Kling v1 Standard Text to Video

kling-v1-standard-text-to-video

Fast Inference

REST API

Try in Console API Docs Examples

Model Information

Response Time~300 sec

StatusActive

Version

0.0.1

Updated1 day ago

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-standard-text-to-video",
            "version": "0.0.1",
            "input": {
  "camera_control_config_value": -10,
  "camera_control_config_type": "your camera control config type here",
  "camera_control": "your camera control here",
  "cfg_scale": 0.5,
  "negative_prompt": "blur, distort, and low quality",
  "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: ~300 seconds
Rate limit: 60 requests/minute
Concurrent requests: 10 maximum
Use long-polling to check prediction status until completion

Overview

The Kling v1 Standard Text-to-Video model generates short video clips from text descriptions, enabling users to create dynamic visual content. It supports customizable camera movements and aspect ratios, making it suitable for various creative purposes.

Technical Specifications

Output Format: Generates MP4 video files suitable for various platforms.

Video Duration: Supports clip lengths of 5 or 10 seconds.

Resolution: Produces videos in standard resolutions compatible with selected aspect ratios (16:9, 9:16, 1:1).

Frame Rate: Delivers smooth motion at a standard frame rate optimized for short-form content.

Processing: Utilizes advanced algorithms to interpret text prompts and render realistic or stylized visuals

Key Considerations

Kling v1 Standard Text to Video performs best with realistic, physically plausible scenes.

Complex textual prompts may increase inference time or result in unstable outputs.

Overlapping or conflicting camera parameters may cause visual artifacts.

The model does not generate audio or interactive content — video is silent and pre-rendered.

Motion logic is constrained to predefined configurations; freeform camera motion is not supported.

Legal Information for Kling v1 Standard Text to Video

By using this Kling v1 Standard Text to Video, you agree to:

Kling Privacy
Kling SERVICE AGREEMENT

Tips & Tricks

Use the following guidance to adjust input values for better results:

Prompt
Craft prompts that include visual nouns and cinematic adjectives.
Example:
✅ "a sunset over a mountain, cinematic lighting, slow motion"
❌ "freedom and hope"
Negative Prompt
Filter out distractions:
Example: "no text, no distortion, no watermark"
Aspect Ratio
Choose based on platform or target look:
- 16:9 for landscape video
- 9:16 for portrait/vertical formats
- 1:1 for square framing
Duration
Use 5 for quicker outputs, 10 for more narrative room.
CFG Scale
Controls prompt adherence:
- 0.6–0.8: balanced creativity and precision
- >0.9: strict prompt following, may reduce diversity
Camera Control
Motion presets:
- down_back: pulls away from subject
- forward_up: dynamic forward crane motion
- right_turn_forward: dramatic sweeping motion from the side
- left_turn_forward: mirrored motion variant
Camera Control Config Type
Axis of motion:
- pan: horizontal movement
- tilt: vertical framing change
- roll: rotational movement
- zoom: push in/pull out for dramatic focus
Camera Control Config Value
Range from 0.1 to 1.0:
- ~0.3: subtle movement
- ~0.7: more pronounced, cinematic motion

Capabilities

Generates coherent, cinematic short videos from descriptive text

Allows directional control over camera movement

Supports negative prompt input for content refinement

Produces outputs with temporal continuity and consistent framing

Adaptable to various aspect ratios for multiple formats

What can I use for?

Creating visual concepts for short-form storytelling

Designing mood-based video clips for creative projects

Generating thematic content for social media

Building AI-assisted visual narratives

Exploring motion ideas in film pre-production

Things to be aware of

Use zoom + tilt for simulated dolly shots

Combine “forward_up” camera motion with scenic prompts for immersive effects

Test square aspect ratio with centered compositions for stylized looks

Add environment-based keywords like “fog”, “sunlight”, “neon lights” to enrich atmosphere

Use negative prompts to remove "watermark", "glitch", or undesired elements for cleaner outputs

Limitations

Limited to short clips (5–10 seconds)

Cannot generate audio or subtitles

Only supports predefined aspect ratios and motion types

Results may vary with overly abstract or poetic prompts

May exhibit frame jittering in fast-moving scenes

Output Format: MP4

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