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Face Swap

face-swap-new

Face Swap model seamlessly exchanges faces in photos or videos, delivering realistic and high-quality results.

Fast Inference
REST API
Try in ConsoleAPI DocsExamples

Model Information

Response Time~38 sec
StatusActive
Version
0.0.1
Updated8 days ago
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": "face-swap-new",
            "version": "0.0.1",
            "input": {
  "swap_image": "your_file.image/jpeg",
  "input_image": "your_file.image/jpeg"
}
        }
    )
    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: ~38 seconds
  • Rate limit: 60 requests/minute
  • Concurrent requests: 10 maximum
  • Use long-polling to check prediction status until completion

Overview

AI Face Swap tools are advanced applications that utilize artificial intelligence to seamlessly exchange faces between images. These tools analyze facial features and expressions to produce realistic and convincing face swaps, finding applications in entertainment, digital art, and content creation.

Technical Specifications

Model Architecture

  • Features robust algorithms for handling diverse facial structures and expressions.

Resolution Support:

  • Supports high-resolution images up to 2048x2048 pixels for detailed outputs.

Compatibility:

  • Works with common image formats such as JPG and PNG.

Key Considerations

Ensure the input images are of high resolution and well-lit for better results.

Validate the format  input images before running the model.

Check that the faces in the input images are clearly visible and unobstructed.

Perform basic preprocessing, such as cropping or resizing images if necessary.

Tips & Tricks

Use images with similar lighting conditions for a more natural swap.

Test with different face angles to achieve the desired outcome.

Capabilities

Advanced face detection and alignment for realistic swaps.

Handles various face angles, lighting conditions, and skin tones.

Preserves image quality during the face-swapping process.

Optimized for single-face manipulations in high-resolution images.

What can I use for?

Entertainment: Create humorous or creative content by swapping faces in photos.


Digital Art: Incorporate face swapping into artistic projects to explore new creative possibilities.


Content Creation: Enhance marketing materials or social media posts with engaging face-swapped images


Creating fun and realistic face swaps for personal or professional projects.


Generating personalized content for social media or marketing.


Experimenting with creative applications in design and photography.

Things to be aware of

Historical Reimaginings: Swap modern faces onto historical portraits for educational or entertainment purposes.


Character Mashups: Combine faces of different fictional characters to create unique hybrids.


Personalized Greetings: Create customized greeting cards featuring face swaps of friends or family members.

Limitations

Maximum file size for each image is limited to 10 MB.

The quality of the output depends on the clarity and lighting of the input images.

The model is optimized for single-face swapping and may not work accurately on images with multiple faces.
Output Format: JPG

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