arXiv Computer Vision September 30, 2026 By Shuai Yang, Luozhou Wang, Wei Huang, ZhiFei Chen, Bohan Zhang, Xiao Fu, Qianli Ma, Chen-Hsuan Lin, Weian Mao, Bryan Chu, Song Han, Yukang Chen

LongLive-Plug: Once-for-All Distillation for Video Generation

Read the original on arXiv Computer Vision →

LongLive‑Plug is a once‑for‑all distillation framework that learns reusable LoRA adapters on a base video diffusion model, enabling training‑free, plug‑and‑play deployment to a wide range of downstream models. These adapters provide single‑pass classifier‑free guidance, few‑step sampling, and long‑context error correction for autoregressive generation, and remain effective even when downstream models add conditioning branches or expand output channels. The authors demonstrate that the approach works on 54 downstream models across three backbone families and eight task categories, including world modeling, robotics, editing, and multimodal generation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

arXiv Computation and Language

Sep 10

On-Policy Distillation for Vision-Language Model Adaptation, an Effective Paradigm on Low-Quality Multimodal Data

arXiv:2609.10321v1 Announce Type: new Abstract: Knowledge distillation offers an efficient route to transfer a task-adapted vision-language teacher to a compact student. The training target in curren...

By Hongyuan Zhang, Xianda Guo, Yanlun Peng, Qianlong Yang, Yubin Guo, Pinhan Fu, Mulin Chen, Xiaozhen Qiao, Ping Luo

llms efficiency multimodal benchmarks

arXiv Computer Vision

Aug 24

Prompt2Effect: Training-Free Image-to-Video Model Specialization via LoRA Generation

arXiv:2606.13971v3 Announce Type: replace Abstract: While personalizing Image-to-Video (I2V) diffusion models with specific visual effects is increasingly demanded for high-end generation, current pr...

By Xiaomeng Yang, Yanyu Li, Gordon Guocheng Qian, Ivan Skorokhodov, Viacheslav Ivanov, Avalon Vinella, Xuan Zhang, Yanzhi Wang, Sergey Tulyakov, Anil Kag

diffusion fine-tuning safety

Hugging Face Trending Papers

Aug 17

MLLM-Guided Semantic Correction for Text-to-Video Generation

Recent advances in diffusion models and Transformer architectures have led to significant progress in text-to-video generation. However, these models often suffer from semantic errors such as missing...

llms diffusion multimodal benchmarks safety

arXiv Machine Learning

Jun 26

DanceOPD: On-Policy Generative Field Distillation

arXiv:2606. 27377v1 Announce Type: cross Abstract: Modern image generation demands a single model that unifies diverse capabilities, including text-to-image (T2I), local editing, and global editing.

By Wei Zhou, Xiongwei Zhu, Zelin Xu, Bo Dong, Lixue Gong, Yongyuan Liang, Meng Chu, Leigang Qu, Lingdong Kong, Wei Liu, Tat-Seng Chua

diffusion efficiency

arXiv Computer Vision

3d ago

Uncertainty-Aware Consistency Distillation for Few-Step Video Generation

arXiv:2609.39132v1 Announce Type: new Abstract: We study few-step video generation, i.e., distilling a multi-step video generator, which typically requires tens of sampling steps, incurring substanti...

By Lingyu Liu, Yaxiong Wang, Li Zhu, Zhedong Zheng

diffusion fine-tuning efficiency benchmarks safety

Hugging Face Trending Papers

Sep 17

Video DeltaNet: A Video-Native Hybrid Attention for Livestream Video Generation

Video DeltaNet (VDN) introduces a hybrid attention mechanism for video diffusion models, combining local Softmax attention with a bidirectional linear memory branch called Video Delta Attention (VDA). The design updates memory once per frame, uses separate output projections and learnable gates to balance the two branches, and employs a staged teacher‑alignment recipe to integrate the new pathway into pretrained models. When applied to MiniMax H3, VDN achieves a 14.5× speedup, completing 14.3‑second, 768p video denoising in 6.70 seconds on eight NVIDIA B200 GPUs compared to the 50‑step dense baseline.

llms diffusion efficiency safety