PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior

By Sang-gil Lee, Heeseung Kim, Chaehun Shin, Xu Tan, Chang Liu, Qi Meng, Tao Qin, Wei Chen, Sung-Hoon Yoon, Tie-Yan Liu. 2021

123 citations recorded by the source corpus.

Denoising diffusion probabilistic models have been recently proposed to generate high-quality samples by estimating the gradient of the data density. The framework defines the prior noise as a standard Gaussian distribution, whereas the corresponding data distribution may be more complicated than the standard Gaussian distribution, which potentially introduces inefficiency in denoising the prior noise into the data sample because of the discrepancy between the data and the prior. In this paper, we propose PriorGrad to improve the efficiency of the conditional diffusion model for speech synthesis (for example, a vocoder using a mel-spectrogram as the condition) by applying an adaptive prior derived from the data statistics based on the conditional information. We formulate the training and sampling procedures of PriorGrad and demonstrate the advantages of an adaptive prior through a theoretical analysis. Focusing on the speech synthesis domain, we consider the recently proposed diffusion-based speech generative models based on both the spectral and time domains and show that PriorGrad achieves faster convergence and inference with superior performance, leading to an improved perceptual quality and robustness to a smaller network capacity, and thereby demonstrating the efficiency of a data-dependent adaptive prior.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

BigVGAN: A Universal Neural Vocoder with Large-Scale Training (2022, methodology): “…et al., 2019; Prenger et al., 2019; Kim et al., 2019; Ping et al., 2020; Lee et al., 2020), GAN-based models (Donahue et al., 2019; Kumar et al., 2019; Bi´nkowski et al., 2020; Yamamoto et al., 2020; Kong et al., 2020), and diffusion models (Kong et al., 2021; Chen et al., 2021; Lee et al., 2022).”
SpecGrad: Diffusion Probabilistic Model based Neural Vocoder with Adaptive Noise Spectral Shaping (2022, methodology): “proposed PriorGrad [24] by introducing an adaptive prior N (0,Σ), where Σ is computed from c.”