Couple Image Generation

eachlabs-couple

Couple Image Generation by Eachlabs is an image model that generates a couple using two images and a prompt.

Fast Inference

REST API

Try in Console API Docs Examples

Model Information

Response Time~40 sec

StatusActive

Version

0.0.1

Updated3 days ago

Live Demo

Average runtime: ~40 seconds

Input

Configure model parameters

Prompt

enter a prompt

Enter your prompt

Face Image 2

Enter second face image to swap.

File upload is currently disabled

Face Image 1

Enter a face image.

File upload is currently disabled

Output

View generated results

Result

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

Click to get incredible results!

Each execution costs $0.2 With $1 you can run this model about 5 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": "eachlabs-couple",
            "version": "0.0.1",
            "input": {
  "prompt": "a couple in cristmas market and looking at the camera",
  "reference_image": "https://storage.googleapis.com/magicpoint/models/women.png",
  "input_image": "https://storage.googleapis.com/magicpoint/models/man.png"
}
        }
    )
    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

Overview

Couple Image Generation is an image-to-image model designed to merge two separate face images into a single, cohesive image. This model ensures a seamless blend while maintaining the unique characteristics of each individual. The process leverages deep learning techniques to create natural and aesthetically pleasing results.

Technical Specifications

Face Recognition & Blending: Advanced feature mapping ensures that each individual's distinct features are preserved while blending the images smoothly.
Resolution Optimization: The model processes images at a high resolution to maintain clarity and detail.
Adaptive Color Matching: The color tones of both images are adjusted automatically to ensure a natural-looking composition.

Key Considerations

The model is designed for merging two human faces; results may not be reliable for non-human objects.
Large variations in lighting, angles, or image quality between the two input images can affect the final result.
Some artifacts may appear if the input images contain extreme expressions, accessories, or occlusions.
Ethical considerations should be taken into account when using this model, ensuring responsible usage of generated images.

Tips & Tricks

prompt: Provide a concise textual description to influence the style of the generated image.
reference_image: This should be a high-quality face image with good lighting and clear facial details.
input_image: The main face image to be merged with the reference image; ensure that it is well-captured and similar in angle to the reference.

Additional Tips

Using images taken in similar lighting conditions enhances the blending quality.
Avoid extreme close-ups or low-resolution images to prevent loss of detail.
If merging images from different sources, pre-editing to match their brightness and contrast may help in achieving a more natural look.

Capabilities

Seamlessly merges two separate face images into a single, natural-looking image.
Retains individual facial characteristics while ensuring smooth transitions.
Adapts color tones and lighting conditions for better visual consistency.
Works with a variety of human face types and expressions.

What can I use for?

Couple Portraits: Merge two separate images into one for creative and sentimental portraits.
Virtual Reunions: Create images of individuals who were not photographed together.
Photo Restoration & Editing: Blend old or damaged images with newer ones for restoration purposes.
Art & Visualization: Generate artistic compositions by merging different facial elements.

Things to be aware of

Experiment with different face angles to see how well the model blends unique features.
Use images with different lighting conditions and compare the results.
Try adding a textual prompt to slightly influence the final image style.
Merge images of family members to explore genetic similarities in a single portrait.

Limitations

Struggles with extreme facial angles, occlusions, and heavily distorted images.
May not always perfectly align features when there are significant differences in facial structure.
Performance may vary based on skin tones, lighting conditions, and image resolutions.

Output Format: JPG

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