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Kontext-Watermark-Remover: Remove Watermarks from Images with FLUX

13 days ago

Kontext-Watermark-Remover: Remove Watermarks from Images with FLUX

As the FLUX model series gains popularity, Black Forest Labs' FLUX.1-Kontext-dev has become an important foundation model in the image editing space. In 2026, developer prithivMLmods released the Kontext-Watermark-Remover adapter based on FLUX.1-Kontext-dev, designed specifically for precisely removing watermarks, logos, and text from images.

This article introduces the model's capabilities, how to use it, and its real-world performance.

What is Kontext-Watermark-Remover

Kontext-Watermark-Remover is a LoRA adapter for FLUX.1-Kontext-dev that has been fine-tuned for precise watermark removal. Instead of simply blurring out watermark areas, it leverages FLUX's image generation capabilities to intelligently reconstruct pixels under the watermark, blending them seamlessly with surrounding background.

The model is open-source on HuggingFace with 74 likes and is used by 19 Spaces in the ecosystem.

Key Features

Precise Watermark Detection — Automatically identifies text watermarks, logo watermarks, and stamp watermarks regardless of position (corners, center, edges).

Background Preservation — After removal, the model intelligently fills the removed area while preserving the original texture, lighting, and overall realism. No blurry patches or mosaic artifacts.

Multiple Watermark Types — Handles transparent, semi-transparent, and opaque watermarks. Also works with overlapping and repeating watermarks.

Open-Source & Local — Fully open weights with local GPU inference support. No need to upload images to third-party servers, offering better privacy.

Training Details

The model was trained on 150 image pairs (75 before/after pairs), annotated with Florence-2-large-ft. Training parameters include:

  • Base Model: FLUX.1-Kontext-dev (fp8)
  • Network Dimension: 64
  • Network Alpha: 32
  • Training Steps: 2900 + 400 warmup
  • Optimizer: AdamW8bit
  • Sampler: euler

How to Use

Local Deployment (Diffusers)

import torch
from diffusers import FluxKontextPipeline
from diffusers.utils import load_image

pipe = FluxKontextPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Kontext-dev",
    torch_dtype=torch.bfloat16
).to("cuda")

pipe.load_lora_weights(
    "prithivMLmods/Kontext-Watermark-Remover",
    weight_name="Kontext-Watermark-Remover.safetensors",
    adapter_name="watermark_remover"
)

pipe.set_adapters(["watermark_remover"], adapter_weights=[1.0])

input_image = load_image("your_watermarked_image.jpg")

prompt = ("[photo content], remove any watermark text or logos from the image "
         "while preserving the background, texture, lighting, and overall realism. "
         "Ensure the edited areas blend seamlessly with surrounding details, "
         "leaving no visible traces of watermark removal.")

result = pipe(
    image=input_image,
    prompt=prompt,
    guidance_scale=2.5,
    num_inference_steps=28,
    generator=torch.Generator().manual_seed(42),
).images[0]

result.save("cleaned_image.jpg")

Online Demo

For users who prefer not to deploy locally, try:

  • HuggingFace Spaces — Search for this model to find related Spaces
  • GenImg AI — Watermark remover app built on FLUX.1 Kontext, 10 credits for registered users
  • Flux Context Online Tools — No registration required

Performance Comparison

Compared to traditional methods:

Blur Tools (Photoshop, etc.): Leave obvious blur patches that are easily spotted.

Content-Aware Fill: Unstable results for large watermarks, often producing texture misalignment.

Kontext-Watermark-Remover: Uses FLUX's powerful generation capability to produce significantly better quality, especially on complex textures like grass, water, and fabric.

  • PSNR: 30.5 dB (vs 25-28 dB for comparable models)
  • SSIM: 0.92 (vs 0.80-0.87 for comparable models)

Use Cases

Personal Photos — Remove date stamps, app watermarks, and editor corner badges.

Design Assets — Clean reference images and template placeholder marks.

Screenshots & Documents — Remove unwanted watermarks from screenshots and overlapping stamps from scanned documents.

E-Commerce Product Images — Clean supplier-provided images for display use.

Requirements

  • GPU with at least 12GB VRAM (RTX 3060 or higher)
  • Processing time: 5-30 seconds per image
  • The trigger prompt is crucial for good results — use the recommended long prompt template
  • For complex textures or large watermarks, try adjusting guidance_scale (recommended range: 2.0-3.5)

Summary

Kontext-Watermark-Remover is a high-quality watermark removal adapter based on FLUX.1-Kontext-dev. Its key advantages are open-source availability, local deployment, privacy safety, and results that surpass traditional blur and content-aware fill methods. For anyone who regularly deals with image watermarks, this is a tool worth exploring.

Note: This article introduces an open-source technical tool. Please comply with applicable laws when using watermark removal features — only process images you have rights to modify.

Author
Admin
Category
Technical