Subspace Diffusion Generative Models

• URL: http://arxiv.org/abs/2205.01490v1
• Date: Tue, 3 May 2022 13:43:47 GMT
• Title: Subspace Diffusion Generative Models
• Authors: Bowen Jing, Gabriele Corso, Renato Berlinghieri, Tommi Jaakkola
• Abstract summary: Score-based models generate samples by mapping noise to data (and vice versa) via a high-dimensional diffusion process. We restrict the diffusion via projections onto subspaces as the data distribution evolves toward noise. Our framework is fully compatible with continuous-time diffusion and retains its flexible capabilities.
• Score: 4.310834990284412
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Score-based models generate samples by mapping noise to data (and vice versa) via a high-dimensional diffusion process. We question whether it is necessary to run this entire process at high dimensionality and incur all the inconveniences thereof. Instead, we restrict the diffusion via projections onto subspaces as the data distribution evolves toward noise. When applied to state-of-the-art models, our framework simultaneously improves sample quality -- reaching an FID of 2.17 on unconditional CIFAR-10 -- and reduces the computational cost of inference for the same number of denoising steps. Our framework is fully compatible with continuous-time diffusion and retains its flexible capabilities, including exact log-likelihoods and controllable generation. Code is available at https://github.com/bjing2016/subspace-diffusion.

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