Kling v1.6 Pro Text to Video

kling-v1-6-pro-text-to-video

Text inputs smoothly turn into consistent, detailed videos using Kling v1.6 Pro Text to Video, designed for high-quality output.

Fast Inference

REST API

Try in Console API Docs Examples

Model Information

Response Time~270 sec

StatusActive

Version

0.0.1

Updatedabout 9 hours ago

Live Demo

Average runtime: ~270 seconds

Input

Configure model parameters

Prompt

A futuristic motorcycle drifts around a wet neon-lit street corner in slow motion, sparks flying from the tires, reflections dancing on the slick pavement — the camera orbits smoothly as the rider leans into the turn, seamless loop.

Output

View generated results

Result

Preview, share or download your results with a single click.

Each execution costs $0.49 With $1 you can run this model about 2 times.

API Reference

View Full Documentation

Prerequisites

Create an API Key from the Eachlabs Console
Install the required dependencies for your chosen language (e.g., requests for Python)

API Integration Steps

1. Create a Prediction

Send a POST request to create a new prediction. This will return a prediction ID that you'll use to check the result. The request should include your model inputs and API key.

import requests
import time

API_KEY = "YOUR_API_KEY"  # Replace with your API key
HEADERS = {
    "X-API-Key": API_KEY,
    "Content-Type": "application/json"
}

def create_prediction():
    response = requests.post(
        "https://api.eachlabs.ai/v1/prediction/",
        headers=HEADERS,
        json={
            "model": "kling-v1-6-pro-text-to-video",
            "version": "0.0.1",
            "input": {
  "cfg_scale": 0.5,
  "negative_prompt": "your negative prompt here",
  "aspect_ratio": "16:9",
  "duration": "5",
  "prompt": "your prompt here"
},
            "webhook_url": ""
        }
    )
    prediction = response.json()
    
    if prediction["status"] != "success":
        raise Exception(f"Prediction failed: {prediction}")
    
    return prediction["predictionID"]

2. Get Prediction Result

Poll the prediction endpoint with the prediction ID until the result is ready. The API uses long-polling, so you'll need to repeatedly check until you receive a success status.

def get_prediction(prediction_id):
    while True:
        result = requests.get(
            f"https://api.eachlabs.ai/v1/prediction/{prediction_id}",
            headers=HEADERS
        ).json()
        
        if result["status"] == "success":
            return result
        elif result["status"] == "error":
            raise Exception(f"Prediction failed: {result}")
        
        time.sleep(1)  # Wait before polling again

3. Complete Example

Here's a complete example that puts it all together, including error handling and result processing. This shows how to create a prediction and wait for the result in a production environment.

try:
    # Create prediction
    prediction_id = create_prediction()
    print(f"Prediction created: {prediction_id}")
    
    # Get result
    result = get_prediction(prediction_id)
    print(f"Output URL: {result['output']}")
    print(f"Processing time: {result['metrics']['predict_time']}s")
except Exception as e:
    print(f"Error: {e}")

Additional Information

The API uses a two-step process: create prediction and poll for results
Response time: ~270 seconds
Rate limit: 60 requests/minute
Concurrent requests: 10 maximum
Use long-polling to check prediction status until completion

Overview

Kling v1.6 Pro Text to Video is a video generation model that converts descriptive text prompts into short, realistic video clips. It uses advanced generative AI techniques to visualize textual descriptions as dynamic video content. This makes it possible to create video sequences based solely on written input without requiring any pre-existing images or footage.

Technical Specifications

Kling v1.6 Pro Text to Video generates short, continuous video clips based entirely on text input.

Operates on a transformer-based text-to-video architecture with frame interpolation and smooth transition optimization.

Supports high-definition video output in standard aspect ratios like 16:9, 9:16, and 1:1.

Designed for short-duration outputs, optimized for 5 to 10-second clips.

Integrates natural motion modeling to maintain consistency in object movement across frames.

Prioritizes prompt coherence and text-image alignment for accurate visual representation.

Key Considerations

Highly abstract or overly vague prompts can lead to inconsistent results.

Extremely high cfg_scale values (approaching 1) may cause overfitting to prompt details, reducing creative motion and natural appearance.

Longer durations increase generation time and processing requirements.

Video transitions and object motion depend heavily on the clarity and structure of the prompt.

Complex scene descriptions should be simplified for improved visual accuracy.

Legal Information for Kling v1.6 Pro Text to Video

By using this Kling v1.6 Pro Text to Video, you agree to:

Kling Privacy
Kling SERVICE AGREEMENT

Tips & Tricks

prompt: Use clear, specific, and vivid descriptions. Include object names, actions, and background details for better results.

Example: "A futuristic city skyline at sunset, flying cars passing by, glowing neon signs."

negative_prompt: List any unwanted objects, styles, or visual elements to exclude from the video.

Example: "blurry, low quality, text overlays, watermarks"

cfg_scale:

Recommended range: 0.5 – 0.9
Use 0.5–0.7 for creative flexibility, 0.8–0.9 for strict prompt adherence.

aspect_ratio:

16:9 for widescreen displays.
9:16 for vertical video (social media use).
1:1 for square formats.

duration:

Choose 5 seconds for quick, sharp visuals.
Use 10 seconds for more detailed scenes or storytelling sequences.

Capabilities

Generates dynamic videos based solely on text descriptions.

Supports prompt-based control over scene composition, objects, lighting, and atmosphere.

Allows exclusion of specific elements using negative prompts.

Offers control over aspect ratio and video length for format versatility.

Provides adjustable adherence to the prompt with cfg_scale.

What can I use for?

Creating short promotional videos based on descriptive ideas.

Generating conceptual video content for storyboarding.

Producing AI-generated video samples for creative projects.

Crafting abstract or atmospheric video loops for social media.

Visualizing scenes for writing, design, or multimedia projects.

Things to be aware of

Describe surreal or fantastical scenes to explore Kling v1.6 Pro Text to Video's creative visualizations.

Combine multiple actions in one prompt, like "a bird flying over a waterfall during a sunset."

Use negative_prompt to refine output style, e.g. "no cartoon style, no blurriness."

Experiment with different aspect ratios to suit various content needs.

Adjust cfg_scale to balance between creative interpretation and precise prompt following.

Limitations

Restricted to short video durations (5 or 10 seconds).

Complex or contradictory prompts may lead to visual artifacts or incomplete scenes.

Rapid movement or highly detailed scenes can reduce visual consistency.

Limited to supported aspect ratios and cfg_scale values between 0 and 1.

Excessively abstract descriptions may produce less predictable results.

Output Format: MP4

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