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cs.LG2025
Selective Underfitting in Diffusion Models
Kiwhan Song, Jaeyeon Kim, Sitan Chen +3
Diffusion models have emerged as the principal paradigm for generative modeling across various domains. During training, they learn the score function, which in turn is used to gen…
cs.LG2025
History-Guided Video Diffusion
Kiwhan Song, Boyuan Chen, Max Simchowitz +3
Classifier-free guidance (CFG) is a key technique for improving conditional generation in diffusion models, enabling more accurate control while enhancing sample quality. It is nat…
cs.LG2024
Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion
Boyuan Chen, Diego Marti Monso, Yilun Du +3
This paper presents Diffusion Forcing, a new training paradigm where a diffusion model is trained to denoise a set of tokens with independent per-token noise levels. We apply Diffu…