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Flux Redux Schnell

flux-redux-schnell

Flux Redux Schnell Model is a high-speed transformation model for efficient and precise image editing.

Partner Model
Fast Inference
REST API
Try in ConsoleAPI DocsExamples

Model Information

Response Time~2 sec
StatusActive
Version
0.0.1
Updated11 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-redux-schnell",
            "version": "0.0.1",
            "input": {
  "seed": null,
  "megapixels": "1",
  "num_outputs": "1",
  "redux_image": "your_file.image/jpeg",
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "output_quality": "80",
  "num_inference_steps": "4",
  "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: ~2 seconds
  • Rate limit: 60 requests/minute
  • Concurrent requests: 10 maximum
  • Use long-polling to check prediction status until completion

Overview

Flux Redux Schnell is a versatile tool designed for generating detailed, high-quality outputs tailored to user-defined prompts and configurations. Its robust customization options allow users to produce visuals that align with their creative, professional, or exploratory needs. By adjusting various parameters, users can achieve a wide range of outputs, from abstract art to photorealistic renders.

Technical Specifications

  • Advanced Customization: Offers fine control over outputs through adjustable parameters such as format, and quality.
  • Image Input Support: Seamlessly process image-based inputs for enhanced usability.
  • High-Resolution Outputs: Supports outputs at varying megapixel levels for diverse requirements.

Key Considerations

  • Processing Time: Increasing num_inference_steps or output_quality will extend the generation time.
  • Input Compatibility: Ensure the redux_image matches the intended style  of the output.
  • Safety Settings: Keep the safety checker enabled unless you are familiar with the input data and output requirements.
  • Parameter Interactions: Test different combinations of aspect_ratio, megapixels, and output_quality to find the ideal configuration.


Legal Information

By using this model, you agree to:

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

Tips & Tricks

  • Optimize Resolution: Start with medium megapixel for drafts, and switch to high resolution for final outputs.
  • Blend Inputs: Combine a redux_image with a descriptive prompt for unique results.
  • Aspect Ratio Framing: Choose an aspect ratio that complements the subject. For example, use 4:5 for portraits and 16:9 for cinematic scenes.
  • Seed Experimentation: Adjust the seed value to explore creative possibilities, and lock it once satisfied.
  • Prompt Crafting: Use descriptive and precise prompts to guide the model effectively. Combining visual and contextual details enhances output accuracy.
  • Inference Steps: Increase num_inference_steps for more detailed and refined outputs, but note the trade-off with processing time.
  • Output Quality: Adjust output_quality based on the use case. Higher quality is suited for final renders, while medium quality is optimal for drafts.
  • Safety Checker: Disable the safety_checker only if you are confident about the safety of the input data.

Capabilities

  • Generates outputs from textual, visual, or combined inputs.
  • Supports a wide range of resolutions and aspect ratios.
  • Flexible configurations for diverse artistic and professional use cases.

What can I use for?

  • Creative Projects: Concept art, digital illustrations, and visual storytelling.
  • Professional Applications: Branding, marketing visuals, and mockups.
  • Exploration and Prototyping: Experiment with ideas and refine designs.

Things to be aware of

  • Style Fusion: Input a redux_image and a contrasting prompt to blend styles.
  • Resolution Scaling: Generate low-resolution drafts and upscale the best ones.
  • Seed Consistency: Lock the seed to refine a specific output across iterations.
  • Custom Aspect Ratios: Experiment with unique ratios to fit specific projects, like banners or profile images.

Limitations

  • Abstract Prompts: The model may struggle with overly complex or vague prompts.
  • High Parameters: Extreme values for num_inference_steps can result in slower processing times.
  • Safety Checker: Disabling the checker may increase the risk of unintended outputs.

Output Format:WEBP,JPG,PNG

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