Dual-End Consistency Model

2026-02-11

AI Summary

This work proposes the Dual-Ended Consistency Model (DE-CM) to address the training instability and inflexible sampling of consistency models in large-scale applications. By introducing a trajectory selection mechanism, the method designs a three-segment key sub-trajectory optimization strategy and integrates continuous-time consistency objectives with flow-matching boundary regularization to enable few-step distillation. Additionally, a noise-to-noise (N2N) mapping is introduced to mitigate error accumulation at the initial step. The proposed approach significantly enhances both training stability and sampling flexibility, achieving a state-of-the-art one-step generation FID of 1.70 on ImageNet 256×256, which represents the best reported result for one-step consistency models to date.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Generation

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

The slow iterative sampling nature remains a major bottleneck for the practical deployment of diffusion and flow-based generative models. While consistency models (CMs) represent a state-of-the-art distillation-based approach for efficient generation, their large-scale application is still limited by two key issues: training instability and inflexible sampling. Existing methods seek to mitigate these problems through architectural adjustments or regularized objectives, yet overlook the critical reliance on trajectory selection. In this work, we first conduct an analysis on these two limitations: training instability originates from loss divergence induced by unstable self-supervised term, whereas sampling inflexibility arises from error accumulation. Based on these insights and analysis, we propose the Dual-End Consistency Model (DE-CM) that selects vital sub-trajectory clusters to achieve stable and effective training. DE-CM decomposes the PF-ODE trajectory and selects three critical sub-trajectories as optimization targets. Specifically, our approach leverages continuous-time CMs objectives to achieve few-step distillation and utilizes flow matching as a boundary regularizer to stabilize the training process. Furthermore, we propose a novel noise-to-noisy (N2N) mapping that can map noise to any point, thereby alleviating the error accumulation in the first step. Extensive experimental results show the effectiveness of our method: it achieves a state-of-the-art FID score of 1.70 in one-step generation on the ImageNet 256x256 dataset, outperforming existing CM-based one-step approaches.

Problem

• diffusion models
• consistency models
• training instability
• sampling inflexibility
• generative modeling

Innovation

• Consistency Models
• Trajectory Selection
• Flow Matching
• Noise-to-Noisy Mapping
• One-step Generation