RefineAnything

RefineAnything is built for local refinement, not broad restyling. You give it an image, a tight region cue such as a box or scribble mask, and a short prompt for what should be fixed. The model then focuses its resolution budget on that region and pastes the result back while trying to keep the untouched background stable. That makes it easier to test on messy but common failures like blurry storefront text, weak product logos, or thin structures that a general editor would often disturb along with everything around them. The public Space gives you a fast proof, and the public repo shows the same path locally with the released LoRA on top of Qwen Image Edit.

Start with the Hugging Face Space and use one image with a single obvious local problem, not a full-scene rewrite request. Good first tests are one blurry logo, one sign with damaged text, or one thin object that lost detail. On the first run, watch three things: whether the repaired region actually becomes more readable, whether the edit stays inside the marked area, and whether nearby pixels keep their original look instead of shifting with the fix. If it passes, move to the public repo and run `python scripts/fast_inference.py` with your own image and mask so you can test the same workflow locally on a CUDA GPU.

Treat this as local repair, not full-scene editing. It works best when the problem area is narrow and clearly marked, while dense text, heavy blur, or weak masks can still produce unstable fixes, and the local path remains GPU-heavy.

• Repair one blurred logo in a product or packaging image without regenerating the full shot.
• Improve local text readability on signs, labels, or UI mockups when the rest of the image is already usable.
• Recover thin structures or small damaged regions that generic image editors tend to smear or overwrite.
• Compare a region-specific repair workflow against broader image editing tools on the same source image.

Try the demo

View model page