Accelerating diffusion model sampling is crucial for efficient AIGC deployment. While diffusion distillation methods -- based on distribution matching and trajectory matching -- reduce sampling to as few as one step, they fall short on complex tasks like text-to-image generation. Few-step generation offers a better balance between speed and quality, but existing approaches face a persistent trade-off: distribution matching lacks flexibility for multi-step sampling, while trajectory matching often yields suboptimal image quality. To bridge this gap, we propose learning few-step diffusion models by Trajectory Distribution Matching (TDM), a unified distillation paradigm that combines the strengths of distribution and trajectory matching. Our method introduces a data-free score distillation objective, aligning the student's trajectory with the teacher's at the distribution level. Further, we develop a sampling-steps-aware objective that decouples learning targets across different steps, enabling more adjustable sampling. This approach supports both deterministic sampling for superior image quality and flexible multi-step adaptation, achieving state-of-the-art performance with remarkable efficiency. Our model, TDM, outperforms existing methods on various backbones, such as SDXL and PixArt-$\alpha$, delivering superior quality and significantly reduced training costs. In particular, our method distills PixArt-$\alpha$ into a 4-step generator that outperforms its teacher on real user preference at 1024 resolution. This is accomplished with 500 iterations and 2 A800 hours -- a mere 0.01% of the teacher's training cost. In addition, our proposed TDM can be extended to accelerate text-to-video diffusion. Notably, TDM can outperform its teacher model (CogVideoX-2B) by using only 4 NFE on VBench, improving the total score from 80.91 to 81.65. Project page: https://tdm-t2x.github.io/

翻译：加速扩散模型采样对于高效部署AIGC至关重要。基于分布匹配和轨迹匹配的扩散蒸馏方法可将采样步骤减少至单步，但在文本到图像生成等复杂任务上表现不足。少步生成在速度与质量间提供了更好的平衡，但现有方法面临持续权衡：分布匹配缺乏多步采样的灵活性，而轨迹匹配往往产生次优图像质量。为弥合这一差距，我们提出通过轨迹分布匹配学习少步扩散模型，这是一种融合分布匹配与轨迹匹配优势的统一蒸馏范式。我们的方法引入无数据分数蒸馏目标，在分布层级对齐学生轨迹与教师轨迹。进一步，我们开发了采样步数感知目标，将不同步骤的学习目标解耦，实现更可调节的采样。该方法同时支持确定性采样以获得卓越图像质量，以及灵活的多步适应能力，以显著效率实现最先进性能。我们的TDM模型在SDXL和PixArt-$\alpha$等多种骨干网络上超越现有方法，提供更优质量并大幅降低训练成本。特别地，我们的方法将PixArt-$\alpha$蒸馏为4步生成器，在1024分辨率真实用户偏好评估中超越其教师模型。这仅需500次迭代和2个A800 GPU小时——仅占教师模型训练成本的0.01%。此外，所提出的TDM可扩展至加速文本到视频扩散。值得注意的是，TDM在VBench基准上仅用4次NFE即可超越其教师模型（CogVideoX-2B），将总分从80.91提升至81.65。项目页面：https://tdm-t2x.github.io/