Diffusion models are a generative AI technique that transforms random noise into realistic data by reversing a gradual noising process that corrupts training data. Initially, Gaussian noise is added step-by-step until the data loses its original structure. The model is trained to denoise this input, predicting cleaner versions iteratively. This method is grounded in physics principles, where pixel values diffuse into noise similar to molecular diffusion.
Diffusion models excel in generating high-quality images and have outperformed older methods like GANs and VAEs in tasks such as super-resolution, inpainting, and text-to-image generation, powering systems like DALL-E 2 and Stable Diffusion. Their advancements hinge on a structured training approach using Markov chains and variational inference, enabling them to produce diverse and coherent outputs effectively. This breakthrough technique has expanded applications beyond images, branching into audio, video, and molecular modeling, cementing diffusion models as a cornerstone of modern generative AI.
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