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FINE: Factorizing Knowledge for Initialization of Variable-sized Diffusion Models
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 04:13
摘要
The training of diffusion models is computationally intensive, making effective pre-training essential. However, real-world deployments often demand models of variable sizes due to diverse memory and computational constraints, posing challenges when corresponding pre-trained versions are unavailable.To address this, we propose FINE, a novel pre-training method whose resulting model can flexibly factorize its knowledge into fundamental components, termed learngenes, enabling direct initialization of models of various sizes and eliminating the need for repeated pre-training.Rather than optimizing a conventional full-parameter model, FINE represents each layer's weights as the product of U_ \star , \Sigma_ \star ^ (l) , and V_ \star ^\top, where U_ \star and V_ \star serve as size-agnostic learngenes shared across layers, while \Sigma_ \star ^ (l) remains layer-specific.By jointly training these components, FINE forms a decomposable and transferable knowledge structure that allows efficient initialization through flexible recombination of learngenes, requiring only light retraining of \Sigma_ \star ^ (l) on limited data.Extensive experiments demonstrate the efficiency of FINE, achieving state-of-the-art performance in initializing variable-sized models across diverse resource-constrained deployments. Furthermore, models initialized by FINE effectively adapt to diverse tasks, showcasing the task-agnostic versatility of learngenes.
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记录 #1 · 2026 · 最新观测版本
FINE: Factorizing Knowledge for Initialization of Variable-sized Diffusion Models
The training of diffusion models is computationally intensive, making effective pre-training essential. However, real-world deployments often demand models of variable sizes due to diverse memory and computational constraints, posing challenges when corresponding pre-trained versions are unavailable.To address this, we propose FINE, a novel pre-training method whose resulting model can flexibly factorize its knowledge into fundamental components, termed learngenes, enabling direct initialization of models of various sizes and eliminating the need for repeated pre-training.Rather than optimizing a conventional full-parameter model, FINE represents each layer's weights as the product of U_ \star , \Sigma_ \star ^ (l) , and V_ \star ^\top, where U_ \star and V_ \star serve as size-agnostic learngenes shared across layers, while \Sigma_ \star ^ (l) remains layer-specific.By jointly training these components, FINE forms a decomposable and transferable knowledge structure that allows efficient initialization through flexible recombination of learngenes, requiring only light retraining of \Sigma_ \star ^ (l) on limited data.Extensive experiments demonstrate the efficiency of FINE, achieving state-of-the-art performance in initializing variable-sized models across diverse resource-constrained deployments. Furthermore, models initialized by FINE effectively adapt to diverse tasks, showcasing the task-agnostic versatility of learngenes.
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