Sample Creation

Learn about the process of sample creation by testing against the pretrained model.


The sampling process in diffusion models involves manipulating noise and iteratively modifying the initial noise to generate new data samples that follow the distribution learned during the model training phase. This process allows us to visualize the generation of images or data points and observe how the model creates new samples based on the learned patterns from the training data.


In this lesson, we’ll discuss sample creation, its details, and how it works across multiple different iterations of neural networks. Once the neural network is trained, sampling comes into play.

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