Product image cleanup
Clear flattened price labels and promotional callouts from images you are authorized to edit.
For a text label flattened into an image, upload the file and identify the label by location, color or wording. AI can remove the label graphic and rebuild nearby product, surface and shadow detail. This page covers visual text labels inside image files, not physical adhesive stickers on real objects.
Try it on your image
Upload JPG, PNG or WebP files. Add a short instruction when only selected text should be removed.
Drag and drop images here, or click to choose files.
JPG, JPEG, PNG or WebP · Up to 20 MB each · Up to 10 images per batch
Step by step
Use the original marketing asset if possible so small labels and product edges stay sharp.
Say which price, sale or callout should go and which product name or packaging text must remain.
Inspect bottle edges, reflections, props and surface shadows around the removed label.
This workflow is for visual labels already flattened into a photo or graphic, such as a price bubble, sale badge, product callout, old campaign line or annotation.
It is not a method for peeling a physical adhesive label from an object. A photographed sticker may be edited visually, but the output is still a modified image rather than a change to the real product.
Product photos often contain both temporary campaign text and permanent package branding. Use specific instructions so the model does not interpret every word as a removal target.
Review cap shapes, bottle outlines, reflected highlights and cast shadows after cleanup. These cues make a product scene look grounded and should remain consistent.
FAQ
Yes. Target the price bubble or promotional label and keep the underlying product and surface details in the instruction.
Yes. Say to remove only the external label or sale text and preserve all packaging and brand details.
No. It edits the appearance of an image file. It does not provide instructions for removing adhesive from a real object.
Yes. Remove all visible temporary labels or list the exact positions that should be cleaned.
Keep exploring
Upload a supported image, target all text or selected words and review the reconstructed background.
