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Kling Image O1

kwaivgi /

Kling Omni Image O1 is Kuaishou's multi-modal image generation model with MVL technology. Supports up to 10 reference images for feature consistency, precise detail editing (add/remove/modify), style control, and series content creation. Perfect for IP character design, comic panels, and brand merchandise. Ready-to-use REST API, best performance, no coldstarts, affordable pricing.

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Change her hair color to light red.

$0.028cho mỗi lần chạy·~35 / $1

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Change her hair color to light red.

Change her hair color to light red.

Cinematic post-apocalyptic landscape. Wide shot of the ruins of Times Square, New York, completely overgrown with massive tropical jungle plants and vines. A waterfall is cascading down a crumbled skyscraper. Sunset lighting, volumetric fog, birds flying in the distance, matte painting style, 8k, hyper-detailed.

Cinematic post-apocalyptic landscape. Wide shot of the ruins of Times Square, New York, completely overgrown with massive tropical jungle plants and vines. A waterfall is cascading down a crumbled skyscraper. Sunset lighting, volumetric fog, birds flying in the distance, matte painting style, 8k, hyper-detailed.

A cozy modern coffee shop interior with warm ambient lighting and wood textures. On the table, place a clear glass iced latte topped with whipped cream and caramel drizzle. The drink should look naturally photographed within the environment, with realistic reflections and subtle depth of field.

A cozy modern coffee shop interior with warm ambient lighting and wood textures. On the table, place a clear glass iced latte topped with whipped cream and caramel drizzle. The drink should look naturally photographed within the environment, with realistic reflections and subtle depth of field.

A clean modern kitchen with marble countertops and soft morning sunlight. On the countertop, place a freshly baked croissant on a white ceramic plate. The croissant should look crisp and buttery, integrated naturally with the environment with accurate lighting and shadows.

A clean modern kitchen with marble countertops and soft morning sunlight. On the countertop, place a freshly baked croissant on a white ceramic plate. The croissant should look crisp and buttery, integrated naturally with the environment with accurate lighting and shadows.

Add a Nike cap to the character.

Add a Nike cap to the character.

Mô hình liên quan

README

Kling Omni Image O1

Kling Omni Image O1 is Kuaishou's advanced multi-modal image generation model, featuring MVL (Multi-modal Visual Language) technology that combines natural language with image references for unprecedented creative control.

🌟 Four Key Advantages

1. Feature Consistency

Maintains subject characteristics across multiple images:

  • Preserved outlines and core elements
  • Consistent color tones and lighting
  • Unified style across series

2. Precise Detail Modifications

Edit images without professional skills:

  • Add new elements naturally
  • Remove unwanted objects cleanly
  • Modify specific details precisely
  • Maintain original style and texture

3. Style Control

Apply and maintain artistic styles:

  • Consistent visual language
  • Brand-aligned aesthetics
  • Cross-image style coherence

4. Rich Imagination

Generate creative variations while preserving identity:

  • New poses and scenarios
  • Environmental changes
  • Creative interpretations

🎯 Use Cases

  • IP Character Design — Create consistent character series
  • Comic Panel Creation — Maintain character identity across panels
  • Brand Merchandise — Unified styling for product lines
  • Image Editing — Professional modifications without skills
  • Series Content — Cohesive visual storytelling

🎬 Core Features

  • Multi-Reference Support — Up to 10 reference images simultaneously
  • Feature Extraction — Intelligent understanding of subject characteristics
  • Cross-Image Consistency — Stable identity across generations
  • Natural Language Control — Guide creation with text prompts

🚀 How to Use

  1. Upload Reference Images Provide 1-10 reference images of your subject.

  2. Describe Your Intent Write a prompt for the desired output.

Example: "The character in a winter coat, standing in a snowy forest, same art style"

  1. Set Parameters Choose resolution and output format.

  2. Generate Receive images with consistent subject features.

💡 Pro Tips

  • Use multiple angles of the same subject for better feature extraction
  • Provide clear, high-resolution reference images
  • Specify style elements you want to maintain
  • For character series, include various expressions and poses in references

Price

  • $0.028 per run

📝 Example Workflows

WorkflowDescription
Character SeriesCreate consistent characters across different scenes
Product VariationsGenerate product images with unified branding
Comic CreationMaintain character identity across story panels
Style TransferApply consistent artistic style to new subjects
Lưu ý:Trang web này sử dụng các mô hình AI do bên thứ ba cung cấp.

Kling Image O1 API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/kwaivgi/kling-image-o1 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 O1 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",
    "aspect_ratio": "1:1",
    "resolution": "1k",
    "num_images": 1
}
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-o1" \
  -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-o1";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "aspect_ratio": "1:1",
        "resolution": "1k",
        "num_images": 1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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",
    "aspect_ratio": "1:1",
    "resolution": "1k",
    "num_images": 1
}

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-o1", 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 O1 API — Frequently asked questions

What is the Kling Image O1 API?

Kling Image O1 is a Kuaishou model for image editing, exposed as a REST API on WaveSpeedAI. Kling Omni Image O1 is Kuaishou's multi-modal image generation model with MVL technology. Supports up to 10 reference images for feature consistency, precise detail editing (add/remove/modify), style control, and series content creation. Perfect for IP character design, comic panels, and brand merchandise. Ready-to-use REST 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 O1 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-o1.

How much does Kling Image O1 cost per run?

Kling Image O1 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 O1 accept?

Key inputs: `prompt`, `images`, `aspect_ratio`, `resolution`, `num_images`. 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-o1.

How long does Kling Image O1 take to generate?

Median end-to-end generation time on WaveSpeedAI is around 11 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 O1 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 O1 | Fast Image Editing API | WaveSpeedAI