Unverified50% confidenceFactExact time
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
1
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50%
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Long-term
Relevance
7/20/2026
First Seen
Sources
Related Claims
UnverifiedIn 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.77% similarUnverifiedConsistency models attempt to compress the multi-step denoising process of diffusion models into 1–4 steps, theoretically capable of improving inference speed by 10–50 times.76% similarUnverifiedDiffusion models typically require dozens to hundreds of denoising steps when generating images, with each step having a U-Net or Transformer network predict the noise component.73% similarVerified扩散模型通过在海量图像数据上训练，学会了从随机噪声中逐步去噪生成图像的能力73% similarUnverifiedThe 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 10–50.73% similar
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