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Denoising Diffusion Implicit Models
Authors:
,
,
Abstract
DDIMs, an efficient variant of DDPMs, accelerate image sampling by leveraging non-Markovian processes, offering faster sample generation and improved interpolation capabilities.
Denoising diffusion probabilistic models (DDPMs) have achieved high quality
image generation without adversarial training, yet they require simulating a
Markov chain for many steps to produce a sample. To accelerate sampling, we
present denoising diffusion implicit models (DDIMs), a more efficient class of
iterative implicit probabilistic models with the same training procedure as
DDPMs. In DDPMs, the generative process is defined as the reverse of a
Markovian diffusion process. We construct a class of non-Markovian diffusion
processes that lead to the same training objective, but whose reverse process
can be much faster to sample from. We empirically demonstrate that DDIMs can
produce high quality samples 10 times to 50 times faster in terms of
wall-clock time compared to DDPMs, allow us to trade off computation for sample
quality, and can perform semantically meaningful image interpolation directly
in the latent space.