title: Unified Continuous Generative Models
summary: Recent advances in continuous generative models, including multi-step
  approaches like diffusion and flow-matching (typically requiring 8-1000
  sampling steps) and few-step methods such as consistency models (typically 1-8
  steps), have demonstrated impressive generative performance. However, existing
  work often treats these approaches as distinct paradigms, resulting in separate
  training and sampling methodologies. We introduce a unified framework for
  training, sampling, and analyzing these models. Our implementation, the Unified
  Continuous Generative Models Trainer and Sampler (UCGM-{T,S}), achieves
  state-of-the-art (SOTA) performance. For example, on ImageNet 256x256 using a
  675M diffusion transformer, UCGM-T trains a multi-step model achieving 1.30 FID
  in 20 steps and a few-step model reaching 1.42 FID in just 2 steps.
  Additionally, applying UCGM-S to a pre-trained model (previously 1.26 FID at
  250 steps) improves performance to 1.06 FID in only 40 steps. Code is available
  at: https://github.com/LINs-lab/UCGM.
paper_authors._id: 6822c7d78fc623ca8c6b2578
paper_authors.name: Peng Sun
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-13T08:11:37.679Z
paper_authors._id: 6305a4cf5054a1c5b82ae8f7
paper_authors.avatarUrl: /avatars/7138103d5b86d8b0b6cdd71e1dd4f1eb.svg
paper_authors.fullname: Black Box
paper_authors.type: user
paper_authors.user: sp12138sp
paper_authors._id: 6822c7d78fc623ca8c6b2579
paper_authors.name: Yi Jiang
paper_authors._id: 6822c7d78fc623ca8c6b257a
paper_authors.name: Tao Lin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-13T08:57:54.259Z
paper_authors._id: 65296b8a6cdea40585bd81e2
paper_authors.avatarUrl: /avatars/824452e8b8fd056eaf7549c46393c47b.svg
paper_authors.fullname: Tao LIN
paper_authors.type: user
paper_authors.user: tlin-taolin
paper_title: Unified Continuous Generative Models
paper_summary: Recent advances in continuous generative models, including multi-step
  approaches like diffusion and flow-matching (typically requiring 8-1000
  sampling steps) and few-step methods such as consistency models (typically 1-8
  steps), have demonstrated impressive generative performance. However, existing
  work often treats these approaches as distinct paradigms, resulting in separate
  training and sampling methodologies. We introduce a unified framework for
  training, sampling, and analyzing these models. Our implementation, the Unified
  Continuous Generative Models Trainer and Sampler (UCGM-{T,S}), achieves
  state-of-the-art (SOTA) performance. For example, on ImageNet 256x256 using a
  675M diffusion transformer, UCGM-T trains a multi-step model achieving 1.30 FID
  in 20 steps and a few-step model reaching 1.42 FID in just 2 steps.
  Additionally, applying UCGM-S to a pre-trained model (previously 1.26 FID at
  250 steps) improves performance to 1.06 FID in only 40 steps. Code is available
  at: https://github.com/LINs-lab/UCGM.
paper_ai_summary: A unified framework for continuous generative models improves training and sampling performance across multi-step and few-step methods.
paper_ai_keywords: continuous generative models diffusion flow-matching consistency models Unified Continuous Generative Models Trainer and Sampler (UCGM-T S) FID