Optimize Kling AI performance for speed and quality. Use when improving generation times, reducing costs, or enhancing output quality. Trigger with phrases like 'klingai performance', 'kling ai optimization', 'faster klingai', 'klingai quality settings'.
npxskills add jeremylongshore/claude-code-plugins-plus-skills--skill klingai-performance-tuningLoading…
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.
| 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 |
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")
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)
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"
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
kling-v2-5-turbo for iteration, v2-6 for finalstandard mode until final renderrequests.Session()callback_url instead of polling
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Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).