Baby Generator

each-baby

Baby Generator creates baby images based on input images as father and mother, offering realistic predictions.

L40S 45GB

Fast Inference

REST API

Try in Console API Docs Examples

Model Information

Response Time~13 sec

StatusActive

Version

0.0.1

Updated2 months ago

Live Demo

Average runtime: ~13 seconds

Input

Configure model parameters

Image2

Man's image

File upload is currently disabled

JPEGPNGJPGWEBP

gender

An enumeration.

Image

Woman's image

File upload is currently disabled

JPEGPNGJPGWEBP

Output

View generated results

Result

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

Cost is calculated based on execution time.The model is charged at $0.00108 per second. With a $1 budget, you can run this model approximately 71 times, assuming an average execution time of 13 seconds per run.

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": "each-baby",
            "version": "0.0.1",
            "input": {
  "seed": 0,
  "image": "your_file.image/jpeg",
  "steps": 25,
  "width": 512,
  "gender": "boy",
  "height": 728,
  "image2": "your_file.image/jpeg"
},
            "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: ~13 seconds
Rate limit: 60 requests/minute
Concurrent requests: 10 maximum
Use long-polling to check prediction status until completion

Overview

The Baby Generator model uses Realistic Vision v5.1 to generate potential baby images from photos of each parent. This model helps couples imagine how their future children might look.

Technical Specifications

Supports customization to cater to diverse artistic preferences and creative needs.

Key Considerations

Creative Purpose: The model is a tool for artistic and exploratory use, not for predictive or diagnostic applications.

Respect Privacy: Always ensure ethical use, especially when working with images that may involve personal or sensitive content.

Understand Limitations: While highly sophisticated, the model has boundaries in realism and does not account for complex natural variations.

Tips & Tricks

For better results, the image input should be a woman's photo, and the image2 input should be a man's photo.

Capabilities

Excels at creating highly realistic and imaginative visualizations of potential offspring from provided parental photos. Powered by Realistic Vision v5.1, it seamlessly blends and merges the unique features of both parents to generate compelling and lifelike baby images.

What can I use for?

Offers a unique way to explore genetic inheritance and visualize potential future children. It can be especially meaningful for couples planning a family, providing a glimpse into their child's possible appearance. Beyond this, the model can inspire creative projects, such as artistic interpretations or exploring different genetic combinations.

Things to be aware of

One remarkable feature of this model is its ability to generate a variety of baby images by fine-tuning input parameters such as seed values, step count, and preferred gender. Exploring these settings can reveal unique variations and captivating interpretations of parental characteristics. Additionally, integrating this model with other AI tools like Real-ESRGAN or Edge-of-Realism v2.0 can result in even more refined and visually striking baby representations.

Limitations

The model can create a child’s photo (boy or girl) based on parent photos, but the result might not always match your expectations perfectly. You may need to try a few times to get the desired outcome.

Output Format: PNG

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