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20222026
most citedCompressing Models with Few Samples: Mimicking then Replacing

2 citations · 4 across the 10 of their papers we have counts for

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7 papers · 1 filter

cs.CV2026

LightVAE: Towards Compact and Efficient Video Autoencoders

Shiqiao Gu, Yang Yong, Fengjun Zhong +5

Latent diffusion models enable high-quality video generation, but video VAE encoding and decoding remain computationally expensive. Existing methods use stage-specific lightweight…

cs.CV2026

RealtimeWAM: One-Step Asynchronous World Action Models

Chengtao Lv, Jinyang Du, Shuyi Feng +7

World Action Models (WAMs) incorporate visual representations from video generation backbones to guide action prediction. Recent efficient WAMs adopt Mixture-of-Transformers (MoT)…

cs.CV2026

SenseNova-U1.5: Towards Native Unified Visual Intelligence

Haiwen Diao, Jiahao Wang, Chenjing Ding +62

We launch SenseNova-U1.5, an 8B-MoT native unified multimodal model that understands, reasons about, and generates visual content within an encoder-free and VAE-free architecture.…

cs.CV2026

MaskFlow: Precise, Consistent and Seamless Regional Image Editing

Rui Xu, Yang Yong, Shunzi Yang +2

Regional image editing has attracted considerable attention for its spatial controllability. Although instruction-based and mask-based editing methods can achieve strong semantic a…

cs.CV2026

Collaborative Few-Step Distillation and Low-Bit Quantization for Wan2.2 Dual-Expert Video Diffusion Models

Jinyang Du, Shenghao Jin, Ziqian Xu +5

Large video diffusion models achieve strong visual quality but remain expensive to deploy because each sample requires many denoising steps and a large resident parameter footprint…

cs.DC2026

DisagFusion: Asynchronous Pipeline Parallelism and Elastic Scheduling for Disaggregated Diffusion Serving

Hantian Zha, Teng Ma, Yang Yong +7

Diffusion-based generation is increasingly powering production content pipelines; however, deploying these models at scale remains a significant challenge. Model weights frequently…

cs.CV2026

SGMD: Score Gradient Matching Distillation for Few-Step Video Diffusion Distillation

Zhuguanyu Wu, Ruihao Gong, Yang Yong +5

Distribution Matching Distillation (DMD) is a widely used paradigm for accelerating inference in few-step video diffusion models. However, DMD-style video distillation faces two co…