{"ID":2833402,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.11831","arxiv_id":"2512.11831","title":"On the Design of One-step Diffusion via Shortcutting Flow Paths","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-256x256 under the classifier-free guidance setting with one step generation, and further reaches FID50k of 2.53 with 2x 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.","short_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...","url_abs":"https://arxiv.org/abs/2512.11831","url_pdf":"https://arxiv.org/pdf/2512.11831v5","authors":"[\"Haitao Lin\",\"Peiyan Hu\",\"Minsi Ren\",\"Zhifeng Gao\",\"Zhi-Ming Ma\",\"Guolin ke\",\"Tailin Wu\",\"Stan Z. Li\"]","published":"2025-12-03T09:28:29Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.CV\"]","methods":"[\"Diffusion Model\",\"LoRA\"]","has_code":false}