klingai-performance-tuning

# 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 ```python 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"]...