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

Haitao Lin, Peiyan Hu, Minsi Ren, Zhifeng Gao, Zhi-Ming Ma, Guolin Ke, Tailin Wu, Stan Z. Li

Keywords: Few-step Diffusion, Shortcut Model, Diffusion Model, Flow Matching

Abstract:

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 (\emph{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.

Primary Area: generative models

Submission Number: 524