web-stable-diffusion VS onnxruntime

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

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/

[https://github.com/mlc-ai/web-stable-diffusion

You can run it directly in the browser with WebGPU, https://mlc.ai/web-stable-diffusion/

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.

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

ONNX Runtime: ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

Embedding generation uses the ONNX runtime under the hood. This is a cross-platform inferencing library that supports multiple execution providers from CPU to specialized GPUs.

tfjs is dead, looking at the commit history. The standard now is to convert PyTorch to onnx, then use onnxruntime (https://github.com/microsoft/onnxruntime/tree/main/js/web) to run the model on the browsdr.

- slang[2] has the potential, but the meta programming part is not as strong as C++, existing libraries cannot be used.

The above conclusion is drawn from my work https://github.com/microsoft/onnxruntime/tree/dev/opencl, purely nightmare to work with thoes drivers and jit compilers. Hopefully Vcc can take compute shader more seriously.

[1]: https://www.circle-lang.org/

I did. It depends what you want, for an overview of how ONNX Runtime works then Microsoft have a bunch of things on https://onnxruntime.ai, but the Java content is a bit lacking on there as I've not had time to write much. Eventually I'll probably write something similar to the C# SD tutorial they have on there but for the Java API.

For writing ONNX models from Java we added an ONNX export system to Tribuo in 2022 which can be used by anything on the JVM to export ONNX models in an easier way than writing a protobuf directly. Tribuo doesn't have full coverage of the ONNX spec, but we're happy to accept PRs to expand it, otherwise it'll fill out as we need it.

What about models besides GPT? Most of the popular vector encoding models aren't using this architecture.

If you really didn't want PyTorch/Transformers, you could consider exporting your models to ONNX (https://github.com/microsoft/onnxruntime).