Background Remover - Stockphotos.com vs InstructPix2Pix

Upload a JPG, PNG, TIFF, WEBP, or BMP file and Stockphotos.com's Background Remover strips the backdrop in about 8 seconds, then lets you download a transparent PNG or drop in a new background. The browser tool handles people, products, animals, and graphics without manual masking, with a 10 MB upload limit per file.

Most background removers push monthly subscriptions, but this one sells one-time removal packs after 3 free credits on signup. You pay when you need more, removals expire on a fixed schedule, and the same Stockphotos API also handles upscaling and EPS conversion for e-commerce workflows.

E-commerce sellers prepping product shots, designers building banners, and developers embedding removal via API are the use cases Stockphotos.com highlights on the site. Free tier caps export at 1200 pixels with slower processing; paid packs unlock 8000-pixel output and priority speed.

Signup includes 3 free removals plus 10 free upscaler credits. Paid packs start at $4.90 for 20 removals or $15 for 100 removals as one-time purchases with no subscription required.

InstructPix2Pix edits a photo from a plain-language instruction such as "make the sky sunset orange" or "turn this into a watercolor." You provide an input image and a short text command, and the model returns an edited version in seconds without training on that specific photo first. It was published as a CVPR 2023 Highlight by researchers at UC Berkeley.

Mask-based editors make you paint regions before changing anything. InstructPix2Pix runs a single forward pass from text alone, skipping per-image fine-tuning or inversion steps that slow other diffusion edit workflows. The trade-off shows up in the paper's failure cases: viewpoint changes, object swaps, and tight local edits remain weak spots.

The project ships open source code on GitHub and a Hugging Face demo space for trying edits in a browser. Training combined GPT-3 and Stable Diffusion to synthesize instruction pairs, and the released model generalizes to real photos up to about 768 pixels wide despite 256x256 training resolution.