Image Object Removal API

The interesting thing about this model is how narrow it is. It does one job — fill a masked region convincingly — and it does not pretend to do the segmentation, the UI, or the workflow orchestration around it. That narrowness is exactly why it is cheap. You are renting a few hundred milliseconds of T4 time, not a product. Strengths worth naming. LaMa with Fast Fourier Convolutions is a real architectural choice for inpainting large areas; it is not a diffusion model and it does not hallucinate new content, which means results are conservative and repeatable — good for product photography where you want the background texture to continue, not a creatively reinterpreted surface. At roughly $0.00077 per run with about 1,298 runs per dollar on the model page, the cost per image is small enough that you can run it as a blanket step on every upload rather than gating it behind a review queue. Predictions typically finish within 4 seconds on T4, so a modest batch job is throughput-friendly. And because the weights are published on GitHub under an open-source license, self-hosting with Docker and Cog is a genuine escape hatch from per-run billing if your volume grows large enough to justify a box. Weaknesses, stated plainly. You must bring your own mask. The model has no segmentation stage, so "remove the person on the left" is not a prompt you can type — it is a mask you must compute with something else and then hand over. That added dependency is the real cost of the low per-run price. Resolution tops out at 2048px, so anything from a modern full-frame camera needs downscaling first, which is a quality decision you have to make upstream. The first request of a session can take up to 20 seconds to warm the server, which is fine for batch jobs and awkward for anything user-facing and synchronous. And there is no GUI at all: iteration means writing code and inspecting output. Where it fits. Batch photo cleanup at volume, particularly e-commerce and real estate where the objects to remove are predictable and the masks can be generated by a rule or a companion model. Teams already running a segmentation model who need a cheap inpainting stage downstream. Anyone building an internal tool who would rather own the pipeline than pay a per-image SaaS markup. Where it does not. One-off manual edits, interactive brushing, images above 2048px, and any workflow where a non-developer is expected to drive. In those cases Cleanup.pictures or Photoshop is simply the faster path, and bria/eraser is the managed alternative if you want erasure plus segmentation bundled together and are willing to pay the premium.