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Diffusion models are generative tools that create novel images and other complex data by learning to reverse a noising process, transforming random noise back into meaningful outputs. A key question about these models is how they generate genuinely new data rather than simply copying their training examples, a capability known as creativity. Researchers have found that this creativity arises from how neural networks naturally learn "smoothed" versions of the mathematical function that guides the denoising process, causing the model to generate data points that fall between training examples rather than exactly replicating them. This smoothing effect occurs due to regularization during neural network training and is essential for diffusion models to discover and generate points along the underlying structure of complex data like images.
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