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Flux 1.1 Pro Ultra

flux-1.1-pro-ultra

Experience the ultimate AI performance with Flux 1.1 Pro Ultra, designed for precision and power.

Partner Model
Fast Inference
REST API
Try in ConsoleAPI DocsExamples

Model Information

Response Time~13 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": "flux-1.1-pro-ultra",
            "version": "0.0.1",
            "input": {
  "image_prompt_strength": "0.1",
  "raw": false,
  "seed": null,
  "prompt": "your prompt here",
  "output_format": "jpg",
  "safety_tolerance": "2",
  "aspect_ratio": "1:1",
  "image_prompt": "your_file.jpeg"
}
        }
    )
    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: ~13 seconds
  • Rate limit: 60 requests/minute
  • Concurrent requests: 10 maximum
  • Use long-polling to check prediction status until completion

Overview

Flux 1.1 Pro Ultra is designed for generating high-quality images based on detailed textual and visual prompts. With a focus on flexibility and precision, it supports advanced customization through adjustable parameters. Whether you're creating artistic visuals, concept art, or professional imagery, this model provides the model to bring your ideas to life.

Technical Specifications

  • Text-to-Image and Image-to-Image Capability: Combines textual and visual prompts to create stunning visuals.
  • Customizable Parameters: Allows fine-tuning of outputs through various adjustable settings.
  • Flexible Aspect Ratios: Supports multiple aspect ratios for diverse creative needs.

Key Considerations

  • Prompt and Image Balance: When using both prompt and image_prompt, ensure they complement each other for harmonious results.
  • Aspect Ratio and Composition: Select aspect_ratio carefully to match your creative vision.
  • Seed Usage: To maintain consistency, use the same seed value across iterations.
  • Safety Guidelines: Adjust safety_tolerance based on your project needs, balancing creativity and compliance.


Legal Information

By using this model, you agree to:

  • Black Forest Labs API agreement
  • Black Forest Labs Terms of Service

Tips & Tricks

  • Optimize Image Prompt Influence: Start with image_prompt_strength at 0.5 and adjust based on the desired emphasis on the visual input.
  • Aspect Ratio Exploration: Experiment with different aspect_ratio values to discover unique perspectives and compositions.
  • Safety Tolerance Tweaks: Increase safety_tolerance for more experimental outputs, but monitor results to ensure they align with your goals.
  • Seed for Variations: Vary the seed value slightly to explore creative variations while retaining a core theme.

Capabilities

  • Generates high-resolution, visually appealing images using textual and visual inputs.
  • Supports adjustable parameters for enhanced creativity and precision.
  • Offers repeatable outputs with the seed parameter.

What can I use for?

  • Artistic Creations: Generate concept art, illustrations, or unique designs.
  • Professional Projects: Design marketing visuals, product mockups, or presentation materials.
  • Creative Exploration: Experiment with prompts to explore new artistic styles and ideas.

Things to be aware of

  • Detailed Scenes: Describe intricate settings (e.g., "a bustling city at night with neon signs and rain-soaked streets").
  • Image Combinations: Upload  photo as image_prompt and refine with textual descriptions.
  • Aspect Ratio Variations: Explore unique compositions with non-standard ratios like "3:2" or "1:1."
  • Creative Styles: Adjust image_prompt_strength  to discover new artistic influences.


Limitations

  • Complex Prompts: Overly detailed or conflicting prompts may lead to unpredictable outputs.
  • Image Prompt Dependency: High image_prompt_strength values may overshadow textual descriptions.
  • Resolution Constraints: Extremely high resolutions may impact processing time and performance.

Output Format: JPG,PNG

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