x-Prediction Is All You Need:Training-Free Accelerated Generation via Endpoint Decodability

arXiv:2607.06114v1 Announce Type: cross 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 extbf{endpoint decodability} and show that the decoder is the minimum-MSE estimator E[x_0mid x_t] under the usual ell_2 objective. This yields extbf{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.

Source: arXiv cs.AI | 2026-07-08