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kwaivgi/

Kling Effects is a fast AI image-to-video effects model that creates 5-second videos from a single image in styles ranging from futuristic to realistic for social media, product demos, and creative visual content. Ready-to-use REST inference API for image animation, visual effects, product showcases, advertising creatives, social media clips, and professional image-to-video workflows with simple integration, no coldstarts, and affordable pricing.

image-to-video
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निष्क्रिय

$0.14प्रति रन·~71 / $10

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README

Kling Effects

Kling Effects turns a single input image into a short stylized video using a curated set of official effect templates. It is designed for social content, product demos, character moments, pet clips, seasonal visuals, cinematic transitions, and other quick image-to-video effect workflows.

Why Choose This?

  • One-image video effects
    Upload one image and transform it into a short animated effect video.

  • Curated effect templates
    Choose from a controlled list of supported effect_scene options.

  • Simple image-to-video workflow
    Select an effect, upload an image, and generate a video.

  • Great for social and creative content
    Suitable for pets, portraits, products, holiday visuals, action scenes, and short-form video ideas.

  • Predictable entry pricing
    Most effects start from a clear base price, with some premium effects priced slightly higher.

Parameters

ParameterRequiredDescription
effect_sceneYesThe effect template to apply.
imageYesInput image used to generate the effect video.

How to Use

  1. Choose an effect — select the effect_scene you want to apply.
  2. Upload your image — provide the image to animate.
  3. Submit — run the model and get the generated effect video.

Pricing

Pricing depends on the selected effect_scene.

ItemStarting Cost
Base effect price$0.84

Billing Rules

  • Most effects start at $0.84
  • Some premium effects may cost slightly more
  • Pricing depends on the selected effect_scene
  • The input image size does not affect pricing

Best Use Cases

  • Pet effect videos — Turn pet photos into playful animated clips.
  • Portrait transformations — Create stylish or character-driven video moments.
  • Product and lifestyle content — Add motion and visual interest to product images.
  • Seasonal social posts — Generate holiday, celebration, or themed short videos.
  • Action and cinematic effects — Apply dynamic movement, transitions, or dramatic visual effects.

Pro Tips

  • Use a clear subject image for better results.
  • Choose an effect that matches the main subject of the image.
  • Portrait and full-body images often work better for character-style effects.
  • Pet-focused effects work best with clear animal photos.
  • Try a base-price effect first when testing a new image.

Notes

  • effect_scene is required.
  • image is required.
  • Only supported effect templates can be selected.
  • Different effects may have slightly different prices.
  • Results may vary depending on image quality, framing, and subject visibility.
नोट:यह वेबसाइट तृतीय पक्षों द्वारा प्रदान किए गए AI मॉडलों का उपयोग करती है। दस्तावेज़ की कीमतें केवल संदर्भ के लिए हैं और पुरानी हो सकती हैं। Generate बटन अनुमान दिखाता है; टास्क का अंतिम शुल्क ही मान्य होगा।

Kling Effects API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/kwaivgi/kling-effects 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 Effects below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
    "effect_scene": "daily_ootd"
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/kwaivgi/kling-effects" \
  -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-effects";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
        "effect_scene": "daily_ootd"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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 = {
    "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
    "effect_scene": "daily_ootd"
}

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-effects", 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 Effects API — Frequently asked questions

What is the Kling Effects API?

Kling Effects is a Kuaishou model for video generation from images, exposed as a REST API on WaveSpeedAI. Kling Effects is a fast AI image-to-video effects model that creates 5-second videos from a single image in styles ranging from futuristic to realistic for social media, product demos, and creative visual content. Ready-to-use REST inference API for image animation, visual effects, product showcases, advertising creatives, social media clips, and professional image-to-video workflows with simple integration, no coldstarts, and affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Kling Effects 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-effects.

How much does Kling Effects cost per run?

Kling Effects starts at $0.14 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 Effects accept?

Key inputs: `image`, `effect_scene`. 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-effects.

How long does Kling Effects take to generate?

Median end-to-end generation time on WaveSpeedAI is around 216 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 Effects 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 Effects API on WaveSpeedAI