axodox / axodox-machinelearning Goto Github PK

Does the API support LCM onnx models?

Hello!
Very excited for this project!

Attempting to add Axodox.Common via NuGet to an Unreal Engine 5 project fails with the following error:

Severity Code Description Project File Line Suppression State
Error The expression "[System.IO.Directory]::GetFiles(P:\Unreal\TestProject\packages\Axodox.Common.1.0.14\build\native....\lib\desktop\x64\Development_Editor, *.lib)" cannot be evaluated. Could not find a part of the path 'P:\Unreal\TestProject\packages\Axodox.Common.1.0.14\lib\desktop\x64\Development_Editor'. P:\Unreal\TestProject\packages\Axodox.Common.1.0.14\build\native\Axodox.Common.targets

This is after setting the C++ standard to C++20 in visual studio as well as:

public class TestProject: ModuleRules
{
	public TestProject(ReadOnlyTargetRules Target) : base(Target)
	{
		CppStandard = CppStandardVersion.Cpp20
                ...

Hi,

First, nice project.

1. It has iOS/Android support?
2. What i need to run in iOS?
3. And what models are supported?
4. Can i use safetensors or ckpt models?

Thanks.

Hi! Amazing project, and I wonder if it is possible to build the project in the Mac m model or the x84_64 platform? I am not familiar with the Windows platform. Could you please provide a CmakeList.txt to build the project? Thanks for your answer in advance.

This implementation is really awesome.Is onnx running on the onnxruntime?

Because this thing is x64 only now.

Workaround: remove the win32 target from your project.

Hi,
I made sure I have all the dependencies listed in the readme, but I still can't build the project. It looks like there's at least one more dependency that I need to install, but I have no idea what it might be. Build output (see attached file for entire output):

Build started...
1>------ Build started: Project: Axodox.MachineLearning.Desktop, Configuration: Debug x64 ------
1>pch.cpp
1>C:\github2\axodox-machinelearning\packages\Axodox.Common.1.0.12\include\Graphics\Devices\GraphicsTypes.h(7,38): error C4430: missing type specifier - int assumed. Note: C++ does not support default-int
1>C:\github2\axodox-machinelearning\packages\Axodox.Common.1.0.12\include\Graphics\Devices\GraphicsTypes.h(7,25): error C2146: syntax error: missing ';' before identifier 'IDXGIFactoryT'
1>C:\github2\axodox-machinelearning\packages\Axodox.Common.1.0.12\include\Graphics\Devices\GraphicsTypes.h(8,38): error C4430: missing type specifier - int assumed. Note: C++ does not support default-int
1>C:\github2\axodox-machinelearning\packages\Axodox.Common.1.0.12\include\Graphics\Devices\GraphicsTypes.h(8,25): error C2146: syntax error: missing ';' before identifier 'IDXGIAdapterT'
// more errors follow ...

Looks like for whatever reason, the symbols in GraphicsTypes.h (IDXGIFactory4, IDXGIAdapter3, ID3D11Device5, etc) are undefined.
BuildErrors.txt

It compiles fine, but does not generate anything (not sure if it is meant to).
I also wonder where I have to put the model files exactly for that (libfolder)...
What I would like to do: Run stable diffusion with Open Frameworks (maybe as an addon - there is already one for .onnx files).
For that I do not need Axodox::Graphics, because I would copy the pixels of the resulting tensor to an openGL texture...

Recently I tried to use library in my console application. It is important to note that application is not UWP based. Unfortunately I encountered error - Exception thrown at 0x00007FFD7857CF19 (KernelBase.dll) in Console.exe: WinRT originate error - 0x80073D54 : 'The process has no package identity.'. It seems like that WinRt restricts model of application. Is there any way to exclude usage of Winrt? or another way to combine WinRt library with non uwp application?

Is there any way to use LoRA models with the library? Should be they converted like main models?

Hi axodox,

I want to run the test, but all of the test results show "Failed to set to the execution context."

I have run update_dependencies.ps1 and build_nuget.ps1.

Did I miss something?

Thanks!

Some SD finetunes (and the official Stable Diffusion 2) use v-prediction because they observe that it's better
Currently, v-prediction models will export with Olive and run fine with the ONNX diffusers pipeline, but not here. This is "a castle" on one I exported

I tried modifying DpmPlusPlus2MScheduler::ApplyStep by modifying the predictedOriginalSample based off one of the diffusers implementations

        auto predictedOriginalSample = output.BinaryOperation<float>(input, [currentSigma](float a, float b) {
            float sigmaSquared = currentSigma * currentSigma;
            return (a * (-currentSigma / sqrt(sigmaSquared + 1))) + (b / sqrt(sigmaSquared + 1));
            });

I could only make the result semi-coherent

In DPMPlusPlus2MScheduler, the formula from HF's euler discrete works

        auto predictedOriginalSample = output.BinaryOperation<float>(input, [currentSigma](float model_output, float sample){
            float sigmaSquaredPlusOne = currentSigma * currentSigma + 1;
            return (model_output * (-currentSigma / std::sqrt(sigmaSquaredPlusOne))) + (sample / sigmaSquaredPlusOne);
            });

If you could finish this (haven't figured out EulerAncestralScheduler) (I have everything here), it would greatly improve compatibility. I uploaded the model in case it's useful.

Hello,

I'd like to ask for clarification on how to set up the following instructions specifically.

Once you have built the library, you override your existing nuget package install by setting the AxodoxMachineLearning-Location environment variable to point to your local build.

For example C:\dev\axodox-machinelearning\Axodox.MachineLearning.Universal for an UWP app and C:\dev\axodox-machinelearning\Axodox.MachineLearning.Desktop for a desktop app.

Then add the project pointed by the path to the solution of your own project. This allows to add all projects into the same solution and make changes on the library and your app seamlessly without copying files repeatedly.

I'm not very familiar with these, so I apologize for any inconvenience. Thank you for your assistance.

where do I find this model? Seems like it's hardcoded into TextTokenizer::TextTokenizer