Omni Zero
omni-zero
Omni Zero is an AI model for generating high-quality, realistic images using advanced algorithms.
Model Information
Input
Configure model parameters
Output
View generated results
Result
Preview, share or download your results with a single click.
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 requestsimport timeAPI_KEY = "YOUR_API_KEY" # Replace with your API keyHEADERS = {"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 resultelif 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 predictionprediction_id = create_prediction()print(f"Prediction created: {prediction_id}")# Get resultresult = 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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