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Omni Zero

omni-zero

Omni Zero is an AI model for generating high-quality, realistic images using advanced algorithms.

L40S 45GB
Fast Inference
REST API
Try in ConsoleAPI DocsExamples

Model Information

Response Time~12 sec
StatusActive
Version
0.0.1
Updated30 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",
            "version": "0.0.1",
            "input": {
  "seed": "42",
  "image": "your_file.image/jpeg",
  "model": "omni-zero",
  "prompt": "A person",
  "depth_image": "your_file.image/jpeg",
  "style_image": "your_file.image/jpeg",
  "depth_strength": "0.5",
  "guidance_scale": "3",
  "identity_image": "your_file.image/jpeg",
  "image_strength": "0.15",
  "style_strength": "1",
  "negative_prompt": "blurry, out of focus",
  "number_of_steps": "10",
  "number_of_images": "1",
  "composition_image": "your_file.image/jpeg",
  "identity_strength": "1",
  "composition_strength": "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

The Omni-Zero model is a versatile and advanced AI designed for generating, enhancing, and transforming images. It leverages a blend of techniques, enabling users to achieve highly detailed, stylistically coherent, and personalized outputs. With customizable parameters, the model provides flexibility for a variety of creative and professional use cases.

Technical Specifications

  • Guidance Scaling: Allows fine control over the influence of the prompt.
  • Image Strength Integration: Enables blending of input images with stylistic or compositional elements.
  • Identity Preservation: Retains core features of an input image while applying desired transformations.
  • Step-Based Refinement: Provides iterative image refinement for higher fidelity and detail.
  • Multi-Layered Inputs: Supports depth, style, and compositional layers for complex visual outputs.

Key Considerations

Computational Load: Higher values for number_of_steps or guidance_scale increase processing time.

Input Consistency: Ensure input images are of high resolution and quality to avoid subpar results.

Strength Overlap: Avoid setting multiple strength parameters (e.g., style_strength, composition_strength) to maximum simultaneously, as this can cause unpredictable outcomes.

Realism vs. Creativity: Choose the appropriate model variant to match your goal, balancing realism and creative abstraction.

Tips & Tricks

Input Optimization for Omni Zero

  • Seed: Use a fixed seed for reproducible results. Randomize it for diverse outputs.
  • Model Selection:
    • omni-zero: Ideal for abstract or creative projects.
    • omni-zero-realism: Best for photorealistic outputs.
  • Prompt Crafting:
    • Be concise and specific to guide the model effectively.
    • Avoid contradictory terms in prompts and negative prompts.
  • Negative Prompt:
    • Specify artifacts or elements to exclude (e.g., "blurred edges," "distorted shapes").

Strength Parameters for Omni Zero

  • Guidance Scale: Keep within the range of 7-10 for balanced creativity and prompt adherence.
  • Image Strength: For transformations, values around 0.6-0.8 yield noticeable but controlled alterations.
  • Composition Strength: Ideal range: 0.5-0.7 for blending composition images without overpowering the base.
  • Style Strength: Stay within 0.3-0.6 for subtle stylistic effects, avoiding oversaturation.
  • Identity Strength: Use 0.7-1 to retain key identity features while applying transformations.
  • Depth Strength: Values of 0.4-0.6 work well for adding depth while maintaining clarity.

Iterative Refinement

  • Steps: Start with 20-30 steps for a balance of quality and speed. Increase to 40-50 for detailed outputs.

Capabilities

  • Creative Outputs: Generate artistic and abstract imagery.
  • Photorealistic Transformations: Create lifelike images with realistic textures and details.
  • Customizable Styling: Apply diverse styles, compositions, and identity elements to base images.
  • Depth Integration: Incorporate depth maps for advanced 3D-like effects.

What can I use for?

  • Visual Design: Generate concept art, posters, or illustrations.
  • Photo Enhancements: Apply stylistic filters or refine photo quality.
  • Content Creation: Produce unique visuals for marketing or storytelling.

Things to be aware of

Abstract Art: Use omni-zero with high style_strength for vibrant and surreal outputs.

Realistic Portraits: Select omni-zero-realism, set identity_strength to 0.9, and use detailed prompts.

Stylized Compositions: Combine composition_image and style_image with moderate strength values (0.5-0.7) for balanced effects.

Depth Enhancement: Add a depth_image with depth_strength set to 0.4-0.6 for enriched spatial dynamics.

Limitations

  • Overlapping Strengths: Simultaneously high values across multiple strength parameters may lead to chaotic results.
  • Complex Prompts: Overly detailed prompts can confuse the model, reducing output quality.
  • Photorealism Limits: Achieving extreme photorealism may require fine-tuning prompts and inputs.

Output Format: JPG

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