Paper page - Q-ARVD: Quantizing Autoregressive Video Diffusion Models

Source: https://huggingface.co/papers/2605.21072

Abstract

Autoregressive video diffusion models face high inference costs that limit practical deployment, prompting the development of Q-ARVD, a novel quantization framework addressing frame-wise sensitivity imbalance and weight outlier patterns specific to these models.

Autoregressive video diffusion models(ARVDs) have emerged as a promising architecture for streaming video generation, paving the way for real-time interactive video generation and world modeling. Despite their potential, the substantial inference cost of ARVDs remains a major obstacle to practical deployment, making modelquantizationa natural direction for improving efficiency. However,quantizationfor ARVDs remains largely unexplored. Our empirical analysis shows that directly applying existingquantizationschemes developed for standarddiffusion transformersto ARVDs leads to suboptimal performance, revealingquantizationbehaviors that differ from those observed in bidirectional diffusion models. In this paper, we identify two critical challenges in quantizing ARVDs: (C1) Highly unbalancedframe-wise quantization sensitivity.Error accumulationduring autoregressive generation can induce severely skewedquantizationsensitivity across frames, following an exponential-like decay pattern. (C2) Prominent and heterogeneous outlier patterns in weights.Weight distributionsexhibit pronouncedoutlier channels, whose patterns vary substantially across layer types and block depths. To address these issues, we propose Q-ARVD, a novel framework for accurate ARVDquantization. (S1) To tackle the highly unbalanced frame-wise sensitivity, Q-ARVD incorporates afinal-quality aware frame-weightingmechanism into thequantizationobjective. (S2) To prevent heterogeneous outliers from degrading performance, Q-ARVD introduces an outlier-awareadaptive dual-scale quantization, which automatically detects the presence and quantity ofoutlier channelsfor an arbitrary layer, and isolates them to protect normal channels. Extensive experiments demonstrate the superiority of Q-ARVD.

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