Generate videos from static images using Kling AI. Use when animating images, creating motion from stills, or building image-based content. Trigger with phrases like 'klingai image to video', 'kling ai animate image', 'klingai img2vid', 'animate picture klingai'.
npxskills add jeremylongshore/claude-code-plugins-plus-skills--skill klingai-image-to-videoLoading…
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
| 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 |
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"])
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",
})
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
]
}
],
})
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
})
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 |
| 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 | 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 |
Extract frames or short clips from videos using ffmpeg.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
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Batch-generate images via OpenAI Images API. Random prompt sampler + `index.html` gallery.
Extract frames or short clips from videos using ffmpeg.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Batch-generate images via OpenAI Images API. Random prompt sampler + `index.html` gallery.