Face Enhancer Fast

real-esrgan-a100

Face Enhancer Fast is a lightweight version of the Face Enhancer model for quick and accurate facial detail improvement.

A100 40GB

Fast Inference

REST API

Try in Console API Docs Examples

Model Information

Response Time~62 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": "real-esrgan-a100",
            "version": "0.0.1",
            "input": {
  "image": "your_file.image/jpeg",
  "scale": "4",
  "face_enhance": false
}
        }
    )
    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: ~62 seconds
Rate limit: 60 requests/minute
Concurrent requests: 10 maximum
Use long-polling to check prediction status until completion

Overview

Real-ESRGAN (Enhanced Super-Resolution Generative Adversarial Networks) uses advanced deep learning techniques to upscale low-resolution images. It not only improves the resolution but also restores details, textures, and sharpness, making it ideal for professional photographers, graphic designers, and everyday users.

Technical Specifications

Model Architecture:

Based on ESRGAN (Enhanced Super-Resolution GAN).
Trained on large datasets of diverse images for generalization.

Input Requirements:

Formats: JPEG, PNG, TIFF.
Recommended Resolution: Up to 720p for optimal performance on the 4x upscale model.
Max File Size: 20 MB.

Output:

Formats: JPEG, PNG, TIFF.
Maximum Resolution: Supports 8K output with no visible loss of quality.

Key Considerations

Artifacts in Low-Quality Images:

Overly compressed or noisy images may introduce artifacts during upscaling.

Output Consistency:

Results may vary for complex or heavily edited images.

Tips & Tricks

Maximize Detail Restoration:

Use the scale 4 model for images requiring the most detail recovery.

Image Cropping:

Crop images into smaller sections for faster processing of high-res files.

Enable the "Face Enhance" feature for better facial details in portraits.

Capabilities

Super-Resolution Upscaling:

Enhance images by up to 4x their original resolution without losing quality.

Detail Restoration:

Rebuild lost textures and sharpen edges for a natural look.

What can I use for?

Photography Enhancement:

Perfect for improving resolution and details in professional or personal photos.

Graphic Design:

Enhance assets like icons, logos, or textures for high-res displays.

Video Frame Upscaling:

Use upscaled images as keyframes in video editing or restoration projects.

Art Preservation:

Restore and upscale digital or scanned artworks.

E-Commerce and Marketing:

Improve product images for websites or advertisements.

Restoring old or low-resolution photos.

Enhancing image quality for professional use.

Upscaling images for printing or large-format displays.

Improving facial details in portrait photography.

Things to be aware of

Upscale Old Photos:

Revive family photos by enhancing resolution and restoring lost details.

Improve Social Media Images:

Transform compressed images into sharp, professional-looking visuals.

Enhance Game Textures:

Use for modding or improving in-game textures in older titles.

Create Print-Ready Images:

Prepare low-res digital images for high-quality printing.

Test on Unique Styles:

Try upscaling cartoon, anime, or stylized images to explore model versatility.

Restoration: Upload a vintage photo and upscale it using a scale of 2x.

Face Enhancement: Enable the face_enhance option for high-quality portraits.

Creative Editing: Experiment with different scale values to achieve desired effects.

Limitations

Texture Consistency:

Fine textures like grass or water may occasionally look unnatural after enhancement.

Output Format: PNG

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