11 citations · 22 across the 23 of their papers we have counts for
11 papers · 1 filter
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…
TurboEdit: Instant text-based image editing
Zongze Wu, Nicholas Kolkin, Jonathan Brandt +2
We address the challenges of precise image inversion and disentangled image editing in the context of few-step diffusion models. We introduce an encoder based iterative inversion t…
Image Neural Field Diffusion Models
Yinbo Chen, Oliver Wang, Richard Zhang +3
Diffusion models have shown an impressive ability to model complex data distributions, with several key advantages over GANs, such as stable training, better coverage of the traini…
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…
Magic Fixup: Streamlining Photo Editing by Watching Dynamic Videos
Hadi Alzayer, Zhihao Xia, Xuaner Zhang +3
We propose a generative model that, given a coarsely edited image, synthesizes a photorealistic output that follows the prescribed layout. Our method transfers fine details from th…
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.…