8 papers
Does Hearing Help Seeing? Investigating Audio-Video Joint Denoising for Video Generation
Jianzong Wu, Hao Lian, Dachao Hao +5
Recent audio-video generative systems suggest that coupling modalities benefits not only audio-video synchrony but also the video modality itself. We pose a fundamental question: D…
MMaDA-Parallel: Multimodal Large Diffusion Language Models for Thinking-Aware Editing and Generation
Ye Tian, Ling Yang, Jiongfan Yang +10
While thinking-aware generation aims to improve performance on complex tasks, we identify a critical failure mode where existing sequential, autoregressive approaches can paradoxic…
VMoBA: Mixture-of-Block Attention for Video Diffusion Models
Jianzong Wu, Liang Hou, Haotian Yang +5
The quadratic complexity of full attention mechanisms poses a significant bottleneck for Video Diffusion Models (VDMs) aiming to generate long-duration, high-resolution videos. Whi…
MMaDA: Multimodal Large Diffusion Language Models
Ling Yang, Ye Tian, Bowen Li +4
We introduce MMaDA, a novel class of multimodal diffusion foundation models designed to achieve superior performance across diverse domains such as textual reasoning, multimodal un…
Training-free Diffusion Acceleration with Bottleneck Sampling
Ye Tian, Xin Xia, Yuxi Ren +6
Diffusion models have demonstrated remarkable capabilities in visual content generation but remain challenging to deploy due to their high computational cost during inference. This…
Diffusion-Sharpening: Fine-tuning Diffusion Models with Denoising Trajectory Sharpening
Ye Tian, Ling Yang, Xinchen Zhang +3
We propose Diffusion-Sharpening, a fine-tuning approach that enhances downstream alignment by optimizing sampling trajectories. Existing RL-based fine-tuning methods focus on singl…