Kling AI

Overview

Kling AI is a state-of-the-art text-to-video generation platform developed by Kuaishou Technology, one of China's leading short-video platforms. With over 22 million users and 168 million videos generated, Kling has established itself as a major player in the AI video generation space, rivaling international competitors like OpenAI's Sora and Runway.

The platform utilizes a sophisticated diffusion transformer architecture combined with a 3D Variational Autoencoder (VAE) to produce high-quality, coherent video content from text descriptions. This advanced architecture allows Kling to generate videos with smooth motion, realistic physics, and strong adherence to text prompts.

Kling AI has evolved rapidly through multiple versions: Kling 1.6 (December 2024), Kling 2.0 (April 2025), and the latest Kling 2.1 (May 2025). Each iteration has brought significant improvements in video quality, generation speed, and creative control, making it a powerful tool for content creators, marketers, and AI researchers looking to leverage Chinese AI innovation.

Key Features

• Diffusion transformer architecture with 3D VAE for high-quality video generation
• Support for various video lengths and resolutions up to HD quality
• Advanced motion synthesis with realistic physics simulation
• Strong text-to-video alignment with nuanced prompt understanding
• Multiple model versions: Kling 1.6, 2.0, and 2.1 with progressive improvements
• Massive user base with 22M+ users and 168M+ videos generated
• Competitive alternative to Western models like Sora and Runway
• Optimized for creative content generation and commercial applications

Use Cases

• Marketing video creation for Chinese and international markets
• Social media content generation for platforms like Douyin and TikTok
• Creative storytelling and narrative video production
• Product demonstration and explainer videos
• AI-powered video prototyping and concept visualization
• Research into diffusion-based video generation models

Model Versions

Kling AI has evolved through three major versions: Kling 1.6 (December 2024) introduced the foundational diffusion transformer architecture, Kling 2.0 (April 2025) brought significant quality improvements and faster generation speeds, and Kling 2.1 (May 2025) represents the current state-of-the-art with enhanced motion synthesis and prompt understanding.

Technical Architecture

The platform employs a diffusion transformer architecture combined with 3D VAE technology. Videos are output in MP4 format with various resolutions available up to HD quality. The platform is accessible through both web-based interface and mobile applications, serving over 22 million users who have collectively generated more than 168 million videos.

Pricing and Availability

Kling AI operates on a freemium model with usage limits. Limited free generation credits are available for all users, while subscription plans provide increased generation limits. Pricing is primarily in CNY (Chinese Yuan) and targeted at the Chinese market, though international users can access the platform.

Resources and Links

Official website: https://klingai.com | Documentation: https://klingai.com/docs | Example gallery: https://klingai.com/gallery

Code Example: API Integration

Integrate Kling AI into your applications using the REST API for scalable text-to-video generation. This production-ready implementation demonstrates authentication, request handling, polling for completion, and batch processing for commercial video workflows targeting Chinese and international markets.

import requests
import time
import os
import json
from pathlib import Path
from typing import Optional, Dict, Any, List
from enum import Enum

class KlingModel(Enum):
    """Available Kling AI model versions"""
    KLING_16 = "kling-1.6"
    KLING_20 = "kling-2.0"
    KLING_21 = "kling-2.1"  # Latest with best quality

class KlingAIClient:
    """
    Production-ready client for Kling AI video generation API
    Supports all model versions with comprehensive error handling
    """
    
    def __init__(self, api_key: str, api_secret: str):
        self.api_key = api_key
        self.api_secret = api_secret
        self.base_url = "https://api.klingai.com/v1"
        self.session = requests.Session()
        self._authenticate()
    
    def _authenticate(self) -> None:
        """Authenticate and obtain access token"""
        try:
            response = self.session.post(
                f"{self.base_url}/auth/token",
                json={
                    "api_key": self.api_key,
                    "api_secret": self.api_secret
                },
                timeout=30
            )
            response.raise_for_status()
            
            data = response.json()
            self.access_token = data["access_token"]
            self.session.headers.update({
                "Authorization": f"Bearer {self.access_token}",
                "Content-Type": "application/json"
            })
            
            print("Authentication successful")
            
        except requests.exceptions.RequestException as e:
            print(f"Authentication failed: {e}")
            raise
    
    def generate_video(
        self,
        prompt: str,
        model: KlingModel = KlingModel.KLING_21,
        duration: float = 5.0,
        aspect_ratio: str = "16:9",
        resolution: str = "1080p",
        negative_prompt: Optional[str] = None
    ) -> Dict[str, Any]:
        """
        Generate video from text prompt
        
