wgpu-mm
web-stable-diffusion
wgpu-mm | web-stable-diffusion | |
---|---|---|
1 | 21 | |
50 | 3,455 | |
- | 1.6% | |
8.7 | 4.4 | |
about 2 months ago | about 2 months ago | |
WGSL | Jupyter Notebook | |
- | Apache License 2.0 |
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wgpu-mm
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Chrome Ships WebGPU
This is very exciting! (I had suspected it would slip to 114)
WebGPU implementations are still pretty immature, but certainly enough to get started with. I've been implementing a Rust + WebGPU ML runtime for the past few months and have enjoyed writing WGSL.
I recently got a 250M parameter LLM running in the browser without much optimisation and it performs pretty well! (https://twitter.com/fleetwood___/status/1638469392794091520)
That said, matmuls are still pretty handicapped in the browser (especially considering the bounds checking enforced in the browser). From my benchmarking I've struggled to hit 50% of theoretical FLOPS, which is cut down to 30% when the bounds checking comes in. (Benchmarks here: https://github.com/FL33TW00D/wgpu-mm)
I look forward to accessing shader cores as they mentioned in the post.
web-stable-diffusion
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GPU-Accelerated LLM on a $100 Orange Pi
Yup, here's their web stable diffusion repo: https://github.com/mlc-ai/web-stable-diffusion
The input is a model (weights + runtime lib) compiled via the mlc-llm project: https://mlc.ai/mlc-llm/docs/compilation/compile_models.html
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StableDiffusion can now run directly in the browser on WebGPU
The MLC team got that working back in March: https://github.com/mlc-ai/web-stable-diffusion
Even more impressively, they followed up with support for several Large Language Models: https://webllm.mlc.ai/
- Web StableDiffusion
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[Stable Diffusion] Diffusion stable Web: exécution de diffusion stable directement dans le navigateur sans serveur GPU
[https://github.com/mlc-ai/web-stable-diffusion
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Now that they started banning stable diffusion on google colab, what's the cheapest and the best way to deploy stable diffusion?
You can run it directly in the browser with WebGPU, https://mlc.ai/web-stable-diffusion/
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I've got Stable Diffusion integrated into my site now, fully client side with no setup or servers.
Using the amazing work of https://mlc.ai/web-stable-diffusion/ I've got the code moved into a Web Worker and running fully local client side. It does require 2GB's of model files be downloaded (automatically), and takes a few minutes for the first load, but it works and once it's going it only takes 20s to make a 512x512 image.
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Chrome Ships WebGPU
The Apache TVM machine learning compiler has a WASM and WebGPU backend, and can import from most DNN frameworks. Here's a project running Stable Diffusion with webgpu and TVM [1].
Questions exist around post-and-pre-processing code in folks' Python stacks, with e.g. NumPy and opencv. There's some NumPy to JS transpilers out there, but those aren't feature complete or fully integrated.
[1] https://github.com/mlc-ai/web-stable-diffusion
- Bringing stable diffusion models to web browsers
- mlc-ai/web-stable-diffusion: Bringing stable diffusion models to web browsers. Everything runs inside the browser with no server support.
- Web Stable Diffusion: Running Diffusion Models with WebGPU
What are some alternatives?
SHA256-WebGPU - Implementation of sha256 in WGSL
stable-diffusion-webui-directml - Stable Diffusion web UI
stablehlo - Backward compatible ML compute opset inspired by HLO/MHLO
rust-bert - Rust native ready-to-use NLP pipelines and transformer-based models (BERT, DistilBERT, GPT2,...)
wgpu-py - Next generation GPU API for Python
pygfx - A python render engine running on wgpu.
tfjs - A WebGL accelerated JavaScript library for training and deploying ML models.
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
webgpu-blas - Fast matrix-matrix multiplication on web browser using WebGPU
js-promise-integration - JavaScript Promise Integration