Developer details three silent failures when running AI image models entirely in the browser

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A developer rebuilt a photo-editing site to run AI models — including object removal, background removal, and 4× upscaling — entirely client-side using ONNX Runtime Web with WebGPU and a WebAssembly fallback. Three unexpected failures emerged, none of which produced clear error messages pointing to the root cause. On Apple GPUs, the preferred background-removal model crashed due to a WebGPU shader buffer limit, forcing a switch to a less accurate but compatible convolutional model. A separate inpainting model produced near-white output on WebGPU due to incorrect Fourier convolution results, while an upscaling model returned a black image because Chrome's new native Float16Array changed how output data was typed. The developer concluded that browser-based AI pipelines require pixel-level output testing and GPU-specific benchmarking, not just error-free execution checks.

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