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cs.CV2025
Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility
Yiheng Li, Feng Liang, Dan Kondratyuk +3
The substantial training cost of diffusion models hinders their deployment. Immiscible Diffusion recently showed that reducing diffusion trajectory mixing in the noise space via li…
cs.CV2024
Immiscible Diffusion: Accelerating Diffusion Training with Noise Assignment
Yiheng Li, Heyang Jiang, Akio Kodaira +3
In this paper, we point out that suboptimal noise-data mapping leads to slow training of diffusion models. During diffusion training, current methods diffuse each image across the…
cs.CV2023
Pre-training on Synthetic Driving Data for Trajectory Prediction
Yiheng Li, Seth Z. Zhao, Chenfeng Xu +5
Accumulating substantial volumes of real-world driving data proves pivotal in the realm of trajectory forecasting for autonomous driving. Given the heavy reliance of current trajec…