Distribution Backtracking Builds A Faster Convergence Trajectory for
  One-step Diffusion Distillation
Accelerating the sampling speed of diffusion models remains a significant
challenge. Recent score distillation methods distill a heavy teacher model into
an one-step student generator, which is optimized by calculating the difference
between the two score functions on the samples generated by the student model.
However, there is a score mismatch issue in the early stage of the distillation
process, because existing methods mainly focus on using the endpoint of
pre-trained diffusion models as teacher models, overlooking the importance of
the convergence trajectory between the student generator and the teacher model.
To address this issue, we extend the score distillation process by introducing
the entire convergence trajectory of teacher models and propose Distribution
Backtracking Distillation (DisBack) for distilling student generators. DisBask
is composed of two stages: Degradation Recording and Distribution Backtracking.
Degradation Recording is designed to obtain the convergence trajectory of
teacher models, which records the degradation path from the trained teacher
model to the untrained initial student generator. The degradation path
implicitly represents the intermediate distributions of teacher models. Then
Distribution Backtracking trains a student generator to backtrack the
intermediate distributions for approximating the convergence trajectory of
teacher models. Extensive experiments show that DisBack achieves faster and
better convergence than the existing distillation method and accomplishes
comparable generation performance. Notably, DisBack is easy to implement and
can be generalized to existing distillation methods to boost performance. Our
code is publicly available on https://github.com/SYZhang0805/DisBack.
66cffb5bd58394b62bf24301
Shengyuan Zhang
claimed_verified
2024-08-29T07:22:58.760Z
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Shengyuan Zhang
user
SYZhang0805
66cffb5bd58394b62bf24302
Ling Yang
claimed_verified
2025-02-11T15:34:30.378Z
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Ling Yang
user
Lingaaaaaaa
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Zejian Li
admin_assigned
2024-08-29T08:09:16.254Z
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Zejian Li
user
hyllbd
66cffb5bd58394b62bf24304
An Zhao
66cffb5bd58394b62bf24305
Chenye Meng
66cffb5bd58394b62bf24306
Changyuan Yang
66cffb5bd58394b62bf24307
Guang Yang
66cffb5bd58394b62bf24308
Zhiyuan Yang
66cffb5bd58394b62bf24309
Lingyun Sun
admin_assigned
2024-08-29T08:07:24.113Z
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Lingyun Sun
user
slysun
Distribution Backtracking Builds A Faster Convergence Trajectory for
  One-step Diffusion Distillation
Accelerating the sampling speed of diffusion models remains a significant
challenge. Recent score distillation methods distill a heavy teacher model into
an one-step student generator, which is optimized by calculating the difference
between the two score functions on the samples generated by the student model.
However, there is a score mismatch issue in the early stage of the distillation
process, because existing methods mainly focus on using the endpoint of
pre-trained diffusion models as teacher models, overlooking the importance of
the convergence trajectory between the student generator and the teacher model.
To address this issue, we extend the score distillation process by introducing
the entire convergence trajectory of teacher models and propose Distribution
Backtracking Distillation (DisBack) for distilling student generators. DisBask
is composed of two stages: Degradation Recording and Distribution Backtracking.
Degradation Recording is designed to obtain the convergence trajectory of
teacher models, which records the degradation path from the trained teacher
model to the untrained initial student generator. The degradation path
implicitly represents the intermediate distributions of teacher models. Then
Distribution Backtracking trains a student generator to backtrack the
intermediate distributions for approximating the convergence trajectory of
teacher models. Extensive experiments show that DisBack achieves faster and
better convergence than the existing distillation method and accomplishes
comparable generation performance. Notably, DisBack is easy to implement and
can be generalized to existing distillation methods to boost performance. Our
code is publicly available on https://github.com/SYZhang0805/DisBack.
DisBack, an extended score distillation method, accelerates diffusion model sampling by leveraging the entire convergence trajectory of the teacher model, improving student generator performance and reducing the score mismatch issue.
diffusion models score distillation score functions teacher models convergence trajectory student generator Degradation Recording Distribution Backtracking intermediate distributions generation performance