Trajectory Consistency Distillation
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.
65e15242b8517a94d137ccfd
Jianbin Zheng
claimed_verified
2024-03-01T09:26:04.302Z
641870135d6f3d15c64d074e
/avatars/35f4b33fd1d4db326e4ee4300b26db72.svg
Jianbin Zheng
user
jabir-zheng
65e15242b8517a94d137ccfe
Minghui Hu
claimed_verified
2024-03-01T05:40:10.665Z
630b77f68b327c7b8b98c409
/avatars/2fe95c9ac95f34dbb031f2ec018f68b0.svg
Minghui Hu
user
h1t
65e15242b8517a94d137ccff
Zhongyi Fan
admin_assigned
2024-03-01T10:43:45.711Z
642e74226a378e41aa551994
/avatars/3a3618a214dcf5913e8a3d2f4a1cd5b8.svg
Zhongyi Fan
user
zyfan
65e15242b8517a94d137cd00
Chaoyue Wang
65e15242b8517a94d137cd01
Changxing Ding
65e15242b8517a94d137cd02
Dacheng Tao
65e15242b8517a94d137cd03
Tat-Jen Cham
Trajectory Consistency Distillation
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.
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.
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)