Omni Zero Couple

omni-zero-couples

Omni Zero Couple is a model designed for generating high-quality couple-themed realistic images.

A40 48GB

Fast Inference

REST API

Try in Console API Docs Examples

Model Information

Response Time~12 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": "omni-zero-couples",
            "version": "0.0.1",
            "input": {
  "seed": "-1",
  "prompt": "Cinematic still photo of a couple. emotional, harmonious, vignette, 4k epic detailed, shot on kodak, 35mm photo, sharp focus, high budget, cinemascope, moody, epic, gorgeous, film grain, grainy",
  "base_image": "your_file.image/jpeg",
  "depth_image": "your_file.image/jpeg",
  "style_image": "your_file.image/jpeg",
  "guidance_scale": "3",
  "negative_prompt": "anime, cartoon, graphic, (blur, blurry, bokeh), text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
  "number_of_steps": "10",
  "identity_image_1": "your_file.image/jpeg",
  "identity_image_2": "your_file.image/jpeg",
  "number_of_images": "1",
  "mask_guidance_end": "1",
  "base_image_strength": "0.2",
  "mask_guidance_start": "0",
  "depth_image_strength": "0.2",
  "style_image_strength": "1",
  "identity_image_strength_1": "1",
  "identity_image_strength_2": "1"
}
        }
    )
    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: ~12 seconds
Rate limit: 60 requests/minute
Concurrent requests: 10 maximum
Use long-polling to check prediction status until completion

Overview

Omni-Zero-Couples is a diffusion-based text-to-image model that enables the creation of high-quality, stylized portraits of couples without the need for prior training on specific data. By leveraging advanced diffusion techniques, it allows for seamless blending of styles and identities, facilitating the generation of unique and personalized couple portraits with minimal effort. This model builds upon the foundation of the Omni-Zero model, which focused on single-subject portraits, expanding its capabilities to accommodate couples.

Technical Specifications

Model Architecture:

Built on diffusion-based techniques optimized for style and subject blending.
Fine-tuned for zero-shot generalization across diverse artistic genres.

Training Data:

Trained on a diverse dataset containing a mix of portrait, artistic, and stylized images, ensuring wide applicability.

Key Considerations

Input Quality: High-resolution and relevant images enhance output quality.

Processing Time: Complex prompts and higher step counts may increase generation time.

Tips & Tricks

Style Exploration: Use various style images to create portraits in different artistic genres, such as Impressionism or Pop Art.

Identity Blending: Combine features from multiple identity images to produce composite portraits.

Negative Prompts: Specify elements to avoid (e.g., "no background clutter") to refine outputs.

Detailed Prompts: Use clear and precise language in prompts for more accurate results. For example, instead of "a couple in love," try "a romantic couple in a winter forest, Impressionist art style."

Experiment with Parameters: Adjust parameters like guidance scale, image strength, and number of steps to refine outputs.

High-Quality Inputs: If you use reference images, ensure they are of high resolution for better results.

Capabilities

Zero-Shot Generation: Creates stylized portraits without prior training on specific data.

Customization: Allows detailed personalization through prompts and images.

High-Quality Outputs: Produces detailed and aesthetically pleasing images suitable for various applications.

What can I use for?

Artistic Projects: Generate unique couple portraits for digital art, prints, or gifts.

Content Creation: Enhance blogs, social media, or publications with custom imagery.

Design Inspiration: Explore different styles and compositions for creative projects.

Personal Use: Create personalized portraits for special occasions or keepsakes.

Things to be aware of

Historical Styles: Generate portraits in the style of different art movements, such as Baroque or Art Nouveau.

Fantasy Themes: Create imaginative portraits with mythical or sci-fi elements.

Cultural Attire: Produce images featuring traditional clothing from various cultures.

Seasonal Scenes: Design portraits set in different seasons or festive environments.

Limitations

Niche Styles: May struggle with highly specific or obscure artistic styles.

Identity Accuracy: The resemblance to provided identity images might vary, especially with low-quality inputs.

Computational Demand: High-resolution outputs and numerous steps require significant processing power.

Output Format: JPG

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