title: Beyond U: Making Diffusion Models Faster & Lighter
summary: 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.
paper_authors._id: 6541c2065019954eef9e6b7f
paper_authors.name: Sergio Calvo-Ordonez
paper_authors._id: 6541c2065019954eef9e6b80
paper_authors.name: Jiahao Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-02T16:16:24.695Z
paper_authors._id: 63a8683732ed73936eb96d10
paper_authors.avatarUrl: /avatars/be3bf6a83cf5875aeeb8f23d3ce2f947.svg
paper_authors.fullname: Jiahao Huang
paper_authors.type: user
paper_authors.user: JiahaoHuang
paper_authors._id: 6541c2065019954eef9e6b81
paper_authors.name: Lipei Zhang
paper_authors._id: 6541c2065019954eef9e6b82
paper_authors.name: Guang Yang
paper_authors._id: 6541c2065019954eef9e6b83
paper_authors.name: Carola-Bibiane Schonlieb
paper_authors._id: 6541c2065019954eef9e6b84
paper_authors.name: Angelica I Aviles-Rivero
paper_title: Beyond U: Making Diffusion Models Faster & Lighter
paper_summary: 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.
paper_ai_summary: A novel denoising network using continuous dynamical systems improves diffusion models by reducing parameters and FLOPs while enhancing convergence speed and noise robustness.
paper_ai_keywords: 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