Kling AI - Image & Video Generation When to use this skill Generating images from text prompts (Text-to-Image) Creating videos from text descriptions (Text-to-Video) Animating static images into videos (Image-to-Video) Producing high-quality AI video content (up to 1080p, 30fps) Building applications with AI-generated media Instructions Step 1: Authentication Setup

Kling AI uses JWT (JSON Web Tokens) for authentication.

Required credentials:

Access Key (AK) - Public identifier Secret Key (SK) - Private signing key

Get credentials: app.klingai.com/global/dev

Generate JWT token (Python):

import jwt import time

def generate_kling_token(access_key: str, secret_key: str) -> str: """Generate JWT token for Kling AI API.""" headers = { "alg": "HS256", "typ": "JWT" } payload = { "iss": access_key, "exp": int(time.time()) + 1800, # 30 minutes "nbf": int(time.time()) - 5 } return jwt.encode(payload, secret_key, algorithm="HS256", headers=headers)

Usage

access_key = "YOUR_ACCESS_KEY" secret_key = "YOUR_SECRET_KEY" token = generate_kling_token(access_key, secret_key)

Headers for all requests:

headers = { "Authorization": f"Bearer {token}", "Content-Type": "application/json" }

Step 2: Text-to-Image Generation

Endpoint: POST https://api.klingai.com/v1/images/generations

Request:

import requests

def generate_image(prompt: str, token: str) -> dict: """Generate image from text prompt.""" url = "https://api.klingai.com/v1/images/generations" headers = { "Authorization": f"Bearer {token}", "Content-Type": "application/json" } data = { "model_name": "kling-v1", "prompt": prompt, "negative_prompt": "blur, distortion, low quality", "n": 1, "aspect_ratio": "16:9", # 16:9, 9:16, 1:1, 4:3, 3:4, 3:2, 2:3 "image_fidelity": 0.5, # 0-1 "callback_url": "" # Optional webhook } response = requests.post(url, json=data, headers=headers) return response.json()

Usage

result = generate_image( prompt="A futuristic cityscape at sunset, cinematic lighting, ultra-realistic", token=token ) task_id = result["data"]["task_id"]

Parameters:

Parameter Type Required Description model_name string Yes kling-v1 prompt string Yes Image description (max 500 chars) negative_prompt string No Elements to avoid n integer No Number of images (1-9, default 1) aspect_ratio string No 16:9, 9:16, 1:1, 4:3, 3:4 image_fidelity float No 0-1, default 0.5 callback_url string No Webhook for completion Step 3: Text-to-Video Generation

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

Request:

def generate_video_from_text(prompt: str, token: str, duration: int = 5) -> dict: """Generate video from text prompt.""" url = "https://api.klingai.com/v1/videos/text2video" headers = { "Authorization": f"Bearer {token}", "Content-Type": "application/json" } data = { "model_name": "kling-v1", # kling-v1, kling-v1-5, kling-v1-6 "prompt": prompt, "negative_prompt": "blur, distortion", "duration": duration, # 5 or 10 seconds "aspect_ratio": "16:9", # 16:9, 9:16, 1:1 "mode": "std", # std (standard) or pro "cfg_scale": 0.5, # 0-1 "camera_control": { "type": "simple", "config": { "horizontal": 0, # -10 to 10 "vertical": 0, # -10 to 10 "zoom": 0, # -10 to 10 "tilt": 0, # -10 to 10 "pan": 0, # -10 to 10 "roll": 0 # -10 to 10 } }, "callback_url": "" } response = requests.post(url, json=data, headers=headers) return response.json()

Usage

result = generate_video_from_text( prompt="A dragon flying over mountains at sunset, epic cinematic shot", token=token, duration=5 ) task_id = result["data"]["task_id"]

Camera control presets:

camera_presets = { "zoom_in": {"zoom": 5}, "zoom_out": {"zoom": -5}, "pan_left": {"horizontal": -5}, "pan_right": {"horizontal": 5}, "tilt_up": {"vertical": 5}, "tilt_down": {"vertical": -5}, "dolly_forward": {"zoom": 3, "vertical": 2}, "orbit_right": {"horizontal": 5, "pan": 3} }

Step 4: Image-to-Video Generation

Endpoint: POST https://api.klingai.com/v1/videos/image2video

Request:

import base64

def generate_video_from_image( image_path: str, prompt: str, token: str, duration: int = 5 ) -> dict: """Animate an image into a video.""" url = "https://api.klingai.com/v1/videos/image2video" headers = { "Authorization": f"Bearer {token}", "Content-Type": "application/json" }

