Kling v1.5 Pro Image to Video
kling-v1-5-pro-image-to-video
Kling v1.5 Pro Image to Video reliably converts images into videos, emphasizing sharpness and seamless motion.
Model Information
Input
Configure model parameters
Output
View generated results
Result
Preview, share or download your results with a single click.
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 requestsimport timeAPI_KEY = "YOUR_API_KEY" # Replace with your API keyHEADERS = {"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-v1-5-pro-image-to-video","version": "0.0.1","input": {"cfg_scale": 0.5,"negative_prompt": "blur, distort, and low quality","tail_image_url": "your tail image url here","aspect_ratio": "16:9","duration": "5","image_url": "your image url here","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 resultelif 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 predictionprediction_id = create_prediction()print(f"Prediction created: {prediction_id}")# Get resultresult = 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: ~200 seconds
- Rate limit: 60 requests/minute
- Concurrent requests: 10 maximum
- Use long-polling to check prediction status until completion
Overview
Kling v1.5 Pro Image to Video is a video generation model designed to convert static images into short, coherent motion clips. By combining image input with a descriptive prompt, Kling v1.5 Pro Image to Video synthesizes dynamic and realistic animations. It supports advanced control through text prompts, transition planning, and tail image sequencing, making it suitable for storytelling and visual content generation where seamless motion and image consistency are important.
Technical Specifications
Supports bidirectional temporal rendering with improved interpolation between source and tail images.
Tailored for short video generation up to 10 seconds, maintaining coherence across motion and lighting.
Incorporates a fine-tuned motion engine designed to preserve spatial elements of the input image while generating realistic depth shifts.
Optimized rendering pipeline ensures prompt-guided influence remains prominent while honoring source image structure.
Aspect ratio rendering is aligned with final frame cropping to ensure minimal deformation or artifacting.
Key Considerations
Output quality is strongly dependent on the alignment between prompt and image. Misaligned prompts may cause artifacts or hallucinated elements.
Using tail image drastically changes the ending sequence. Ensure tail image matches the visual tone of the main input image.
Avoid cluttered or text-heavy input images as they may result in motion artifacts or frame instability.
Aspect ratio mismatch between image and selected ratio can cause unwanted cropping or stretching.
Longer durations (10s) might lead to subtle blurriness near the end if motion is too intense or unstructured in the prompt.
Legal Information for Kling v1.5 Pro Image to Video
By using this Kling v1.5 Pro Image to Video, you agree to:
- Kling Privacy
- Kling SERVICE AGREEMENT
Tips & Tricks
prompt: Be specific. Include motion type (e.g., “slow pan forward”, “dynamic camera tilt”), environmental details, and lighting conditions. Example:
"a majestic mountain range under golden sunset, slow cinematic zoom forward"
negative_prompt: Use to exclude unwanted elements like “blur”, “distortion”, “extra limbs”, “low resolution”, or style mismatches.
Example: "blurry, cartoon, abstract, low quality, extra objects"
cfg_scale:
- Range: 0–1
- Values between 0.6–0.8 generally offer a balance between creativity and fidelity to the input image.
- Lower values (e.g., 0.4) reduce prompt influence and preserve image structure more.
- Higher values (0.9+) may introduce more creative interpretation but can deviate from image details.
aspect_ratio:
- 16:9: Best for horizontal videos or cinematic presentation.
- 9:16: Ideal for mobile or vertical social content.
- 1:1: Square format for symmetrical scenes or specific platform needs.
duration:
- 5s: Recommended for dynamic, punchy motion.
- 10s: Suitable for smooth, narrative transitions or sequences with tail image usage.
image_url: Should be a clean, single-subject composition. Avoid cluttered scenes. Backgrounds should support the motion direction suggested in the prompt.
tail_image_url: Helps define the ending visual frame. Ensure it's visually compatible with the main image. Example use: character turning around, scene fading into night, etc.
Capabilities
Generates short animated video clips (5–10 seconds) from a single input image.
Supports visual transitions using both a starting and tail image.
Creates realistic camera motion such as panning, zooming, or dolly shots.
Preserves subject integrity while interpreting motion cues from the prompt.
Aligns prompt context with visual depth, environment, and tone.
What can I use for?
Creating dynamic animated intros for static visual assets.
Generating teaser videos from posters, key visuals, or product shots.
Producing cinematic clips for social media with added movement.
Enhancing still photography with fluid camera motions.
Extending visual narratives using tail-to-tail image sequencing.
Things to be aware of
Try combining a calm scene input with a tail image that introduces lighting change for a day-to-night transition.
Animate artwork or illustrations using rich camera motion prompts.
Use negative prompts to eliminate specific artifacts like “frame tearing” or “double shadows.”
Combine a high cfg_scale (0.9) with strong prompts for stylized artistic movement.
Match aspect ratio with platform destination before generation for better composition alignment.
Limitations
Motion generation may lose consistency in highly abstract or surreal prompts.
Faces and text in the image may become distorted if not supported by the prompt.
Not suitable for videos requiring object-level animation or complex scene changes.
Output may suffer from flickering if background has too much texture variation.
Transitions between main and tail image work best with similar subject positions and angles.
Output Format: MP4
