webgpu-blas
web-stable-diffusion
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| webgpu-blas | web-stable-diffusion | |
|---|---|---|
| 3 | 21 | |
| 97 | 3,434 | |
| - | 2.3% | |
| 4.8 | 4.4 | |
| 3 months ago | about 2 months ago | |
| TypeScript | Jupyter Notebook | |
| MIT License | Apache License 2.0 |
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webgpu-blas
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Chrome Ships WebGPU
Looks like no -- there appears to be no tensor core or similar support and this SGEMM (fp32 matrix multiply) benchmark gets awful results (my laptop gets 330gflops on this when it should be capable of 13000 gflops).
https://github.com/milhidaka/webgpu-blas
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Modern JavaScript:Everything you missed over the last 10 years(ECMAScript 2020)
I think you will be interested to read this article about the future of data programming in JavaScript (http://benschmidt.org/post/2020-01-15/2020-01-15-webgpu/).
I do think that this kind of thing will be able to be built on top of WebGPU (I saw this experimental POC that did so recently: https://github.com/milhidaka/webgpu-blas). The only issue is that since JavaScript doesn't support operator overloading, the code might be a little less readable.
- JavaScript for Data Science
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?
numjs - Like NumPy, in JavaScript
rust-bert - Rust native ready-to-use NLP pipelines and transformer-based models (BERT, DistilBERT, GPT2,...)
Material UI - Ready-to-use foundational React components, free forever. It includes Material UI, which implements Google's Material Design.
stable-diffusion-webui-directml - Stable Diffusion web UI
SHA256-WebGPU - Implementation of sha256 in WGSL
wgpu-mm
wgpu-py - Next generation GPU API for Python
next-auth - Authentication for the Web.
js-promise-integration - JavaScript Promise Integration
icpts - TypeScript implementation of iterative closest point (ICP) for point cloud registration
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator