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In Umasou’s 2026 browser-inference case study, converting a compact Real-ESRGAN x4v3 model from FP32 to FP16 cut a 24-frame, 720p video task from 31.4 seconds to 6.6 seconds. The same precision change badly reduced the tested LaMa inpainting model’s masked-region score. These are results from one implementation, not a guarantee that FP16 will speed up every browser model or preserve its quality.
What the 4.8× result actually measures
Umasou reports that the browser tool used ONNX Runtime Web with WebGPU for super-resolution and watermark removal. In the reported page-level test, a 24-frame 720p video task took 31.4 seconds with the x4v3 upscaler in FP32 and 6.6 seconds after converting that model to FP16. That is the source of the 4.8× figure.
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The author also reports a single-tile WebGPU inference time of 403 ms in FP32 and 113 ms in FP16, described as 3.5× faster. The tile measurement and the end-to-end video task are different workloads: the latter includes more than a single model inference. Neither figure is an independent benchmark, and both depend on the author’s implementation and test environment.
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Architecture and precision were separate changes
The team first changed the model architecture, replacing Real-ESRGAN x4plus, based on RRDBNet, with the more compact realesr-general-x4v3, identified as SRVGGNetCompact. Only after that selection did it compare FP32 and FP16 for x4v3. The 4.8× page-level comparison is specifically the FP32-to-FP16 result for the compact model; it should not be credited to FP16 alone as a combined improvement over the original x4plus setup.
Umasou reports that the architecture switch changed per-frame processing from about 75 seconds to about 7 seconds in its setup. That is a separate, author-reported comparison, not a transferable speed estimate for other machines or images.
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Why the team chose a smaller upscaler first
In the author’s comparison, SRVGGNetCompact x4v3 was substantially smaller than RRDBNet x4plus and won a three-case PSNR comparison. Those cases are a narrow basis for choosing between models; they do not establish that x4v3 is generally more accurate or better for every image.
| Author-reported comparison | RRDBNet x4plus | SRVGGNetCompact x4v3 |
|---|---|---|
| Graph nodes | 1,187 | 71 |
| Model file size in the architecture comparison | 67 MB | 4.9 MB |
| CPU speed comparison | The author reports x4v3 was 14–27× faster; this is the author’s comparison, not a general hardware result. | |
| PSNR across three test cases | The author reports x4v3 ahead by 0.1–1.5 dB across those cases. | |
The file-size figures in this architecture comparison are not the FP32-versus-FP16 comparison. For the x4v3 model, Umasou separately reports a reduction from 4.9 MB in FP32 to 2.4 MB in FP16. The author also reports more than 57 dB PSNR between the FP16 and FP32 x4v3 outputs. That indicates close agreement between those outputs in the tested comparison; it is not a universal quality guarantee or a substitute for checking the restored images.
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Why FP16 failed the tested LaMa inpainting case
The same article reports a different result for LaMa. After conversion to FP16, masked-region PSNR fell from 21.1 dB to 11.9 dB, and the watermark remained visible. Umasou says the output looked plausible despite the low score, illustrating why a metric alone cannot determine whether an inpainting result is usable.
The author attributes the failure to the behavior of mask-gated inpainting: the model must distinguish pixels to preserve from pixels to regenerate. In that test, reducing precision coincided with a major quality regression. It is a warning about the tested model and setup—not proof that all inpainting models fail in FP16.
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The article also reports an INT8 result for a dark-text case: PSNR decreased from 24.5 dB to 15.6 dB. This is a separate precision experiment from the LaMa FP16 comparison, and should not be read as a result for every INT8 model or workload.
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How to evaluate a precision change responsibly
The useful lesson from these results is to admit a model and precision choice based on representative output tests, not speed or a paper score alone. Umasou describes its own quality admission principle as: “Quality admission is always the benchmark, never the paper’s numbers and never ‘it looks fine.’” For a practical evaluation, compare outputs and runtime under the same conditions.
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- Choose the model for the deployment target. Compare candidate architectures on the images and operation you actually need. The author’s three-case PSNR lead for x4v3 is evidence about those three cases only.
- Establish a baseline. Record FP32 output, inference time, end-to-end processing time, model size and the browser/runtime/backend used. Keep single-tile inference distinct from a multi-frame page task.
- Convert one model and test representative cases. Include ordinary inputs and difficult examples, such as fine lines for upscaling or dark text and masked boundaries for inpainting. Compare both numerical metrics and visual results.
- Check the whole application path. Umasou says its ONNX Runtime Web setup used
keep_io_typesto retain an FP32 tensor interface at the JavaScript boundary, alongside an FP32 fallback. That is an implementation detail from this case, not a setting that guarantees compatibility or quality in another application. - Measure on the intended devices. A WebGPU speed result on one system does not establish the result on a different browser, operating system or GPU. Confirm both output quality and completion time where the tool will be used.
Browser, runtime and CPU caveats
ONNX Runtime Web supports multiple execution paths, and its compatibility documentation describes browser and operating-system differences. The compatibility notes in the documentation referenced for this case specify Chromium 113 or later on Windows for WebGPU, and Chrome 121 or later or Edge 122 or later on Windows for float16 support. The displayed matrix marks WebGPU unavailable for iOS and Firefox on Windows. Browser support changes, so these figures describe the cited documentation snapshot rather than a permanent compatibility promise; check the official ONNX Runtime Web compatibility matrix before relying on a particular combination.
Backend matters too. The Real-ESRGAN project’s FAQ says half-precision operators may be unavailable in CPU mode and recommends its --fp32 option for that error. That project guidance concerns its inference implementation; it does not independently validate the ONNX Runtime Web measurements in Umasou’s article. A model format, runtime and execution provider that work together on one setup may not do so on another.
Umasou’s profiling also cautions against assuming that adding workers or optimizing tensor transfers will solve every slowdown. The author reports a worker-pool run of 396 seconds versus 393 seconds for a serial run, and tensor-transfer overhead of 1–2% of total runtime in the profiled task. Those observations led the team to interpret that workload as memory-bandwidth-bound. They are not a general finding about all browser inference or all worker configurations.
Output quality still depends on the image
Even without a precision conversion, Real-ESRGAN has restoration limits. Its paper describes training with synthetic degradations intended to approximate real-world image corruption, while also noting that some results can contain twisted lines or GAN artifacts and that complicated, out-of-distribution degradations can defeat the method. Treat a high PSNR comparison between two model outputs as one useful check, not proof that either output is faithful or suitable for a particular image.
In this case, the practical distinction is clear: FP16 was a strong speed-and-size trade-off for the author’s tested x4v3 upscaler, while FP16 damaged the tested LaMa inpainting result. The evidence supports testing each model and task separately, rather than applying one precision setting across an entire browser image pipeline.
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