FLUX.1 Kontext – Text Removal - AI Image Tools Tool

Overview

FLUX.1 Kontext – Text Removal is a specialized image-editing model from Black Forest Labs, hosted on Replicate, that automatically detects and removes visible text from images. It’s designed as a focused tool for workflows that need to redact, de‑watermark, or visually clean images by removing overlaid text while attempting to retain the surrounding background and structure. The Replicate listing provides a web playground for quick experimentation and an API endpoint for programmatic integration. The model is positioned for tasks such as sanitizing screenshots, preparing images for reuse where text is unwanted, or cleaning up photographic signage and captions. Public-facing documentation and the Replicate playground are the primary entry points for testing; broader implementation details (training data, architecture, and per-inference compute costs) are provided on the Replicate model page by the publisher. According to the model page on Replicate, the tool’s primary capability is end-to-end text removal with API access and a visual demo for iterative tuning and evaluation.

Key Features

• Automated removal of visible text from images without manual masking
• Hosted on Replicate with a web playground for visual experimentation
• API-accessible for programmatic batch processing and pipeline integration
• Aims to preserve surrounding pixels and background context after removal
• Suitable for screenshots, photos, and images with overlaid captions

Example Usage

Example (python):

import replicate

# Replace with your Replicate API token
client = replicate.Client(api_token="YOUR_API_TOKEN")

# Get the model hosted on Replicate
model = client.models.get("flux-kontext-apps/text-removal")

# Predict by passing an image URL or local file path (depends on model I/O)
# Many Replicate models accept either a public image URL or a file-like object.
result = model.predict(image="https://example.com/input_image_with_text.jpg")

# The model typically returns one or more output image URLs or base64 data.
print("Model output:", result)

# If the output is a URL, download and save it (example using requests):
# import requests
# r = requests.get(result)
# open('output.png', 'wb').write(r.content)

# Note: Check the Replicate model page for exact input/output field names.

Key Information

• Category: Image Tools
• Type: AI Image Tools Tool