OnnxStack transforms machine learning in .NET, Seamlessly integrating with ONNX Runtime and Microsoft ML, this library empowers you to build, deploy, and execute machine learning models entirely within the .NET ecosystem. Bid farewell to Python dependencies and embrace a new era of intelligent applications tailored for .NET
Model Inference with C# and ONNX Runtime
OnnxStack.Core is a .NET library designed to facilitate seamless interaction with the OnnxRuntime C# API. This project simplifies the creation and disposal of OrtValues and offers straightforward services for loading and running inferences on a variety of models. With a focus on improving developer efficiency, the library abstracts complexities, allowing for smoother integration of OnnxRuntime into .NET applications.
More information and examples can be found in the OnnxStack.Core project README
Stable Diffusion Inference with C# and ONNX Runtime
OnnxStack.StableDiffusion is a .NET library for latent diffusion in C#, Leveraging OnnxStack.Core, this library seamlessly integrates many StableDiffusion capabilities, including:
- Text to Image
- Image to Image
- Image Inpaint
- Video to Video
- Control Net
OnnxStack.StableDiffusion provides compatibility with a diverse set of models, including
- StableDiffusion 1.5
- StableDiffusion Inpaint
- StableDiffusion ControlNet
- Stable-Cascade
- SDXL
- SDXL Inpaint
- SDXL-Turbo
- LatentConsistency
- LatentConsistency XL
- Instaflow
More information can be found in the OnnxStack.StableDiffusion project README
Image upscaler with C# and ONNX Runtime
OnnxStack.ImageUpscaler is a library designed to elevate image quality through superior upscaling techniques. Leveraging OnnxStack.Core, this library provides seamless integration for enhancing image resolution and supports a variety of upscaling models, allowing developers to improve image clarity and quality. Whether you are working on image processing, content creation, or any application requiring enhanced visuals, the ImageUpscale project delivers efficient and high-quality upscaling solutions.
More information and examples can be found in the OnnxStack.ImageUpscaler project README
Image recognition with ResNet50v2 and ONNX Runtime
Harness the accuracy of the ResNet50v2 deep learning model for image recognition, seamlessly integrated with ONNX for efficient deployment. This combination empowers your applications to classify images with precision, making it ideal for tasks like object detection, content filtering, and image tagging across various platforms and hardware accelerators. Achieve high-quality image recognition effortlessly with ResNet50v2 and ONNX integration.
work in progress
Object detection with Faster RCNN Deep Learning with C# and ONNX Runtime
Enable robust object detection in your applications using RCNN (Region-based Convolutional Neural Network) integrated with ONNX. This powerful combination allows you to accurately locate and classify objects within images. Whether for surveillance, autonomous vehicles, or content analysis, RCNN and ONNX integration offers efficient and precise object detection across various platforms and hardware, ensuring your solutions excel in recognizing and localizing objects in images.
work in progress
We welcome contributions to OnnxStack! If you have any ideas, bug reports, or improvements, feel free to open an issue or submit a pull request.
- Join our Discord: OnnxStack Discord
- Chat to us here: Project Discussion Board
Special thanks to the creators of the fantastic repositories below; all were instrumental in the creation of OnnxStack.
- Stable Diffusion with C# and ONNX Runtime by Cassie Breviu (@cassiebreviu)
- Diffusers by Huggingface (@huggingface)
- Onnx-Web by Sean Sube (@ssube)
- Axodox-MachineLearning by Péter Major @(axodox)
- ControlNet by Lvmin Zhang (@lllyasviel)