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 Console API Docs Examples

Model Information

Response Time~15 sec

StatusActive

Version

0.0.1

Updated2 months ago

Live Demo

Average runtime: ~15 seconds

Input

Configure model parameters

Prompt

Prompt is the input text or question that the model uses to generate a response or output.

A photo of a woman, headshot, realistic

aspect_ratio

Aspect ratio specifies the proportion between the width and height of an image or visual output.

Guidance

Guidance refers to the additional information or instructions provided to the model to help shape its outputs.

3.5

output_format

Output format is the type or structure of the generated result, such as text, image, or audio.

Output

View generated results

Result

Preview, share or download your results with a single click.

Cost is calculated based on execution time.The model is charged at $0.00154 per second. With a $1 budget, you can run this model approximately 43 times, assuming an average execution time of 15 seconds per run.

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": 0,
  "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
},
            "webhook_url": ""
        }
    )
    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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