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cs.CV2026
SelfLift: Accelerating Few-Step Diffusion via Self-Recovering Resolution Transition
Tingyan Wen, Chenqian Yan, Xurui Peng +4
Few-step diffusion models substantially compress temporal computation, making the spatial cost of each model evaluation an increasingly dominant source of inference latency. Progre…
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…