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

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.