Beyond U: Making Diffusion Models Faster & Lighter
Diffusion models are a family of generative models that yield record-breaking
performance in tasks such as image synthesis, video generation, and molecule
design. Despite their capabilities, their efficiency, especially in the reverse
denoising process, remains a challenge due to slow convergence rates and high
computational costs. In this work, we introduce an approach that leverages
continuous dynamical systems to design a novel denoising network for diffusion
models that is more parameter-efficient, exhibits faster convergence, and
demonstrates increased noise robustness. Experimenting with denoising
probabilistic diffusion models, our framework operates with approximately a
quarter of the parameters and 30% of the Floating Point Operations (FLOPs)
compared to standard U-Nets in Denoising Diffusion Probabilistic Models
(DDPMs). Furthermore, our model is up to 70% faster in inference than the
baseline models when measured in equal conditions while converging to better
quality solutions.
6541c2065019954eef9e6b7f
Sergio Calvo-Ordonez
6541c2065019954eef9e6b80
Jiahao Huang
admin_assigned
2023-11-02T16:16:24.695Z
63a8683732ed73936eb96d10
/avatars/be3bf6a83cf5875aeeb8f23d3ce2f947.svg
Jiahao Huang
user
JiahaoHuang
6541c2065019954eef9e6b81
Lipei Zhang
6541c2065019954eef9e6b82
Guang Yang
6541c2065019954eef9e6b83
Carola-Bibiane Schonlieb
6541c2065019954eef9e6b84
Angelica I Aviles-Rivero
Beyond U: Making Diffusion Models Faster & Lighter
Diffusion models are a family of generative models that yield record-breaking
performance in tasks such as image synthesis, video generation, and molecule
design. Despite their capabilities, their efficiency, especially in the reverse
denoising process, remains a challenge due to slow convergence rates and high
computational costs. In this work, we introduce an approach that leverages
continuous dynamical systems to design a novel denoising network for diffusion
models that is more parameter-efficient, exhibits faster convergence, and
demonstrates increased noise robustness. Experimenting with denoising
probabilistic diffusion models, our framework operates with approximately a
quarter of the parameters and 30% of the Floating Point Operations (FLOPs)
compared to standard U-Nets in Denoising Diffusion Probabilistic Models
(DDPMs). Furthermore, our model is up to 70% faster in inference than the
baseline models when measured in equal conditions while converging to better
quality solutions.
A novel denoising network using continuous dynamical systems improves diffusion models by reducing parameters and FLOPs while enhancing convergence speed and noise robustness.
diffusion models generative models image synthesis video generation molecule design reverse denoising process continuous dynamical systems denoising network parameter-efficient faster convergence noise robustness denoising probabilistic diffusion models DDPMs Floating Point Operations inference