Klingai text to video

Kling AI Text-to-Video

Overview

Generate videos from text prompts using the /v1/videos/text2video endpoint. Supports models v1 through v2.6, standard/professional modes, camera control, negative prompts, and native audio (v2.6+).

Endpoint: POST https://api.klingai.com/v1/videos/text2video

Request Parameters

Parameter	Type	Required	Description
model_name	string	Yes	Model version (see model catalog)
prompt	string	Yes	Video description, max 2500 chars
negative_prompt	string	No	What to exclude from generation
duration	string	Yes	"5" or "10" seconds
aspect_ratio	string	No	"16:9" (default), "9:16", "1:1", etc.
mode	string	No	"standard" (default) or "professional"
cfg_scale	float	No	Prompt adherence (0.0-1.0, default 0.5)
camera_control	object	No	Camera movement config
callback_url	string	No	Webhook URL for completion notification

Complete Example — Python

import jwt, time, os, requests

BASE = "https://api.klingai.com/v1"

def get_headers():
    ak, sk = os.environ["KLING_ACCESS_KEY"], os.environ["KLING_SECRET_KEY"]
    token = jwt.encode(
        {"iss": ak, "exp": int(time.time()) + 1800, "nbf": int(time.time()) - 5},
        sk, algorithm="HS256", headers={"alg": "HS256", "typ": "JWT"}
    )
    return {"Authorization": f"Bearer {token}", "Content-Type": "application/json"}

# Create text-to-video task
response = requests.post(f"{BASE}/videos/text2video", headers=get_headers(), json={
    "model_name": "kling-v2-6",
    "prompt": "Aerial drone shot of a coral reef at golden hour, "
              "tropical fish swimming through crystal clear water, "
              "sun rays penetrating the surface, cinematic 4K",
    "negative_prompt": "blurry, low quality, distorted, watermark",
    "duration": "5",
    "aspect_ratio": "16:9",
    "mode": "professional",
    "cfg_scale": 0.5,
})

task = response.json()
task_id = task["data"]["task_id"]

# Poll for completion
while True:
    time.sleep(15)
    result = requests.get(
        f"{BASE}/videos/text2video/{task_id}", headers=get_headers()
    ).json()

    status = result["data"]["task_status"]
    if status == "succeed":
        video = result["data"]["task_result"]["videos"][0]
        print(f"Video URL: {video['url']}")
        print(f"Duration: {video['duration']}s")
        break
    elif status == "failed":
        raise RuntimeError(result["data"]["task_status_msg"])
    # else: submitted/processing — keep polling

With Camera Control

# Camera movement types: pan, tilt, zoom, roll
response = requests.post(f"{BASE}/videos/text2video", headers=get_headers(), json={
    "model_name": "kling-v2-6",
    "prompt": "A medieval castle on a cliff at sunrise, fog in the valley",
    "duration": "5",
    "mode": "standard",
    "camera_control": {
        "type": "simple",
        "config": {
            "horizontal": 5,    # pan right (negative = left), range -10 to 10
            "vertical": 0,      # tilt (negative = down, positive = up)
            "zoom": 3,          # zoom in (positive) or out (negative)
            "roll": 0,          # rotation
            "pan": 0,           # dolly left/right
            "tilt": -2,         # dolly up/down
        }
    },
})

Rule: Only one non-zero field in config for type: "simple".

With Native Audio (v2.6 only)

response = requests.post(f"{BASE}/videos/text2video", headers=get_headers(), json={
    "model_name": "kling-v2-6",
    "prompt": "A jazz band performing in a dimly lit club, saxophone solo, "
              "audience clapping, warm amber lighting",
    "duration": "10",
    "mode": "professional",
    "motion_has_audio": True,  # generates synchronized audio
})

Prompt Engineering Tips

Technique	Example
Scene + action + style	"A samurai walking through cherry blossoms, cinematic slow motion"
Lighting cues	"golden hour", "neon-lit", "overcast diffused light"
Camera language	"close-up", "wide establishing shot", "tracking shot"
Negative prompt	"blurry, watermark, text overlay, distorted faces"
Material/texture	"brushed steel", "hand-painted watercolor", "photorealistic"

Cost Reference

Duration	Standard	Professional
5 seconds	10 credits	35 credits
10 seconds	20 credits	70 credits

Error Handling

Error	Cause	Fix
400 invalid prompt	Empty or >2500 chars	Check prompt length
400 invalid model	Unsupported model_name	Use valid model ID from catalog
402 insufficient credits	Not enough credits	Top up account
task_status: failed	Content policy violation or complexity	Simplify prompt, remove restricted content

Resources

• Text-to-Video API
• Camera Control Guide
• Content Guidelines