Unverified60% confidenceFactExact time
The 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.
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60%
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Long-term
Relevance
7/21/2026
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Related Claims
UnverifiedYang Song et al. unified the diffusion process under the framework of continuous-time stochastic differential equations and proposed score-based generative models.77% similarUnverified2020年后兴起的扩散模型（Diffusion Models）直接建立在随机微分方程与概率流的数学框架之上74% 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73% similarUnverified扩散模型推理速度较慢，通常需要数十步去噪迭代才能生成一张图像，这催生了DDIM、DPM-Solver等加速采样算法73% 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.73% similar
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