","type":"http","description":"Bearer key","in":"header"}}},"paths":{"/v2/video/generations":{"get":{"operationId":"_v2_video_generations","parameters":[{"name":"generation_id","required":true,"in":"query","schema":{"type":"string"}}],"responses":{"200":{"content":{"application/json":{"schema":{"type":"object","properties":{"id":{"type":"string","description":"The ID of the generated video."},"status":{"type":"string","enum":["queued","generating","completed","error"],"description":"The current status of the generation task."},"video":{"type":"object","nullable":true,"properties":{"url":{"type":"string","format":"uri","description":"The URL where the file can be downloaded from."}},"required":["url"]},"error":{"type":"object","nullable":true,"properties":{"name":{"type":"string"},"message":{"type":"string"}},"required":["name","message"],"description":"Description of the error, if any."},"meta":{"type":"object","nullable":true,"properties":{"usage":{"type":"object","nullable":true,"properties":{"credits_used":{"type":"number","description":"The number of tokens consumed during generation."}},"required":["credits_used"]}},"description":"Additional details about the generation."}},"required":["id","status"]}}}}}}}}}
```
## Code Example
The code below creates a video generation task, then automatically polls the server every **15** seconds until it finally receives the video URL.
Two reference videos are supplied via URLs, and the prompt defines how the model should use them.
{% tabs %}
{% tab title="Python" %}
import requests
import time
# Insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>:
api_key = "<YOUR_AIMLAPI_KEY>"
base_url = "https://api.aimlapi.com/v2"
# Creating and sending a video generation task to the server
def generate_video():
url = f"{base_url}/video/generations"
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
payload = {
"model": "alibaba/wan-2-7-r2v",
"prompt": '''
Use the woman from the second reference video as Mona Lisa — keep her face, clothing, colors, lighting, camera angle, and background exactly as in the second reference video.
Do not replace her with a different person and do not change the environment.
Take the raccoon only from the first reference video — keep the same fur pattern, colors, proportions, and appearance.
Do not generate a different raccoon.
Place the raccoon gently in Mona Lisa’s arms, as if he is her pet. She softly pets the raccoon. The raccoon affectionately licks her face, and Mona Lisa reacts with a warm, joyful laugh.
The audio must be in English. Mona Lisa says the short line:
"Oh, you sweet little one!"
Synchronize her lip movement to this line only. Do not generate Chinese speech.
Keep the visual style, motion, framing, and atmosphere realistic and consistent with the second reference video.
''',
"video_urls":[
"https://raw.githubusercontent.com/aimlapi/api-docs/main/reference-files/racoon-in-the-forest.mp4",
"https://raw.githubusercontent.com/aimlapi/api-docs/main/reference-files/monalisa.mp4"
],
# "duration": "5",
}
response = requests.post(url, json=payload, 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}/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():
# Running video generation and getting a task id
gen_response = generate_video()
print(gen_response)
gen_id = gen_response.get("id")
print("Generation ID: ", gen_id)
# Try to retrieve the video from the server every 15 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")
if status in ["queued", "generating"]:
print(f"Status: {status}. Checking again in 15 seconds.")
time.sleep(15)
else:
print("Processing complete:\n", response_data)
return response_data
print("Timeout reached. Stopping.")
return None
if __name__ == "__main__":
main()
{% endtab %}
{% tab title="JS" %}
{% code overflow="wrap" %}
```javascript
const https = require("https");
const { URL } = require("url");
// Replace with your actual AI/ML API key
const apiKey = "";
const baseUrl = "https://api.aimlapi.com/v2";
// Creating and sending a video generation task to the server
function generateVideo(callback) {
const data = JSON.stringify({
model: "alibaba/wan-2-7-r2v",
prompt: `Use the woman from the second reference video as Mona Lisa — keep her face, clothing, colors, lighting, camera angle, and background exactly as in the second reference video.
Do not replace her with a different person and do not change the environment.
Take the raccoon only from the first reference video — keep the same fur pattern, colors, proportions, and appearance.
Do not generate a different raccoon.
Place the raccoon gently in Mona Lisa’s arms, as if he is her pet. She softly pets the raccoon. The raccoon affectionately licks her face, and Mona Lisa reacts with a warm, joyful laugh.
The audio must be in English. Mona Lisa says the short line:
"Oh, you sweet little one!"
Synchronize her lip movement to this line only. Do not generate Chinese speech.
Keep the visual style, motion, framing, and atmosphere realistic and consistent with the second reference video.`,
video_urls: [
"https://raw.githubusercontent.com/aimlapi/api-docs/main/reference-files/racoon-in-the-forest.mp4",
"https://raw.githubusercontent.com/aimlapi/api-docs/main/reference-files/monalisa.mp4"
]
};
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} - ${body}`);
callback(null);
} else {
const parsed = JSON.parse(body);
callback(parsed);
}
});
});
req.on("error", (err) => console.error("Request error:", err));
req.write(data);
req.end();
}
// Requesting the result of the task from the server using the generation_id
function getVideo(genId, callback) {
const url = new URL(`${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 parsed = JSON.parse(body);
callback(parsed);
});
});
req.on("error", (err) => console.error("Request error:", err));
req.end();
}
// Initiates video generation and checks the status every 15 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 = 15 * 1000; // 15 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;
if (["queued", "generating"].includes(status)) {
console.log(`Status: ${status}. Checking again in 15 seconds.`);
setTimeout(checkStatus, interval);
} else {
console.log("Processing complete:\n", responseData);
}
});
};
checkStatus();
})
}
main();
```
{% endcode %}
{% endtab %}
{% endtabs %}
Response
{% code overflow="wrap" %}
```json5
{'id': 'B4s9SMzh12gKpK9EGuzez', 'status': 'queued', 'meta': {'usage': {'credits_used': 3900000, 'usd_spent': 1.95}}}
Generation ID: B4s9SMzh12gKpK9EGuzez
Status: queued. Checking again in 15 seconds.
Status: generating. Checking again in 15 seconds.
Status: generating. Checking again in 15 seconds.
Status: generating. Checking again in 15 seconds.
Status: generating. Checking again in 15 seconds.
...
Status: generating. Checking again in 15 seconds.
Status: generating. Checking again in 15 seconds.
Status: generating. Checking again in 15 seconds.
Status: generating. Checking again in 15 seconds.
Processing complete:
{'id': 'B4s9SMzh12gKpK9EGuzez', 'status': 'completed', 'video': {'url': 'https://cdn.aimlapi.com/generations/alligator/1778021564136-f9fa8fdc-17a9-4b89-91f7-27eacc440a87.mp4'}}
```
{% endcode %}
**Processing time**: \~ 8 min 42 sec.
**Generated video** (1920x1080, with sound):
{% embed url="" %}
---
# Agent Instructions: Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter:
```
GET https://docs.aimlapi.com/api-references/video-models/alibaba-cloud/wan-2.7-reference-to-video.md?ask=
```
The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.