klingai-image-to-video

Animate static images into video using Kling AI. Use when converting images to video, adding motion to stills, or building I2V pipelines. Trigger with phrases like 'klingai image to video', 'kling ai animate image', 'klingai img2vid', 'animate picture klingai'.

Quality

77%

Does it follow best practices?

Impact

Pending

No eval scenarios have been run

Securityby

Advisory

Suggest reviewing before use

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Kling AI Image-to-Video

Overview

Animate static images using the /v1/videos/image2video endpoint. Supports motion prompts, camera control, dynamic masks (motion brush), static masks, and tail images for start-to-end transitions.

Endpoint: POST https://api.klingai.com/v1/videos/image2video

Request Parameters

Parameter	Type	Required	Description
`model_name`	string	Yes	`kling-v1-5`, `kling-v2-1`, `kling-v2-master`, etc.
`image`	string	Yes	URL of the source image (JPG, PNG, WebP)
`prompt`	string	No	Motion description for the animation
`negative_prompt`	string	No	What to exclude
`duration`	string	Yes	`"5"` or `"10"` seconds
`aspect_ratio`	string	No	`"16:9"` default
`mode`	string	No	`"standard"` or `"professional"`
`cfg_scale`	float	No	Prompt adherence (0.0-1.0)
`image_tail`	string	No	End-frame image URL (mutually exclusive with masks/camera)
`camera_control`	object	No	Camera movement (mutually exclusive with masks/image_tail)
`static_mask`	string	No	Mask image URL for fixed regions
`dynamic_masks`	array	No	Motion brush trajectories
`callback_url`	string	No	Webhook for completion

Basic Image-to-Video

import jwt, time, os, requests

BASE = "https://api.klingai.com/v1"

def get_headers():
    ak, sk = os.environ["KLING_ACCESS_KEY"], os.environ["KLING_SECRET_KEY"]
    token = jwt.encode(
        {"iss": ak, "exp": int(time.time()) + 1800, "nbf": int(time.time()) - 5},
        sk, algorithm="HS256", headers={"alg": "HS256", "typ": "JWT"}
    )
    return {"Authorization": f"Bearer {token}", "Content-Type": "application/json"}

# Animate a landscape photo
response = requests.post(f"{BASE}/videos/image2video", headers=get_headers(), json={
    "model_name": "kling-v2-1",
    "image": "https://example.com/landscape.jpg",
    "prompt": "Clouds slowly drifting across the sky, gentle wind rustling through trees",
    "negative_prompt": "static, frozen, blurry",
    "duration": "5",
    "mode": "standard",
})

task_id = response.json()["data"]["task_id"]

# Poll for result
while True:
    time.sleep(15)
    result = requests.get(
        f"{BASE}/videos/image2video/{task_id}", headers=get_headers()
    ).json()
    if result["data"]["task_status"] == "succeed":
        print(f"Video: {result['data']['task_result']['videos'][0]['url']}")
        break
    elif result["data"]["task_status"] == "failed":
        raise RuntimeError(result["data"]["task_status_msg"])

Start-to-End Transition (image_tail)

Use image_tail to specify both the first and last frame. Kling interpolates the motion between them.

response = requests.post(f"{BASE}/videos/image2video", headers=get_headers(), json={
    "model_name": "kling-v2-master",
    "image": "https://example.com/sunrise.jpg",        # first frame
    "image_tail": "https://example.com/sunset.jpg",    # last frame
    "prompt": "Time lapse of sun moving across the sky",
    "duration": "5",
    "mode": "professional",
})

Motion Brush (dynamic_masks)

Draw motion paths for specific elements in the image. Up to 6 motion paths per image in v2.6.

response = requests.post(f"{BASE}/videos/image2video", headers=get_headers(), json={
    "model_name": "kling-v2-6",
    "image": "https://example.com/person-standing.jpg",
    "prompt": "Person walking forward naturally",
    "duration": "5",
    "dynamic_masks": [
        {
            "mask": "https://example.com/person-mask.png",  # white = selected region
            "trajectories": [
                {"x": 0.5, "y": 0.7, "t": 0.0},   # start position (normalized 0-1)
                {"x": 0.5, "y": 0.5, "t": 0.5},   # midpoint
                {"x": 0.5, "y": 0.3, "t": 1.0},   # end position
            ]
        }
    ],
})

Static Mask (freeze regions)

Keep specific areas of the image static while animating the rest.

response = requests.post(f"{BASE}/videos/image2video", headers=get_headers(), json={
    "model_name": "kling-v2-master",
    "image": "https://example.com/scene.jpg",
    "prompt": "Water flowing in the river, birds flying",
    "duration": "5",
    "static_mask": "https://example.com/buildings-mask.png",  # white = frozen
})

Mutual Exclusivity Rules

These features cannot be combined in a single request:

Feature Set A	Feature Set B
`image_tail`	`dynamic_masks`, `static_mask`, `camera_control`
`dynamic_masks` / `static_mask`	`image_tail`, `camera_control`
`camera_control`	`image_tail`, `dynamic_masks`, `static_mask`

Image Requirements

Constraint	Value
Formats	JPG, PNG, WebP
Max size	10 MB
Min resolution	300x300 px
Max resolution	4096x4096 px
Mask format	PNG with white (selected) / black (excluded)

Error Handling

Error	Cause	Fix
`400` invalid image	URL unreachable or wrong format	Verify image URL is publicly accessible
`400` mutual exclusivity	Combined incompatible features	Use only one feature set per request
`task_status: failed`	Image too complex or low quality	Use higher resolution, clearer source
Mask mismatch	Mask dimensions differ from source	Ensure mask matches source image dimensions

Resources

Repository: jeremylongshore/claude-code-plugins-plus-skills
Commit: 3e83543

Last updated: 2 days ago
Created: 2 days ago

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