Kling AI Performance Tuning

Overview

This skill demonstrates optimizing Kling AI for better performance including faster generation, improved quality, cost optimization, and efficient resource usage.

Prerequisites

• Kling AI API key configured
• Understanding of performance tradeoffs
• Python 3.8+

Instructions

Follow these steps for performance tuning:

1. Benchmark Baseline: Measure current performance
2. Identify Bottlenecks: Find slow areas
3. Apply Optimizations: Implement improvements
4. Measure Results: Compare before/after
5. Balance Tradeoffs: Find optimal settings

Performance Factors

Kling AI Performance Factors:

GENERATION SPEED:
- Model selection (kling-v1 faster, kling-pro slower)
- Duration (5s faster than 10s)
- Resolution (lower = faster)
- Queue position (varies)

QUALITY:
- Model selection (kling-pro highest quality)
- Duration (longer = more coherent)
- Prompt clarity (specific = better)
- Resolution (higher = more detail)

COST:
- Model (kling-v1 cheapest, kling-pro expensive)
- Duration (linear cost increase)
- Retries (failed jobs still cost)
- Resolution multiplier

TRADEOFF PROFILES:
- Speed: kling-v1, 5s, 720p
- Quality: kling-pro, 10s, 1080p
- Balanced: kling-v1.5, 5s, 1080p
- Budget: kling-v1, 5s, 720p

Performance Profiler

import time
import statistics
from dataclasses import dataclass, field
from typing import List, Dict, Optional
from datetime import datetime
import requests
import os

@dataclass
class PerformanceMetric:
    job_id: str
    model: str
    duration: int
    resolution: str
    submit_time: float
    complete_time: Optional[float] = None
    total_time: Optional[float] = None
    status: str = "pending"
    credits_used: int = 0

@dataclass
class PerformanceProfile:
    name: str
    metrics: List[PerformanceMetric] = field(default_factory=list)

    def add_metric(self, metric: PerformanceMetric):
        self.metrics.append(metric)

    def get_stats(self) -> Dict:
        completed = [m for m in self.metrics if m.total_time is not None]
        if not completed:
            return {"no_data": True}

        times = [m.total_time for m in completed]
        credits = [m.credits_used for m in completed]

        return {
            "count": len(completed),
            "avg_time": statistics.mean(times),
            "min_time": min(times),
            "max_time": max(times),
            "std_dev": statistics.stdev(times) if len(times) > 1 else 0,
            "p50_time": statistics.median(times),
            "p95_time": sorted(times)[int(len(times) * 0.95)] if len(times) > 1 else times[0],
            "total_credits": sum(credits),
            "avg_credits": statistics.mean(credits),
            "success_rate": len(completed) / len(self.metrics) * 100
        }

class KlingAIPerformanceProfiler:
    """Profile and optimize Kling AI performance."""

    def __init__(self, api_key: str = None):
        self.api_key = api_key or os.environ["KLINGAI_API_KEY"]
        self.base_url = "https://api.klingai.com/v1"
        self.profiles: Dict[str, PerformanceProfile] = {}

    def create_profile(self, name: str) -> PerformanceProfile:
        """Create a new performance profile."""
        profile = PerformanceProfile(name=name)
        self.profiles[name] = profile
        return profile

    def benchmark(
        self,
        profile_name: str,
        prompt: str,
        model: str = "kling-v1.5",
        duration: int = 5,
        resolution: str = "1080p",
        iterations: int = 3
    ) -> Dict:
        """Run benchmark tests."""
        if profile_name not in self.profiles:
            self.create_profile(profile_name)

        profile = self.profiles[profile_name]
        results = []

        for i in range(iterations):
            print(f"Benchmark iteration {i+1}/{iterations}...")

            metric = self._run_single_benchmark(prompt, model, duration, resolution)
            profile.add_metric(metric)
            results.append(metric)

