On the Design of One-step Diffusion via Shortcutting Flow Paths

Recent advances in few-step diffusion models have demonstrated their efficiency and effectiveness by shortcutting the probabilistic paths of diffusion models, especially in training one-step diffusion models from scratch (a.k.a. shortcut models). However, their theoretical derivation and practical implementation are often closely coupled, which obscures the design space. To address this, we propose a common design framework for representative shortcut models. This framework provides theoretical justification for their validity and disentangles concrete component-level choices, thereby enabling systematic identification of improvements. With our proposed improvements, the resulting one-step model achieves a new state-of-the-art FID50k of 2.85 on ImageNet-256×256 under the classifier-free guidance setting with one step generation, and further reaches FID50k of 2.53 with 2× training steps. Remarkably, the model requires no pre-training, distillation, or curriculum learning. We believe our work lowers the barrier to component-level innovation in shortcut models and facilitates principled exploration of their design space. INTRODUCTION Diffusion-based models have become the dominant paradigm in deep generative modeling (Sohl-Dickstein et al., 2015; Ho et al., 2020; Song et al., 2020), progressively transforming samples from a prior distribution toward the data distribution. However, dozens or even hundreds of neural function evaluations (NFEs) are typically required, resulting in slow inference and limited real-time use (