Image Upscaler by Each AI

each-upscaler

Image Upscaler by Each AI improves image resolution and sharpness without losing detail, ideal for professional use.

A100 40GB

Fast Inference

REST API

Try in Console API Docs Examples

Model Information

Response Time~53 sec

StatusActive

Version

0.0.1

Updatedabout 1 month ago

Live Demo

Average runtime: ~53 seconds

Input

Configure model parameters

Prompt

masterpiece, best quality, highres, <lora:more_details:0.5> <lora:SDXLrender_v2.0:1>

Negative Prompt

(worst quality, low quality, normal quality:2) JuggernautNegative-neg

output_format

An enumeration.

Image

input 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 $0.001265 per second. With a $1 budget, you can run this model approximately 14 times, assuming an average execution time of 53 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-upscaler",
            "version": "0.0.1",
            "input": {
  "mask": "your_file.image/jpeg",
  "seed": 1337,
  "image": "your_file.image/jpeg",
  "prompt": "masterpiece, best quality, highres, <lora:more_details:0.5> <lora:SDXLrender_v2.0:1>",
  "dynamic": 6,
  "handfix": "disabled",
  "pattern": false,
  "sharpen": 0,
  "sd_model": "juggernaut_reborn.safetensors [338b85bc4f]",
  "scheduler": "DPM++ 3M SDE Karras",
  "creativity": 0.35,
  "lora_links": "your lora links here",
  "downscaling": false,
  "resemblance": 0.6,
  "scale_factor": 2,
  "tiling_width": 112,
  "output_format": "png",
  "tiling_height": 144,
  "custom_sd_model": "your custom sd model here",
  "negative_prompt": "(worst quality, low quality, normal quality:2) JuggernautNegative-neg",
  "num_inference_steps": 18,
  "downscaling_resolution": 768
},
            "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: ~53 seconds
Rate limit: 60 requests/minute
Concurrent requests: 10 maximum
Use long-polling to check prediction status until completion

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