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This is a Quantitative Study study.

September 29, 2025Open Access

How to build a consistency model: Learning flow maps via self-distillation

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Learning flow maps via self-distillation improves efficiency in generative models based on differential equations.
The study identifies that high-dimensional tasks benefit from objectives avoiding temporal derivatives of flow maps.
Lower-dimensional tasks can use higher-order derivatives to better capture sharp features during model training.
Self-distillation techniques eliminate the necessity for pre-trained models, streamlining the learning process.

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Implication

Systematic approach reveals efficiency improvements in generative models using flow maps, indicating higher-order derivatives enhance performance in varied dimensions.

Cite This Study

Boffi et al. (2025) studied this question.

synapsesocial.com/papers/68da58d8c1728099cfd111e8 https://doi.org/10.48550/arxiv.2505.18825

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Implication

Systematic approach reveals efficiency improvements in generative models using flow maps, indicating higher-order derivatives enhance performance in varied dimensions.

How to build a consistency model: Learning flow maps via self-distillation

Key Points

Discussion

Implication

Cite This Study

How to build a consistency model: Learning flow maps via self-distillation

Key Points

Discussion

Implication

Cite This Study