klingai-batch-processing

# Kling AI Batch Processing

## Overview

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`.

## Batch Submission with Rate Limiting

```python
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")
```

## Collect Results

```python
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
```

## Async Batch with asyncio

```python
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
```

## Batch with Callbacks (No Polling)

```python
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
```

## Cost Estimation Before Batch

```python
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")
```

## Resources

- [API Reference](https://app.klingai.com/global/dev/document-api/apiReference/model/textToVideo)
- [Developer Portal](https://app.klingai.com/global/dev)