FINE: Factorizing Knowledge for Initialization of Variable-sized Diffusion Models
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912940575162368 |
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| author | Xie, Yucheng Feng, Fu Shi, Ruixiao Shen, Jianlu Wang, Jing Rui, Yong Geng, Xin |
| author_facet | Xie, Yucheng Feng, Fu Shi, Ruixiao Shen, Jianlu Wang, Jing Rui, Yong Geng, Xin |
| contents | 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 $U_{\star}$, $Σ_{\star}^{(l)}$, and $V_{\star}^\top$, where $U_{\star}$ and $V_{\star}$ serve as size-agnostic learngenes shared across layers, while $Σ_{\star}^{(l)}$ 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 $Σ_{\star}^{(l)}$ 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_19289 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FINE: Factorizing Knowledge for Initialization of Variable-sized Diffusion Models Xie, Yucheng Feng, Fu Shi, Ruixiao Shen, Jianlu Wang, Jing Rui, Yong Geng, Xin Computer Vision and Pattern Recognition 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 $U_{\star}$, $Σ_{\star}^{(l)}$, and $V_{\star}^\top$, where $U_{\star}$ and $V_{\star}$ serve as size-agnostic learngenes shared across layers, while $Σ_{\star}^{(l)}$ 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 $Σ_{\star}^{(l)}$ 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. |
| title | FINE: Factorizing Knowledge for Initialization of Variable-sized Diffusion Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.19289 |