Kling 3.0 Pro delivers top-tier image-to-video generation with smooth motion, cinematic visuals, accurate prompt adherence, and native audio for ready-to-share clips. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing.
就緒
$0.56每次運行·~17 / $10
A couple sits face to face at an elegant white-clothed dinner table in an upscale restaurant. The woman in a beaded black evening dress gazes at the man across the table, her lips parting slightly as she speaks, her blonde hair catching the warm ambient light. The man in a dark navy suit leans slightly forward, listening attentively, then responds with a subtle nod and a gentle smile. His right hand gestures softly near his plate as he talks. Between them, two wine glasses with red wine catch and refract the golden chandelier light — the liquid shimmers faintly as the table vibrates with subtle movement. The woman reaches for her wine glass, lifts it gracefully, and takes a slow sip. In the background, a gold-framed mirror reflects the dim restaurant interior, and a crystal chandelier overhead casts warm, flickering candlelight-style glow across the scene. Other white-clothed tables sit softly out of focus. Intimate atmosphere, warm golden tones, cinematic shallow depth of field, slow elegant pacing. 4K, 24fps.
Kling V3.0 Pro Image-to-Video is Kuaishou's premium image-to-video model, delivering the highest visual quality and motion realism in the V3.0 family. Upload a reference image and describe the motion — the model generates cinematic video with superior detail, optional start-to-end frame guidance, and synchronized sound. Supports flexible duration from 3 to 15 seconds.
V3.0 Pro quality The highest visual fidelity and motion realism in the Kling V3.0 family.
Flexible duration Generate videos from 3 to 15 seconds — any length you need.
Start-end frame guidance Optional end image for controlled transitions between two frames.
Sound generation Optional synchronized sound effects generated alongside the video.
Negative prompt support Specify what you don't want in the video for more precise control.
Multi-prompt and element list support Chain prompt segments for scene transitions and lock in specific visual elements for consistency.
Prompt Enhancer Built-in tool to automatically improve your motion descriptions.
| Parameter | Required | Description |
|---|---|---|
| image | Yes | Start frame image to animate (URL or upload). |
| prompt | No | Text description of the desired motion and action. |
| negative_prompt | No | Elements to exclude from the video. |
| end_image | No | End frame image for guided transitions. |
| duration | No | Video length in seconds. Range: 3–15. Default: 5. |
| cfg_scale | No | Prompt guidance strength. Default: 0.5. |
| sound | No | Generate synchronized sound alongside the video. Default: disabled. |
| shot_type | No | Editing mode: intelligent (default, auto-determines scope) or customize. |
| multi_prompt | No | Additional prompts for complex scene compositions. |
| element_list | No | List of visual elements to maintain consistency throughout the clip. |
| Duration | Without Sound | With Sound |
|---|---|---|
| 3s | $0.336 | $0.504 |
| 5s | $0.560 | $0.840 |
| 10s | $1.120 | $1.680 |
| 15s | $1.680 | $2.520 |
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/kwaivgi/kling-v3.0-pro/image-to-video with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Kling v3.0 Pro Image To Video below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"duration": 5,
"cfg_scale": 0.5,
"shot_type": "customize"
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/kwaivgi/kling-v3.0-pro/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
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/kwaivgi/kling-v3.0-pro/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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"duration": 5,
"cfg_scale": 0.5,
"shot_type": "customize"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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));
}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",
"duration": 5,
"cfg_scale": 0.5,
"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-v3.0-pro/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 v3.0 Pro Image To Video is a Kuaishou model for video generation from images, exposed as a REST API on WaveSpeedAI. Kling 3.0 Pro delivers top-tier image-to-video generation with smooth motion, cinematic visuals, accurate prompt adherence, and native audio for ready-to-share clips. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing. You can call it programmatically or try it from the playground above.
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-v3.0-pro-image-to-video.
Kling v3.0 Pro Image To Video starts at $0.56 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.
Key inputs: `prompt`, `image`, `duration`, `negative_prompt`, `cfg_scale`, `element_list`. 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-v3.0-pro-image-to-video.
Median end-to-end generation time on WaveSpeedAI is around 176 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.
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.