            # Small delay between tests
            time.sleep(2)

        return {
            "profile": profile_name,
            "iterations": iterations,
            "config": {
                "model": model,
                "duration": duration,
                "resolution": resolution
            },
            "stats": profile.get_stats()
        }

    def _run_single_benchmark(
        self,
        prompt: str,
        model: str,
        duration: int,
        resolution: str
    ) -> PerformanceMetric:
        """Run a single benchmark iteration."""
        submit_time = time.time()

        # Submit job
        response = requests.post(
            f"{self.base_url}/videos/text-to-video",
            headers={
                "Authorization": f"Bearer {self.api_key}",
                "Content-Type": "application/json"
            },
            json={
                "prompt": prompt,
                "duration": duration,
                "model": model,
                "resolution": resolution
            }
        )

        if response.status_code != 200:
            return PerformanceMetric(
                job_id="failed",
                model=model,
                duration=duration,
                resolution=resolution,
                submit_time=submit_time,
                status="failed"
            )

        job_id = response.json()["job_id"]

        metric = PerformanceMetric(
            job_id=job_id,
            model=model,
            duration=duration,
            resolution=resolution,
            submit_time=submit_time
        )

        # Poll until complete
        while True:
            status_response = requests.get(
                f"{self.base_url}/videos/{job_id}",
                headers={"Authorization": f"Bearer {self.api_key}"}
            )
            status_data = status_response.json()

            if status_data["status"] == "completed":
                metric.complete_time = time.time()
                metric.total_time = metric.complete_time - metric.submit_time
                metric.status = "completed"
                metric.credits_used = status_data.get("credits_used", duration * 2)
                break
            elif status_data["status"] == "failed":
                metric.status = "failed"
                break

            time.sleep(5)

        return metric

    def compare_configs(
        self,
        prompt: str,
        configs: List[Dict],
        iterations: int = 2
    ) -> Dict:
        """Compare different configurations."""
        results = {}

        for config in configs:
            profile_name = f"{config['model']}_{config['duration']}s_{config.get('resolution', '1080p')}"

            result = self.benchmark(
                profile_name=profile_name,
                prompt=prompt,
                iterations=iterations,
                **config
            )

            results[profile_name] = result

        # Find optimal
        valid_results = {k: v for k, v in results.items() if "no_data" not in v["stats"]}

        if valid_results:
            fastest = min(valid_results.items(), key=lambda x: x[1]["stats"]["avg_time"])
            cheapest = min(valid_results.items(), key=lambda x: x[1]["stats"]["avg_credits"])

            return {
                "results": results,
                "recommendations": {
                    "fastest": fastest[0],
                    "fastest_time": fastest[1]["stats"]["avg_time"],
                    "cheapest": cheapest[0],
                    "cheapest_credits": cheapest[1]["stats"]["avg_credits"]
                }
            }

        return {"results": results, "recommendations": None}

# Usage
profiler = KlingAIPerformanceProfiler()

# Single benchmark
result = profiler.benchmark(
    profile_name="baseline",
    prompt="A peaceful forest scene with sunlight filtering through trees",
    model="kling-v1.5",
    duration=5,
    iterations=3
)

print(f"Average time: {result['stats']['avg_time']:.1f}s")

# Compare configurations
comparison = profiler.compare_configs(
    prompt="A serene mountain landscape",
    configs=[
        {"model": "kling-v1", "duration": 5},
        {"model": "kling-v1.5", "duration": 5},
        {"model": "kling-v1.5", "duration": 10},
    ],
    iterations=2
)

print(f"Fastest: {comparison['recommendations']['fastest']}")
print(f"Cheapest: {comparison['recommendations']['cheapest']}")

Optimization Strategies

class OptimizationStrategy:
    """Optimization strategies for different use cases."""

