Z-Image-Turbo Image-to-Image is a 6 billion parameter model that enhances the quality of reference images (similar to upscaling) in sub-second time. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing.
निष्क्रिय

$0.005प्रति रन·~200 / $1

A realistic, high-resolution photograph of an American woman, captured in natural daylight with soft, diffused lighting that enhances her features gently. She is positioned in a candid, relaxed pose—standing outdoors near a park bench, wearing casual, contemporary clothing: a light blue denim jacket, a white t-shirt, and dark jeans. Her long, wavy brown hair falls loosely over her shoulders, and she has a subtle, thoughtful expression as she looks slightly off-camera. The background is softly blurred (shallow depth of field), with hints of greenery, a distant streetlamp, and a few passing pedestrians. Shot on a full-frame DSLR with a 50mm prime lens, emphasizing natural textures, skin tones, and environmental detail. Cinematic composition, eye-level shot, natural color grading, and a subtle film grain texture for authentic photographic realism.

An old lady sit on the floor

Transform into a long-haired woman wearing glasses, dressed in the same clothes, posing in the same way.
Z-Image Turbo Image-to-Image is a versatile image generation model that offers a spectrum of modifications — from subtle enhancement to dramatic reimagination. The key is the strength parameter: at low values, it preserves your original image while enhancing quality (similar to upscaling); at high values, it uses your image as loose inspiration for entirely new creations.
This is not a simple editing tool — it's a generation engine that lets you control exactly how much of the original image to preserve.
Flexible transformation spectrum From near-lossless enhancement to complete style overhaul — all controlled by a single strength slider.
Quality enhancement mode At low strength, improve image quality, add detail, and sharpen without changing content.
Creative reimagination mode At high strength, use your image as a reference while dramatically changing style, composition, or subject.
Custom output sizing Set exact width and height for your output, independent of input dimensions.
Prompt Enhancer Built-in tool to automatically improve your prompts for better results.
Fast and affordable Turbo-optimized for quick generation at just $0.005 per image.
The strength parameter is the core of this model. It controls how much the output differs from your input image:
| Strength | Effect | Use Case |
|---|---|---|
| 0.0 - 0.3 | Minimal change — enhances quality, adds detail, sharpens | Upscaling, quality improvement, subtle refinement |
| 0.3 - 0.6 | Moderate change — preserves structure, adjusts style | Style tweaks, color grading, texture enhancement |
| 0.6 - 0.8 | Significant change — keeps composition, transforms content | Style transfer, artistic reinterpretation |
| 0.8 - 1.0 | Maximum change — uses image as loose reference only | Creative reimagination, dramatic transformation |
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text description guiding the transformation |
| image | Yes | Source image to transform (upload or URL) |
| width | No | Output width in pixels (default: 1024) |
| height | No | Output height in pixels (default: 1024) |
| strength | No | Transformation intensity 0-1 (default: 0.6) |
| seed | No | Random seed for reproducibility (-1 for random) |
| Item | Cost |
|---|---|
| Per image | $0.005 |
Simple flat-rate pricing regardless of image size or strength setting.
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/z-image-turbo/image-to-image with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Z Image Turbo Image To Image below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"size": "1024*1024",
"strength": 0.6,
"seed": -1,
"output_format": "jpeg"
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/z-image-turbo/image-to-image" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $WAVESPEED_API_KEY" \
-d "$REQUEST_BODY")
TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
printf 'Submission response did not contain a prediction id
' >&2
exit 1
fi
RESULT_URL=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi
# 2. Poll until the prediction finishes.
while true; do
RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
-H "Authorization: Bearer $WAVESPEED_API_KEY")
RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
case "$STATUS" in
completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
created|processing) sleep 2 ;;
*) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
esac
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/wavespeed-ai/z-image-turbo/image-to-image";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');
async function requestJson(url, options = {}) {
const response = await fetch(url, options);
if (!response.ok) throw new Error(await response.text());
return response.json();
}
// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
method: "POST",
headers: {
"Authorization": `Bearer ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"size": "1024*1024",
"strength": 0.6,
"seed": -1,
"output_format": "jpeg"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;
// 2. Poll until the prediction finishes.
while (true) {
const resultBody = await requestJson(resultUrl, {
headers: { "Authorization": `Bearer ${apiKey}` },
});
const result = resultBody.data ?? resultBody;
if (result.status === "completed") {
console.log(result.outputs);
break;
}
if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
await new Promise(resolve => setTimeout(resolve, 2000));
}import json
import os
import time
from urllib.request import Request, urlopen
api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"size": "1024*1024",
"strength": 0.6,
"seed": -1,
"output_format": "jpeg"
}
def request_json(url, data=None):
request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
with urlopen(request) as response:
return json.load(response)
# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/wavespeed-ai/z-image-turbo/image-to-image", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"
# 2. Poll until the prediction finishes.
while True:
result_body = request_json(result_url)
result = result_body.get("data", result_body)
status = result.get("status")
if status == "completed":
print(result.get("outputs", []))
break
if status in {"failed", "cancelled", "timeout"}:
raise RuntimeError(result)
if status not in {"created", "processing"}:
raise RuntimeError(f"Unexpected status: {status}")
time.sleep(2)Z Image Turbo Image To Image is a WaveSpeedAI model for image editing, exposed as a REST API on WaveSpeedAI. Z-Image-Turbo Image-to-Image is a 6 billion parameter model that enhances the quality of reference images (similar to upscaling) in sub-second time. 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 result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/wavespeed-ai/z-image-turbo-image-to-image.
Z Image Turbo Image To Image starts at $0.005 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`, `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-image-to-image.
Median end-to-end generation time on WaveSpeedAI is around 4 seconds per request, based on recent successful runs. Queue time varies 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.