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.

News

• 2026-04-21 — Environment pinning update. For best results (and to avoid color shifts), please use exactly the versions pinned in requirement.txt: diffusers==0.36.0, transformers==4.55.0, safetensors==0.5.3, peft==0.17.0. See Environment Notice below for a visual comparison.
• 2026-04-21 — Hugging Face Space environment fixed. The online demo now runs on the correct dependency versions, so refinement results are noticeably better: https://huggingface.co/spaces/limuloo1999/RefineAnything.
• 2026-04-14 — Community ComfyUI integration by @smthemex: ComfyUI_RefineAnything. Thanks for the great work!
• 2026-04-14 — Local Gradio demo (app.py) is available for interactive testing.
• 2026-04-12 — Hugging Face Space demo is live: https://huggingface.co/spaces/limuloo1999/RefineAnything.
• 2026-04-09 — Checkpoint released on Hugging Face: https://huggingface.co/limuloo1999/RefineAnything.
• 2026-04-09 — Release inference scripts.
• 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.

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Comparisons

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Installation

pip install -r requirement.txt

Important — pin these versions exactly. RefineAnything is sensitive to small numerical differences in the underlying libraries. Please install exactly the versions below; using newer or older releases can cause visible artifacts such as color shifts in the refined region.

diffusers==0.36.0
transformers==4.55.0
safetensors==0.5.3
peft==0.17.0

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Environment Notice

We have observed that mismatched versions of diffusers / transformers / safetensors / peft can introduce color shifts in the refined region, even when everything else is identical. The example below uses the prompt "remove the hand":

Input (masked region = hand)	Correct environment	Wrong environment (color shift)

If your output shows a mild color/tone mismatch inside the mask while the rest of the image looks fine, the first thing to check is your package versions.

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Quick Start

Only three things are required to run RefineAnything:

Argument	Description
--input	Source image
--mask	Binary mask (white = region to refine)
--prompt	What to refine
--ref	(optional) Reference image for guided refinement

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Demo 1 — Reference-based Logo Refinement

Refine a blurry logo on a pillow using a reference image.

python scripts/fast_inference.py \
    --input  src/input1.png \
    --mask   src/mask1.png \
    --prompt "Refine the LOGO." \
    --ref    src/ref1.png \
    --output output/demo1.png

Input	Reference	Prompt
		"Refine the LOGO."
Output

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Demo 2 — Reference-free Text Refinement

Refine blurry Chinese text on a building sign — no reference image needed.

python scripts/fast_inference.py \
    --input  src/input2.png \
    --mask   src/mask2.png \
    --prompt "refine the text '鼎好商城'" \
    --output output/demo2.png

Input	Prompt
	"refine the text '鼎好商城'"
Output

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Local Gradio Demo

We also provide a Gradio-based web UI for interactive testing. You can brush regions, upload reference images, and adjust all inference parameters in the browser.

python app.py

Then open http://localhost:7860 in your browser. The app will automatically download the base model (Qwen/Qwen-Image-Edit-2511) and the RefineAnything LoRA from Hugging Face on first launch.

You can specify a custom base model path via the MODEL_DIR environment variable:

MODEL_DIR=/path/to/local/Qwen-Image-Edit-2511 python app.py

Features of the Gradio demo:

• Brush-to-select: paint directly on the source image to define the refinement region.
• Optional reference image: upload a second image and optionally brush to crop a specific reference area.
• Focus crop: automatically crops and zooms into the edit region for higher detail fidelity, then composites back seamlessly.
• Lightning LoRA: one-click toggle for faster inference with fewer steps.
• Before / After slider: instantly compare input and output.

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Citation

If you use this repository, please cite:

@article{zhou2026refineanything,
  title={RefineAnything: Multimodal Region-Specific Refinement for Perfect Local Details},
  author={Zhou, Dewei and Li, You and Yang, Zongxin and Yang, Yi},
  journal={arXiv preprint arXiv:2604.06870},
  year={2026}
}

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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.