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In 2021, Song et al. unified DDPM and score matching under a continuous-time SDE framework, establishing the Score-based Generative Modeling through SDEs paradigm.
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9/11/2026
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UnverifiedThe theoretical foundation of diffusion models was unified by Song Yang et al. in 2020 through a stochastic differential equation (SDE) framework; the discovery of probability flow ODEs enabled deterministic sampling and gave rise to accelerated sampling methods such as DDIM, compressing the number of sampling steps from 1000 to 10–50.72% similarVerifiedIn 2020, DDPM (Denoising Diffusion Probabilistic Models) pioneered a paradigm in the image generation field of achieving high-quality generation through progressive noise addition followed by progressive denoising.67% similarUnverifiedStable Diffusion 基于去噪扩散概率模型（DDPM）理论，将扩散过程从像素空间转移到低维潜在空间，由VAE负责编解码，CLIP模型将文本提示编码为语义向量61% similarUnverifiedThe core mathematical logic of diffusion models is based on stochastic differential equations (SDE) and denoising score matching, generating images through network architectures such as U-Net or DiT via tens to hundreds of iterative sampling steps.60% similarVerifiedThe denoising process of diffusion models is based on the DDPM principle, which involves progressively adding Gaussian noise to data during training and then training a neural network to learn the reverse denoising process.59% similar
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