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Scaling Graphically Structured Diffusion Models
Christian Weilbach · William Harvey · Hamed Shirzad · Frank Wood
Event URL: https://openreview.net/forum?id=pzH65nCyCN »
Applications of the recently introduced graphically structured diffusion model (GSDM) family show that sparsifying the transformer attention mechanism within a diffusion model and meta-training on a variety of conditioning tasks can yield an efficiently learnable diffusion model artifact that is capable of flexible, in the sense of observing different subsets of variables at test-time, amortized conditioning in probabilistic graphical models. While extremely promising in terms of applicability and utility, implementations of GSDMs prior to this work were not scalable beyond toy graphical model sizes. We overcome this limitation by describing and and solving two scaling issues related to GSDMs; one engineering and one methodological. We additionally propose a new benchmark problem of weight inference for a convolutional neural network applied to $14\times14$ MNIST.

Author Information

Christian Weilbach (Department of Computer Science, University of British Columbia)
William Harvey (University of British Columbia)
Hamed Shirzad (University of British Columbia)
Frank Wood (UBC + inverted.ai)

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