CppFast Diffusers Inference (CFDI) is a C++ project. Its purpose is to leverage the acceleration capabilities of ONNXRuntime and the high compatibility of the .onnx model format to provide a convenient solution for the engineering deployment of Stable Diffusion.
The project aims to implement a high-performance SD inference library based on C/C++ using ONNXRuntime, comparable to HuggingFace Diffusers, with high model interchangeability.
Why choose ONNXRuntime as our Inference Engine?
Open Source: ONNXRuntime is an open-source project, allowing users to freely use and modify it to suit different application scenarios.
Scalability: It supports custom operators and optimizations, allowing for extensions and optimizations based on specific needs.
High Performance: ONNXRuntime is highly optimized to provide fast inference speeds, suitable for real-time applications.
Strong Compatibility: It supports model conversion from multiple deep learning frameworks (such as PyTorch, TensorFlow), making integration and deployment convenient.
Cross-Platform Support: ONNXRuntime supports multiple hardware platforms, including CPU, GPU, TPU, etc., enabling efficient execution on various devices.
Community and Enterprise Support: Developed and maintained by Microsoft, it has an active community and enterprise support, providing continuous updates and maintenance.
Below show What actually happened in [Example: 1-step img2img inference] in Latent Space (Skip All Models):
CppFast Diffusers Inference (CFDI)
CppFast Diffusers Inference (CFDI) is a C++ project. Its purpose is to leverage the acceleration capabilities of ONNXRuntime and the high compatibility of the .onnx model format to provide a convenient solution for the engineering deployment of Stable Diffusion.
You can find Project here: https://github.com/Windsander/CFDI-StableDiffusionONNXFast
The project aims to implement a high-performance SD inference library based on C/C++ using ONNXRuntime, comparable to HuggingFace Diffusers, with high model interchangeability.
Why choose ONNXRuntime as our Inference Engine?
Open Source: ONNXRuntime is an open-source project, allowing users to freely use and modify it to suit different application scenarios.
Scalability: It supports custom operators and optimizations, allowing for extensions and optimizations based on specific needs.
High Performance: ONNXRuntime is highly optimized to provide fast inference speeds, suitable for real-time applications.
Strong Compatibility: It supports model conversion from multiple deep learning frameworks (such as PyTorch, TensorFlow), making integration and deployment convenient.
Cross-Platform Support: ONNXRuntime supports multiple hardware platforms, including CPU, GPU, TPU, etc., enabling efficient execution on various devices.
Community and Enterprise Support: Developed and maintained by Microsoft, it has an active community and enterprise support, providing continuous updates and maintenance.
Below show What actually happened in [Example: 1-step img2img inference] in Latent Space (Skip All Models):
See Details on the Project Main Page