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klingai-text-to-video

Generate videos from text prompts with Kling AI. Use when creating videos from descriptions, learning prompt techniques, or building T2V pipelines. Trigger with phrases like 'kling ai text to video', 'klingai prompt', 'generate video from text', 'text2video kling'.

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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

ParameterTypeRequiredDescription
model_namestringYesModel version (see model catalog)
promptstringYesVideo description, max 2500 chars
negative_promptstringNoWhat to exclude from generation
durationstringYes"5" or "10" seconds
aspect_ratiostringNo"16:9" (default), "9:16", "1:1", etc.
modestringNo"standard" (default) or "professional"
cfg_scalefloatNoPrompt adherence (0.0-1.0, default 0.5)
camera_controlobjectNoCamera movement config
callback_urlstringNoWebhook 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

TechniqueExample
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

DurationStandardProfessional
5 seconds10 credits35 credits
10 seconds20 credits70 credits

Error Handling

ErrorCauseFix
400 invalid promptEmpty or >2500 charsCheck prompt length
400 invalid modelUnsupported model_nameUse valid model ID from catalog
402 insufficient creditsNot enough creditsTop up account
task_status: failedContent policy violation or complexitySimplify prompt, remove restricted content

Resources

  • Text-to-Video API
  • Camera Control Guide
  • Content Guidelines
Repository
jeremylongshore/claude-code-plugins-plus-skills
Last updated
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