Flux Fill Pro

flux-fill-pro

Flux Fill Pro Model fills missing or unwanted areas in images seamlessly, ensuring realistic outcomes

Partner Model

Fast Inference

REST API

Try in Console API Docs Examples

Model Information

Response Time~9 sec

StatusActive

Version

0.0.1

Updatedabout 1 hour ago

Live Demo

Average runtime: ~9 seconds

Input

Configure model parameters

Prompt

Text prompt for image generation

movie poster says "FLUX FILL"

Image

The image to inpaint. Can contain an alpha mask.

File upload is currently disabled

JPEGPNGJPGWEBP

Mask

A black-and-white image that describes the part of the image to inpaint. Black areas will be preserved while white areas will be inpainted. Must have the same size as image. Optional if you provide an alpha mask in the original image.

File upload is currently disabled

JPEGPNGJPGWEBP

Guidance

Controls the balance between adherence to the text prompt and image quality/diversity. Higher values make the output more closely match the prompt but may reduce overall image quality. Lower values allow for more creative freedom but might produce results less relevant to the prompt.

output_format

An enumeration.

Output

View generated results

Result

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

Each execution costs $0.055 With $1 you can run this model about 18 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-fill-pro",
            "version": "0.0.1",
            "input": {
  "mask": "your_file.image/jpeg",
  "seed": null,
  "image": "your_file.image/jpeg",
  "steps": "50",
  "prompt": "your prompt here",
  "guidance": "3",
  "output_format": "jpg",
  "safety_tolerance": "2",
  "prompt_upsampling": 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: ~9 seconds
Rate limit: 60 requests/minute
Concurrent requests: 10 maximum
Use long-polling to check prediction status until completion

Overview

Flux Fill Pro, developed by Black Forest Lab, offers advanced inpainting capabilities, enabling users to effortlessly fill, modify, and enhance images with exceptional precision. This model shines in complex scenarios, providing high-quality image synthesis and editing for precise and detailed results.

Technical Specifications

Model Objective: Flux Fill Pro for intelligent inpainting, leveraging advanced algorithms to fill gaps in images or modify specific regions without losing contextual integrity.

Input Types: Flux Fill Pro supports string-based textual prompts, image files, masks, and numerical parameters.

Parameter Flexibility: Fine-tuning inputs like guidance, safety tolerance, and steps offers users a tailored approach to image modification.

Key Considerations

Model Constraints: While Flux Fill Pro is powerful, it relies heavily on the quality and accuracy of inputs. Poorly designed masks or vague prompts may lead to suboptimal results.

Safety Tolerance: Higher safety tolerance values may limit creativity, while lower values allow more experimental outputs.

Parameter Limits: Inputs like steps and guidance have predefined ranges. Exceeding these may result in errors or inefficiencies.

Legal Information

By using this model, you agree to:

Black Forest Labs API agreement
Black Forest Labs Terms of Service
Black Forest Labs Privacy Policy

Tips & Tricks

Prompt: Craft specific, concise, and descriptive prompts. For instance, instead of “a forest,” use “a dense, foggy forest during sunrise with soft light breaking through the trees.”
Steps :
- Use lower values (e.g., 10-20) for quicker results when precision is not critical.
- Opt for higher values (e.g., 40-50) when generating detailed outputs, especially for intricate modifications.
Guidance :
- A guidance value of 2-3 maintains the prompt’s influence while allowing creative freedom.
- A value of 4-5 ensures stricter adherence to the prompt but may reduce variation in outputs.
Safety Tolerance:
- Set safety tolerance to 1-2 for highly creative outputs, though this increases the risk of artifacts.
- Use 5-6 for more conservative and polished results, particularly in professional applications.
Image and Mask: Ensure high-resolution images and precise masks for more refined outputs. Misaligned or low-quality masks can lead to inaccuracies.
Prompt Upsampling: Enable this option for prompts requiring enhanced detail or sharper focus on specific areas.

Capabilities

Image Inpainting: Modify or fill specific regions in an image while maintaining contextual consistency.

Creative Edits: Generate variations of an image based on textual descriptions, blending creativity with realism with Flux Fill Pro.

Refinement: Adjust existing images with enhanced textures, colors, and details.

What can I use for?

Content Creation with Flux Fill Pro: Generate visually compelling images for artistic or commercial use.

Restoration: Repair damaged or incomplete images by filling in missing areas.

Customization: Adapt images for marketing, branding, or personalized projects.

Things to be aware of

Experiment with steps to balance speed and detail.

Test different guidance values to explore the creative flexibility of your prompt.

Use masks creatively to isolate specific areas for focused modifications.

Combine prompts with high-resolution inputs to push the boundaries of inpainting capabilities.

Limitations

Flux Fill Pro may struggle with extremely vague prompts or poorly defined masks.

Outputs depend heavily on the accuracy of input parameters, requiring careful calibration.

Overly complex prompts may lead to unexpected or less cohesive results.

Output Format: JPG,PNG

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