1 citations · 2 across the 8 of their papers we have counts for
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Bend the Basics: Degradation-Aware Deformable Tokenization for All-in-One Image Restoration
Zihao He, Yunfeng Wu, Xinchao Wang +1
All-in-one image restoration seeks a single model that can recover images degraded by diverse and spatially non-uniform corruptions. However, many unified Transformers rely on fixe…
Visual Generation in the New Era: An Evolution from Atomic Mapping to Agentic World Modeling
Keming Wu, Zuhao Yang, Kaichen Zhang +24
Recent visual generation models have made major progress in photorealism, typography, instruction following, and interactive editing, yet they still struggle with spatial reasoning…
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…
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…
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…
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…
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…
CLEAR: Conv-Like Linearization Revs Pre-Trained Diffusion Transformers Up
Songhua Liu, Zhenxiong Tan, Xinchao Wang
Diffusion Transformers (DiT) have become a leading architecture in image generation. However, the quadratic complexity of attention mechanisms, which are responsible for modeling t…
OminiControl: Minimal and Universal Control for Diffusion Transformer
Zhenxiong Tan, Songhua Liu, Xingyi Yang +2
We present OminiControl, a novel approach that rethinks how image conditions are integrated into Diffusion Transformer (DiT) architectures. Current image conditioning methods eithe…
LinFusion: 1 GPU, 1 Minute, 16K Image
Songhua Liu, Weihao Yu, Zhenxiong Tan +1
Modern diffusion models, particularly those utilizing a Transformer-based UNet for denoising, rely heavily on self-attention operations to manage complex spatial relationships, thu…