Kandinsky 5 (Text-to-Video)
A diffusion model designed for text-to-video generation with a resolution slightly above standard definition (SD).
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.
How to Make a Call
Full Example: Generating and Retrieving the Video From the Server
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
# Insert your AI/ML API key instead of <YOUR_AIMLAPI_KEY>:
api_key = "<YOUR_AIMLAPI_KEY>"
# Creating and sending a video generation task to the server
def generate_video():
url = "https://api.aimlapi.com/v2/generate/video/pixverse/generation"
headers = {
"Authorization": f"Bearer {api_key}",
}
data = {
"model": "sber-ai/kandinsky5-distill-t2v",
"prompt": "A menacing evil dragon appears in a distance above the tallest mountain, then rushes toward the camera with its jaws open, revealing massive fangs. We see it's coming.",
"resolution": "1080p",
"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()
print(response_data)
return response_data
# Requesting the result of the task from the server using the generation_id
def get_video(gen_id):
url = "https://api.aimlapi.com/v2/generate/video/pixverse/generation"
params = {
"generation_id": gen_id,
}
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
response = requests.get(url, params=params, headers=headers)
return response.json()
def main():
# Generate video
gen_response = generate_video()
gen_id = gen_response.get("id")
print("Generation ID: ", gen_id)
# Try to retrieve the video from the server every 10 sec
if gen_id:
start_time = time.time()
timeout = 600
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()Processing time: ~2 min 28 sec.
Generated Video (768x512, without sound):
API Schemas
Create a video generation task and send it to the server
You can generate a video using this API. In the basic setup, you only need a prompt. This endpoint creates and sends a video generation task to the server — and returns a generation ID.
The text description of the scene, subject, or action to generate in the video.
The aspect ratio of the generated video.
16:9Possible values: The length of the output video in seconds.
Number of inference steps for sampling. Higher values give better quality but take longer.
30No content
POST /v2/video/generations HTTP/1.1
Host: api.aimlapi.com
Content-Type: application/json
Accept: */*
Content-Length: 110
{
"model": "sber-ai/kandinsky5-t2v",
"prompt": "text",
"aspect_ratio": "16:9",
"duration": 5,
"num_inference_steps": 30
}No content
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.
No content
GET /v2/video/generations?generation_id=text HTTP/1.1
Host: api.aimlapi.com
Accept: */*
No content
Last updated
Was this helpful?