Kling v1.6 Standard Elements

kling-v1-6-standard-elements

Fast Inference

REST API

Try in Console API Docs Examples

Model Information

Response Time~180 sec

StatusActive

Version

0.0.1

Updated1 day ago

Live Demo

Average runtime: ~180 seconds

Input

Configure model parameters

Prompt

A man walks into an alley. Very natural and realistic video

Reference Image

File upload is currently disabled

Reference Image 2

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Duration

Output

View generated results

Result

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

Each execution costs $0.28 With $1 you can run this model about 3 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-standard-elements",
            "version": "0.0.1",
            "input": {
  "negative_prompt": "your negative prompt here",
  "aspect_ratio": "16:9",
  "duration": 5,
  "prompt": "your prompt here",
  "image_url_4": "your reference image 4 here",
  "image_url_3": "your reference image 3 here",
  "image_url_2": "your reference image 2 here",
  "image_url_1": "your reference image 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: ~180 seconds
Rate limit: 60 requests/minute
Concurrent requests: 10 maximum
Use long-polling to check prediction status until completion

Overview

Kling v1.6 Standard Elements is designed to create smooth and coherent video sequences based on multiple reference images and a guiding prompt. It combines image-to-video synthesis with prompt-driven animation logic to generate short-form videos. Kling v1.6 Standard Elements supports both horizontal and vertical formats and is optimized for generating realistic and consistent visuals over time.

Technical Specifications

Kling v1.6 Standard Elements supports multi-image input for guided video generation.

It uses temporal consistency mechanisms to ensure smoother transitions between frames.

Model is fine-tuned for short video outputs (5 or 10 seconds).

Supports natural camera motion simulation such as zoom, pan, and rotation based on text prompts.

Supports generation in common aspect ratios (16:9, 9:16, 1:1).

Key Considerations

All reference images should be thematically related to avoid conflicting visual outputs.

For best results, use 2 to 4 reference images. Using fewer than 2 may result in low diversity, while more than 4 may reduce consistency.

Long prompts with conflicting instructions may confuse motion generation.

Kling v1.6 Standard Elements is optimized for short clips; using it for storytelling longer than 10 seconds may not yield meaningful results.

If reference images include text, logos, or watermarks, these may be reproduced or distorted in the output.

Legal Information for Kling v1.6 Standard Elements

By using this Kling v1.6 Standard Elements, you agree to:

Kling Privacy
Kling SERVICE AGREEMENT

Tips & Tricks

prompt
Write concise and visual descriptions. For example:
"A person turning around slowly while smiling"
Avoid using overly abstract language. Keep it to 10-20 words for better results.

negative_prompt
Use it to exclude unwanted effects or styles. Example:
"blurry, distorted, extra limbs, glitch"
This helps improve visual clarity and coherence.

aspect_ratio

16:9: Best for landscape and desktop-style content.
9:16: Ideal for social media stories and mobile viewing.
1:1: Useful for platform-neutral square compositions.

duration

5: Use for quick actions or short expressions.
10: Suitable for extended motion or multi-scene effects.

image_url_1 to image_url_4

Use at least two reference images for effective guidance.
Maintain similar lighting, facial angle, and background.
Use four images to add variation across time but ensure visual consistency.
If facial detail is important, choose high-resolution images with a neutral expression.

Capabilities

Generates video clips from a blend of prompt guidance and image references.

Supports simple motion like walking, turning, smiling, or reacting to prompt descriptions.

Maintains temporal consistency across frames.

Can generate realistic character-focused videos or concept-style animations.

Enables portrait and landscape animation with flexible input formats.

What can I use for?

Creating character animations based on photos.

Producing short social content with dynamic visual transitions.

Generating AI-driven portraits that simulate natural motion.

Visual storytelling in creative or artistic projects.

Enhancing static designs with subtle movements.

Things to be aware of

Animate a single character across 4 facial angles to simulate head movement.

Use a prompt like "person looks left then smiles" with 9:16 aspect ratio for social media output.

Apply negative prompts such as "extra hands, deformed, low quality" to reduce visual errors.

Combine 3 reference images of different emotions and use a prompt like "slow emotional change from serious to happy".

Limitations

May not accurately replicate complex camera movements like dolly zooms or intricate 3D transitions.

Consistency between reference image content is crucial; mismatched inputs can degrade video quality.

Does not generate audio; outputs are silent.

Limited control over background unless clearly defined in the prompt.

Subject identity may slightly drift over time if reference images are inconsistent.

Output Format: MP4

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