# Load and encode image
with open(image_path, "rb") as f:
    image_base64 = base64.b64encode(f.read()).decode()

data = {
    "model_name": "kling-v1",
    "image": image_base64,              # Base64 or URL
    "image_tail": "",                   # Optional: end frame image
    "prompt": prompt,
    "negative_prompt": "blur, distortion",
    "duration": duration,
    "mode": "std",
    "cfg_scale": 0.5,
    "callback_url": ""
}
response = requests.post(url, json=data, headers=headers)
return response.json()

Usage

result = generate_video_from_image( image_path="portrait.jpg", prompt="Person smiling and waving at camera", token=token )

With URL instead of base64:

data = { "model_name": "kling-v1", "image": "https://example.com/image.jpg", "prompt": "The subject slowly turns their head", "duration": 5 }

Step 5: Check Task Status (Polling)

All generation tasks are asynchronous. Poll for completion:

Endpoint: GET https://api.klingai.com/v1/videos/text2video/{task_id}

import time

def poll_task_status(task_id: str, token: str, task_type: str = "text2video") -> dict: """Poll task status until completion.""" url = f"https://api.klingai.com/v1/videos/{task_type}/{task_id}" headers = {"Authorization": f"Bearer {token}"}

while True:
    response = requests.get(url, headers=headers)
    result = response.json()
    status = result["data"]["task_status"]

    print(f"Status: {status}")

    if status == "succeed":
        return result
    elif status == "failed":
        raise Exception(f"Task failed: {result['data'].get('task_status_msg')}")

    time.sleep(10)  # Poll every 10 seconds

Usage

result = poll_task_status(task_id, token) video_url = result["data"]["task_result"]["videos"][0]["url"] print(f"Video URL: {video_url}")

Task status values:

Status Description submitted Task queued processing Currently generating succeed Completed successfully failed Generation failed Step 6: Download Generated Media def download_media(url: str, output_path: str) -> None: """Download generated image or video.""" response = requests.get(url, stream=True) with open(output_path, "wb") as f: for chunk in response.iter_content(chunk_size=8192): f.write(chunk) print(f"Downloaded: {output_path}")

Usage

download_media(video_url, "output.mp4")

Examples Example 1: Complete Text-to-Video Workflow import jwt import time import requests

class KlingClient: BASE_URL = "https://api.klingai.com/v1"

def __init__(self, access_key: str, secret_key: str):
    self.access_key = access_key
    self.secret_key = secret_key

def _get_token(self) -> str:
    headers = {"alg": "HS256", "typ": "JWT"}
    payload = {
        "iss": self.access_key,
        "exp": int(time.time()) + 1800,
        "nbf": int(time.time()) - 5
    }
    return jwt.encode(payload, self.secret_key, algorithm="HS256", headers=headers)

def _headers(self) -> dict:
    return {
        "Authorization": f"Bearer {self._get_token()}",
        "Content-Type": "application/json"
    }

def text_to_video(self, prompt: str, duration: int = 5, mode: str = "std") -> str:
    """Generate video and return URL."""
    # Create task
    response = requests.post(
        f"{self.BASE_URL}/videos/text2video",
        json={
            "model_name": "kling-v1",
            "prompt": prompt,
            "duration": duration,
            "mode": mode,
            "aspect_ratio": "16:9"
        },
        headers=self._headers()
    )
    task_id = response.json()["data"]["task_id"]

    # Poll for completion
    while True:
        result = requests.get(
            f"{self.BASE_URL}/videos/text2video/{task_id}",
            headers=self._headers()
        ).json()

        status = result["data"]["task_status"]
        if status == "succeed":
            return result["data"]["task_result"]["videos"][0]["url"]
        elif status == "failed":
            raise Exception("Generation failed")

        time.sleep(10)

Usage

client = KlingClient("YOUR_ACCESS_KEY", "YOUR_SECRET_KEY") video_url = client.text_to_video( prompt="A majestic eagle soaring through clouds at golden hour", duration=5 ) print(f"Generated video: {video_url}")

Example 2: Batch Image Generation def batch_generate_images(prompts: list, token: str) -> list: """Generate multiple images in parallel.""" import concurrent.futures

results = []

with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
    futures = {
        executor.submit(generate_image, prompt, token): prompt
        for prompt in prompts
    }

    for future in concurrent.futures.as_completed(futures):
        prompt = futures[future]
        try:
            result = future.result()
            results.append({"prompt": prompt, "task_id": result["data"]["task_id"]})
        except Exception as e:
            results.append({"prompt": prompt, "error": str(e)})

return results

Usage

prompts = [ "A serene Japanese garden with cherry blossoms", "A cyberpunk street scene at night", "An underwater coral reef teeming with life" ] tasks = batch_generate_images(prompts, token)

