6 citations · 6 across the 8 of their papers we have counts for
3 papers · 2 filters
Contrastive-Augmented Flow Matching for Style-Content Disentanglement
Yusong Li, Pingchuan Ma, Ming Gui +2
Learning representations that separate content and style is crucial for controllable generation and compositional generalization. However, diffusion and flow-based models trained p…
Denoising, Fast and Slow: Difficulty-Aware Adaptive Sampling for Image Generation
Johannes Schusterbauer, Ming Gui, Yusong Li +3
Diffusion- and flow-based models usually allocate compute uniformly across space, updating all patches with the same timestep and number of function evaluations. While convenient,…
Guiding Token-Sparse Diffusion Models
Felix Krause, Stefan Andreas Baumann, Johannes Schusterbauer +4
Diffusion models deliver high quality in image synthesis but remain expensive during training and inference. Recent works have leveraged the inherent redundancy in visual content t…