Normalizing Trajectory Models (NTM) 提出一种基于标准化流的轨迹建模方法
Normalizing Trajectory Models
Normalizing Trajectory Models (NTM) 将扩散模型的反向生成步骤建模为具有精确似然训练能力的条件标准化流，解决了生成过程压缩至少量步骤时性能下降的问题。该模型通过结合浅层可逆块与深层轨迹预测器，支持从零训练或基于预训练流匹配模型初始化。在文本生成图像任务中，NTM 仅需 4 个采样步骤即可匹配或超越现有基准，并能通过自蒸馏生成高质量样本。
AuthorsJiatao Gu†, Tianrong Chen, Ying Shen‡**, David Berthelot, Shuangfei Zhai, Josh Susskind
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 score function induced by the model itself 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.
- † University of Pennsylvania
- ‡ UIUC
- ** Work done while at Apple
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来源：Apple Machine Learning Research · machinelearning.apple.com