plain English to specify what to remove or what should replace the masked area.
Fast iteration at scale Built for rapid reruns—ideal for design iteration, batch processing, and automation.
Simple API integration Straightforward inputs (image, mask, prompt) with stable behavior and no cold starts.
FLUX.2 [max] Edit — Production-grade AI image editing with natural-language instructions and precise control for high-fidelity commercial creative workflows.
FLUX.2 [flex] Edit — Fast, controllable AI image editing for clean inpainting, color-accurate adjustments, and consistent style preservation at scale.
Qwen Edit Plus — Prompt-driven AI inpainting and local image edits with strong composition preservation, ideal for background cleanup and targeted refinements.
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/z-image/turbo-inpaint 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 Inpaint below.
# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/z-image/turbo-inpaint" \
-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"
}'
# 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].// 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-inpaint", {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://example.com/your-input.jpg"
});
console.log(result.outputs[0]); // → URL of the generated output# pip install wavespeed
import wavespeed
output = wavespeed.run(
"wavespeed-ai/z-image/turbo-inpaint",
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://example.com/your-input.jpg"
}
)
print(output["outputs"][0]) # → URL of the generated outputZ Image Turbo Inpaint is a WaveSpeedAI model for image editing, exposed as a REST API 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. You can call it programmatically or try it from the playground above.
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-inpaint.
Z Image Turbo Inpaint starts at $0.020 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.
Key inputs: `prompt`, `image`, `mask_image`. 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-inpaint.
Average end-to-end generation time on WaveSpeedAI is around 7 seconds per request — measured across recent runs. Queue time scales with global demand; live status is visible in the prediction record.
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.