        Args:
            prompt: Detailed text description (Chinese or English)
            model: Kling model version to use
            duration: Video duration in seconds (1-10s)
            aspect_ratio: Video aspect ratio (16:9, 9:16, 1:1)
            resolution: Output resolution (720p, 1080p)
            negative_prompt: Elements to avoid in generation
        
        Returns:
            Dictionary with task_id and metadata
        """
        try:
            print(f"Submitting video generation request...")
            print(f"Model: {model.value}")
            print(f"Prompt: {prompt[:100]}...")
            
            payload = {
                "model": model.value,
                "prompt": prompt,
                "duration": duration,
                "aspect_ratio": aspect_ratio,
                "resolution": resolution
            }
            
            if negative_prompt:
                payload["negative_prompt"] = negative_prompt
            
            response = self.session.post(
                f"{self.base_url}/videos/generate",
                json=payload,
                timeout=30
            )
            response.raise_for_status()
            
            result = response.json()
            task_id = result["task_id"]
            
            print(f"Task submitted: {task_id}")
            print(f"Estimated wait time: {result.get('estimated_time', 'unknown')}s")
            
            return result
            
        except requests.exceptions.RequestException as e:
            print(f"API request failed: {e}")
            if hasattr(e.response, 'text'):
                print(f"Response: {e.response.text}")
            raise
        except Exception as e:
            print(f"Generation error: {e}")
            raise
    
    def check_status(self, task_id: str) -> Dict[str, Any]:
        """Check generation task status"""
        try:
            response = self.session.get(
                f"{self.base_url}/videos/status/{task_id}",
                timeout=30
            )
            response.raise_for_status()
            return response.json()
            
        except requests.exceptions.RequestException as e:
            print(f"Status check failed: {e}")
            raise
    
    def wait_for_completion(
        self,
        task_id: str,
        max_wait_time: int = 600,
        poll_interval: int = 5
    ) -> Dict[str, Any]:
        """
        Poll task until completion
        
        Args:
            task_id: Task identifier from generate_video
            max_wait_time: Maximum time to wait in seconds
            poll_interval: Time between status checks in seconds
        
        Returns:
            Final task result with video URL
        """
        print(f"Waiting for task {task_id} to complete...")
        
        start_time = time.time()
        attempts = 0
        
        while time.time() - start_time < max_wait_time:
            status_result = self.check_status(task_id)
            status = status_result["status"]
            
            if status == "completed":
                print(f"\nGeneration complete!")
                return status_result
            
            elif status == "failed":
                error_msg = status_result.get("error", "Unknown error")
                raise Exception(f"Generation failed: {error_msg}")
            
            elif status == "processing":
                progress = status_result.get("progress", 0)
                print(f"Progress: {progress}%", end="\r")
            
            time.sleep(poll_interval)
            attempts += 1
        
        raise TimeoutError(f"Task did not complete within {max_wait_time}s")
    
    def download_video(self, video_url: str, output_path: Path) -> Path:
        """Download generated video"""
        try:
            print(f"Downloading video from: {video_url}")
            
            response = requests.get(video_url, stream=True, timeout=300)
            response.raise_for_status()
            
            # Ensure directory exists
            output_path.parent.mkdir(parents=True, exist_ok=True)
            
            with open(output_path, "wb") as f:
                for chunk in response.iter_content(chunk_size=8192):
                    if chunk:
                        f.write(chunk)
            
            print(f"Video saved to: {output_path}")
            return output_path
            
        except Exception as e:
            print(f"Download failed: {e}")
            raise
    
    def generate_and_download(
        self,
        prompt: str,
        output_path: Path,
        **kwargs
    ) -> Path:
        """Complete workflow: generate and download video"""
        # Submit generation
        result = self.generate_video(prompt, **kwargs)
        task_id = result["task_id"]
        
        # Wait for completion
        completed = self.wait_for_completion(task_id)
        video_url = completed["video_url"]
        
        # Download
        return self.download_video(video_url, output_path)

# Business use case: Marketing campaign for Chinese market
def douyin_marketing_campaign():
    """
    Generate video content for Douyin (Chinese TikTok) marketing
    Demonstrates targeting Chinese social media with Kling AI
    """
    
    API_KEY = os.getenv("KLING_API_KEY", "your_api_key")
    API_SECRET = os.getenv("KLING_API_SECRET", "your_api_secret")
    
    client = KlingAIClient(API_KEY, API_SECRET)
    output_dir = Path("douyin_campaign")
    output_dir.mkdir(exist_ok=True)
    
