RefineAnything

Multimodal Region-Specific Refinement for Perfect Local Details

RefineAnything targets region-specific image refinement: given an input image and a user-specified region (e.g., scribble mask or bounding box), it restores fine-grained details—text, logos, thin structures—while keeping all non-edited pixels unchanged. It supports both reference-based and reference-free refinement.

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News

• 2026-04-08 — Documentation skeleton added; code release coming this month (inference scripts, environment, and checkpoints will be linked here).
• TBD — Checkpoints and training/evaluation resources will be announced once finalized.

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Highlights

• Region-accurate refinement — Explicit region cues (scribbles or boxes) steer edits to the target area.
• Reference-based and reference-free — Optional reference image for guided local detail recovery.
• Strict background preservation — Edits stay inside the target region; training emphasizes seamless boundaries.
• Data and benchmark — A training corpus spanning reference-based and reference-free settings, plus evaluation focused on region fidelity and background consistency (details ship with the code release).

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Comparisons

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Installation

Coming with the code release. Versions below are placeholders.

# git clone https://github.com/limuloo/RefineAnything.git
# cd RefineAnything
# conda create -n refineanything python=3.10 -y
# conda activate refineanything
# pip install -r requirements.txt
# pip install -e .

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Quick start

Coming with the code release.

# Example (final CLI may differ):
# python scripts/infer.py --image path/to/image.png --mask path/to/mask.png \
#   --prompt "Refine the text on the sign." [--reference path/to/ref.png]

Optional Gradio demo and HTTP API will be documented here if included in the release.

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Citation

If you use this repository, please cite:

@article{refineanything2026,
  title        = {RefineAnything: Multimodal Region-Specific Refinement for Perfect Local Details},
  author       = {TBD},
  year         = {2026},
  eprint       = {2604.06870},
  archivePrefix= {arXiv},
  primaryClass = {cs.CV},
  url          = {https://arxiv.org/abs/2604.06870},
}

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Acknowledgements and license

RefineAnything builds on ideas and components from the broader diffusion and multimodal ecosystem (including Qwen2.5-VL, Qwen-Image, and latent diffusion with VAE + MMDiT). Base model weights and API terms are subject to their respective licenses—verify compliance before redistributing checkpoints or derived weights.

Repository code license: TBD (e.g., Apache-2.0 or MIT)—set LICENSE when you open-source the implementation.