Logo Generator

Fast Inference

REST API

Model Information

Response Time:~20 sec

Status:Active

Version:

0.0.1

Updated:3 days ago

logo-generator

Live Demo

Average runtime: ~20 seconds

Input

Configure model parameters

Prompt

Create a professional logo for Eachlabs Coffee. This is a premium coffee company that specializes in roasted beans and finely ground coffee. The logo design should align with the coffee industry while reflecting elegance, warmth, and refined quality. Incorporate a single, small and stylized coffee bean into the design — subtly, not prominently. Use a sophisticated color palette including mocha brown, muted gold, and soft beige tones. Ensure the typography is elegant and premium — preferably a custom serif font with balanced spacing and graceful curves. The design must be modern yet timeless, memorable, and highly versatile for both digital and print applications. Use clean lines, vector-based styling, and a subtle 3D or embossed look for a luxurious finish. The background should be light-toned and textured, such as warm off-white paper or soft natural stone. Every visual element — color, shape, and font — should feel cohesive, intentional, and high-end.

Output

View generated results

Result

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

Each execution costs $0.09 With $1 you can run this model about 11 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": "logo-generator",
            "version": "0.0.1",
            "input": {
  "aspect_ratio": "1:1",
  "prompt": "your prompt here"
},
            "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: ~20 seconds
Rate limit: 60 requests/minute
Concurrent requests: 10 maximum
Use long-polling to check prediction status until completion

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