    # Marketing videos optimized for Chinese audience
    campaigns = [
        {
            "name": "product_launch_cn",
            "prompt": "高端智能手机展示，现代简约风格，柔和灯光，产品特写镜头，专业商业广告" , # High-end smartphone, modern minimalist, soft lighting
            "duration": 6.0,
            "aspect_ratio": "9:16"  # Vertical for Douyin
        },
        {
            "name": "lifestyle_beauty",
            "prompt": "年轻女性在咖啡店使用护肤产品，自然光线，温馨氛围，真实生活场景",  # Young woman using skincare in cafe
            "duration": 8.0,
            "aspect_ratio": "9:16"
        },
        {
            "name": "ecommerce_food",
            "prompt": "美食特写镜头，食材新鲜，烹饪过程，食欲诱人，高清画质",  # Food close-up, fresh ingredients, cooking
            "duration": 5.0,
            "aspect_ratio": "1:1"  # Square for multiple platforms
        }
    ]
    
    results = []
    
    for campaign in campaigns:
        print(f"\n{'='*60}")
        print(f"Generating: {campaign['name']}")
        print(f"{'='*60}")
        
        output_path = output_dir / f"{campaign['name']}.mp4"
        
        try:
            # Generate with Kling 2.1 (latest)
            video_path = client.generate_and_download(
                prompt=campaign["prompt"],
                output_path=output_path,
                model=KlingModel.KLING_21,
                duration=campaign["duration"],
                aspect_ratio=campaign["aspect_ratio"],
                resolution="1080p"
            )
            
            results.append({
                "campaign": campaign["name"],
                "path": video_path,
                "success": True
            })
            
            print(f"✓ Success: {campaign['name']}")
            
        except Exception as e:
            print(f"✗ Failed: {campaign['name']} - {e}")
            results.append({
                "campaign": campaign["name"],
                "error": str(e),
                "success": False
            })
        
        # Rate limiting
        time.sleep(2)
    
    # Summary
    print("\n=== Campaign Summary ===")
    successful = sum(1 for r in results if r["success"])
    print(f"Successful: {successful}/{len(results)}")
    
    for r in results:
        if r["success"]:
            print(f"✓ {r['campaign']}: {r['path']}")
        else:
            print(f"✗ {r['campaign']}: {r['error']}")
    
    return results

# International marketing use case
def international_product_videos():
    """Generate product demo videos for international markets"""
    
    API_KEY = os.getenv("KLING_API_KEY")
    API_SECRET = os.getenv("KLING_API_SECRET")
    
    client = KlingAIClient(API_KEY, API_SECRET)
    
    # English prompts for international audience
    products = [
        "Luxury watch on black velvet, rotating slowly, studio lighting, premium commercial",
        "Electric vehicle driving on coastal highway, sunset, cinematic camera movement",
        "Smartphone with holographic display, futuristic technology, clean background"
    ]
    
    for idx, prompt in enumerate(products, 1):
        print(f"\nGenerating product video {idx}/{len(products)}")
        
        output_path = Path(f"product_{idx}.mp4")
        client.generate_and_download(
            prompt=prompt,
            output_path=output_path,
            model=KlingModel.KLING_21,
            duration=7.0,
            aspect_ratio="16:9",
            resolution="1080p"
        )

if __name__ == "__main__":
    # Run Douyin marketing campaign
    douyin_marketing_campaign()
    
    # Uncomment for international product videos
    # international_product_videos()

Professional Integration Services by 21medien

Kling AI's position as China's leading video generation platform with 22 million users makes it essential for businesses targeting Chinese markets, but successful integration requires expertise in both the technical API and the cultural nuances of Chinese digital ecosystems. 21medien provides specialized integration services to help international businesses leverage Kling AI for Chinese and global markets.

Our comprehensive services include: China Market Strategy consulting on using Kling AI for Douyin, WeChat, and other Chinese social platforms with culturally-appropriate content strategies, API Integration and Workflow Automation for seamlessly incorporating Kling AI into your content production pipelines with robust error handling and retry logic, Multi-platform Content Optimization generating videos in appropriate aspect ratios and formats for Douyin (9:16), Kuaishou, international platforms, and e-commerce channels, Bilingual Prompt Engineering maximizing generation quality for both Chinese and English prompts with understanding of linguistic and cultural optimization techniques, Model Version Management helping you choose between Kling 1.6, 2.0, and 2.1 based on your specific quality, speed, and budget requirements, Cross-border Content Compliance ensuring your AI-generated videos meet content regulations for both Chinese and international markets, and Production Pipeline Development for high-volume video generation including queue management, progress tracking, automatic retries, and quality validation.

Whether you're an international brand entering the Chinese market, a Chinese company expanding globally, or a content platform serving both audiences, our team brings deep expertise in Chinese AI technology and cross-cultural digital marketing. We help you navigate API implementation, optimize costs through intelligent batching and caching, and build culturally-resonant video content that performs across markets. Schedule a free consultation call through our contact page to discuss how Kling AI can power your video strategy for the world's largest digital market and beyond.