gen3a_turbo
Overview
An advanced AI model designed for converting images into high-quality videos. It allows users to generate dynamic video content with smooth motion and detailed textures from still images or text prompts, significantly enhancing creative workflows in multimedia production.
Each generation costs 525 000 AI/ML Tokens.
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
API Schemas
Video Generation
You can generate a video using this API. In the basic setup, you need only an image URL and the aspect ratio of the desired result. The model can detect and use the aspect ratio from the input image, but for correct operation in this case, the image's width-to-height ratio must be between 0.5 and 2.
The text description of the scene, subject, or action to generate in the video.
A HTTPS URL or data URI containing an encoded image to be used as the first frame of the generated video.
A HTTPS URL or data URI containing an encoded image to be used as the last frame of the generated video.
The length of the output video in seconds.
The aspect ratio of the generated video.
Varying the seed integer is a way to get different results for the same other request parameters. Using the same value for an identical request will produce similar results. If unspecified, a random number is chosen.
POST /v2/generate/video/runway/generation HTTP/1.1
Host: api.aimlapi.com
Authorization: Bearer <YOUR_AIMLAPI_KEY>
Content-Type: application/json
Accept: */*
Content-Length: 149
{
"model": "gen3a_turbo",
"prompt": "text",
"image_url": "https://example.com",
"last_image_url": "https://example.com",
"duration": 5,
"ratio": "16:9",
"seed": 1
}{
"id": "a12b3456-7c89-0de1-23f4-g567d584f98d",
"status": "queued"
}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 complete, the response will include the final result — with the generated video URL and additional metadata.
Generation ID
a12b3456-7c89-0de1-23f4-g567d584f98dGET /v2/generate/video/runway/generation HTTP/1.1
Host: api.aimlapi.com
Authorization: Bearer <YOUR_AIMLAPI_KEY>
Accept: */*
{
"id": "a12b3456-7c89-0de1-23f4-g567d584f98d",
"status": "queued",
"video": [
"https://example.com"
]
}Example
import requests
def main():
url = "https://api.aimlapi.com/v2/generate/video/runway/generation"
payload = {
"model": "gen3a_turbo",
"prompt": "A jellyfish in the ocean",
"ratio": "16:9",
"image_url": "https://upload.wikimedia.org/wikipedia/commons/3/35/Maldivesfish2.jpg",
}
headers = {"Authorization": "Bearer <YOUR_AIMLAPI_KEY>", "Content-Type": "application/json"}
response = requests.post(url, json=payload, headers=headers)
print("Generation:", response.json())
if __name__ == "__main__":
main()
Full Example: Generating and Retrieving the Video From the Server
Let’s take a beautiful but somewhat barren mountain landscape:
Then ask Gen4 Turbo to populate it with an epic reptilian creature using the following 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"
We combine both methods above in one program: first it sends a video generation request to the server, then it checks for results every 10 seconds.
Don’t forget to replace <YOUR_AIMLAPI_KEY> with your actual AI/ML API key from your API Key management page — in both places in the code!
import time
import requests
# Creating and sending a video generation task to the server (returns a generation ID)
def generate_video():
url = "https://api.aimlapi.com/v2/generate/video/runway/generation"
payload = {
"model": "gen3a_turbo",
"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",
"ratio": "16:9",
"image_url": "https://upload.wikimedia.org/wikipedia/commons/thumb/6/68/Liebener_Spitze_SW.JPG/1280px-Liebener_Spitze_SW.JPG",
}
# Insert your AI/ML API key instead of <YOUR_AIMLAPI_KEY>:
headers = {"Authorization": "Bearer <YOUR_AIMLAPI_KEY>", "Content-Type": "application/json"}
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()
print("Generation:", response_data)
return response_data
# Requesting the result of the generation task from the server using the generation_id:
def retrieve_video(gen_id):
url = "https://api.aimlapi.com/v2/generate/video/runway/generation"
params = {
"generation_id": gen_id,
}
# Insert your AI/ML API key instead of <YOUR_AIMLAPI_KEY>:
headers = {"Authorization": "Bearer <YOUR_AIMLAPI_KEY>", "Content-Type": "application/json"}
response = requests.get(url, params=params, headers=headers)
return response.json()
# This is the main function of the program. From here, we sequentially call the video generation and then repeatedly request the result from the server every 10 seconds:
def main():
generation_response = generate_video()
gen_id = generation_response.get("id")
if gen_id:
start_time = time.time()
timeout = 600
while time.time() - start_time < timeout:
response_data = retrieve_video(gen_id)
if response_data is None:
print("Error: No response from API")
break
status = response_data.get("status")
if status == "generating" or status == "queued" or status == "waiting":
print("Still waiting... Checking again in 10 seconds.")
time.sleep(10)
else:
print("Generation complete:/n", response_data)
return response_data
print("Timeout reached. Stopping.")
return None
if __name__ == "__main__":
main()
The following video was generated by running the code example above. Processing time: ~25 sec. You may also check out the original video in 1280×720 resolution.
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