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Kling O3 Edit is an AI image editing model with 4K resolution and multi-image reference support, enabling high-quality transformations with multiple reference inputs. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

image-to-image
Input

Idle

Have the people in picture 1 and picture 2 take a selfie together.

$0.028per run·~35 / $1

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ExamplesView all

Have the people in picture 1 and picture 2 take a selfie together.

Have the people in picture 1 and picture 2 take a selfie together.

Related Models

README

Kling Image O3 Edit

Kling Image O3 Edit is Kuaishou's next-generation image editing model from the O3 architecture. Upload up to 10 reference images and describe how to combine or transform them — the model generates new images that blend characters, styles, and elements from your references while following your text instructions. Supports flexible aspect ratios, up to 4K resolution, and batch generation.

Why Choose This?

  • O3-generation quality The latest architecture with improved detail, composition, and prompt understanding.

  • Multi-reference support Upload up to 10 reference images to combine characters, objects, or styles in a single output.

  • Text-guided editing Describe changes or compositions in natural language — no manual masking required.

  • Up to 4K resolution Choose output resolution from 1K to 4K based on your quality requirements.

  • Flexible aspect ratios Auto-detect or manually select from multiple options including 1:1, 3:4, 4:3, 9:16, 16:9.

  • Batch generation Generate multiple variations in a single request for rapid iteration.

Parameters

ParameterRequiredDescription
promptYesText description of the desired edit or composition
imagesYesReference images (up to 10) for characters, styles, etc.
aspect_ratioNoOutput aspect ratio (default: auto)
resolutionNoOutput resolution: 1k, 2k, or 4k (default: 1k)
num_imagesNoNumber of images to generate (default: 1)
output_formatNoOutput format: png or jpeg (default: png)

How to Use

  1. Upload your images — add up to 10 reference images containing the characters, objects, or styles you want.
  2. Write your prompt — describe how to combine or transform them (e.g., "Have the people in picture 1 and picture 2 take a selfie together.").
  3. Choose aspect ratio — select auto or a specific format for your use case.
  4. Set resolution — choose 1k/2k for speed or 4k for maximum detail.
  5. Set num_images — generate multiple variations if needed.
  6. Run — submit and download your edited images.

Pricing

ResolutionCost per Image
1K$0.028
2K$0.028
4K$0.056

Billing Rules

  • Base rate: $0.028 per image (1K/2K)
  • 4K rate: $0.056 per image (2× base)
  • Total cost = num_images × per-image rate

Best Use Cases

  • Character Composition — Combine people from different photos into a single scene.
  • Style Fusion — Blend visual styles from multiple reference images.
  • Creative Mashups — Merge characters, objects, or environments from separate sources.
  • Marketing & Ads — Create composite visuals featuring multiple products or people.
  • Social Content — Generate imaginative scenes combining friends, celebrities, or fictional characters.

Pro Tips

  • Reference images with clear subjects and good lighting produce the best results.
  • Use "picture 1", "picture 2" etc. in your prompt to refer to specific reference images in order.
  • Generate multiple images (num_images > 1) to explore different interpretations.
  • Use 4K resolution for print-quality or large-format outputs.
  • Auto aspect ratio adapts to your reference images — use manual selection for specific platforms.

Notes

  • Both prompt and images are required fields.
  • Maximum 10 reference images per request.
  • 4K resolution costs 2× the base rate.
  • Ensure uploaded image URLs are publicly accessible.

Related Models

Note:This website uses AI models provided by third parties. Documentation prices are for reference and may be outdated. The Generate button shows an estimate; the final task charge prevails.

Kling Image O3 Edit API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/kwaivgi/kling-image-o3/edit 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 Kling Image O3 Edit below.

HTTP example
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",
    "images": [
        "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
    ],
    "aspect_ratio": "auto",
    "resolution": "1k",
    "num_images": 1,
    "output_format": "png",
    "shot_type": "customize"
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/kwaivgi/kling-image-o3/edit" \
  -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
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/kwaivgi/kling-image-o3/edit";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "images": [
                "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
        ],
        "aspect_ratio": "auto",
        "resolution": "1k",
        "num_images": 1,
        "output_format": "png",
        "shot_type": "customize"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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));
}
Python example
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",
    "images": [
        "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
    ],
    "aspect_ratio": "auto",
    "resolution": "1k",
    "num_images": 1,
    "output_format": "png",
    "shot_type": "customize"
}

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/kwaivgi/kling-image-o3/edit", 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)

Kling Image O3 Edit API — Frequently asked questions

What is the Kling Image O3 Edit API?

Kling Image O3 Edit is a Kuaishou model for image editing, exposed as a REST API on WaveSpeedAI. Kling O3 Edit is an AI image editing model with 4K resolution and multi-image reference support, enabling high-quality transformations with multiple reference inputs. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Kling Image O3 Edit 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 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/kwaivgi/kwaivgi-kling-image-o3-edit.

How much does Kling Image O3 Edit cost per run?

Kling Image O3 Edit starts at $0.028 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 Kling Image O3 Edit accept?

Key inputs: `prompt`, `images`, `aspect_ratio`, `resolution`, `num_images`, `output_format`. 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/kwaivgi/kwaivgi-kling-image-o3-edit.

How long does Kling Image O3 Edit take to generate?

Median end-to-end generation time on WaveSpeedAI is around 62 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.

Can I use Kling Image O3 Edit outputs commercially?

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

Kling Image O3 Edit | Fast Image Editing API on WaveSpeedAI