Diffusion models are a class of generative AI models that create data by simulating a gradual process of denoising or reversing noise added to an initial signal. In contrast to traditional models like GANs, diffusion models start with pure noise and iteratively refine it to generate realistic outputs, such as images, audio, or text. These models have gained popularity for their ability to produce high-quality, diverse results and are widely used in applications like image generation and enhancement in AI systems.

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