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Kling V2.6 Std Text to Video | Powerful Text-to-Video

kwaivgi/

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

text-to-video
इनपुट

निष्क्रिय

$0.21प्रति रन·~47 / $10

आगे:

उदाहरणसभी देखें

1950s Film Noir style. A detective sitting in a smoky, dimly lit office, shadows from Venetian blinds cast across his face. He is peeking out the window. Black and white, film grain, mysterious atmosphere, dramatic lighting.

A stylish woman in a transparent raincoat walking down a futuristic neon-lit Tokyo street at night. Rain droplets hitting her umbrella, surrounded by holographic billboards. Tracking camera shot, bokeh background, cinematic lighting, wet pavement reflecting red and blue lights, photorealistic

Extreme macro shot of a blue eye suddenly opening. The texture of the iris is clearly visible, with tiny water droplets on the eyelashes. Pupil contracting in response to light. Hyper-realistic, 8k, BBC documentary style.

Pixar animation style. A cute fluffy blue monster sitting in a pile of cookies, munching on a cookie happily. Crumbs flying, adorable expression. Bright lighting, vibrant colors, high-quality 3D render, C4D.

संबंधित मॉडल

README

Kling V2.6 Standard Text-to-Video

Kling V2.6 Standard is Kuaishou's text-to-video generation model that creates high-quality videos directly from text descriptions. With support for negative prompts, multiple aspect ratios, and flexible duration, it delivers cinematic results with rich detail and natural motion.

Why Choose This?

  • Pure text-driven generation Create videos from scratch using detailed text descriptions.

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

  • Multiple aspect ratios Support for 1:1, 9:16, and 16:9 to fit any platform.

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

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

Parameters

ParameterRequiredDescription
promptYesText description of the video scene and motion
negative_promptNoElements to exclude from generation
aspect_ratioNoOutput ratio: 1:1, 9:16, 16:9 (default: 16:9)
durationNoVideo length: 5 or 10 seconds (default: 5)

How to Use

  1. Write your prompt — describe the scene, characters, motion, and style in detail.
  2. Add negative prompt (optional) — specify what you want to avoid in the output.
  3. Select aspect ratio — choose based on your target platform.
  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

  • Social Media Content — Create short-form videos for TikTok, Reels, and Stories.
  • Concept Visualization — Bring creative ideas to life without filming.
  • Film Noir & Artistic Styles — Generate stylized footage with specific aesthetics.
  • Marketing Videos — Produce promotional content from text descriptions.
  • Storyboarding — Visualize narrative scenes for pre-production.

Pro Tips

  • Use the Prompt Enhancer to refine your descriptions automatically.
  • Be specific about style, lighting, atmosphere, and camera movement.
  • Use negative prompts to avoid common issues (e.g., "blurry, low quality, distorted").
  • Match aspect ratio to your platform: 16:9 for YouTube, 9:16 for TikTok/Reels, 1:1 for Instagram.
  • 5s videos are more cost-effective for testing; use 10s for final production.

Notes

  • Only prompt is required; other parameters have defaults.
  • Duration options are 5 or 10 seconds only.
  • For best results, write detailed prompts with scene, motion, and style information.

Related Models

नोट:यह वेबसाइट तृतीय पक्षों द्वारा प्रदान किए गए AI मॉडलों का उपयोग करती है।

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

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/kwaivgi/kling-v2.6-std/text-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 Text 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",
    "aspect_ratio": "16:9",
    "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/text-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/text-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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "aspect_ratio": "16:9",
        "duration": 5
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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",
    "aspect_ratio": "16:9",
    "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/text-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 Text To Video API — Frequently asked questions

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

Kling v2.6 Std Text To Video is a Kuaishou model for video generation, exposed as a REST API on WaveSpeedAI. Kling 2.6 Standard offers cost-effective text-to-video generation with smooth motion, cinematic visuals, and strong 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 Text 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-text-to-video.

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

Kling v2.6 Std Text 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 Text To Video accept?

Key inputs: `prompt`, `aspect_ratio`, `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-text-to-video.

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

Median end-to-end generation time on WaveSpeedAI is around 69 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 Text 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 Text to Video | Powerful Text-to-Video API on WaveSpeedAI