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The term "forward diffusion" in the context of model training refers to the incremental addition of noise over time steps. In diffusion models, this process involves gradually transforming data into a noise distribution through a series of iterative steps. At each time step, a controlled amount of noise is added to the data, leading to a diffusion process where the original data becomes increasingly obscured. This technique is often used in training generative models, where the goal is to learn to reverse the diffusion process by denoising the data step-by-step.

The correct understanding of forward diffusion is crucial for training models that rely on generating new data from learned representations, as it emphasizes how data can be progressively altered, thereby aiding in the understanding of the eventual denoising step in reverse diffusion processes.