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Kling V2.6 Std Image to Video | Fast Image-to-Video

kwaivgi/

Kling 2.6 Standard offers cost-effective image-to-video generation with smooth motion, cinematic visuals, and accurate prompt adherence. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

image-to-video
输入

就绪

$0.21每次运行·~47 / $10

下一步:

示例查看全部

Close-up shot, the craftsman is fully focused on assembling a complex brass machine. His right hand holding the tool carefully turns a component, and the small gears inside the machine rotate slowly in response. Faint steam or smoke drifts in the background. The metal surfaces reflect warm lighting. 4k quality, macro details.

The athlete looks down at the basketball in his hands, slowly rotating the ball to feel the texture, then raises his head to look confidently at the camera again. Light reflects off his skin and the surface of the ball. Slow motion, sports documentary style.

相关模型

README

Kling V2.6 Standard Image-to-Video

Kling V2.6 Standard Image-to-Video is Kuaishou's image-to-video generation model that brings static images to life. Upload a reference image and describe the motion — the model generates smooth, natural video with detailed animation and cinematic quality.

Why Choose This?

  • Image-driven generation Transform any image into dynamic video with natural motion.

  • Negative prompt support Exclude unwanted elements for more precise control over the output.

  • Flexible duration Generate 5-second or 10-second videos.

  • Detail preservation Maintains fine details from the source image during animation.

  • Prompt Enhancer Built-in tool to automatically improve your motion descriptions.

Parameters

ParameterRequiredDescription
promptNoText description of the desired motion and action
negative_promptNoElements to exclude from generation
imageYesReference image to animate (URL or upload)
durationNoVideo length: 5 or 10 seconds (default: 5)

How to Use

  1. Upload your image — provide the reference image to animate.
  2. Write your prompt (optional) — describe the motion, camera movement, and action.
  3. Add negative prompt (optional) — specify what you want to avoid.
  4. Set duration — 5 seconds or 10 seconds.
  5. Run — submit and download your video.

Pricing

DurationCost
5s$0.21
10s$0.42

Best Use Cases

  • Photo Animation — Bring portraits, landscapes, and product images to life.
  • Social Media Content — Create engaging video from static images.
  • Marketing & Ads — Generate dynamic promotional videos from product photos.
  • Storytelling — Animate illustrations and artwork for narratives.
  • Creative Projects — Explore motion concepts from reference images.

Pro Tips

  • Use the Prompt Enhancer to refine your motion descriptions.
  • Be specific about movement direction, speed, and camera angles in the prompt.
  • Use negative prompts to avoid common artifacts (e.g., "blurry, distorted, low quality").
  • Use high-quality source images for better video results.
  • 5s videos are more cost-effective for testing; use 10s for final production.

Notes

  • Image is the only required field; prompt is optional but recommended.
  • Duration options are 5 or 10 seconds only.
  • Ensure uploaded image URLs are publicly accessible.

Related Models

提示:本网站部分功能由第三方 AI 模型提供支持。

Kling v2.6 Std Image To Video API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/kwaivgi/kling-v2.6-std/image-to-video 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 v2.6 Std Image To Video 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",
    "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
    "duration": 5
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/kwaivgi/kling-v2.6-std/image-to-video" \
  -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-v2.6-std/image-to-video";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
        "duration": 5
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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",
    "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
    "duration": 5
}

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-v2.6-std/image-to-video", 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 v2.6 Std Image To Video API — Frequently asked questions

What is the Kling v2.6 Std Image To Video API?

Kling v2.6 Std Image To Video is a Kuaishou model for video generation from images, exposed as a REST API on WaveSpeedAI. Kling 2.6 Standard offers cost-effective image-to-video generation with smooth motion, cinematic visuals, and accurate prompt adherence. 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 v2.6 Std Image To Video 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-v2.6-std-image-to-video.

How much does Kling v2.6 Std Image To Video cost per run?

Kling v2.6 Std Image To Video starts at $0.21 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 v2.6 Std Image To Video accept?

Key inputs: `prompt`, `image`, `duration`, `negative_prompt`. 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-v2.6-std-image-to-video.

How long does Kling v2.6 Std Image To Video take to generate?

Median end-to-end generation time on WaveSpeedAI is around 48 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 v2.6 Std Image To Video 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 V2.6 Std Image to Video | Fast Image-to-Video API on WaveSpeedAI