Achieving Stable Diffusion in ONNX: A Comprehensive Guide #ONNX

The article discusses the ONNX runtime, a way to speed up Stable Diffusion inference on NVIDIA GPUs. Installing the right versions of dependencies can be challenging due to the evolving ecosystem. The article provides a debugging guide for addressing compatibility issues during installation. ONNX can refer to a format for storing ML models and a runtime to run models in ONNX format. The article focuses on running Stable Diffusion models using the ONNX runtime and discusses the pros and cons of using ONNX. It also provides options for installation from source or via PyPI. The article also explores the use of Hugging Face’s Optimum library for running models on various accelerators. The author shares struggles with the Optimum library and recommends using Microsoft’s official scripts for SDXL inference. The ONNX runtime promises significant latency gains but comes with engineering overhead and limitations in dynamic modifications. It is recommended for efficient production code after the experimentation phase. The article concludes with resources for optimizing inference time.

Source link

Source link: https://towardsdatascience.com/how-to-run-stable-diffusion-with-onnx-dafd2d29cd14