Apple 提出 Normalizing Trajectory Models（NTM）：四步采样同时保留精确似然
Normalizing Trajectory Models - Apple Machine Learning Research
Apple 提出 Normalizing Trajectory Models（NTM），将扩散模型的每个反向步骤建模为带精确似然训练的条件归一化流，结合每步浅层可逆模块与跨轨迹的深层并行预测器，可从头训练或从预训练 flow-matching 模型初始化。其精确轨迹似然支持自蒸馏，轻量去噪器四步即可生成高质量样本。在文生图基准上，NTM 仅用四步采样即匹配或超越强基线，并保留生成轨迹的精确似然。
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Normalizing Trajectory Models
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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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