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Recraft Clarity Upcale

recraft-clarity-upscale

Designed to make images sharper and cleaner, Recraft Clarity Upscale increases overall quality, making visuals suitable for web use or print-ready materials.

Fast Inference
REST API
Try in ConsoleAPI DocsExamples

Model Information

Response Time~15 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": "recraft-clarity-upscale",
            "version": "0.0.1",
            "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: ~15 seconds
  • Rate limit: 60 requests/minute
  • Concurrent requests: 10 maximum
  • Use long-polling to check prediction status until completion

Overview

The Recraft Clarity Upscale Model is designed to enhance and upscale raster images, increasing their resolution and clarity. By utilizing advanced AI algorithms, it sharpens details and reduces noise, resulting in images suitable for both web and print applications.

Technical Specifications

Model Architecture:

  • Employs deep learning techniques to predict and generate missing details, enhancing image sharpness and clarity

Noise Reduction:

  • Utilizes advanced AI methods to denoise images, preserving essential details while eliminating unwanted artifacts

Key Considerations

  • Image Quality:
    • Higher-quality input images yield better upscaling results.
    • Extremely low-resolution images may not achieve the desired enhancement.
  • File Size:
    • Larger images may require longer processing times.

Legal Information for Recraft Clarity Upscale

By using this model, you agree to:

Tips & Tricks

Optimal Image Selection:

  • Choose images with good lighting and minimal compression artifacts for best results.

Post-Processing:

  • After upscaling, consider additional editing to fine-tune sharpness and contrast according to your specific needs.

Capabilities

Image Enhancement:

  • Improves the clarity and sharpness of images, making them more visually appealing.

Resolution Increase:

  • Upscales images to higher resolutions without significant loss of quality.

What can I use for?

Web Content:

  • Enhance images for websites to ensure they are clear and professional-looking.

Print Materials:

  • Prepare images for print, ensuring they meet the necessary resolution and clarity standards.

Things to be aware of

Different Image Types:

  • Experiment with various image genres, such as portraits, landscapes, and graphics, to see how the Recraft Clarity Upcale enhances different content.

Comparative Analysis:

  • Compare the upscaled image with the original to assess the improvements and understand the Recraft Clarity Upcale's impact.

Limitations

Over-Enhancement:

  • In some cases, the Recraft Clarity Upcale may introduce artifacts if the input image has significant noise or compression issues.

Subjectivity:

  • Perceived improvements can vary based on individual preferences and the specific characteristics of the input image.

Output Format: WEBP

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