Diffusion Models With Implicit Conditions Driven by Latent Shifts

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 Fréchet 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.