v2-master/image-to-video
Compared to v1.6, this Kling model better aligns with the prompt and delivers more dynamic and visually appealing results.
Setup your API Key
If you don’t have an API key for the AI/ML API yet, feel free to use our Quickstart guide.
Code Example
The code below creates a video generation task, then automatically polls the server every 10 seconds until it finally receives the video URL.
import requests
import time
base_url = "https://api.aimlapi.com/v2"
api_key = "<YOUR_AIMLAPI_KEY>"
# Creating and sending a video generation task to the server
def generate_video():
url = f"{base_url}/generate/video/kling/generation"
headers = {
"Authorization": f"Bearer {api_key}",
}
data = {
"model": "klingai/v2-master-image-to-video",
"prompt": "Mona Lisa puts on glasses with her hands.",
"image_url": "https://s2-111386.kwimgs.com/bs2/mmu-aiplatform-temp/kling/20240620/1.jpeg",
"duration": "5",
}
response = requests.post(url, json=data, headers=headers)
if response.status_code >= 400:
print(f"Error: {response.status_code} - {response.text}")
else:
response_data = response.json()
return response_data
# Requesting the result of the task from the server using the generation_id
def get_video(gen_id):
url = f"{base_url}/generate/video/kling/generation"
params = {
"generation_id": gen_id,
}
# Insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>:
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
response = requests.get(url, params=params, headers=headers)
# print("Generation:", response.json())
return response.json()
def main():
# Running video generation and getting a task id
gen_response = generate_video()
gen_id = gen_response.get("id")
print("Gen_ID: ", gen_id)
# Trying to retrieve the video from the server every 10 sec
if gen_id:
start_time = time.time()
timeout = 1000
while time.time() - start_time < timeout:
response_data = get_video(gen_id)
if response_data is None:
print("Error: No response from API")
break
status = response_data.get("status")
print("Status:", status)
if status == "waiting" or status == "active" or status == "queued" or status == "generating":
print("Still waiting... Checking again in 10 seconds.")
time.sleep(10)
else:
print("Processing complete:/n", response_data)
return response_data
print("Timeout reached. Stopping.")
return None
if __name__ == "__main__":
main()
API Schemas
Generating a video using this model involves sequentially calling two endpoints:
The first one is for creating and sending a video generation task to the server (returns a generation ID).
The second one is for requesting the generated video from the server using the generation ID received from the first endpoint.
Below, you can find two corresponding API schemas and an example with both endpoint calls.
Create a video generation task and send it to the server
A direct link to an online image or a Base64-encoded local image that will serve as the visual base or the first frame for the video.
URL of the image to be used as the last frame of the video.
URL of the image for Static Brush Application Area (Mask image created by users using the motion brush).
The description of elements to avoid in the generated video.
The length of the output video in seconds.
The CFG (Classifier Free Guidance) scale is a measure of how close you want the model to stick to your prompt.
0.5Customized Task ID
The text description of the scene, subject, or action to generate in the video.
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Retrieve the generated video from the server
After sending a request for video generation, this task is added to the queue. This endpoint lets you check the status of a video generation task using its generation_id, obtained from the endpoint described above.
If the video generation task status is complete, the response will include the final result — with the generated video URL and additional metadata.
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