title: Diffusion Models without Classifier-free Guidance
summary: This paper presents Model-guidance (MG), a novel objective for training
  diffusion model that addresses and removes of the commonly used Classifier-free
  guidance (CFG). Our innovative approach transcends the standard modeling of
  solely data distribution to incorporating the posterior probability of
  conditions. The proposed technique originates from the idea of CFG and is easy
  yet effective, making it a plug-and-play module for existing models. Our method
  significantly accelerates the training process, doubles the inference speed,
  and achieve exceptional quality that parallel and even surpass concurrent
  diffusion models with CFG. Extensive experiments demonstrate the effectiveness,
  efficiency, scalability on different models and datasets. Finally, we establish
  state-of-the-art performance on ImageNet 256 benchmarks with an FID of 1.34.
  Our code is available at https://github.com/tzco/Diffusion-wo-CFG.
paper_authors._id: 67b400719ff3ff79dae14701
paper_authors.name: Zhicong Tang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-18T09:31:45.361Z
paper_authors._id: 6372f265112fb535baf254c4
paper_authors.avatarUrl: /avatars/9b821bc533175c7dded48cdb3a3e1a12.svg
paper_authors.fullname: tzco
paper_authors.type: user
paper_authors.user: tzco
paper_authors._id: 67b400719ff3ff79dae14702
paper_authors.name: Jianmin Bao
paper_authors._id: 67b400719ff3ff79dae14703
paper_authors.name: Dong Chen
paper_authors._id: 67b400719ff3ff79dae14704
paper_authors.name: Baining Guo
paper_title: Diffusion Models without Classifier-free Guidance
paper_summary: This paper presents Model-guidance (MG), a novel objective for training
  diffusion model that addresses and removes of the commonly used Classifier-free
  guidance (CFG). Our innovative approach transcends the standard modeling of
  solely data distribution to incorporating the posterior probability of
  conditions. The proposed technique originates from the idea of CFG and is easy
  yet effective, making it a plug-and-play module for existing models. Our method
  significantly accelerates the training process, doubles the inference speed,
  and achieve exceptional quality that parallel and even surpass concurrent
  diffusion models with CFG. Extensive experiments demonstrate the effectiveness,
  efficiency, scalability on different models and datasets. Finally, we establish
  state-of-the-art performance on ImageNet 256 benchmarks with an FID of 1.34.
  Our code is available at https://github.com/tzco/Diffusion-wo-CFG.
paper_ai_keywords: diffusion model Model-guidance (MG) Classifier-free guidance (CFG) posterior probability inference speed FID (Fréchet Inception Distance) ImageNet 256 benchmarks