Example 3: Image-to-Video with Motion Control def animate_portrait(image_url: str, token: str) -> str: """Animate a portrait with subtle motion.""" url = "https://api.klingai.com/v1/videos/image2video"

response = requests.post(
    url,
    json={
        "model_name": "kling-v1",
        "image": image_url,
        "prompt": "Subtle head movement, natural blinking, gentle smile",
        "negative_prompt": "distortion, unnatural movement, glitch",
        "duration": 5,
        "mode": "pro",  # Higher quality for portraits
        "cfg_scale": 0.7
    },
    headers={
        "Authorization": f"Bearer {token}",
        "Content-Type": "application/json"
    }
)

task_id = response.json()["data"]["task_id"]
return poll_task_status(task_id, token, "image2video")

Usage

result = animate_portrait("https://example.com/portrait.jpg", token)

Example 4: Video with Camera Movement def cinematic_video(prompt: str, camera_motion: str, token: str) -> str: """Generate video with specific camera movement.""" camera_configs = { "epic_reveal": {"zoom": -5, "vertical": 3}, "dramatic_push": {"zoom": 5}, "orbit_subject": {"horizontal": 7, "pan": 3}, "crane_up": {"vertical": 5, "zoom": 2}, "dolly_back": {"zoom": -3, "horizontal": 2} }

config = camera_configs.get(camera_motion, {})

response = requests.post(
    "https://api.klingai.com/v1/videos/text2video",
    json={
        "model_name": "kling-v1-6",
        "prompt": prompt,
        "duration": 10,
        "mode": "pro",
        "aspect_ratio": "16:9",
        "camera_control": {
            "type": "simple",
            "config": {
                "horizontal": config.get("horizontal", 0),
                "vertical": config.get("vertical", 0),
                "zoom": config.get("zoom", 0),
                "tilt": config.get("tilt", 0),
                "pan": config.get("pan", 0),
                "roll": config.get("roll", 0)
            }
        }
    },
    headers={
        "Authorization": f"Bearer {token}",
        "Content-Type": "application/json"
    }
)

task_id = response.json()["data"]["task_id"]
return poll_task_status(task_id, token)

Usage

video_url = cinematic_video( prompt="Ancient temple emerging from jungle mist", camera_motion="epic_reveal", token=token )

Model Comparison Video Models Model Resolution Duration Quality Cost kling-v1 (std) 720p 5-10s Good $ kling-v1 (pro) 1080p 5-10s Better $$ kling-v1-5 (std) 720p 5-10s Good $ kling-v1-5 (pro) 1080p 5-10s Better $$ kling-v1-6 (std) 720p 5-10s Great $$ kling-v1-6 (pro) 1080p 5-10s Excellent $$$ Image Models Model Max Resolution Quality kling-v1 1024x1024 Standard kling-v1 (2k) 2048x2048 High Pricing Reference Feature Standard Mode Pro Mode 5s video ~$0.14 ~$0.49 10s video ~$0.28 ~$0.98 Image ~$0.01 - Best practices Prompt quality: Be specific and descriptive. Include style keywords like "cinematic", "photorealistic", "4K" Negative prompts: Always include to avoid common artifacts: "blur, distortion, low quality, watermark" Start with standard mode: Test with std mode first, upgrade to pro for final output Use 5-second videos: More cost-effective for testing; use 10s only when needed Handle rate limits: Implement exponential backoff for retries Cache tokens: Reuse JWT tokens within their validity period (30 min recommended) Webhook for production: Use callback_url instead of polling in production environments Common pitfalls Token expiration: JWT tokens expire. Regenerate before making requests if expired Prompt length: Text-to-image max 500 chars, text-to-video max 2500 chars Image format: Image-to-video supports JPEG, PNG. Max size varies by model Polling too fast: Don't poll faster than every 5-10 seconds to avoid rate limits Missing negative prompt: Always include to prevent common artifacts Wrong aspect ratio: Match aspect ratio to your use case (16:9 for landscape, 9:16 for portrait) Error Handling class KlingAPIError(Exception): pass

def handle_kling_response(response: requests.Response) -> dict: """Handle API response with proper error handling.""" data = response.json()

if response.status_code == 401:
    raise KlingAPIError("Authentication failed. Check your credentials.")
elif response.status_code == 429:
    raise KlingAPIError("Rate limit exceeded. Wait and retry.")
elif response.status_code >= 400:
    error_msg = data.get("message", "Unknown error")
    raise KlingAPIError(f"API Error: {error_msg}")

if data.get("code") != 0:
    raise KlingAPIError(f"Request failed: {data.get('message')}")

return data

References Kling AI Developer Portal API Documentation Python SDK (Community) Pricing Guide