147 citations · 391 across the 75 of their papers we have counts for
13 papers · 2 filters
DiffusionTrend: A Minimalist Approach to Virtual Fashion Try-On
Wengyi Zhan, Mingbao Lin, Shuicheng Yan +1
We introduce DiffusionTrend for virtual fashion try-on, which forgoes the need for retraining diffusion models. Using advanced diffusion models, DiffusionTrend harnesses latent inf…
AccDiffusion v2: Towards More Accurate Higher-Resolution Diffusion Extrapolation
Zhihang Lin, Mingbao Lin, Wengyi Zhan +1
Diffusion models suffer severe object repetition and local distortion when the inference resolution differs from its pre-trained resolution. We propose AccDiffusion v2, an accurate…
TextDestroyer: A Training- and Annotation-Free Diffusion Method for Destroying Anomal Text from Images
Mengcheng Li, Fei Chao
In this paper, we propose TextDestroyer, the first training- and annotation-free method for scene text destruction using a pre-trained diffusion model. Existing scene text removal…
UniVST: A Unified Framework for Training-free Localized Video Style Transfer
Quanjian Song, Mingbao Lin, Wengyi Zhan +3
This paper presents UniVST, a unified framework for localized video style transfer based on diffusion models. It operates without the need for training, offering a distinct advanta…
Advancing Multimodal Large Language Models with Quantization-Aware Scale Learning for Efficient Adaptation
Jingjing Xie, Yuxin Zhang, Mingbao Lin +2
This paper presents the first study to explore the potential of parameter quantization for multimodal large language models to alleviate the significant resource constraint encount…
EasyInv: Toward Fast and Better DDIM Inversion
Ziyue Zhang, Mingbao Lin, Shuicheng Yan +1
This paper introduces EasyInv, an easy yet novel approach that significantly advances the field of DDIM Inversion by addressing the inherent inefficiencies and performance limitati…
AccDiffusion: An Accurate Method for Higher-Resolution Image Generation
Zhihang Lin, Mingbao Lin, Meng Zhao +1
This paper attempts to address the object repetition issue in patch-wise higher-resolution image generation. We propose AccDiffusion, an accurate method for patch-wise higher-resol…
Move and Act: Enhanced Object Manipulation and Background Integrity for Image Editing
Pengfei Jiang, Mingbao Lin, Fei Chao
Current methods commonly utilize three-branch structures of inversion, reconstruction, and editing, to tackle consistent image editing task. However, these methods lack control ove…
AnySR: Realizing Image Super-Resolution as Any-Scale, Any-Resource
Wengyi Zhan, Mingbao Lin, Chia-Wen Lin +1
In an effort to improve the efficiency and scalability of single-image super-resolution (SISR) applications, we introduce AnySR, to rebuild existing arbitrary-scale SR methods into…
UniPTS: A Unified Framework for Proficient Post-Training Sparsity
Jingjing Xie, Yuxin Zhang, Mingbao Lin +3
Post-training Sparsity (PTS) is a recently emerged avenue that chases efficient network sparsity with limited data in need. Existing PTS methods, however, undergo significant perfo…
Boosting Multimodal Large Language Models with Visual Tokens Withdrawal for Rapid Inference
Zhihang Lin, Mingbao Lin, Luxi Lin +1
Multimodal large language models (MLLMs) demand considerable computations for inference due to the extensive parameters and the additional input tokens needed for visual informatio…
CutDiffusion: A Simple, Fast, Cheap, and Strong Diffusion Extrapolation Method
Mingbao Lin, Zhihang Lin, Wengyi Zhan +2
Transforming large pre-trained low-resolution diffusion models to cater to higher-resolution demands, i.e., diffusion extrapolation, significantly improves diffusion adaptability.…
ObjectAdd: Adding Objects into Image via a Training-Free Diffusion Modification Fashion
Ziyue Zhang, Mingbao Lin, Quanjian Song +2
We introduce ObjectAdd, a training-free diffusion modification method to add user-expected objects into user-specified area. The motive of ObjectAdd stems from: first, describing e…