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Flux Realism

flux-dev-realism

Flux Realism focuses on generating hyper-realistic images for professional-grade visuals.

Partner Model
A100 80GB
Fast Inference
REST API
Try in ConsoleAPI DocsExamples

Model Information

Response Time~15 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-dev-realism",
            "version": "0.0.1",
            "input": {
  "seed": null,
  "prompt": "A photo of a woman, headshot, realistic",
  "guidance": "3.5",
  "num_outputs": "1",
  "aspect_ratio": "1:1",
  "lora_strength": "0.8",
  "output_format": "webp",
  "output_quality": "80",
  "num_inference_steps": "30"
}
        }
    )
    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: ~15 seconds
  • Rate limit: 60 requests/minute
  • Concurrent requests: 10 maximum
  • Use long-polling to check prediction status until completion

Overview

The flux-dev-realism model is a variant of the FLUX.1-dev, a 12-billion-parameter rectified flow transformer designed to generate high-quality, photorealistic images from text descriptions. Developed by XLabs AI, this model excels in producing realistic images across various subjects, including portraits, landscapes, and imaginative scenes.

Technical Specifications

Model Architecture: FLUX.1-dev is a rectified flow transformer with 12 billion parameters, optimized for text-to-image generation tasks.

Performance: The model achieves a balance between high-quality image output and computational efficiency, making it suitable for various non-commercial applications.

Key Considerations

Non-Commercial Use: The flux-dev-realism model is intended for non-commercial applications, such as research, education, and personal projects.


Complete necessary data preprocessing steps: Ensure that input data is appropriately prepared before using the model.


Validate input data format and structure: Confirm that the data format matches the model's expectations.

Legal Information

By using this model, you agree to:

  • XLabs AI API agreement
  • XLabs AI Terms of Service

Tips & Tricks

Realistic Outputs: To achieve more realistic images, experiment with different prompts and adjust model parameters accordingly. Engaging with community forums can provide valuable insights and shared experiences.


Optimal parameter settings for training and inference: Adjust parameters to achieve the best results.

Capabilities

Image Generation: Capable of producing a wide range of photorealistic images, from detailed portraits to complex landscapes and imaginative scenes.


Style Diversity: The model supports various artistic styles, allowing users to generate images that align with specific aesthetic preferences.


Photorealistic Image Generation: Creates lifelike visuals based on textual prompts.


Optimized for Realism: Prioritizes realistic details and image fidelity.


Enhanced Stability: Ensures consistent results across diverse inputs.

What can I use for?

Graphic Design: Create detailed and visually appealing graphics for design projects, enhancing both efficiency and creativity.


Digital Art: Produce high-quality, photorealistic digital artwork, making it a valuable tool for artists and hobbyists alike.


Educational Purposes: Serve as a resource for teaching AI-driven design techniques and exploring creative possibilities in academic settings.


Realistic Visual Content Creation: Generate images for design, marketing, or creative projects.


Research Applications: Employ the model for experiments requiring photorealistic images.


Simulation and Prototyping: Use generated images for testing and prototyping purposes.

Things to be aware of

Experiment with Prompts: Test various text descriptions to explore the model's range and versatility in image generation.


Combine with Other Tools: Integrate the model with other AI tools and extensions, such as ControlNet and LoRA models, to enhance output quality and control


Specific Examples: Experiment with detailed text prompts to achieve varied outputs.


Practical Use Cases: Utilize the model in real-world scenarios for impactful results.


Parameter Adjustments: Explore different settings to fine-tune results.


Creative Applications: Push the boundaries of creativity by experimenting with unique prompts.


Integration Scenarios: Combine the model with other models to enhance workflows.

Limitations

Resource Intensive: The model's size and complexity may lead to high computational demands, potentially limiting its use on less powerful hardware.


Scope of Use: Designed primarily for non-commercial purposes, the model may not be suitable for commercial projects without appropriate licensing.


Maximum data size: Be mindful of the model's capacity to handle large datasets.


Output Format: PNG,JPG,WEBP


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