Diffusion models are introduced to incrementally reconstruct images through a degradation process involving forward and reverse Markov chains, effectively denoising corrupted data.
The forward process transforms a sample from the data distribution into progressively noised data, ultimately leading to Gaussian noise, while the reverse chain recovers images.
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