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Kling v1.6 Image to Video

Kling AI Image to Video

Partner Model
Fast Inference
REST API
Try in ConsoleAPI DocsExamples

Model Information

Response Time~440 sec
StatusActive
Version
0.0.1
Updated8 days 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 AI Image to Video",
            "version": "0.0.1",
            "input": {
  "duration": "10",
  "dynamic_masks": "your dynamic masks here",
  "static_mask": "your_file.image/jpg",
  "mode": "PRO",
  "cfg_scale": null,
  "image_tail": "your_file.image/jpeg",
  "image_url": "https://storage.googleapis.com/magicpoint/models/man.png",
  "negative_prompt": "your negative prompt here",
  "prompt": "a man is talking"
}
        }
    )
    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: ~440 seconds
  • Rate limit: 60 requests/minute
  • Concurrent requests: 10 maximum
  • Use long-polling to check prediction status until completion

Overview

Kling v1.6 Image to Video is a model designed to generate high-quality videos from a single image. By providing key parameters such as prompts, duration, and control settings, users can produce smooth and coherent video sequences based on their desired output.

Technical Specifications

  • Kling v1.6 onverts a ingle image into a video sequence.
  • Supports dynamic and static masks for localized modifications.
  • Adjustable configuration settings for precise control over animation.
  • Provides multiple processing modes for varied quality and speed requirements.

Key Considerations

  • Higher cfg_scale values might lead to unnatural motion, whereas lower values may reduce coherence with the input prompt.
  • Using negative_prompt effectively can significantly improve the relevance of the output.
  • STD mode is optimized for faster processing, while PRO mode focuses on higher quality but requires more computation.
  • Ensure that image_url and image_tail have similar compositions to maintain smooth transitions.


Legal Information for Kling v1.6 Image to Video

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

Tips & Tricks

  • image_url: Choose high-quality images with distinct features to enhance motion clarity.
  • prompt: Be specific about the desired movement and transitions. Avoid vague descriptions.
  • duration: For short clips (5 seconds), focus on subtle transitions; for longer clips (10 seconds), ensure more dynamic variations.
  • mode: Use STD for rapid prototyping and PRO for refined outputs.
  • image_tail: If not provided, the Kling v1.6 Image to Video will generate an automatic fade-out effect.
  • static_mask & dynamic_masks: These should be used carefully to control which parts of the image remain unchanged or move dynamically.
  • cfg_scale: Start with a mid-range value (e.g., 0.5) and adjust based on the results. Higher values (close to 1) enforce stronger adherence to prompts.

Capabilities

  • Transforms static images into smooth, animated video sequences.
  • Supports direct control over animation parameters such as duration and mode selection.
  • Kling v1.6 allows refined adjustments using masks and prompts to guide video generation.

What can I use for?

  • Creating short animated clips from static images for artistic or storytelling purposes.
  • Generating dynamic content for advertisements or promotional materials.
  • Enhancing AI-assisted video generation workflows.
  • Experimenting with creative motion transformations from single images.

Things to be aware of

  • Apply static_mask selectively to keep some areas stable while allowing others to move.
  • Experiment with different cfg_scale values to achieve the right balance between creativity and realism.
  • Use negative_prompt to eliminate unwanted distortions or artifacts in the generated video.
  • Try both STD and PRO modes to see the difference in processing speed and output quality.
  • Create a seamless looping animation by carefully adjusting image_tail settings.

Limitations

  • The Kling v1.6 Image to Video may struggle with extremely abstract or highly complex scene transformations.
  • Some inconsistencies may occur if image_url and image_tail do not align well.
  • Negative_prompt effectiveness varies based on the complexity of the scene.

Output Format: MP4

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