{"ID":6023505,"CreatedAt":"2026-07-08T01:00:23.257252134Z","UpdatedAt":"2026-07-10T10:09:03.016489495Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.06114","arxiv_id":"2607.06114","title":"x-Prediction Is All You Need:Training-Free Accelerated Generation via Endpoint Decodability","abstract":"Diffusion and flow matching models generate high-quality samples, but their ODE samplers often need tens to hundreds of neural function evaluations (NFEs). This remains a practical challenge for released checkpoints, since many accelerators require additional design choices and training cost through retraining, distillation, or trajectory redesign. We investigate a different route based on $x$-prediction. During sampling, standard affine probability paths already expose $x_0$ information: an intermediate state and its path velocity determine a principled estimate of the clean sample. We formalize this property as \\textbf{endpoint decodability} and show that the decoder is the minimum-MSE estimator $\\mathbb{E}[x_0\\mid x_t]$ under the usual $\\ell_2$ objective. This yields \\textbf{Truncated Jump Sampling} (TJS): stop the ODE at an early-exit time $t^*$ and return the decoded $x_0$. TJS requires no retraining, distillation, or architecture change. Across SDXL, SD3.5M, Z-Image-Turbo, and three class-conditional benchmarks, it reduces NFEs by 20--70\\% with near-matched quality. The analysis also shows why endpoint prediction can work without straightening the trajectory, providing inference acceleration without trajectory redesign.","short_abstract":"Diffusion and flow matching models generate high-quality samples, but their ODE samplers often need tens to hundreds of neural function evaluations (NFEs). This remains a practical challenge for released checkpoints, since many accelerators require additional design choices and training cost through retraining, distill...","url_abs":"https://arxiv.org/abs/2607.06114","url_pdf":"https://arxiv.org/pdf/2607.06114v1","authors":"[\"Xin Peng\",\"Ang Gao\"]","published":"2026-07-07T10:24:38Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[\"Diffusion Model\"]","has_code":false}