![]() ![]() num_inference_steps - The number of steps to run inference for.prompt - The text prompt to use for the image.It then calls the UNet.Inference function to run the inference. The main function sets the prompt, number of inference steps, and the guidance scale. The scheduler algorithm and the unet model work together to denoise the image to create an image that represents the text prompt. The latent noisy image is created as a starting point. Now lets start to breakdown how to inference in C#! The unet model takes the text embedding of the user prompt created by the CLIP model that connects text and image. If you want to learn more about how it works check out this amazing blog post for more details! Inference with C# This code is based on the Hugging Face Diffusers Library and Blog. When taking a prebuilt model and operationalizing it, its useful to take a moment and understand the models in this pipeline. Understanding the model in Python with Diffusers from Hugging Face The folders to copy are: unet, vae_decoder, text_encoder, safety_checker. Copy the folders with the ONNX files to the C# project folder \StableDiffusion\StableDiffusion.See the ONNX conversion tutorial for PyTorch for more information. If there isn’t an ONNX model branch available, use the main branch and convert it to ONNX. Once you have selected a model version repo, click Files and Versions, then select the ONNX branch. We will leverage and download the ONNX Stable Diffusion models from Hugging Face. The Hugging Face site has a great library of open source models. Use Hugging Face to download the Stable Diffusion models To run in the cloud with Azure Machine Learning: This was built on a GTX 3070 and it has not been tested on anything smaller.Be sure to clone the direct-ML-EP branch of this repo if you choose this option. Follow this tutorial to configure CUDA and cuDNN for GPU with ONNX Runtime and C# on Windows 11 This tutorial can be run locally or in the cloud by leveraging Azure Machine Learning compute.Ī GPU enabled machine with CUDA or DirectML on Windows Postprocess the output with the VAEDecoder.The Inference Loop: UNet model, Timesteps and LMS Scheduler.Text embedding with the CLIP text encoder model.Tokenization with ONNX Runtime Extensions.Understanding the model in Python with Diffusers from Hugging Face.Use Hugging Face to download the Stable Diffusion models.This site uses Just the Docs, a documentation theme for Jekyll. Object detection with Faster RCNN in C#.Image recognition with ResNet50v2 in C#.Custom Excel Functions for BERT Tasks in JavaScript.Classify images with ONNX Runtime and Next.js. ![]()
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