Exploring Low-Dimensional Subspaces in Diffusion Models for Controllable Image Editing

Understand

Recently, diffusion models have emerged as a powerful class of generative models.

• Despite their success, there is still limited understanding of their semantic spaces.
• This makes it challenging to achieve precise and disentangled image generation without additional training, especially in an unsupervised way.
• In this work, we improve the understanding of their semantic spaces from intriguing observations: among a certain range of noise levels, (1) the learned posterior mean predictor (PMP) in the diffusion model is locally linear, and (2) the singular vectors of its Jacobian lie in low-dimensional semantic subspaces.