Diffusion Image Generation with Explicit Modeling of Data Manifold Geometry
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 into the score function of a continuous diffusion model. This approach successfully leverages both the structural quantification capabilities of discrete tokens and the parallel generation flexibility of continuous diffusion. Moreover, we enable end-to-end differentiable training via a novel soft top-$k$ aggregation mecha...
| Authors | Duoduo Xue, Zhiyu Zhu, Junhui Hou |
|---|---|
| Primary category | cs.CV |
| Published | 25 May 2026 |
| Last updated at source | 1 July 2026 |
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