Seedance V1 Pro Text to Video

Fast Inference

REST API

Model Information

Response Time:~80 sec

Status:Active

Version:

0.0.1

Updated:3 days ago

seedance-v1-pro-text-to-video

Live Demo

Average runtime: ~80 seconds

Input

Configure model parameters

Prompt

The text prompt used to generate the video

A golden retriever puppy runs joyfully through a sunlit forest trail during early morning. The camera follows the puppy with smooth, low-angle tracking shots as it leaps over roots and splashes through puddles. Dew sparkles on the grass, leaves fall gently from the trees, and rays of sunlight stream through the forest canopy. The puppy occasionally glances at the camera, tongue out, full of life. The camera spins gently around the dog as it stops in a clearing, breathing excitedly. Every detail — fur motion, water droplets, lighting shifts — is ultra-realistic and emotionally engaging. Natural forest sounds enhance the immersive feel.

Resolution

Video resolution - 480p for faster generation, 1080p for higher quality

Output

View generated results

Result

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

Each execution costs $0.74 With $1 you can run this model about 1 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": "seedance-v1-pro-text-to-video",
            "version": "0.0.1",
            "input": {
  "seed": 0,
  "camera_fixed": false,
  "duration": 5,
  "aspect_ratio": "16:9",
  "resolution": "1080p",
  "prompt": "your prompt here"
},
            "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: ~80 seconds
Rate limit: 60 requests/minute
Concurrent requests: 10 maximum
Use long-polling to check prediction status until completion

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