Jiuyi (Joey) Xu is a second-year Ph.D. student in Robotics at the Colorado School of Mines under the supervision of Dr. Yangming Shi. His research interests include, but are not limited to, Vision-Language-Action (VLA) models, generative AI (e.g., image generation) and efficient AI (e.g., quantization). He holds an M.S. in Computer Science from the University of Southern California (USC) and a B.E. in Software Engineering from Dalian University of Technology (DLUT). Previously, he was a student research intern at USC’s Institute for Creative Technologies, working on projects related to open-vocabulary object detection (OVOD) and open-vocabulary semantic segmentation (OVSS). Beyond research, Joey is an active peer reviewer for the Journal of Computing in Civil Engineering and has contributed to multiple academic conferences/journals. He is also a student member of IEEE, ACM, and ASCE. In his spare time, he really likes going to the gym and playing basketball.
In this work, we propose LowDiff, a novel and efficient diffusion framework based on a cascaded approach by generating increasingly higher resolution outputs. Besides, LowDiff employs a unified model to progressively refine images from low resolution to the desired resolution. With the proposed architecture design and generation techniques, we achieve comparable or even superior performance with much fewer high-resolution sampling steps. LowDiff is applicable to diffusion models in both pixel space and latent space.
Coming soon