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Last year, I announced Burn (https://github.com/burn-rs/burn), a new deep learning framework written in Rust. The response from the community was very positive, which encouraged me to continue working on the project and solidify the core architecture.
Also perhaps comparing to Neuronika.
Would this mean it could be possible to, say, port the inference code for Stable Diffusion to pure Rust using the NdArrayBackend? (Out of curiosity, what performance reduction percent would it likely mean over the CUDA backend?) I'm working on a pure Rust 2D graphics editor (Graphite) that will integrate lots of ML models for image processing and synthesis, but it's unfortunately looking like we'll have to very messily bundle a bunch of Python projects and somehow call into them. I dream of a pure Rust solution someday, and I'm wondering how feasible that might be over time.
Are you aware of https://github.com/LaurentMazare/diffusers-rs
I would't try to distribute your ml models with the typical frameworks, especially not with python. Have you looked in to ONNX?For example: https://github.com/pykeio/ort
A question I have is: what are the philosophical/design differences with dfdx? As someone who's played around with dfdx and only skimmed the README of burn, it seems like dfdx leans into Rust's type system/type inference for compile time checking of as much as is possible to check at compile time. I wonder if you've gotten a chance to look at dfdx and would like to outline what you think the differences are. Thanks!
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