papers

Publications (5)

cs.CV2026

Error Propagation Mechanisms and Compensation Strategies for Quantized Diffusion

Songwei Liu, Chao Zeng, Chenqian Yan +4

Diffusion models have transformed image synthesis by establishing unprecedented quality and creativity benchmarks. Nevertheless, their large-scale deployment faces challenges due t…

cs.CV2024

Hybrid SD: Edge-Cloud Collaborative Inference for Stable Diffusion Models

Chenqian Yan, Songwei Liu, Hongjian Liu +5

Stable Diffusion Models (SDMs) have shown remarkable proficiency in image synthesis. However, their broad application is impeded by their large model sizes and intensive computatio…

cs.CV2026

ERTACache: Error Rectification and Timesteps Adjustment for Efficient Diffusion

Xurui Peng, Chenqian Yan, Hong Liu +6

Diffusion models suffer from substantial computational overhead due to their inherently iterative inference process. While feature caching offers a promising acceleration strategy…

cs.CV2026

Train Short, Inference Long: Training-free Horizon Extension for Autoregressive Video Generation

Jia Li, Xiaomeng Fu, Xurui Peng +7

Autoregressive video diffusion models have emerged as a scalable paradigm for long video generation. However, they often suffer from severe extrapolation failure, where rapid error…

cs.CV2026

TAP: A Token-Adaptive Predictor Framework for Training-Free Diffusion Acceleration

Haowei Zhu, Tingxuan Huang, Xing Wang +7

Diffusion models achieve strong generative performance but remain slow at inference due to the need for repeated full-model denoising passes. We present Token-Adaptive Predictor (T…