Dual-End Consistency Model

The slow iterative sampling nature remains a major bottle-neck for the practical deployment of diffusion and flow-based genera-tive models. While consistency models (CMs) represent a state-of-the-art distillation-based approach for efficient generation, their large-scaleapplication is still limited by two key issues: training instability andinflexible sampling. Existing methods seek to mitigate these problemsthrough architectural adjustments or regularized objectives, yet over-look the critical reliance on trajectory selection. In this work, we firstconduct an analysis on these two limitations: training instability orig-inates from loss divergence induced by unstable self-supervised term,whereas sampling inflexibility arises from error accumulation. Based onthese insights and analysis, we propose the Dual-End ConsistencyModel (DE-CM) that selects vital sub-trajectory clusters to achievestable and effective training. DE-CM decomposes the PF-ODE trajec-tory and selects three critical sub-trajectories as optimization targets.Specifically, our approach leverages continuous-time CMs objectives toachieve few-step distillation and utilizes flow matching as a boundaryregularizer to stabilize the training process. Furthermore, we propose anovel noise-to-noisy (N2N) mapping that can map noise to any point,thereby alleviating the error accumulation in the first step. Extensiveexperimental results show the effectiveness of our method: it achieves astate-of-the-art FID score of 1.70 in one-step generation on the ImageNet256×256 dataset, outperforming existing CM-based one-step approaches.