65 citations · 69 across the 12 of their papers we have counts for
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cs.AI2026
EpaCache: Error-Propagation-Aware Caching for Accelerating Diffusion-Based Visual Generation
Yuhan Liu, Zongwei Hong, Jinglun Li +3
Diffusion-based visual generative models deliver strong image and video synthesis quality but incur high inference costs because sequential samplers repeatedly evaluate large netwo…
cs.CV2026
SPARE: Structural Parameter-Free Affinity Regularization for Flow Matching
Zong-Wei Hong, Jinglun Li, Shen Zhang +3
Denoising diffusion transformers achieve strong generation quality but converge slowly during training. Regularizing their internal representations has emerged as an effective acce…
cs.CV2026
The Velocity Deficit: Initial Energy Injection for Flow Matching
Linze Li, Zong-Wei Hong, Shen Zhang +4
While Flow Matching theoretically guarantees constant-velocity trajectories, we identify a critical breakdown in high-dimensional practice: the Velocity Deficit. We show that the M…