用极少隐状态训练扩散模型，速度提升4-6倍且效果不降。
Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states
- 通过优化噪声调度，在32个隐状态上实现与1000个状态相当的效果。
- 将隐状态数压缩至1个，仍可生成高质量图像。
- 多模型组合实现快速分布式训练，适合大规模生成任务。
我们挑战了扩散模型的一个基本假设：训练需大量隐状态或时间步以使反向生成过程接近高斯分布。首先，我们证明通过精心设计的噪声调度，仅需约32个隐状态即可达到与约1000个隐状态训练模型相当的性能。其次，我们将该极限推进至单个隐状态，即T空间中的完全解耦。我们发现，通过组合多个独立训练的单隐状态模型，可轻松生成高质量样本。大量实验证明，所提出的解耦模型在两个不同数据集上，各项指标的收敛速度均提升了4-6倍。
原文摘要 · Abstract (English)
We challenge a fundamental assumption of diffusion models, namely, that a large number of latent-states or time-steps is required for training so that the reverse generative process is close to a Gaussian. We first show that with careful selection of a noise schedule, diffusion models trained over a small number of latent states (i.e. $T \sim 32$) match the performance of models trained over a much large number of latent states ($T \sim 1,000$). Second, we push this limit (on the minimum number of latent states required) to a single latent-state, which we refer to as complete disentanglement in T-space. We show that high quality samples can be easily generated by the disentangled model obtained by combining several independently trained single latent-state models. We provide extensive experiments to show that the proposed disentangled model provides 4-6$\times$ faster convergence measured across a variety of metrics on two different datasets.
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