Klingai upgrade migration

Kling AI Upgrade & Migration

Overview

Guide for migrating between Kling AI model versions. Covers breaking changes, parameter differences, feature availability, and parallel testing strategies.

Version History

Version	Release	Key Changes
v1.0	2024-06	Initial T2V + I2V
v1.5	2024-09	1080p, motion brush, I2V-only model
v1.6	2024-11	Lip sync, camera paths, effects API
v2.0	2025-03	Quality leap, kling-v2-master
v2.1	2025-06	Optimized I2V, kling-v2-1-master for T2V
v2.5 Turbo	2025-09	40% faster, best speed/quality ratio
v2.6	2025-12	Native audio, 30-48 FPS, highest quality

Migration: v1.x to v2.x

# v1.x request
body = {
    "model_name": "kling-v1-6",
    "prompt": "A sunset over mountains",
    "duration": "5",
    "mode": "standard",
}

# v2.x -- only model_name changes
body["model_name"] = "kling-v2-master"

Breaking changes:

• kling-v2-1 is I2V-only (no text-to-video support)
• Camera control intensities produce different results at same values
• Generation times differ (v2.x generally slower, higher quality)

Migration: v2.x to v2.6 with Audio

body["model_name"] = "kling-v2-6"
body["motion_has_audio"] = True  # NEW: synchronized audio

# Cost impact: audio multiplies credits 5x
# 5s standard: 10 -> 50 credits

Feature Availability Matrix

Feature	v1.0	v1.5	v1.6	v2.0	v2.1	v2.5T	v2.6
Text-to-video	Y	Y	Y	Y	I2V only	Y	Y
Image-to-video	Y	Y	Y	Y	Y	Y	Y
Camera control	-	-	Y	Y	Y	Y	Y
Motion brush	-	Y	Y	Y	Y	Y	Y
Lip sync	-	-	Y	Y	Y	Y	Y
Effects	-	-	Y	Y	Y	Y	Y
Native audio	-	-	-	-	-	-	Y
1080p	-	Y	Y	Y	Y	Y	Y

Parallel A/B Comparison

def compare_models(prompt, models):
    """Generate same prompt across models for comparison."""
    results = {}
    for model in models:
        r = requests.post(f"{BASE}/videos/text2video", headers=get_headers(), json={
            "model_name": model, "prompt": prompt, "duration": "5", "mode": "standard",
        }).json()
        results[model] = {"task_id": r["data"]["task_id"], "start": time.time()}

    # Poll all
    while any("url" not in r for r in results.values()):
        for model, info in results.items():
            if "url" in info or "error" in info:
                continue
            r = requests.get(
                f"{BASE}/videos/text2video/{info['task_id']}", headers=get_headers()
            ).json()
            if r["data"]["task_status"] == "succeed":
                info["url"] = r["data"]["task_result"]["videos"][0]["url"]
                info["time"] = round(time.time() - info["start"])
            elif r["data"]["task_status"] == "failed":
                info["error"] = r["data"].get("task_status_msg")
        time.sleep(10)

    for model, info in results.items():
        print(f"{model}: {info.get('url', info.get('error'))} ({info.get('time', '?')}s)")
    return results

Rollback Strategy

# Feature flag for instant rollback
KLING_MODEL = os.environ.get("KLING_MODEL_VERSION", "kling-v2-master")
body["model_name"] = KLING_MODEL

# To rollback: export KLING_MODEL_VERSION=kling-v1-6

Resources

• Model Documentation
• Developer Portal