四步生成高质量图像，同时保持精确概率模型。
Normalizing Trajectory Models
- 用可逆块与并行预测构建端到端生成网络，每步建模为精确似然的变换流。
- 在文本到图像任务中，仅用4步即可媲美甚至超越现有强基线模型。
- 支持自蒸馏，用自身得分函数训练轻量去噪器，适合高效生成场景。
基于扩散的模型将采样过程分解为许多小的高斯去噪步骤——这一假设在生成被压缩为少数粗粒度转换时失效。现有少步方法通过蒸馏、一致性训练或对抗目标解决此问题，但牺牲了概率框架。本文提出归一化轨迹模型（NTM），将每个反向步骤建模为具有精确似然训练的表达性强条件变换流。架构上，NTM 在每一步结合浅层可逆块，并在轨迹上使用深层并行预测器，形成可从头训练或由预训练流匹配模型初始化的端到端网络。其精确轨迹似然进一步支持自蒸馏：一个由模型自身得分函数训练的轻量去噪器可在4步内生成高质量样本。在文本到图像基准测试中，NTM 仅用4步采样即达到或超越强图像生成基线，且唯一保留生成轨迹的精确似然。
原文摘要 · Abstract (English)
Diffusion-based models decompose sampling into many small Gaussian denoising steps -- an assumption that breaks down when generation is compressed to a few coarse transitions. Existing few-step methods address this through distillation, consistency training, or adversarial objectives, but sacrifice the likelihood framework in the process. We introduce Normalizing Trajectory Models (NTM), which models each reverse step as an expressive conditional normalizing flow with exact likelihood training. Architecturally, NTM combines shallow invertible blocks within each step with a deep parallel predictor across the trajectory, forming an end-to-end network trainable from scratch or initializable from pretrained flow-matching models. Its exact trajectory likelihood further enables self-distillation: a lightweight denoiser trained on the model's own score produces high-quality samples in four steps. On text-to-image benchmarks, NTM matches or outperforms strong image generation baselines in just four sampling steps while uniquely retaining exact likelihood over the generative trajectory.
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