Denoising Diffusion Implicit Models

by   Jiaming Song, et al.

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 × to 50 × 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.


page 6

page 7

page 16

page 17

page 18

page 19


Semi-Implicit Denoising Diffusion Models (SIDDMs)

Despite the proliferation of generative models, achieving fast sampling ...

Latent Dynamical Implicit Diffusion Processes

Latent dynamical models are commonly used to learn the distribution of a...

Pseudo Numerical Methods for Diffusion Models on Manifolds

Denoising Diffusion Probabilistic Models (DDPMs) can generate high-quali...

Seismic Data Interpolation based on Denoising Diffusion Implicit Models with Resampling

The incompleteness of the seismic data caused by missing traces along th...

Dynamic Dual-Output Diffusion Models

Iterative denoising-based generation, also known as denoising diffusion ...

Unsupervised Denoising of Retinal OCT with Diffusion Probabilistic Model

Optical coherence tomography (OCT) is a prevalent non-invasive imaging m...

Code Repositories


Release for Improved Denoising Diffusion Probabilistic Models

view repo


Denoising Diffusion Implicit Models

view repo

Please sign up or login with your details

Forgot password? Click here to reset