unpaint VS onnx

Here are the details: Running Unpaint on the Xbox Series consoles · axodox/unpaint Wiki (github.com)

I also started to build an app of my own on top of it called Unpaint (which you can download and try following the link), targeting Windows and (for now) DirectML. The app provides the basic Stable Diffusion pipelines - it can do txt2img, img2img and inpainting, it also implements some advanced prompting features (attention, scheduling) and the safety checker. It is lightweight and starts up quickly, and it is just ~2.5GB with a model, so you can easily put it on your fastest drive. Performance wise with single images is on par for me with CUDA and Automatic1111 with a 3080 Ti, but it seems to use more VRAM at higher batch counts, however this is a good start in my opinion. It also has an integrated model manager powered by Hugging Face - though for now I restricted it to avoid vandalism, however you can still convert existing models and install them offline (I will make a guide soon). And as you can see on the above images: it also has a simple but nice user interface.

The source code is on GitHub (here: axodox/native-diffusion (github.com)), there is also an installer in releases, if you want to try it.

I made a C++ test project here: Release Olive Test · axodox/native-diffusion (github.com)

Well, I am working on a port of StableDiffusion which uses C++ only, no python, no other dependencies and crap. I have txt2img, img2img and inpainting fully working now.

Once your model has been trained and validated using Anomalib, the next step is to prepare it for real-time implementation. This is where ONNX (Open Neural Network Exchange) or OpenVINO (Open Visual Inference and Neural network Optimization) comes into play.

ONNX is an open format from the Linux Foundation to represent machine learning models. It is becoming extensively adopted by the Machine Learning community and is compatible with most of the machine learning frameworks like PyTorch, TensorFlow, etc. Converting a model between any of those formats and ONNX is really simple and can be done in most cases with a single command.

ONNX[0], model-as-protosbufs, continuing to gain adoption will hopefully solve this issue.

[0] https://github.com/onnx/onnx

Running inference for large models on CPU is not a new problem and fortunately there has been great development in many different optimization frameworks for speeding up matrix and tensor computations on CPU. We explored multiple optimization frameworks and methods to improve latency, namely TorchScript, BetterTransformer and ONNX.

ONNX is a format for representing machine learning models in a portable way. Additionally, ONNX models can be easily optimized and thus become smaller and faster.

I would say onnx.ai [0] provides more information about ONNX for those who aren’t working with ML/DL.

[0] https://onnx.ai

It's not clear why you thing this sub is appropriate for some third party system with a Python interface. Why don't you try their discussion group: https://github.com/onnx/onnx/discussions

This kind of indirection through standardisation is pretty common to make compatibility between different kinds of software components easier. Some other good examples are the LSP project from Microsoft and ONNX to represent machine learning models. The first provides a standard so that IDEs don't have to re-invent the weel for every programming language. The latter decouples training frameworks from inference frameworks. Going back to WSGI, you can find a pretty extensive rationale for the WSGI standard here if interested.