rembg-webgpu

Blazing fast and Robust Background removal for the Web.

🚀 Try Live Demo – See it in action with your own images

Benchmark

Performance benchmarks on M1 MacBook Pro (WebGPU enabled):

Note: First-time initialization adds delay for model download and compilation (cached thereafter). WASM fallback is approximately 3-5× slower than WebGPU.

Have different hardware? We'd love to see benchmarks from your device! Submit a PR with your results (include device specs, browser, and whether WebGPU or WASM was used).

What is it?

rembg-webgpu is a production-ready, client-side background removal library that runs entirely in the browser. Built on rembg.com's distilled AI model and powered by @huggingface/transformers, it delivers state-of-the-art segmentation without server dependencies or privacy compromises.

Core Features:

• Intelligent Backend Selection – Automatically detects and uses the best available backend:
  • WebGPU with FP16 (shader-f16) for maximum performance
  • WebGPU with FP32 fallback if FP16 unavailable
  • WASM with FP32 as universal fallback
• Runtime Capability Detection – Query device capabilities before initialization via getCapabilities()
• Zero Server Dependency – Complete offline processing; your users' images never leave their device
• Granular Progress Tracking – Advanced hooks for download/building/ready phases with percentage progress
• Advanced Optimization – OffscreenCanvas worker-based compositing with automatic main-thread fallback
• Smart Caching – Memory + browser cache for instant subsequent loads
• Automatic Preview Generation – Returns both full-resolution and optimized preview URLs
• Headless by Design – No opinionated UI; bring your own interface and workflows
• TypeScript Native – Full type safety with exported types for all APIs

Why rembg-webgpu is Different

Unlike most browser-based background removal solutions that are merely thin wrappers around demo code, rembg-webgpu was engineered from the ground up for production environments.

We started with @huggingface/transformers as a foundation, then extensively rebuilt and optimized the entire pipeline with:

• Custom fetch interception for granular download progress tracking
• Intelligent device capability detection and automatic backend selection
• Worker-based compositing architecture to keep the main thread responsive
• Memory-efficient chunked processing for large images
• Sophisticated caching strategies across memory and browser storage

The result is a library that doesn't just work in demos—it scales to real-world applications with thousands of users.

Install

npm i rembg-webgpu

Your bundler must support web workers via new URL('./worker.ts', import.meta.url) (Vite, Webpack 5, etc.).
Modern browsers only.

Sample code

import { removeBackground, subscribeToProgress, getCapabilities } from 'rembg-webgpu';

// Optional: Check device capabilities before initialization
const capability = await getCapabilities();
console.log(`Backend: ${capability.device}, Precision: ${capability.dtype}`);
// Possible results:
// - { device: 'webgpu', dtype: 'fp16' } - Best performance
// - { device: 'webgpu', dtype: 'fp32' } - Good performance
// - { device: 'wasm', dtype: 'fp32' }   - Universal fallback

// Optional: Subscribe to ONNX download/build progress to show a loader
const unsubscribe = subscribeToProgress(({ phase, progress }) => {
  // phase: 'idle' | 'downloading' | 'building' | 'ready' | 'error'
  // progress: 0..100 (ready sets to 100)
  console.log(`${phase}: ${progress}%`);
});

// Remove background from an image
const result = await removeBackground(imageUrl);

// Clean up when done
unsubscribe();

Full Documentation & walkthrough guide

rembg.com's blog

Technical Details

Backend Selection

• Automatically detects WebGPU support and FP16 (shader-f16) capability
• Falls back gracefully: WebGPU FP16 → WebGPU FP32 → WASM FP32

Performance Optimizations

• First call downloads and initializes the model on initial run-up; subsequent calls use memory + browser cache
• Worker-based OffscreenCanvas compositing offloads processing from main thread
• Chunked image processing (512px strips) prevents memory spikes on large images
• Automatic preview generation (≤450px) for instant UI feedback

Resource Management

• You control input image sizing before calling removeBackground(url) for optimal performance
• Blob URLs are not automatically revoked—call URL.revokeObjectURL() when done to prevent memory leaks
• Model weights (~40-50MB) cached in browser after first download

Roadmap

• WebGPU acceleration with FP16/FP32 precision detection
• Automatic WASM fallback
• Runtime device capability detection API
• Granular progress tracking for model downloads
• OffscreenCanvas worker-based compositing
• Memory + browser caching
• Offline-first architecture
• Full TypeScript support
• Native batch processing API
• Custom model support with zero-config
• Mobile-optimized version

Attribution

Background Removal Library provided by www.rembg.com

License

This project is licensed under the RemBG Attribution License (MIT-Compatible). See the LICENSE file for details.