Process multiple video generation requests efficiently with Kling AI. Use when generating multiple videos or building content pipelines. Trigger with phrases like 'klingai batch', 'kling ai bulk', 'multiple videos klingai', 'klingai parallel generation'.
npxskills add jeremylongshore/claude-code-plugins-plus-skills--skill klingai-batch-processingLoading…
Generate multiple videos efficiently using controlled parallelism, rate-limit-aware submission, progress tracking, and result collection. All requests go through https://api.klingai.com/v1.
import jwt, time, os, requests
BASE = "https://api.klingai.com/v1"
def get_headers():
ak, sk = os.environ["KLING_ACCESS_KEY"], os.environ["KLING_SECRET_KEY"]
token = jwt.encode(
{"iss": ak, "exp": int(time.time()) + 1800, "nbf": int(time.time()) - 5},
sk, algorithm="HS256", headers={"alg": "HS256", "typ": "JWT"}
)
return {"Authorization": f"Bearer {token}", "Content-Type": "application/json"}
def submit_batch(prompts, model="kling-v2-master", duration="5",
mode="standard", max_concurrent=3, delay=2.0):
"""Submit batch with controlled concurrency and pacing."""
tasks = []
active = []
for i, prompt in enumerate(prompts):
# Wait if at concurrency limit
while len(active) >= max_concurrent:
active = [t for t in active if not check_complete(t["task_id"])]
if len(active) >= max_concurrent:
time.sleep(5)
response = requests.post(f"{BASE}/videos/text2video", headers=get_headers(), json={
"model_name": model,
"prompt": prompt,
"duration": duration,
"mode": mode,
})
data = response.json()["data"]
task = {"task_id": data["task_id"], "prompt": prompt, "index": i}
tasks.append(task)
active.append(task)
print(f"[{i+1}/{len(prompts)}] Submitted: {data['task_id']}")
time.sleep(delay) # pace requests
return tasks
def check_complete(task_id):
r = requests.get(f"{BASE}/videos/text2video/{task_id}", headers=get_headers()).json()
return r["data"]["task_status"] in ("succeed", "failed")
def collect_results(tasks, timeout=600):
"""Wait for all tasks and collect results."""
results = {}
start = time.monotonic()
while len(results) < len(tasks) and time.monotonic() - start < timeout:
for task in tasks:
if task["task_id"] in results:
continue
r = requests.get(
f"{BASE}/videos/text2video/{task['task_id']}", headers=get_headers()
).json()
status = r["data"]["task_status"]
if status == "succeed":
results[task["task_id"]] = {
"status": "succeed",
"url": r["data"]["task_result"]["videos"][0]["url"],
"prompt": task["prompt"],
}
elif status == "failed":
results[task["task_id"]] = {
"status": "failed",
"error": r["data"].get("task_status_msg", "Unknown"),
"prompt": task["prompt"],
}
if len(results) < len(tasks):
time.sleep(15)
return results
import asyncio
import aiohttp
async def async_batch(prompts, max_concurrent=3):
"""Async batch processing with semaphore-controlled concurrency."""
semaphore = asyncio.Semaphore(max_concurrent)
results = {}
async def generate_one(prompt, index):
async with semaphore:
async with aiohttp.ClientSession() as session:
# Submit
async with session.post(
f"{BASE}/videos/text2video",
headers=get_headers(),
json={"model_name": "kling-v2-master", "prompt": prompt,
"duration": "5", "mode": "standard"},
) as resp:
data = (await resp.json())["data"]
task_id = data["task_id"]
# Poll
while True:
await asyncio.sleep(10)
async with session.get(
f"{BASE}/videos/text2video/{task_id}",
headers=get_headers(),
) as resp:
data = (await resp.json())["data"]
if data["task_status"] == "succeed":
results[index] = data["task_result"]["videos"][0]["url"]
return
elif data["task_status"] == "failed":
results[index] = f"FAILED: {data.get('task_status_msg')}"
return
await asyncio.gather(*[generate_one(p, i) for i, p in enumerate(prompts)])
return results
def submit_batch_with_callbacks(prompts, callback_url):
"""Submit batch with webhook callbacks -- no polling needed."""
tasks = []
for prompt in prompts:
r = requests.post(f"{BASE}/videos/text2video", headers=get_headers(), json={
"model_name": "kling-v2-master",
"prompt": prompt,
"duration": "5",
"mode": "standard",
"callback_url": callback_url,
}).json()
tasks.append(r["data"]["task_id"])
time.sleep(2) # rate limit pacing
return tasks
def estimate_batch_cost(count, duration=5, mode="standard", audio=False):
credits_map = {(5, "standard"): 10, (5, "professional"): 35,
(10, "standard"): 20, (10, "professional"): 70}
per_video = credits_map.get((duration, mode), 10)
if audio:
per_video *= 5
total = count * per_video
print(f"Batch: {count} videos x {per_video} credits = {total} credits")
print(f"Estimated cost: ${total * 0.14:.2f}")
return total
# Check before submitting
needed = estimate_batch_cost(50, duration=5, mode="standard")
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