Towards Faster Training of Diffusion Models: An Inspiration of A Consistency Phenomenon
<|reference_start|>Towards Faster Training of Diffusion Models: An Inspiration of A Consistency Phenomenon: Diffusion models (DMs) are a powerful generative framework that have attracted significant attention in recent years. However, the high computational cost of training DMs limits their practical applications. In this paper, we start with a consistency phenomenon of DMs: we observe that DMs with different initializations or even different architectures can produce very similar outputs given the same noise inputs, which is rare in other generative models. We attribute this phenomenon to two factors: (1) the learning difficulty of DMs is lower when the noise-prediction diffusion model approaches the upper bound of the timestep (the input becomes pure noise), where the structural information of the output is usually generated; and (2) the loss landscape of DMs is highly smooth, which implies that the model tends to converge to similar local minima and exhibit similar behavior patterns. This finding not only reveals the stability of DMs, but also inspires us to devise two strategies to accelerate the training of DMs. First, we propose a curriculum learning based timestep schedule, which leverages the noise rate as an explicit indicator of the learning difficulty and gradually reduces the training frequency of easier timesteps, thus improving the training efficiency. Second, we propose a momentum decay strategy, which reduces the momentum coefficient during the optimization process, as the large momentum may hinder the convergence speed and cause oscillations due to the smoothness of the loss landscape. We demonstrate the effectiveness of our proposed strategies on various models and show that they can significantly reduce the training time and improve the quality of the generated images.<|reference_end|>
@article{xu2024towards,
    title={Towards Faster Training of Diffusion Models: An Inspiration of A
  Consistency Phenomenon},
    author={Tianshuo Xu, Peng Mi, Ruilin Wang, Yingcong Chen},
    journal={arXiv preprint arXiv:2404.07946},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.07946},
    primaryClass={cs.LG cs.AI}
}