Diffusion Models with Implicit Conditions Driven by Latent Shifts

초록

This paper introduces a diffusion model that replaces the target prior distribution from a standard Gaussian to non-zero-mean Gaussian priors, with shifted latent trajectories determined by non-zero-mean Gaussian noise, thereby maintaining the closed-form of conventional diffusion models. Unlike conventional conditional models such as conditional denoising diffusion probabilistic models or classifier guidance, the proposed model implicitly aligns each class with a unique component in the Gaussian priors. Carefully devised positioning strategies uniformly distribute Gaussian components without introducing additional learnable parameters, which is essential to implicit-only conditioning on our model. Qualitative and quantitative experiments demonstrate that, even without additional conditioning layers or a classifier, the proposed framework achieves performance comparable to explicit methods in terms of Fr & eacute;chet Inception Distance, precision, and recall. The framework is further validated under unsupervised settings by replacing class labels with pseudo-labels generated from K-means clustering on feature representations.

키워드