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Kling 2.1 Pro converts images to professional cinematic videos with enhanced fidelity, precise camera moves and dynamic motion control. Ready-to-use REST inference API, top performance, no coldstarts, affordable pricing.

image-to-video
입력

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$0.45실행당·~22 / $10

다음:

예시전체 보기

A beautiful young woman with long wavy hair, laughing loudly, her hair swaying in the wind, dressed in a stylish blue trench coat, standing in a sunny city street, cinematic close-up shot, 8K, high detail, photorealistic, ultra realistic, volumetric lighting.

A majestic waterfall cascades down moss-covered rocks into a pristine mountain lake. The water sparkles under the morning sunlight, and a gentle mist rises from the surface. Leaves on nearby trees rustle in the breeze.

A highly detailed shot of a futuristic racing car speeding on a neon-lit track. The car's tires leave a blue light trail. The background is blurred, motion blur effect, cyberpunk aesthetic, high speed, dynamic angle, 8K, cinematic.

A majestic magic dragon, a close-up shot, breathing fire over an ancient castle on a mountain cliff, surrounded by epic mountains and clouds. Dramatic lighting, fantasy art, cinematic, hyper-realistic, volumetric light, 4K, wide shot.

A cute fawn running through a sun-drenched forest with blooming wildflowers. The sun shines through the trees, casting beautiful light rays. Soft focus, macro photography, fairytale vibe, dreamy atmosphere, vibrant colors, UHD, slow motion.

A busy street in Tokyo at night after rain. People are walking by, blurred reflections of neon signs and city lights on the wet pavement. Tilt-shift effect, cinematic, detailed, street photography, moody lighting, 4K, time-lapse feel.

A striking view of a young female fashion model with flowing blonde hair, wearing a vibrant emerald green dress, dramatically twirling on a rooftop overlooking a bustling cityscape at sunset. The camera is a sweeping orbital shot circling her, capturing the movement of her dress and hair against the urban backdrop. Golden hour light casting long shadows, wind gently blowing her hair, creating a sense of freedom and energy, high fashion editorial, cinematic, 8k, ultra-detailed.

A lone knight on a horse, galloping furiously across a vast, windswept grassland. The clouds above are moving swiftly, and the sun is setting in the background. Wide, epic shot, cinematic, dramatic lighting, 8K, photorealistic.

A futuristic soldier, equipped with advanced gear, running through a neon-lit, cyberpunk city at night. The camera is in a handheld shot, giving it a shaky, first-person perspective. Fast-paced action, blurred lights, high-energy, cinematic.

A young woman standing alone on a bridge at night, her hair gently swaying in the breeze, her gaze distant as she looks at the city lights in the distance. A car drives past the frame, leaving a blur of light and shadow. The camera circles around from behind her to the side, then briefly freezes, capturing her contemplative expression. Melancholic, serene, emotional, cinematic, high contrast.

관련 모델

README

Kling v2.1 I2V Pro — kwaivgi/kling-v2.1-i2v-pro

Kling v2.1 I2V Pro turns a single reference image into a short, cinematic video clip guided by your prompt. Upload an image, describe the motion (subject + camera + environment), and the model animates the scene while keeping the input image as the visual anchor. Built for stable production use with a ready-to-use REST API, no cold starts, and predictable pricing.

Key capabilities

  • Image-to-video generation anchored to your input image
  • Prompt-controlled motion: facial micro-expressions, hair/clothing movement, environment effects
  • Cinematic camera moves: push-in, orbit, pan, tilt, handheld feel
  • Supports negative_prompt to reduce artifacts and unwanted styles

Pricing

DurationPrice
5s$0.45
10s$0.90
15s$1.35
20s$1.80

Inputs

  • image (required): the reference image used as the visual anchor
  • prompt (required): describe what moves and how the camera behaves
  • negative_prompt (optional): describe what to avoid (blur, distortions, artifacts)

Parameters

  • prompt: motion + scene direction for the clip
  • negative_prompt: optional “avoid list”
  • image: input image (upload or URL)
  • guidance_scale: how strongly motion follows your prompt (lower = more natural, higher = more literal)
  • duration: video length in seconds

Prompting guide (I2V)

Write prompts like a director’s brief, focusing on motion:

  • Subject motion: expression change, breathing, walking, turning, hair swaying
  • Environment motion: wind, rain, fog, particles, light rays
  • Camera motion: slow push-in, orbit, dolly, handheld micro-shake
  • Continuity: keep identity, outfit, and scene layout consistent with the input image

Example prompts

  • A cinematic close-up of a woman laughing on a sunny city street. Her hair sways in the wind, coat fabric subtly moves, warm natural light, shallow depth of field, camera slow push-in, smooth motion, 5 seconds.
  • Portrait in golden hour. Gentle breeze, subtle facial motion, soft lens flare, handheld micro-sway, realistic skin texture, 5 seconds.
  • Moody night street scene. Light rain, drifting mist, neon reflections, camera slow orbit around the subject, 5 seconds.

Negative prompt examples

  • blur, distort, low quality
  • jitter, warping, melted details, extra limbs
  • watermark, logo, subtitles, text artifacts
참고:이 웹사이트는 제3자가 제공하는 AI 모델을 사용합니다. 문서 가격은 참고용이며 최신 정보가 아닐 수 있습니다. Generate 버튼에는 예상 금액이 표시되며, 최종 작업 청구액이 적용됩니다.

Kling v2.1 I2v Pro API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/kwaivgi/kling-v2.1-i2v-pro 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 v2.1 I2v Pro 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",
    "guidance_scale": 0.5,
    "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-v2.1-i2v-pro" \
  -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-v2.1-i2v-pro";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
        "guidance_scale": 0.5,
        "duration": 5
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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",
    "guidance_scale": 0.5,
    "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-v2.1-i2v-pro", 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 v2.1 I2v Pro API — Frequently asked questions

What is the Kling v2.1 I2v Pro API?

Kling v2.1 I2v Pro is a Kuaishou model for video generation from images, exposed as a REST API on WaveSpeedAI. Kling 2.1 Pro converts images to professional cinematic videos with enhanced fidelity, precise camera moves and dynamic motion control. Ready-to-use REST inference API, top performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Kling v2.1 I2v Pro 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-v2.1-i2v-pro.

How much does Kling v2.1 I2v Pro cost per run?

Kling v2.1 I2v Pro starts at $0.45 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 v2.1 I2v Pro accept?

Key inputs: `prompt`, `image`, `duration`, `guidance_scale`, `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-v2.1-i2v-pro.

How long does Kling v2.1 I2v Pro take to generate?

Median end-to-end generation time on WaveSpeedAI is around 109 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 v2.1 I2v Pro 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 V2.1 I2V Pro | Fast Image-to-Video API on WaveSpeedAI