Video diffusion models are increasingly developed into specialized models for diverse downstream tasks, and this development often includes a distillation stage, for example to accelerate sampling or to improve long-video generation. This stage is typically repeated for every specialized model. We introduce LongLive-Pl...
Shuai Yang, Luo-Zhou Wang, Wei Huang et al.· 0 citations
Large language models (LLMs) typically generate text autoregressively (AR), predicting one token at a time. Block diffusion language models (dLLMs) instead generate blocks sequentially while denoising multiple tokens in parallel within each block, offering a promising way to accelerate generation. Rather than training...
Video diffusion models are rapidly scaling and exhibiting enhanced generation capabilities. Among these recent advancements, MiniMax-H3 stands out as a highly capable, production-level open-source model. However, its 33-billion parameters and multi-step iterative denoising process introduce substantial computational ov...
Yi-Tong Li, Jin-Cheng Yu, Jun-Song Chen et al.· 0 citations
This work takes an RSI-inspired approach at the harness layer, scaling auto-research loops across increasingly numerous and diverse environments for harness rollouts, and yields reusable improvements that transfer beyond their development setting.
Hao-Zhe Liu, Tian Ye, Sen-Sen Gao et al.· 4 citations
This paper introduces training-free Sol-Attn (Sparsifying online attention), which unifies dynamic routing, sparse computation, and approximation correction in a single online-softmax pass, achieving a better accuracy-efficiency trade-off in sparse attention.
This work introduces SANA-Video 2.0, a hybrid video diffusion transformer instantiated at 5B and 14B scales under a unified architecture that recovers softmax-level expressiveness at substantially reduced cost, unlocking scalable long, high resolution video generation.
Junsong Chen, Jincheng Yu, Yitong Li et al.· arXiv.org· 1 citation· ⚡1
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