Building Browser-Local Video Face Swapping: Lessons from a WebGPU Inference Pipeline
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Video face swapping is usually associated with cloud GPUs: upload a video, process it frame by frame on a server, and download the result. Modern browser APIs make another architecture possible. WebGPU, WebCodecs, Web Workers, and ONNX Runtime Web can move a meaningful part of the pipeline onto the user's device. Source media does not have to be uploaded to an inference server, models can be cached locally, and completed results can go straight into an editable video project. Once we built the…
1Key Takeaways
- Video face swapping is usually associated with cloud GPUs: upload a video, process it frame by frame on a server, and download the result.
- Modern browser APIs make another architecture possible.
- WebGPU, WebCodecs, Web Workers, and ONNX Runtime Web can move a meaningful part of the pipeline onto the user's device.
- Source media does not have to be uploaded to an inference server, models can be cached locally, and completed results can go straight into an editable video project.
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that video face swapping is usually associated with cloud GPUs: upload a video, process it frame by frame on a server, and download the result.
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