17 citations · 19 across the 6 of their papers we have counts for
6 papers
Q-ARVD: Quantizing Autoregressive Video Diffusion Models
Siao Tang, Xinyin Ma, Gongfan Fang +2
Autoregressive video diffusion models (ARVDs) have emerged as a promising architecture for streaming video generation, paving the way for real-time interactive video generation and…
ConciseHint: Boosting Efficient Reasoning via Continuous Concise Hints during Generation
Siao Tang, Xinyin Ma, Gongfan Fang +1
Recent advancements in large reasoning models (LRMs) like DeepSeek-R1 and OpenAI o1 series have achieved notable performance enhancements on complex reasoning tasks by scaling up t…
DisenStudio: Customized Multi-subject Text-to-Video Generation with Disentangled Spatial Control
Hong Chen, Xin Wang, Yipeng Zhang +4
Generating customized content in videos has received increasing attention recently. However, existing works primarily focus on customized text-to-video generation for single subjec…
Lightweight Diffusion Models with Distillation-Based Block Neural Architecture Search
Siao Tang, Xin Wang, Hong Chen +3
Diffusion models have recently shown remarkable generation ability, achieving state-of-the-art performance in many tasks. However, the high computational cost is still a troubling…
Post-training Quantization for Text-to-Image Diffusion Models with Progressive Calibration and Activation Relaxing
Siao Tang, Xin Wang, Hong Chen +4
High computational overhead is a troublesome problem for diffusion models. Recent studies have leveraged post-training quantization (PTQ) to compress diffusion models. However, mos…
Disentangled Representation Learning
Xin Wang, Hong Chen, Si'ao Tang +2
Disentangled Representation Learning (DRL) aims to learn a model capable of identifying and disentangling the underlying factors hidden in the observable data in representation for…