Verified75% confidenceFactExact time
Diffusion models restore target images or video frames from random noise through stepwise denoising, typically requiring dozens of denoising iterations, with extremely high computational overhead.
5
Sources
75%
Confidence
Long-term
Relevance
7/12/2026
First Seen
Sources
Related Claims
VerifiedInference in diffusion models is an iterative denoising process that typically requires dozens or even hundreds of timesteps and is particularly sensitive to numerical errors.83% similarVerifiedThe iterative denoising process of diffusion models is sensitive to numerical errors; small errors at each step accumulate and affect the final image, and the more sampling steps there are, the greater the impact of quantization errors.78% similarVerifiedThe more sampling steps a diffusion model uses, the more thorough the denoising and the higher the image quality, but the computational time also increases proportionally.77% similarUnverifiedDiffusion language models often require dozens or even hundreds of denoising iterations to ensure generation quality, with each step requiring one complete forward pass, so the actual speed advantage is not always apparent.75% similarUnverifiedThe reverse process of a diffusion model can only be approximated as a Markov chain when the number of noise-adding steps is sufficiently large and the noise at each step is sufficiently small.75% similar
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