Scheduler – Computes denoised image embeddings.U-Net – Neural network architecture typically used for the task of image segmentation.Variable Autoencoder (VAE) – Encodes and decodes images to embeddings.The sample referenced in this post uses a combination of ONNX Runtime Extensions implementation of the OpenAI’s Contrastive Language-Image Pre-Training (CLIP) and ONNX models. Text encoder – Encodes text to embeddings.The main components in Stable Diffusion are: Stable Diffusion is an AI model that can generate images based on a text prompt.Īlthough the theory and innovations behind Stable Diffusion can be complex, it’s made up of relatively few components. To learn more, visit the ONNX and ONNX Runtime websites. Load and consume the ONNX model in a different framework or language than the one the model was originally trained with like C#.Train a model in one of the many popular machine learning frameworks that support ONNX conversion.The ONNX Runtime (ORT) is a runtime for ONNX models which provides an interface for accelerating the consumption / inferencing of machine learning models, integrating with hardware-specific libraries, and sharing models across programming languages and frameworks like PyTorch, Tensorflow / Keras, scikit-learn, ML.NET, and others. The Open Neural Network Exchange (ONNX) is an open source format to represent AI models. NET technologies like C# and Visual Studio! What is ONNX Runtime? Using ONNX Runtime you can quickly get started generating AI images locally using your preferred. Stable Diffusion is an AI model used to generate images based on text prompts.
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