A diffusion model designed for fast text-to-video generation (no sound), offered as a compact variant of the Kandinsky 5 (Text-to-Video) model. A resolution is 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
Step-by-Step Instructions
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 both corresponding API schemas.
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.
post
Body
modelstring · enumRequiredPossible values:
promptstringRequired
The text description of the scene, subject, or action to generate in the video.
aspect_ratiostring · enumOptional
The aspect ratio of the generated video.
Default: 3:2Possible values:
durationinteger · enumOptional
The length of the output video in seconds.
Default: 5Possible values:
num_inference_stepsintegerOptional
Number of inference steps for sampling. Higher values give better quality but take longer.
Default: 30
Responses
200Success
application/json
post
/v2/video/generations
200Success
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 id, obtained from the endpoint described above.
If the video generation task status is completed, the response will include the final result — with the generated video URL and additional metadata.
get
Query parameters
generation_idstringRequired
Responses
200
Successfully generated video
application/json
get
/v2/video/generations
200
Successfully generated video
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.
curl -L \
--request POST \
--url 'https://api.aimlapi.com/v2/video/generations' \
--header 'Authorization: Bearer <YOUR_AIMLAPI_KEY>' \
--header 'Content-Type: application/json' \
--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."
}'
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/video/generations"
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.",
"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/video/generations"
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()
// Insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>
const apiKey = "<YOUR_AIMLAPI_KEY>";
const baseUrl = "https://api.aimlapi.com/v2";
const https = require("https");
const { URL } = require("url");
// Creating and sending a video generation task to the server
function generateVideo(callback) {
const data = JSON.stringify({
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.
`,
});
const url = new URL(`${baseUrl}/video/generations`);
const options = {
method: "POST",
headers: {
"Authorization": `Bearer ${apiKey}`,
"Content-Type": "application/json",
"Content-Length": Buffer.byteLength(data)
}
};
const req = https.request(url, options, (res) => {
let body = "";
res.on("data", (chunk) => body += chunk);
res.on("end", () => {
if (res.statusCode >= 400) {
console.error(`Error: ${res.statusCode} - ${body}`);
callback(null);
} else {
const result = JSON.parse(body);
callback(result);
}
});
});
req.on("error", (err) => {
console.error("Request error:", err);
callback(null);
});
req.write(data);
req.end();
}
// Requesting the result of the task from the server using the generation_id
function getVideo(genId, callback) {
const url = new URL(`${baseUrl}/video/generations`);
url.searchParams.append("generation_id", genId);
const options = {
method: "GET",
headers: {
"Authorization": `Bearer ${apiKey}`,
"Content-Type": "application/json"
}
};
const req = https.request(url, options, (res) => {
let body = "";
res.on("data", (chunk) => body += chunk);
res.on("end", () => {
const result = JSON.parse(body);
callback(result);
});
});
req.on("error", (err) => {
console.error("Request error:", err);
callback(null);
});
req.end();
}
// Initiates video generation and checks the status every 10 seconds until completion or timeout
function main() {
generateVideo((genResponse) => {
if (!genResponse || !genResponse.id) {
console.error("No generation ID received.");
return;
}
const genId = genResponse.id;
console.log("Generation ID:", genId);
const timeout = 1000 * 1000; // 1000 sec
const interval = 10 * 1000; // 10 sec
const startTime = Date.now();
const checkStatus = () => {
if (Date.now() - startTime >= timeout) {
console.log("Timeout reached. Stopping.");
return;
}
getVideo(genId, (responseData) => {
if (!responseData) {
console.error("Error: No response from API");
return;
}
const status = responseData.status;
console.log("Status:", status);
if (["waiting", "active", "queued", "generating"].includes(status)) {
console.log("Still waiting... Checking again in 10 seconds.");
setTimeout(checkStatus, interval);
} else {
console.log("Processing complete:\n", responseData);
}
});
};
checkStatus();
});
}
main();
Generation ID: 0a3ca8ba-9af6-41d3-a938-41f762fcedc1:sber-ai/kandinsky5-distill-t2v
Status: queued
Still waiting... Checking again in 10 seconds.
Status: generating
Still waiting... Checking again in 10 seconds.
Status: generating
Still waiting... Checking again in 10 seconds.
Status: generating
Still waiting... Checking again in 10 seconds.
Status: generating
Still waiting... Checking again in 10 seconds.
Status: completed
Processing complete:
{
id: '0a3ca8ba-9af6-41d3-a938-41f762fcedc1:sber-ai/kandinsky5-distill-t2v',
status: 'completed',
video: {
url: 'https://cdn.aimlapi.com/flamingo/files/b/koala/yHNhY22wNAnpCbSqIaV8D_output.mp4'
}
}
