Kling Omni Video O1 Video-Edit enables conversational video editing through natural language commands. Remove objects, change backgrounds, modify styles, adjust weather/lighting, and transform scenes with simple text instructions like 'remove pedestrians' or 'change daytime to dusk'. Ready-to-use REST API, best performance, no coldstarts, affordable pricing.
Siap
$0.45per run·~22 / $10
Change the men's clothes to a red suit.
Use the input video as structure. Keep the exact composition, café interior, and subtle background motions. Add one person, at a table on the right, place a casually dressed person sitting and working on a laptop, with a coffee cup beside them.
Add a flying bird in the sky.
Use the input video as structure. Keep the person, their walking motion, and the camera movement the same, but restyle everything in a soft anime Tokyo evening style. Turn the buildings into detailed Japanese storefronts with subtle signs, warm window light, and distant billboards. Set the time to blue hour, with a gentle gradient sky and a few streetlights already on. Colors are pastel and slightly desaturated, lines are clean and stable, and there are no extra fast-moving elements, only the original walking motion and slow parallax.
Change the blue-black color tone to a red-black color tone.
Kling Omni Video O1 is Kuaishou's groundbreaking unified multi-modal video model. The Video-Edit mode revolutionizes video editing through natural language — simply describe what you want to change, and the AI performs pixel-level semantic reconstruction.
Edit videos with simple text commands:
The model supports editing:
The MVL system interprets your intent and performs:
Upload Your Video Provide the source video you want to edit.
Describe Your Edit Write a natural language command for the modification.
Example: "Remove all cars from the street and change the time to golden hour"
Set Parameters Choose output format and quality settings.
Generate Receive your edited video with seamless modifications.
| Item | Value |
|---|---|
| Per Second | $0.09 |
| Duration Range | 6-20 seconds |
Supports video duration between 6 and 20 seconds. Videos shorter than 6s are billed at 6s; videos longer than 20s are billed at 20s.
| Command | Effect |
|---|---|
| "Remove the logo" | Cleans branded elements |
| "Make it night time" | Adjusts lighting and atmosphere |
| "Add rain effects" | Inserts weather elements |
| "Change hair color to blonde" | Modifies character appearance |
| "Replace background with beach" | Swaps environment |
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/kwaivgi/kling-video-o1/video-edit-fast with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Kling Video O1 Video Edit Fast below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
"keep_original_sound": true,
"aspect_ratio": "16:9"
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/kwaivgi/kling-video-o1/video-edit-fast" \
-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-video-o1/video-edit-fast";
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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
"keep_original_sound": true,
"aspect_ratio": "16:9"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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 = {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
"keep_original_sound": True,
"aspect_ratio": "16:9"
}
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-video-o1/video-edit-fast", 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 Video O1 Video Edit Fast is a Kuaishou model for video editing, exposed as a REST API on WaveSpeedAI. Kling Omni Video O1 Video-Edit enables conversational video editing through natural language commands. Remove objects, change backgrounds, modify styles, adjust weather/lighting, and transform scenes with simple text instructions like 'remove pedestrians' or 'change daytime to dusk'. Ready-to-use REST API, best performance, no coldstarts, 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-video-o1-video-edit-fast.
Kling Video O1 Video Edit Fast 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.
Key inputs: `prompt`, `images`, `video`, `aspect_ratio`, `keep_original_sound`. 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-video-o1-video-edit-fast.
Median end-to-end generation time on WaveSpeedAI is around 235 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.