Unverified50% confidenceFactExact time
DDIM generalizes the stochastic Markov reverse process of standard diffusion models into a non-Markovian, skip-capable deterministic or semi-deterministic process.
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11/2/2026
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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 10–50.75% similarUnverifiedThe denoising sampling process of a diffusion model can be regarded as the solution of a stochastic differential equation, comprising a deterministic drift term and a stochastic diffusion term.73% similarUnverifiedThe sampling process of continuous diffusion models can be realized as a deterministic trajectory via the probability flow ordinary differential equation (ODE), supporting accelerated sampling and consistency distillation.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% similarUnverifiedFor stochastic algorithms with momentum or adaptive step sizes, their SDE approximations are often easier to subject to Lyapunov analysis, and through rate transfer these continuous-time results can be quickly mapped back to the discrete algorithms.73% similar
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