# Show HN： 苹果的Sharp通过ONNX Runtime Web在浏览器中运行

- 来源：Hacker News 热门（buzzing.cc 中文翻译）
- 作者：bring-shrubbery
- 发布时间：2026-05-03 23:43
- AIHOT 分数：67
- AIHOT 链接：https://aihot.virxact.com/items/cmopy5mf2107msll9ah157ea7
- 原文链接：https://github.com/bring-shrubbery/ml-sharp-web

## AI 摘要

苹果的图像处理库Sharp现可通过ONNX Runtime Web在浏览器中直接运行。该项目已在GitHub开源，实现了将原本依赖本地Node.js环境的Sharp功能迁移至Web平台，用户无需本地安装即可在浏览器中进行图像处理。该发布在Hacker News上获得了103点关注度，展示了Web端机器学习与本地工具融合的新进展。

## 正文

ml-sharp-web

A browser-based Gaussian splat generator built on top of Apple SHARP. ✨

This project lets you:

upload one image

generate Gaussian splats in the browser

preview the result

download a .ply file

.ply

Links

Project repo: bring-shrubbery/ml-sharp-web

Follow the author on X: @bringshrubberyy

Upstream SHARP repo (Apple): apple/ml-sharp

SHARP project page: apple.github.io/ml-sharp

SHARP paper: arXiv:2512.10685

Before you start (important license note)

Apple's SHARP repository has separate licenses for code and model weights.

SHARP code license: LICENSE

SHARP model license: LICENSE_MODEL

If you use Apple's released SHARP checkpoint/weights, you must follow LICENSE_MODEL (research-use restrictions apply).

LICENSE_MODEL

What you need

Bun installed

A modern desktop browser (Chrome or Edge recommended)

Enough disk space and RAM for the SHARP model (the exported ONNX sidecar is large, ~2.4 GB)

Quick start (run the app) 🚀

1. Star this repo 🤩

If this project helps you, please star it:

bring-shrubbery/ml-sharp-web

2. Install dependencies

bun install

This also copies ONNX Runtime Web WASM assets into public/ort/ automatically.

public/ort/

3. Start the app

bun dev

Open the URL shown by Vite (usually http://localhost:5173).

http://localhost:5173

4. Use the app

Upload an image.

Click Generate Splat.

Generate Splat

Preview the result and download the .ply file.

.ply

Important model file note (.onnx + .onnx.data)

.onnx

.onnx.data

SHARP exports usually produce two files:

sharp_web_predictor.onnx

sharp_web_predictor.onnx

sharp_web_predictor.onnx.data

sharp_web_predictor.onnx.data

Both files must be served together from the same folder (for example public/models/).

public/models/

Why this matters:

The .onnx file is only the graph and metadata.

.onnx

The .onnx.data file contains most of the model weights.

.onnx.data

For that reason, the app uses the hosted model by default. Uploading only the .onnx file directly in the browser usually will not work because the .onnx.data sidecar is separate.

.onnx

.onnx.data

Export the SHARP model to ONNX (beginner-friendly steps)

Everything runs in the browser, but you still need an exported SHARP ONNX model.

1. Clone Apple's SHARP repo (reference code)

git clone https://github.com/apple/ml-sharp /tmp/ml-sharp-upstream

2. Prepare a Python environment for export

You need Python + SHARP dependencies + ONNX export dependencies.

The easiest route is to follow the upstream SHARP setup first, then run this exporter script from this repo.

3. Export the browser predictor ONNX

From this repo:

python3 scripts/export_sharp_onnx.py \ --sharp-repo /tmp/ml-sharp-upstream \ --output public/models/sharp_web_predictor.onnx

If the model is large (it is), the script will also write:

public/models/sharp_web_predictor.onnx.data

public/models/sharp_web_predictor.onnx.data

Optional export flags

--checkpoint /path/to/sharp_2572gikvuh.pt to use a manually downloaded checkpoint

--checkpoint /path/to/sharp_2572gikvuh.pt

--device cuda to export on GPU (if your environment supports it)

--device cuda

--opset 20 to change ONNX opset (default is 20)

--opset 20

20

Static build (optional)

If you want a static build instead of running bun dev:

bun dev

bun run build bun run preview

Notes:

bun run build copies public/ into dist/, including the model files.

bun run build

public/

dist/

If sharp_web_predictor.onnx.data is present, the build output will be very large.

sharp_web_predictor.onnx.data

How it works (high level)

React + TypeScript UI (src/)

src/

ONNX Runtime Web worker for inference (src/workers/sharpWorker.ts)

src/workers/sharpWorker.ts

Browser-side SHARP postprocessing (NDC -> metric gaussian conversion)

Browser-side PLY writer

In-page preview with @mkkellogg/gaussian-splats-3d

@mkkellogg/gaussian-splats-3d

Troubleshooting 🛠️

"expected magic word ... found 3c 21 64 6f" (WASM error)

This means a WASM file request returned HTML instead.

Try:

run the app with bun dev (not file://...)

bun dev

file://...

restart the dev server after bun install

bun install

verify these load in your browser: /ort/ort-wasm-simd-threaded.asyncify.mjs /ort/ort-wasm-simd-threaded.asyncify.wasm

/ort/ort-wasm-simd-threaded.asyncify.mjs

/ort/ort-wasm-simd-threaded.asyncify.mjs

/ort/ort-wasm-simd-threaded.asyncify.wasm

/ort/ort-wasm-simd-threaded.asyncify.wasm

"Failed to load external data file ... sharp_web_predictor.onnx.data"

This means the ONNX sidecar file is missing or not served correctly.

Check:

public/models/sharp_web_predictor.onnx

public/models/sharp_web_predictor.onnx

public/models/sharp_web_predictor.onnx.data

public/models/sharp_web_predictor.onnx.data

The app can reach the hosted model files in your deployment/browser environment

The app runs, but generation is very slow or crashes

SHARP is large and browser inference is heavy.

Try:

Chrome or Edge (desktop)

smaller Max gaussians in the UI

Max gaussians

closing other memory-heavy tabs/apps

waiting longer on first run (model + runtime initialization can take time)

Tech stack

Bun

React

TypeScript

Vite

ONNX Runtime Web

GaussianSplats3D viewer

Project status

Working prototype / experimental. 🧪

The app runs end-to-end in the browser, but performance and compatibility depend heavily on browser WebGPU/WASM support and your machine's available memory.