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An AI people remover can return an image at the same pixel dimensions as the original without regenerating the entire photo at full resolution. In Jasper Smith’s implementation, the model creates a smaller inpainting result, and the browser composites that result only inside the painted mask over the decoded original. The output canvas stays full size, but the brushed area contains generated, enlarged pixels—not the original detail.
What “never downscales your photo” means
Smith describes two separate ideas: the dimensions of the finished file and the detail in the area being repaired. His tool generates a replacement at either 1K Standard or 2K HD Repair, then scales that result to the original image’s dimensions for compositing. Pixels outside the mask come from the original as decoded by the browser; pixels inside it come from the generated repair.
As Smith puts it, “Everything outside the brush stays identical to what the browser decoded from your file.” That is a description of his implementation, not an independently verified fidelity test. It also does not mean every property of the source file survives: the reported output is PNG, and EXIF metadata and ICC color-profile information are not retained.
How the browser compositing works
- The app sends the full image frame and a black-and-white mask to an inpainting model.
- The model returns a generated image at the selected 1K or 2K mode.
- The browser draws the original into a canvas at its full dimensions, scales the generated result to fit, and clips that layer using the mask’s alpha.
- The clipped repair is composited over the original. The author says the process accounts for alpha-channel handling and antialiased mask edges; repeated edits build on the latest full-size composite.
This design avoids asking the model to regenerate unmasked portions of the frame. It does not guarantee that the generated region will blend perfectly: the model is responsible for inventing plausible replacement content, and the author’s article does not provide an independent quality benchmark.
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Standard 1K or HD Repair 2K?
| Mode | Generated repair detail | What it changes | When it may matter |
|---|---|---|---|
| Standard (1K) | Inpainting result generated at 1K, according to Smith. | Only the brushed area is composited into the full-size canvas; it does not regenerate the unmasked photo. | A reasonable choice when the source is relatively small or the repaired area does not need more generated detail. |
| HD Repair (2K) | Inpainting result generated at 2K, according to Smith. | Intended to add detail within the brushed region, not to improve the whole photograph. | Smith says it makes no difference on images below roughly 1000 pixels on the long side. This is his explanation, not a comparative test. |
Both modes are part of the same compositing approach: generated content is limited to the mask, while the rest of the image is taken from the browser-decoded original. The article does not establish a measured quality difference between modes.
What happens on the server
Smith reports that pixel compositing happens in the browser rather than on the server. The Cloudflare Worker validates uploads, creates and polls model jobs, and copies results into R2. The final composite is uploaded separately; if that upload fails, the described app offers a save-only retry.
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The reported stack is Next.js 15 running on Cloudflare Workers through @opennextjs/cloudflare, with D1 for accounts, jobs, and the credit ledger; R2 for uploads, masks, and results accessed through the S3 SDK; Stripe for credit packs and monthly plans; and Google OAuth as the sole sign-in option. Smith also describes client-generated UUID request IDs to prevent duplicate jobs or charges on retries, transactional refunds for confirmed generation failures, and retryable handling of transient polling or download errors. These are the builder’s implementation claims, not an independent assessment of reliability or security.
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Image limits and metadata to account for
- Input cap: Smith reports a maximum of 25 megapixels or 20 MB. He attributes the limit in part to browser canvas memory constraints, especially on phones.
- Memory: Smith estimates that a 25-megapixel image can require about 100 MB for raw RGBA pixel storage. Actual memory pressure can vary by device and browser.
- Large canvases: The author specifically notes that iOS Safari may refuse very large canvases. No browser compatibility matrix is provided, so support across devices is not established.
- Metadata and color: The output is described as PNG without retained EXIF or ICC information. Preserve the original separately if you need its capture metadata or embedded color profile.
Credits and availability
At the time of Smith’s September 29, 2026 article, he described two credits available without signing in and four additional credits after a first Google sign-in, as well as one-time credit packs and monthly plans. Those are the author’s stated offer and plan types at publication; current availability and prices are not independently verified.
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What the claim does—and does not—establish
The useful distinction is that matching the original canvas dimensions is not the same as preserving all original information. In this approach, the unmasked area is composited from the browser-decoded image, while the masked area is generated at lower resolution and enlarged. Smith’s article describes the pipeline and its limits, but does not independently verify pixel-level fidelity, repair quality, privacy practices, or compatibility across browsers.
Source: Jasper Smith, DEV Community, September 29, 2026. The article is the builder’s account of the implementation.
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