activity
20242026
collaborators

9 papers

cs.CV2026

QK-Edit: Revisiting Attention-based Injection in MM-DiT for Image and Video Editing

Tiancheng Shen, Zilong Huang, Xiangtai Li +6

Multimodal Diffusion Transformers (MM-DiTs) have recently emerged as a powerful framework for unified text-vision synthesis, surpassing traditional U-Net architectures in generativ…

cs.CV2026

DreamSR: Towards Ultra-High-Resolution Image Super-Resolution via a Receptive-Field Enhanced Diffusion Transformer

Qingji Dong, Hang Dong, Mingqin Chen +2

Large-scale pre-trained diffusion models have been extensively adopted for real-world image Super-Resolution because of their powerful generative priors through textual guidance. H…

cs.CV2026

NextFlow: Unified Sequential Modeling Activates Multimodal Understanding and Generation

Huichao Zhang, Liao Qu, Yiheng Liu +33

We present NextFlow, a unified decoder-only autoregressive transformer trained on 6 trillion interleaved text-image discrete tokens. By leveraging a unified vision representation w…

cs.CV2025

DreamOmni3: Scribble-based Editing and Generation

Bin Xia, Bohao Peng, Jiyang Liu +8

Recently unified generation and editing models have achieved remarkable success with their impressive performance. These models rely mainly on text prompts for instruction-based ed…

cs.CV2025

DreamOmni2: Multimodal Instruction-based Editing and Generation

Bin Xia, Bohao Peng, Yuechen Zhang +10

Recent advancements in instruction-based image editing and subject-driven generation have garnered significant attention, yet both tasks still face limitations in meeting practical…

cs.CV2025

DreamVE: Unified Instruction-based Image and Video Editing

Bin Xia, Jiyang Liu, Yuechen Zhang +6

Instruction-based editing holds vast potential due to its simple and efficient interactive editing format. However, instruction-based editing, particularly for video, has been cons…

cs.CV2025

DreamLayer: Simultaneous Multi-Layer Generation via Diffusion Mode

Junjia Huang, Pengxiang Yan, Jinhang Cai +5

Text-driven image generation using diffusion models has recently gained significant attention. To enable more flexible image manipulation and editing, recent research has expanded…

cs.CV2024

DreamOmni: Unified Image Generation and Editing

Bin Xia, Yuechen Zhang, Jingyao Li +5

Currently, the success of large language models (LLMs) illustrates that a unified multitasking approach can significantly enhance model usability, streamline deployment, and foster…

cs.CV2024

CreatiLayout: Siamese Multimodal Diffusion Transformer for Creative Layout-to-Image Generation

Hui Zhang, Dexiang Hong, Yitong Wang +4

Diffusion models have been recognized for their ability to generate images that are not only visually appealing but also of high artistic quality. As a result, Layout-to-Image (L2I…