title: Trajectory Consistency Distillation
summary: Latent Consistency Model (LCM) extends the Consistency Model to the latent
  space and leverages the guided consistency distillation technique to achieve
  impressive performance in accelerating text-to-image synthesis. However, we
  observed that LCM struggles to generate images with both clarity and detailed
  intricacy. To address this limitation, we initially delve into and elucidate
  the underlying causes. Our investigation identifies that the primary issue
  stems from errors in three distinct areas. Consequently, we introduce
  Trajectory Consistency Distillation (TCD), which encompasses trajectory
  consistency function and strategic stochastic sampling. The trajectory
  consistency function diminishes the distillation errors by broadening the scope
  of the self-consistency boundary condition and endowing the TCD with the
  ability to accurately trace the entire trajectory of the Probability Flow ODE.
  Additionally, strategic stochastic sampling is specifically designed to
  circumvent the accumulated errors inherent in multi-step consistency sampling,
  which is meticulously tailored to complement the TCD model. Experiments
  demonstrate that TCD not only significantly enhances image quality at low NFEs
  but also yields more detailed results compared to the teacher model at high
  NFEs.
paper_authors._id: 65e15242b8517a94d137ccfd
paper_authors.name: Jianbin Zheng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-03-01T09:26:04.302Z
paper_authors._id: 641870135d6f3d15c64d074e
paper_authors.avatarUrl: /avatars/35f4b33fd1d4db326e4ee4300b26db72.svg
paper_authors.fullname: Jianbin Zheng
paper_authors.type: user
paper_authors.user: jabir-zheng
paper_authors._id: 65e15242b8517a94d137ccfe
paper_authors.name: Minghui Hu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-03-01T05:40:10.665Z
paper_authors._id: 630b77f68b327c7b8b98c409
paper_authors.avatarUrl: /avatars/2fe95c9ac95f34dbb031f2ec018f68b0.svg
paper_authors.fullname: Minghui Hu
paper_authors.type: user
paper_authors.user: h1t
paper_authors._id: 65e15242b8517a94d137ccff
paper_authors.name: Zhongyi Fan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-01T10:43:45.711Z
paper_authors._id: 642e74226a378e41aa551994
paper_authors.avatarUrl: /avatars/3a3618a214dcf5913e8a3d2f4a1cd5b8.svg
paper_authors.fullname: Zhongyi Fan
paper_authors.type: user
paper_authors.user: zyfan
paper_authors._id: 65e15242b8517a94d137cd00
paper_authors.name: Chaoyue Wang
paper_authors._id: 65e15242b8517a94d137cd01
paper_authors.name: Changxing Ding
paper_authors._id: 65e15242b8517a94d137cd02
paper_authors.name: Dacheng Tao
paper_authors._id: 65e15242b8517a94d137cd03
paper_authors.name: Tat-Jen Cham
paper_title: Trajectory Consistency Distillation
paper_summary: Latent Consistency Model (LCM) extends the Consistency Model to the latent
  space and leverages the guided consistency distillation technique to achieve
  impressive performance in accelerating text-to-image synthesis. However, we
  observed that LCM struggles to generate images with both clarity and detailed
  intricacy. To address this limitation, we initially delve into and elucidate
  the underlying causes. Our investigation identifies that the primary issue
  stems from errors in three distinct areas. Consequently, we introduce
  Trajectory Consistency Distillation (TCD), which encompasses trajectory
  consistency function and strategic stochastic sampling. The trajectory
  consistency function diminishes the distillation errors by broadening the scope
  of the self-consistency boundary condition and endowing the TCD with the
  ability to accurately trace the entire trajectory of the Probability Flow ODE.
  Additionally, strategic stochastic sampling is specifically designed to
  circumvent the accumulated errors inherent in multi-step consistency sampling,
  which is meticulously tailored to complement the TCD model. Experiments
  demonstrate that TCD not only significantly enhances image quality at low NFEs
  but also yields more detailed results compared to the teacher model at high
  NFEs.
paper_ai_summary: Trajectory Consistency Distillation (TCD) improves text-to-image synthesis by addressing errors in consistency models, leading to higher image quality and detail at low numerical flow evaluations.
paper_ai_keywords: Latent Consistency Model (LCM) Consistency Model latent space guided consistency distillation trajectory consistency function strategic stochastic sampling Probability Flow ODE TCD image quality numerical flow evaluations (NFEs)