2 citations · 4 across the 35 of their papers we have counts for
16 papers · 1 filter
SpotEdit: Selective Region Editing in Diffusion Transformers
Zhibin Qin, Zhenxiong Tan, Zeqing Wang +2
Diffusion Transformer models have significantly advanced image editing by encoding conditional images and integrating them into transformer layers. However, most edits involve modi…
RunawayEvil: Jailbreaking the Image-to-Video Generative Models
Songping Wang, Rufan Qian, Yueming Lyu +5
Image-to-Video (I2V) generation synthesizes dynamic visual content from image and text inputs, providing significant creative control. However, the security of such multimodal syst…
CoDA: From Text-to-Image Diffusion Models to Training-Free Dataset Distillation
Letian Zhou, Songhua Liu, Xinchao Wang
Prevailing Dataset Distillation (DD) methods leveraging generative models confront two fundamental limitations. First, despite pioneering the use of diffusion models in DD and deli…
EditMGT: Unleashing Potentials of Masked Generative Transformers in Image Editing
Wei Chow, Linfeng Li, Lingdong Kong +13
Recent advances in diffusion models (DMs) have achieved exceptional visual quality in image editing tasks. However, the global denoising dynamics of DMs inherently conflate local e…
FreeSwim: Revisiting Sliding-Window Attention Mechanisms for Training-Free Ultra-High-Resolution Video Generation
Yunfeng Wu, Jiayi Song, Zhenxiong Tan +2
The quadratic time and memory complexity of the attention mechanism in modern Transformer based video generators makes end-to-end training for ultra high resolution videos prohibit…
Vision Bridge Transformer at Scale
Zhenxiong Tan, Zeqing Wang, Xingyi Yang +2
We introduce Vision Bridge Transformer (ViBT), a large-scale instantiation of Brownian Bridge Models designed for conditional generation. Unlike traditional diffusion models that t…
SkipVAR: Accelerating Visual Autoregressive Modeling via Adaptive Frequency-Aware Skipping
Jiajun Li, Yue Ma, Xinyu Zhang +3
Recent studies on Visual Autoregressive (VAR) models have highlighted that high-frequency components, or later steps, in the generation process contribute disproportionately to inf…
Control and Realism: Best of Both Worlds in Layout-to-Image without Training
Bonan Li, Yinhan Hu, Songhua Liu +1
Layout-to-Image generation aims to create complex scenes with precise control over the placement and arrangement of subjects. Existing works have demonstrated that pre-trained Text…
Image Editing As Programs with Diffusion Models
Yujia Hu, Songhua Liu, Zhenxiong Tan +2
While diffusion models have achieved remarkable success in text-to-image generation, they encounter significant challenges with instruction-driven image editing. Our research highl…
Top-Down Compression: Revisit Efficient Vision Token Projection for Visual Instruction Tuning
Bonan li, Zicheng Zhang, Songhua Liu +2
Visual instruction tuning aims to enable large language models to comprehend the visual world, with a pivotal challenge lying in establishing an effective vision-to-language projec…
Flash Sculptor: Modular 3D Worlds from Objects
Yujia Hu, Songhua Liu, Xingyi Yang +1
Existing text-to-3D and image-to-3D models often struggle with complex scenes involving multiple objects and intricate interactions. Although some recent attempts have explored suc…
Ultra-Resolution Adaptation with Ease
Ruonan Yu, Songhua Liu, Zhenxiong Tan +1
Text-to-image diffusion models have achieved remarkable progress in recent years. However, training models for high-resolution image generation remains challenging, particularly wh…
POSTA: A Go-to Framework for Customized Artistic Poster Generation
Haoyu Chen, Xiaojie Xu, Wenbo Li +6
Poster design is a critical medium for visual communication. Prior work has explored automatic poster design using deep learning techniques, but these approaches lack text accuracy…
OminiControl2: Efficient Conditioning for Diffusion Transformers
Zhenxiong Tan, Qiaochu Xue, Xingyi Yang +2
Fine-grained control of text-to-image diffusion transformer models (DiT) remains a critical challenge for practical deployment. While recent advances such as OminiControl and other…
Understanding Dataset Distillation via Spectral Filtering
Deyu Bo, Songhua Liu, Xinchao Wang
Dataset distillation (DD) has emerged as a promising approach to compress datasets and speed up model training. However, the underlying connections among various DD methods remain…
Unifying Dataset Pruning and Distillation for Efficient Large-scale Compression
Lingao Xiao, Songhua Liu, Yang He +1
Dataset pruning (DP) and dataset distillation (DD) fundamentally differ in their outputs: DP selects original image subsets, while DD generates synthetic images. Recently, DD's inc…