2.5k citations · 3.5k across the 32 of their papers we have counts for
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In-Context Learning Unlocked for Diffusion Models
Zhendong Wang, Yifan Jiang, Yadong Lu +5
We present Prompt Diffusion, a framework for enabling in-context learning in diffusion-based generative models. Given a pair of task-specific example images, such as depth from/to…
Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models
Zhendong Wang, Yifan Jiang, Huangjie Zheng +5
Diffusion models are powerful, but they require a lot of time and data to train. We propose Patch Diffusion, a generic patch-wise training framework, to significantly reduce the tr…
Mixing and Shifting: Exploiting Global and Local Dependencies in Vision MLPs
Huangjie Zheng, Pengcheng He, Weizhu Chen +1
Token-mixing multi-layer perceptron (MLP) models have shown competitive performance in computer vision tasks with a simple architecture and relatively small computational cost. The…