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Shuning Chang

7 accepted papers

2026

Flow Along the $K$-Amplitude for Generative Modeling

ICLR 2026poster

In this work, we propose K-Flow, a novel generative learning paradigm that flows along the $K$-amplitude domain, where $K$ is a scaling parameter that organizes projected coefficients (frequency bands), and amplitude refers to the norm of such coefficients. We instantiate K-Flow with three concrete…

Cited by 0SourcecodeScholar
2026

Lumos-1: On Autoregressive Video Generation with Discrete Diffusion from a Unified Model Perspective

ICLR 2026poster

Autoregressive large language models (LLMs) have unified a vast range of language tasks, inspiring preliminary efforts in autoregressive (AR) video generation. Existing AR video generators either diverge from standard LLM architectures, depend on bulky external text encoders, or incur prohibitive la…

Cited by 0SourcecodeScholar
2025

SparseDiT: Token Sparsification for Efficient Diffusion Transformer

NeurIPS 2025poster

Diffusion Transformers (DiT) are renowned for their impressive generative performance; however, they are significantly constrained by considerable computational costs due to the quadratic complexity in self-attention and the extensive sampling steps required. While advancements have been made in exp…

Cited by 0SourcecodeScholar
2023

Making Vision Transformers Efficient From a Token Sparsification View

CVPR 2023poster

The quadratic computational complexity to the number of tokens limits the practical applications of Vision Transformers (ViTs). Several works propose to prune redundant tokens to achieve efficient ViTs. However, these methods generally suffer from (i) dramatic accuracy drops, (ii) application diffic…

2023

Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Models

NeurIPS 2023poster

Public large-scale text-to-image diffusion models, such as Stable Diffusion, have gained significant attention from the community. These models can be easily customized for new concepts using low-rank adaptations (LoRAs). However, the utilization of multiple-concept LoRAs to jointly support multipl…

Cited by 178SourcePDFScholar
2023

Revisiting Vision Transformer from the View of Path Ensemble

ICCV 2023oral

Vision Transformers (ViTs) are normally regarded as a stack of transformer layers. In this work, we propose a novel view of ViTs showing that they can be seen as ensemble networks containing multiple parallel paths with different lengths. Specifically, we equivalently transform the traditional casca…

Cited by 6PDFcodeScholar
2022

KVT: k-NN Attention for Boosting Vision Transformers

ECCV 2022poster

"Convolutional Neural Networks (CNNs) have dominated computer vision for years, due to its ability in capturing locality and translation invariance. Recently, many vision transformer architectures have been proposed and they show promising performance. A key component in vision transformers is the f…