Post from Oct 24, 2025
Good post on using MLIR for compiling ML models to GPUs. It gives a good broad overview of a GPU architecture, and how MLIR fits into that. The overall series looks pretty interesting too!
Good post on using MLIR for compiling ML models to GPUs. It gives a good broad overview of a GPU architecture, and how MLIR fits into that. The overall series looks pretty interesting too!
Wrote a fresh implementation of most of the popular samplers and schedulers used for image generation (Stable Diffusion and Flux) at https://github.com/cmdr2/samplers.cpp. A few other schedulers (like Align Your Steps) have been left out for now, but are pretty easy to implement.
Some notes on machine-learning compilers, gathered while researching tech for Easy Diffusion's next engine (i.e. sdkit v3). For context, see the design constraints of the new engine.
The design constraints for Easy Diffusion's next engine (i.e. sdkit v3) are:
Cleared the backlog of stale issues on ED's github repo. This brought down the number of open issues from ~350 to 74.
Experimented with TensorRT-RTX (a new library offered by NVIDIA).
Development update for Easy Diffusion - It's chugging along in starts and stops. Broadly, there are three tracks:
Upgraded the default version of Easy Diffusion to Python 3.9. Newer versions of torch don't support Python 3.8, so this became urgent after the release of NVIDIA's 50xx series GPUs.
Easy Diffusion (and sdkit) now also support AMD on Windows automatically (using DirectML), thanks to integrating with torchruntime. It also supports integrated GPUs (Intel and AMD) on Windows, making Easy Diffusion faster on PCs without dedicated graphics cards.
Spent the last week or two getting torchruntime fully integrated into Easy Diffusion, and making sure that it handles all the edge-cases.