Italiano(IT)Q-ARVD: Quantizing Autoregressive Video Diffusion Models
新方法提升视频扩散模型的效率和质量
作者PulseAugur 编辑部·[13 个来源]·
研究人员正在开发新方法来提高视频扩散模型的效率和质量。几篇论文介绍了优化注意力机制的技术，例如稀疏注意力（LVSA、Veda）和线性注意力（ARL2），以降低计算成本并实现更长的视频生成。其他方法侧重于微调和偏好优化，例如用于时空区域对齐的LocalDPO和通过矢量化时间步长适应来实现时间控制的Pusa V1.0。此外，Q-ARVD解决了自回归视频扩散模型特有的量化挑战，而Bernini则统一了大型语言模型和扩散模型以实现语义规划和渲染。
AI
arXiv:2601.21444v2 Announce Type: replace-cross Abstract: The efficiency of long-video inference remains a critical bottleneck, mainly due to the dense computation in the prefill stage of Large Multimodal Models (LMMs). Existing methods either compress visual embeddings or apply …
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Long Video Sparse Attention (LVSA) addresses computational bottlenecks in video diffusion models by introducing a sparse attention mechanism that reduces compute costs while maintaining video quality beyond training horizons.
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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.
Dense self-attention is the compute and quality bottleneck of long-video diffusion inference: cost grows quadratically with the sequence length, and beyond the training horizon the model converges to near-static output, that is, "frozen" repetitive video. State of the art approac…
arXiv:2605.30325v1 Announce Type: new Abstract: Scaling Diffusion Transformers to generate high-resolution, long videos is constrained by the quadratic cost of self-attention, and existing sparse attention methods degrade under high sparsity. We show empirically that generation q…
Scaling Diffusion Transformers to generate high-resolution, long videos is constrained by the quadratic cost of self-attention, and existing sparse attention methods degrade under high sparsity. We show empirically that generation quality is determined not by the sparsity ratio i…
arXiv cs.CV
TIER_1English(EN)·Yaofang Liu, Yumeng Ren, Aitor Artola, Yuxuan Hu, Xiaodong Cun, Xiaotong Zhao, Alan Zhao, Raymond H. Chan, Suiyun Zhang, Rui Liu, Dandan Tu, Jean-Michel Morel·
arXiv:2507.16116v2 Announce Type: replace Abstract: The rapid advancement of video diffusion models has been hindered by fundamental limitations in temporal modeling, particularly the rigid synchronization of frame evolution imposed by conventional scalar timestep variables. Whil…
arXiv cs.CV
TIER_1English(EN)·Bernini Team, Chenchen Liu, Junyi Chen, Lei Li, Lu Chi, Mingzhen Sun, Zhuoying Li, Yi Fu, Ruoyu Guo, Yiheng Wu, Ge Bai, Zehuan Yuan·
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arXiv:2605.22015v1 Announce Type: new Abstract: Diffusion Transformer (DiT) has emerged as a powerful model architecture for generating high-quality images and videos. In the case of video DiT, 3D Spatio-Temporal Attention increases token length in proportion to the number of fra…
Multimodal large language models (MLLMs) and diffusion models have each reached remarkable maturity: MLLMs excel at reasoning over heterogeneous multimodal inputs with strong semantic grounding, while diffusion models synthesize images and videos with photorealistic fidelity. We …
