title: Multistep Consistency Models
summary: Diffusion models are relatively easy to train but require many steps to
  generate samples. Consistency models are far more difficult to train, but
  generate samples in a single step.
    In this paper we propose Multistep Consistency Models: A unification between
  Consistency Models (Song et al., 2023) and TRACT (Berthelot et al., 2023) that
  can interpolate between a consistency model and a diffusion model: a trade-off
  between sampling speed and sampling quality. Specifically, a 1-step consistency
  model is a conventional consistency model whereas we show that a infty-step
  consistency model is a diffusion model.
    Multistep Consistency Models work really well in practice. By increasing the
  sample budget from a single step to 2-8 steps, we can train models more easily
  that generate higher quality samples, while retaining much of the sampling
  speed benefits. Notable results are 1.4 FID on Imagenet 64 in 8 step and 2.1
  FID on Imagenet128 in 8 steps with consistency distillation. We also show that
  our method scales to a text-to-image diffusion model, generating samples that
  are very close to the quality of the original model.
paper_authors._id: 65efcf3387bf55c1da3a6225
paper_authors.name: Jonathan Heek
paper_authors._id: 65efcf3387bf55c1da3a6226
paper_authors.name: Emiel Hoogeboom
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-30T09:56:56.121Z
paper_authors._id: 671feaa3e269c38584cb065f
paper_authors.avatarUrl: /avatars/d7bc35ab98b208ef1d009aa9b629682a.svg
paper_authors.fullname: Emiel Hoogeboom
paper_authors.type: user
paper_authors.user: Emiel2
paper_authors._id: 65efcf3387bf55c1da3a6227
paper_authors.name: Tim Salimans
paper_title: Multistep Consistency Models
paper_summary: Diffusion models are relatively easy to train but require many steps to
  generate samples. Consistency models are far more difficult to train, but
  generate samples in a single step.
    In this paper we propose Multistep Consistency Models: A unification between
  Consistency Models (Song et al., 2023) and TRACT (Berthelot et al., 2023) that
  can interpolate between a consistency model and a diffusion model: a trade-off
  between sampling speed and sampling quality. Specifically, a 1-step consistency
  model is a conventional consistency model whereas we show that a infty-step
  consistency model is a diffusion model.
    Multistep Consistency Models work really well in practice. By increasing the
  sample budget from a single step to 2-8 steps, we can train models more easily
  that generate higher quality samples, while retaining much of the sampling
  speed benefits. Notable results are 1.4 FID on Imagenet 64 in 8 step and 2.1
  FID on Imagenet128 in 8 steps with consistency distillation. We also show that
  our method scales to a text-to-image diffusion model, generating samples that
  are very close to the quality of the original model.
paper_ai_keywords: Diffusion models Consistency models Multistep Consistency Models TRACT 1-step consistency model $\infty$-step consistency model FID Imagenet 64 Imagenet128 consistency distillation text-to-image diffusion model