x-Prediction Is All You Need:Training-Free Accelerated Generation via Endpoint Decodability
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 ...
| Authors | Xin Peng, Ang Gao |
|---|---|
| Primary category | cs.LG |
| Published | 7 July 2026 |
| Last updated at source | 4 August 2026 |
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