How Distributed Collaboration Influences the Diffusion Model Training? A Theoretical Perspective

Jing Qiao, Yu Liu, Yuan Yuan, Xiao Zhang, Zhipeng Cai, Dongxiao Yu
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:50171-50188, 2025.

Abstract

This paper examines the theoretical performance of distributed diffusion models in environments where computational resources and data availability vary significantly among workers. Traditional models centered on single-worker scenarios fall short in such distributed settings, particularly when some workers are resource-constrained. This discrepancy in resources and data diversity challenges the assumption of accurate score function estimation foundational to single-worker models. We establish the inaugural generation error bound for distributed diffusion models in resource-limited settings, establishing a linear relationship with the data dimension $d$ and consistency with established single-worker results. Our analysis highlights the critical role of hyperparameter selection in influencing the training dynamics, which are key to the performance of model generation. This study provides a streamlined theoretical approach to optimizing distributed diffusion models, paving the way for future research in this area.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-qiao25d, title = {How Distributed Collaboration Influences the Diffusion Model Training? {A} Theoretical Perspective}, author = {Qiao, Jing and Liu, Yu and Yuan, Yuan and Zhang, Xiao and Cai, Zhipeng and Yu, Dongxiao}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {50171--50188}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/qiao25d/qiao25d.pdf}, url = {https://proceedings.mlr.press/v267/qiao25d.html}, abstract = {This paper examines the theoretical performance of distributed diffusion models in environments where computational resources and data availability vary significantly among workers. Traditional models centered on single-worker scenarios fall short in such distributed settings, particularly when some workers are resource-constrained. This discrepancy in resources and data diversity challenges the assumption of accurate score function estimation foundational to single-worker models. We establish the inaugural generation error bound for distributed diffusion models in resource-limited settings, establishing a linear relationship with the data dimension $d$ and consistency with established single-worker results. Our analysis highlights the critical role of hyperparameter selection in influencing the training dynamics, which are key to the performance of model generation. This study provides a streamlined theoretical approach to optimizing distributed diffusion models, paving the way for future research in this area.} }
Endnote
%0 Conference Paper %T How Distributed Collaboration Influences the Diffusion Model Training? A Theoretical Perspective %A Jing Qiao %A Yu Liu %A Yuan Yuan %A Xiao Zhang %A Zhipeng Cai %A Dongxiao Yu %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-qiao25d %I PMLR %P 50171--50188 %U https://proceedings.mlr.press/v267/qiao25d.html %V 267 %X This paper examines the theoretical performance of distributed diffusion models in environments where computational resources and data availability vary significantly among workers. Traditional models centered on single-worker scenarios fall short in such distributed settings, particularly when some workers are resource-constrained. This discrepancy in resources and data diversity challenges the assumption of accurate score function estimation foundational to single-worker models. We establish the inaugural generation error bound for distributed diffusion models in resource-limited settings, establishing a linear relationship with the data dimension $d$ and consistency with established single-worker results. Our analysis highlights the critical role of hyperparameter selection in influencing the training dynamics, which are key to the performance of model generation. This study provides a streamlined theoretical approach to optimizing distributed diffusion models, paving the way for future research in this area.
APA
Qiao, J., Liu, Y., Yuan, Y., Zhang, X., Cai, Z. & Yu, D.. (2025). How Distributed Collaboration Influences the Diffusion Model Training? A Theoretical Perspective. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:50171-50188 Available from https://proceedings.mlr.press/v267/qiao25d.html.

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