High-resolution video generation is expensive, as its cost grows rapidly with the number of spatiotemporal tokens. A practical alternative first generates a lower-resolution video and then applies a refiner, but conventional multi-step refinement introduces a second sampling bottleneck. We present SoL-Refiner, a one-st...
Hao-Zhe Liu, Tian Ye, Shuchen Xue et al.· 0 citations
DraftAttention2 is presented, a training-free framework that uses the low-resolution draft attention map to jointly select attention blocks and assign their numerical precision, and achieves a superior quality-efficiency trade-off over existing efficient video generation methods.
Rui Ding, Haopeng Li, Wei-Ze Ma et al.· 0 citations
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.· 7 citations
DC-Gen, a general framework that accelerates text-to-image diffusion models by leveraging a deeply compressed latent space, uses an efficient post-training pipeline to preserve the quality of the base model to reduce the latency of 4K image generation.
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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