Flux Redux Dev

flux-redux-dev

Flux Redux Dev Model enables developers to perform advanced image transformations with custom results

Partner Model

Fast Inference

REST API

Try in Console API Docs Examples

Model Information

Response Time~7 sec

StatusActive

Version

0.0.1

Updatedabout 17 hours ago

Live Demo

Average runtime: ~7 seconds

Input

Configure model parameters

aspect_ratio

An enumeration.

Guidance

Guidance for generated image

output_format

An enumeration.

megapixels

An enumeration.

Redux Image

Input image to condition your output on. This replaces prompt for FLUX.1 Redux models

File upload is currently disabled

JPEGPNGJPGWEBP

Output

View generated results

Result

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

Each execution costs $0.028 With $1 you can run this model about 35 times.

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-redux-dev",
            "version": "0.0.1",
            "input": {
  "seed": null,
  "guidance": "3",
  "megapixels": "1",
  "num_outputs": "1",
  "redux_image": "your_file.image/jpeg",
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "output_quality": "80",
  "num_inference_steps": "28",
  "disable_safety_checker": false
}
        }
    )
    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: ~7 seconds
Rate limit: 60 requests/minute
Concurrent requests: 10 maximum
Use long-polling to check prediction status until completion

Overview

Flux Redux Dev is an open-weight image model developed by Black Forest Labs. It enables the creation of new versions of images while preserving key elements of the original. This model is particularly useful for tasks such as product photography, image editing, and content creation.

Technical Specifications

Model Architecture: Flux Redux Dev is built upon the FLUX.1 framework, designed for image variation tasks.

Input Requirements for Flux Redux Dev: Accepts images in standard formats with adjustable parameters for customization.

Output Capabilities: Generates high-quality image variations based on the provided input and parameters.

Key Considerations

Content Sensitivity: Ensure that the input images comply with ethical guidelines and do not contain sensitive or inappropriate content.
Resource Management: Be mindful of computational resources, especially when generating multiple high-resolution outputs.
Parameter Tuning: Experiment with different parameter settings to achieve the desired balance between image quality and processing efficiency.

Legal Information

By using this Flux Redux Dev, you agree to:

Black Forest Labs Privacy Policy
Black Forest Labs Terms of Service

Tips & Tricks

Aspect Ratio: Selecting an appropriate aspect ratio enhances the composition of the generated images. For example, 1:1 is ideal for portraits, while 16:9 suits landscapes.
Number of Inference Steps: A higher number of inference steps (e.g., 40-50) can improve image quality but will increase processing time.
Guidance Scale: A guidance scale between 7 and 8 often yields a good balance between adherence to the original image and introducing creative variations.
Output Quality: For JPEG format, an output quality setting between 90 and 100 ensures high-quality images with minimal compression artifacts.
Megapixels: Choosing a higher megapixel setting (e.g., 1) results in higher resolution images but requires more processing time and resources.

Capabilities

Image Variation: Generates diverse variations of the input image while maintaining core elements.

Customization: Offers adjustable parameters to fine-tune the output according to user preferences.

What can I use for?

Image Variations – Generate multiple versions of an image while maintaining its core structure.
Product Photography Enhancements – Modify lighting, colors, and textures while keeping the original product recognizable.
Concept Art & Design Iterations – Create multiple artistic variations to explore different styles and compositions.
Social Media Content – Adjust images for various platform aspect ratios and aesthetics.
Photo Restoration & Improvement – Enhance image details and correct minor flaws while preserving realism.

Things to be aware of

Experiment with Aspect Ratios – Test different aspect ratios (e.g., 1:1 for social media, 16:9 for cinematic looks) to see how compositions change.

Optimize Inference Steps – Use a higher number of steps (e.g., 30–50) for detailed and refined outputs, or lower steps (e.g., 10–20) for faster results.

Adjust Guidance for Control – Set guidance between 3–7 for balanced creativity; higher values make the output more faithful to the original image.

Improve Image Sharpness with Output Quality – Increase output quality closer to 100 for high-fidelity results; reduce it to save processing time.

Test Different Megapixel Settings – Use 0.25 MP for quick previews and 1 MP for high-resolution outputs.

Limitations

Input Dependency: The quality of the output is heavily dependent on the quality of the input image.

Computational Load: Generating multiple high-resolution images can be resource-intensive and time-consuming.

Content Restrictions: The Flux Redux Dev may not perform well with images containing complex patterns or excessive noise.

Output Format: PNG,JPG,WEBP

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