Decentralized Diffusion Models

• URL: http://arxiv.org/abs/2501.05450v2
• Date: Fri, 10 Jan 2025 18:58:11 GMT
• Title: Decentralized Diffusion Models
• Authors: David McAllister, Matthew Tancik, Jiaming Song, Angjoo Kanazawa,
• Abstract summary: Large-scale AI model training divides work across thousands of GPU, then synchronizes gradients across them at each step. This incurs a significant network burden that only centralized, monolithic clusters can support. We propose Decentralized Diffusion Models, a scalable framework for distributing diffusion model training across independent clusters.
• Score: 53.89995588977048
• License:
• Abstract: Large-scale AI model training divides work across thousands of GPUs, then synchronizes gradients across them at each step. This incurs a significant network burden that only centralized, monolithic clusters can support, driving up infrastructure costs and straining power systems. We propose Decentralized Diffusion Models, a scalable framework for distributing diffusion model training across independent clusters or datacenters by eliminating the dependence on a centralized, high-bandwidth networking fabric. Our method trains a set of expert diffusion models over partitions of the dataset, each in full isolation from one another. At inference time, the experts ensemble through a lightweight router. We show that the ensemble collectively optimizes the same objective as a single model trained over the whole dataset. This means we can divide the training burden among a number of "compute islands," lowering infrastructure costs and improving resilience to localized GPU failures. Decentralized diffusion models empower researchers to take advantage of smaller, more cost-effective and more readily available compute like on-demand GPU nodes rather than central integrated systems. We conduct extensive experiments on ImageNet and LAION Aesthetics, showing that decentralized diffusion models FLOP-for-FLOP outperform standard diffusion models. We finally scale our approach to 24 billion parameters, demonstrating that high-quality diffusion models can now be trained with just eight individual GPU nodes in less than a week.

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