Vidu 1.5 Text to Video

Fast Inference

REST API

Model Information

Response Time:~40 sec

Status:Active

Version:

0.0.1

Updated:2 days ago

vidu-1-5-text-to-video

Live Demo

Average runtime: ~40 seconds

Input

Configure model parameters

Prompt

Text prompt,A textual description for video generation, with a maximum length of 1500 characters

a lone figure walking through a neon-lit cyberpunk city at night, rain falling steadily, reflections of colorful lights on the wet pavement, flying cars zooming overhead, tall buildings with glowing holograms and billboards, steam rising from street vents, people with umbrellas passing by, camera slowly panning to follow the character from behind, moody and atmospheric, cinematic lighting, ultra-realistic, Blade Runner aesthetic

Output

View generated results

Result

Preview, share or download your results with a single click.

Each execution costs $0.05 With $1 you can run this model about 20 times.

API Reference

View Full Documentation

Prerequisites

Create an API Key from the Eachlabs Console
Install the required dependencies for your chosen language (e.g., requests for Python)

API Integration Steps

1. Create a Prediction

Send a POST request to create a new prediction. This will return a prediction ID that you'll use to check the result. The request should include your model inputs and API key.

import requests
import time

API_KEY = "YOUR_API_KEY"  # Replace with your API key
HEADERS = {
    "X-API-Key": API_KEY,
    "Content-Type": "application/json"
}

def create_prediction():
    response = requests.post(
        "https://api.eachlabs.ai/v1/prediction/",
        headers=HEADERS,
        json={
            "model": "vidu-1-5-text-to-video",
            "version": "0.0.1",
            "input": {
  "resolution": "your resolution here",
  "bgm": false,
  "movement_amplitude": "auto",
  "aspect_ratio": "16:9",
  "seed": 0,
  "duration": 4,
  "prompt": "your prompt here",
  "style": "general"
},
            "webhook_url": ""
        }
    )
    prediction = response.json()
    
    if prediction["status"] != "success":
        raise Exception(f"Prediction failed: {prediction}")
    
    return prediction["predictionID"]

2. Get Prediction Result

Poll the prediction endpoint with the prediction ID until the result is ready. The API uses long-polling, so you'll need to repeatedly check until you receive a success status.

def get_prediction(prediction_id):
    while True:
        result = requests.get(
            f"https://api.eachlabs.ai/v1/prediction/{prediction_id}",
            headers=HEADERS
        ).json()
        
        if result["status"] == "success":
            return result
        elif result["status"] == "error":
            raise Exception(f"Prediction failed: {result}")
        
        time.sleep(1)  # Wait before polling again

3. Complete Example

Here's a complete example that puts it all together, including error handling and result processing. This shows how to create a prediction and wait for the result in a production environment.

try:
    # Create prediction
    prediction_id = create_prediction()
    print(f"Prediction created: {prediction_id}")
    
    # Get result
    result = get_prediction(prediction_id)
    print(f"Output URL: {result['output']}")
    print(f"Processing time: {result['metrics']['predict_time']}s")
except Exception as e:
    print(f"Error: {e}")

Additional Information

The API uses a two-step process: create prediction and poll for results
Response time: ~40 seconds
Rate limit: 60 requests/minute
Concurrent requests: 10 maximum
Use long-polling to check prediction status until completion

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