Consistency Flow Matching: Defining Straight Flows with Velocity
  Consistency
Flow matching (FM) is a general framework for defining probability paths via
Ordinary Differential Equations (ODEs) to transform between noise and data
samples. Recent approaches attempt to straighten these flow trajectories to
generate high-quality samples with fewer function evaluations, typically
through iterative rectification methods or optimal transport solutions. In this
paper, we introduce Consistency Flow Matching (Consistency-FM), a novel FM
method that explicitly enforces self-consistency in the velocity field.
Consistency-FM directly defines straight flows starting from different times to
the same endpoint, imposing constraints on their velocity values. Additionally,
we propose a multi-segment training approach for Consistency-FM to enhance
expressiveness, achieving a better trade-off between sampling quality and
speed. Preliminary experiments demonstrate that our Consistency-FM
significantly improves training efficiency by converging 4.4x faster than
consistency models and 1.7x faster than rectified flow models while achieving
better generation quality. Our code is available at:
https://github.com/YangLing0818/consistency_flow_matching
6684acd0c1cbe5e008c99eab
Ling Yang
claimed_verified
2024-07-12T07:41:34.365Z
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Ling Yang
user
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Zixiang Zhang
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Zhilong Zhang
admin_assigned
2024-07-03T08:20:32.301Z
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Zhilong Zhang
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Xingchao Liu
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2024-07-03T08:20:38.901Z
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Xingchao Liu
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XCLiu
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Minkai Xu
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2024-07-03T08:20:46.679Z
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Minkai Xu
user
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Wentao Zhang
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Chenlin Meng
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Stefano Ermon
admin_assigned
2024-07-03T08:21:23.591Z
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/avatars/5a8b2bb063c2ebc340504b22530f6811.svg
Stefano Ermon
user
ermonste
6684acd0c1cbe5e008c99eb3
Bin Cui
Consistency Flow Matching: Defining Straight Flows with Velocity
  Consistency
Flow matching (FM) is a general framework for defining probability paths via
Ordinary Differential Equations (ODEs) to transform between noise and data
samples. Recent approaches attempt to straighten these flow trajectories to
generate high-quality samples with fewer function evaluations, typically
through iterative rectification methods or optimal transport solutions. In this
paper, we introduce Consistency Flow Matching (Consistency-FM), a novel FM
method that explicitly enforces self-consistency in the velocity field.
Consistency-FM directly defines straight flows starting from different times to
the same endpoint, imposing constraints on their velocity values. Additionally,
we propose a multi-segment training approach for Consistency-FM to enhance
expressiveness, achieving a better trade-off between sampling quality and
speed. Preliminary experiments demonstrate that our Consistency-FM
significantly improves training efficiency by converging 4.4x faster than
consistency models and 1.7x faster than rectified flow models while achieving
better generation quality. Our code is available at:
https://github.com/YangLing0818/consistency_flow_matching
Consistency Flow Matching improves the efficiency and quality of sample generation by enforcing self-consistency in velocity fields and using multi-segment training for flow-based generative models.
flow matching ordinary differential equations ODEs probability paths noise data samples function evaluations iterative rectification optimal transport velocity field self-consistency straight flows multi-segment training sampling quality consistency models rectified flow models