Efficient Generative Modeling with
Residual Vector Quantization-Based Tokens

Abstract. We introduce ResGen, an efficient Residual Vector Quantization (RVQ)-based generative model for high-fidelity generation with fast sampling. RVQ improves data fidelity by increasing the number of quantization steps, referred to as depth, but deeper quantization typically increases inference steps in generative models. To address this, ResGen directly predicts the vector embedding of collective tokens rather than individual ones, ensuring that inference steps remain independent of RVQ depth. Additionally, we formulate token masking and multi-token prediction within a probabilistic framework using discrete diffusion and variational inference. We validate the efficacy and generalizability of the proposed method on two challenging tasks across different modalities: conditional image generation on ImageNet 256×256 and zero-shot text-to-speech synthesis.. Experimental results demonstrate that ResGen outperforms autoregressive counterparts in both tasks, delivering superior performance without compromising sampling speed. Furthermore, as we scale the depth of RVQ, our generative models exhibit enhanced generation fidelity or faster sampling speeds compared to similarly sized baseline models.

Model Overview

An overview of the forward masking and reverse unmasking processes is shown at the top, with a detailed depiction of the reverse unmasking process below. In the top figure, forward masking proceeds from right to left, incrementally masking more tokens, while reverse unmasking progresses from left to right, iteratively revealing the masked tokens. White boxes denote masked tokens and colored boxes represent tokens that have been uncovered. The bottom figure illustrates the reverse unmasking process in detail. Starting from masked residual vector quantization (RVQ) tokens, our method first predicts cumulative RVQ embeddings. These embeddings are then quantized and partially masked again. Through a series of iterations, each round predicts the values of the masked tokens and replaces them until the entire token sequence is filled.