3 citations · 11 across the 13 of their papers we have counts for
15 papers
EditStream: A Unified Autoregressive Framework for Interactive Video Generation and Editing
Yuqian Zhou, Zhenghong Zhou, Zongze Wu +5
Interactive video generation and editing are becoming increasingly important for creative design. In this report, we introduce EditStream: a unified framework for interactive video…
Improved Baselines with Representation Autoencoders
Jaskirat Singh, Boyang Zheng, Zongze Wu +3
Representation Autoencoders (RAE) replace traditional VAE with pretrained vision encoders. In this paper, we systematically investigate several design choices and find three insigh…
What matters for Representation Alignment: Global Information or Spatial Structure?
Jaskirat Singh, Xingjian Leng, Zongze Wu +4
Representation alignment (REPA) guides generative training by distilling representations from a strong, pretrained vision encoder to intermediate diffusion features. We investigate…
Long-Context State-Space Video World Models
Ryan Po, Yotam Nitzan, Richard Zhang +5
Video diffusion models have recently shown promise for world modeling through autoregressive frame prediction conditioned on actions. However, they struggle to maintain long-term m…
SliderSpace: Decomposing the Visual Capabilities of Diffusion Models
Rohit Gandikota, Zongze Wu, Richard Zhang +3
We present SliderSpace, a framework for automatically decomposing the visual capabilities of diffusion models into controllable and human-understandable directions. Unlike existing…
From Slow Bidirectional to Fast Autoregressive Video Diffusion Models
Tianwei Yin, Qiang Zhang, Richard Zhang +4
Current video diffusion models achieve impressive generation quality but struggle in interactive applications due to bidirectional attention dependencies. The generation of a singl…
Improved Distribution Matching Distillation for Fast Image Synthesis
Tianwei Yin, Michaël Gharbi, Taesung Park +4
Recent approaches have shown promises distilling diffusion models into efficient one-step generators. Among them, Distribution Matching Distillation (DMD) produces one-step generat…
Distilling Diffusion Models into Conditional GANs
Minguk Kang, Richard Zhang, Connelly Barnes +6
We propose a method to distill a complex multistep diffusion model into a single-step conditional GAN student model, dramatically accelerating inference, while preserving image qua…
VideoGigaGAN: Towards Detail-rich Video Super-Resolution
Yiran Xu, Taesung Park, Richard Zhang +5
Video super-resolution (VSR) approaches have shown impressive temporal consistency in upsampled videos. However, these approaches tend to generate blurrier results than their image…
Editable Image Elements for Controllable Synthesis
Jiteng Mu, Michaël Gharbi, Richard Zhang +4
Diffusion models have made significant advances in text-guided synthesis tasks. However, editing user-provided images remains challenging, as the high dimensional noise input space…
Lazy Diffusion Transformer for Interactive Image Editing
Yotam Nitzan, Zongze Wu, Richard Zhang +4
We introduce a novel diffusion transformer, LazyDiffusion, that generates partial image updates efficiently. Our approach targets interactive image editing applications in which, s…
Customizing Text-to-Image Diffusion with Object Viewpoint Control
Nupur Kumari, Grace Su, Richard Zhang +3
Model customization introduces new concepts to existing text-to-image models, enabling the generation of these new concepts/objects in novel contexts. However, such methods lack ac…
Jump Cut Smoothing for Talking Heads
Xiaojuan Wang, Taesung Park, Yang Zhou +2
A jump cut offers an abrupt, sometimes unwanted change in the viewing experience. We present a novel framework for smoothing these jump cuts, in the context of talking head videos.…
NewMove: Customizing text-to-video models with novel motions
Joanna Materzynska, Josef Sivic, Eli Shechtman +3
We introduce an approach for augmenting text-to-video generation models with customized motions, extending their capabilities beyond the motions depicted in the original training d…
One-step Diffusion with Distribution Matching Distillation
Tianwei Yin, Michaël Gharbi, Richard Zhang +4
Diffusion models generate high-quality images but require dozens of forward passes. We introduce Distribution Matching Distillation (DMD), a procedure to transform a diffusion mode…