  Diffusion models, which learn to reverse a signal destruction process to
generate new data, typically require the signal at each step to have the same
dimension. We argue that, considering the spatial redundancy in image signals,
there is no need to maintain a high dimensionality in the evolution process,
especially in the early generation phase. To this end, we make a theoretical
generalization of the forward diffusion process via signal decomposition.
Concretely, we manage to decompose an image into multiple orthogonal components
and control the attenuation of each component when perturbing the image. That
way, along with the noise strength increasing, we are able to diminish those
inconsequential components and thus use a lower-dimensional signal to represent
the source, barely losing information. Such a reformulation allows to vary
dimensions in both training and inference of diffusion models. Extensive
experiments on a range of datasets suggest that our approach substantially
reduces the computational cost and achieves on-par or even better synthesis
performance compared to baseline methods. We also show that our strategy
facilitates high-resolution image synthesis and improves FID of diffusion model
trained on FFHQ at $1024\times1024$ resolution from 52.40 to 10.46. Code and
models will be made publicly available.

Machine Learning;Computer Vision and Pattern Recognition