    @staticmethod
    def for_speed() -> Dict:
        """Optimize for fastest generation."""
        return {
            "model": "kling-v1",
            "duration": 5,
            "resolution": "720p",
            "tips": [
                "Use shortest duration",
                "Use base model",
                "Lower resolution reduces processing",
                "Simple prompts generate faster"
            ]
        }

    @staticmethod
    def for_quality() -> Dict:
        """Optimize for highest quality."""
        return {
            "model": "kling-pro",
            "duration": 10,
            "resolution": "1080p",
            "tips": [
                "Use pro model",
                "Longer duration = more coherent",
                "Detailed, specific prompts",
                "Include style and mood descriptors"
            ]
        }

    @staticmethod
    def for_cost() -> Dict:
        """Optimize for lowest cost."""
        return {
            "model": "kling-v1",
            "duration": 5,
            "resolution": "720p",
            "tips": [
                "Use base model (cheapest)",
                "5-second clips only",
                "Validate prompts before generation",
                "Cache and reuse successful outputs"
            ]
        }

    @staticmethod
    def balanced() -> Dict:
        """Balanced optimization."""
        return {
            "model": "kling-v1.5",
            "duration": 5,
            "resolution": "1080p",
            "tips": [
                "Mid-tier model for quality/speed",
                "5 seconds is usually enough",
                "Full HD resolution",
                "Good for most use cases"
            ]
        }

# Usage
print("Speed optimization:")
speed_config = OptimizationStrategy.for_speed()
print(f"  Config: {speed_config['model']}, {speed_config['duration']}s, {speed_config['resolution']}")
for tip in speed_config["tips"]:
    print(f"  - {tip}")

Caching Layer

import hashlib
import json
from pathlib import Path

class VideoCache:
    """Cache generated videos to avoid regeneration."""

    def __init__(self, cache_dir: str = ".video_cache"):
        self.cache_dir = Path(cache_dir)
        self.cache_dir.mkdir(exist_ok=True)
        self.index_file = self.cache_dir / "index.json"
        self.index = self._load_index()

    def _load_index(self) -> Dict:
        if self.index_file.exists():
            return json.loads(self.index_file.read_text())
        return {}

    def _save_index(self):
        self.index_file.write_text(json.dumps(self.index, indent=2))

    def _cache_key(self, prompt: str, model: str, duration: int) -> str:
        """Generate cache key from parameters."""
        data = f"{prompt}:{model}:{duration}"
        return hashlib.sha256(data.encode()).hexdigest()[:16]

    def get(self, prompt: str, model: str, duration: int) -> Optional[Dict]:
        """Get cached result if exists."""
        key = self._cache_key(prompt, model, duration)
        if key in self.index:
            entry = self.index[key]
            print(f"Cache hit: {key}")
            return entry
        return None

    def set(self, prompt: str, model: str, duration: int, result: Dict):
        """Cache a generation result."""
        key = self._cache_key(prompt, model, duration)
        self.index[key] = {
            "prompt": prompt,
            "model": model,
            "duration": duration,
            "video_url": result.get("video_url"),
            "job_id": result.get("job_id"),
            "cached_at": datetime.utcnow().isoformat()
        }
        self._save_index()
        print(f"Cached: {key}")

# Cached client wrapper
class CachedKlingAIClient:
    def __init__(self, api_key: str, cache: VideoCache):
        self.api_key = api_key
        self.cache = cache
        self.base_url = "https://api.klingai.com/v1"

    def generate(self, prompt: str, model: str = "kling-v1.5", duration: int = 5) -> Dict:
        # Check cache first
        cached = self.cache.get(prompt, model, duration)
        if cached:
            return cached

        # Generate new
        # ... generation code ...
        result = {"job_id": "...", "video_url": "..."}

        # Cache result
        self.cache.set(prompt, model, duration, result)

        return result

Output

Successful execution produces:

• Performance benchmarks
• Optimization recommendations
• Configuration comparisons
• Cached generation results

Error Handling

Common errors and solutions:

1. Benchmark Variance: Run more iterations
2. Timeout: Use longer timeout for pro model
3. Cache Stale: Implement cache expiry

Examples

See code examples above for complete, runnable implementations.

Resources

• Kling AI Performance
• Optimization Best Practices
• Caching Strategies