Diffusion Image Generation with Explicit Modeling of Data Manifold Geometry 事件

Diffusion Image Generation with Explicit Modeling of Data Manifold Geometry arXiv:2606.00094v1 Announce Type: new Abstract: Image generative models aim to sample data points from the underlying data manifold, a task that requires learning and decoding a dense, low-dimensional, and compact parameterization space. To achieve this, we propose the Data Manifold-aware Image diffusioN moDel (MIND), a novel framework that explicitly models manifold geometry by integrating discrete patch tokenization i