AI Image Cleaner is a free online AI image cleaning tool that can be used directly in the browser. It focuses on using brush masks to precisely remove unwanted content from photos, such as people, passersby, power lines, clutter, stamps, text, and semi-transparent watermarks, and rebuilds the background of the removed areas through AI inpainting.
[Uses] This tool is used for photo cleanup and is suitable for removing intruders from landmark photos, power lines in landscape photos, corner date stamps, burned-in subtitles, stickers, props in product still lifes, etc., making the image look clean and natural.
[Main capabilities]
- Precise brush erasing: use the brush to cover the object to be removed. You can cover the entire object and extend slightly beyond its edges, rather than only outlining it.
- Provides an eraser, undo, and redo for precise edge control.
- Automatically handles oversized edges after upload to ensure processing reliability (the documentation states that oversized edges will be resized).
- After running cleanup, you can view a before-and-after comparison preview and download the result.
- The filling part references AI inpainting ideas from large-mask research such as LaMa (the documentation cites https://arxiv.org/abs/2102.09437）。
- Failed jobs refund credits (original text: Failed jobs refund credits).
- Supports JPG / PNG / WebP uploads; the page indicates you can drag and drop or click to browse.
[How to use] The material describes three steps: 01 Upload a photo: drag a JPEG or PNG into the workspace; oversized edges are automatically scaled to keep processing stable. 02 Paint the erase mask: use the brush to paint only the content that needs to disappear, such as people, clutter, signs, or text; use the eraser, undo, and redo to keep edges precise. 03 Run cleanup and download: start cleanup, review the before-and-after preview, then download. About free use and sign-in: the material states "Paint free — sign in to generate," meaning painting is free and you must sign in to generate; Google sign-in gives 6 credits (~3 cleans), and each clean costs 2 credits (original text: Google sign-in gives 6 credits (~3 cleans), 2 each).
[Use cases]
- Remove tourists or passersby from landmark photos, preserve building edges, and restore a clean skyline without manual cloning.
- Remove thin power lines crossing the sky or trees in landscape photos, with the model filling in texture so the horizon looks natural.
- Remove date stamps from photo corners, covering the stamp and surrounding pixels so the fill matches the grain and color of the original scan.
- Remove watermarks you own from photos (such as diagonal stock marks, corner logos, or semi-transparent overlays); the material includes a lawful step-by-step guide.
- Remove text from images (subtitles, captions, overlay text) without Photoshop.
- Landing pages for different cleanup needs: object removal, watermark removal, text removal, people removal, image cleanup, photo eraser, etc., all based on the same brush workspace.
[Limitations]
- The generation feature requires sign-in (Google sign-in); painting is free.
- Credit mechanism: Google sign-in gives 6 credits, and each cleanup costs about 2 credits (about 3 cleans); failed jobs refund credits.
- The material states that for batch retouching or prepress preparation, desktop tools still have advantages; browser workflows are better suited to quick social posts, product listing photos, and one-off fixes.
- The material does not provide more detailed facts such as pricing, subscriptions, prices, other sign-in methods, platform compatibility, or privacy policy, so they are not provided for now.
- The material mentions that the "last updated" date for the blog and tutorial pages is 2026-09-27.
[Comparison explanation] The material compares this tool with desktop Content-Aware Fill and common mobile eraser apps, noting that choosing a photo cleanup workflow usually depends on cost, control, and how natural the fill looks. AI Image Cleaner combines the precision of manual brush masks with AI inpainting to naturally reconstruct the background, avoiding the problem of automatic detection tools often selecting the wrong object; its underlying model uses large-mask inpainting technology to fill removed areas with believable texture, grain, and lighting.
