Kling AI Performance Tuning Skill

Kling AI Performance Tuning

Overview

Optimize video generation for your use case by choosing the right model, mode, and parameters. Covers benchmarking, speed vs. quality trade-offs, connection pooling, and caching strategies.

Speed vs. Quality Matrix

| Config | ~Gen Time | Quality | Credits (5s) | Best For | |--------|-----------|---------|-------------|----------| | v2.5-turbo + standard | 30-60s | Good | 10 | Drafts, iteration | | v2-master + standard | 60-90s | High | 10 | Production previews | | v2.6 + standard | 60-120s | Highest | 10 | Quality-sensitive | | v2.6 + professional | 120-300s | Highest+ | 35 | Final output | | v2.6 + prof + audio | 180-400s | Highest+ | 200 | Full production |

Benchmarking Tool

import time, requests, json

def benchmark_model(prompt: str, model: str, mode: str = "standard",
                    runs: int = 3) -> dict:
    """Benchmark generation time for a model/mode combination."""
    times = []

    for i in range(runs):
        start = time.monotonic()

        # Submit
        r = requests.post(f"{BASE}/videos/text2video", headers=get_headers(), json={
            "model_name": model, "prompt": prompt, "duration": "5", "mode": mode,
        }).json()
        task_id = r["data"]["task_id"]

        # Poll
        while True:
            time.sleep(10)
            result = requests.get(
                f"{BASE}/videos/text2video/{task_id}", headers=get_headers()
            ).json()
            if result["data"]["task_status"] in ("succeed", "failed"):
                break

        elapsed = time.monotonic() - start
        times.append(elapsed)
        print(f"  Run {i+1}/{runs}: {elapsed:.1f}s ({result['data']['task_status']})")

    return {
        "model": model,
        "mode": mode,
        "avg_sec": round(sum(times) / len(times), 1),
        "min_sec": round(min(times), 1),
        "max_sec": round(max(times), 1),
        "runs": runs,
    }

# Compare models
prompt = "A waterfall in a tropical forest, cinematic"
for model in ["kling-v2-5-turbo", "kling-v2-master", "kling-v2-6"]:
    result = benchmark_model(prompt, model, runs=2)
    print(f"{model}: avg={result['avg_sec']}s, min={result['min_sec']}s")

Connection Pooling

import requests

# Without pooling: new TCP connection per request (slow)
# With pooling: reuse connections (fast)

session = requests.Session()
adapter = requests.adapters.HTTPAdapter(
    pool_connections=5,     # number of connection pools
    pool_maxsize=10,        # max connections per pool
    max_retries=3,          # auto-retry on connection errors
)
session.mount("https://", adapter)

# Use session instead of requests directly
response = session.post(f"{BASE}/videos/text2video", headers=get_headers(), json=body)

Prompt Optimization

Prompts that generate faster:

| Technique | Why It Helps | |-----------|-------------| | Clear single subject | Less complexity to resolve | | Specify camera angle | Reduces ambiguity | | Avoid conflicting styles | "realistic anime" confuses the model | | Keep under 200 words | Shorter prompts process faster | | Use negative prompts | Removes processing of unwanted elements |

# Slow prompt (vague, conflicting)
slow = "A scene with many things happening, realistic but also artistic"

# Fast prompt (specific, clear)
fast = "A single red fox walking through snow, side view, natural lighting, 4K"

Caching Strategy

import hashlib

class PromptCache:
    """Cache results to avoid regenerating identical videos."""

    def __init__(self):
        self._cache = {}

    def _key(self, prompt: str, model: str, duration: int, mode: str) -> str:
        raw = f"{prompt}|{model}|{duration}|{mode}"
        return hashlib.sha256(raw.encode()).hexdigest()[:16]

    def get(self, prompt, model, duration, mode):
        key = self._key(prompt, model, duration, mode)
        return self._cache.get(key)

    def set(self, prompt, model, duration, mode, video_url):
        key = self._key(prompt, model, duration, mode)
        self._cache[key] = {
            "url": video_url,
            "cached_at": time.time(),
        }

cache = PromptCache()

def generate_with_cache(prompt, model="kling-v2-master", duration=5, mode="standard"):
    cached = cache.get(prompt, model, duration, mode)
    if cached:
        print(f"Cache hit: {cached['url']}")
        return cached["url"]

    # Generate
    result = client.text_to_video(prompt, model=model, duration=duration, mode=mode)
    url = result["videos"][0]["url"]
    cache.set(prompt, model, duration, mode, url)
    return url

Optimization Checklist

[ ] Use kling-v2-5-turbo for iteration, v2-6 for final
[ ] Use standard mode until final render
[ ] Connection pooling via requests.Session()
[ ] Cache identical prompt+param combinations
[ ] Prompt: specific, single subject, < 200 words
[ ] Batch submissions paced at 2-3s intervals
[ ] Use callback_url instead of polling
[ ] Download videos async (don't block on CDN download)

Agent Skills: Kling AI Performance Tuning

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