10 papers · 1 filter
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…
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…
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…
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…
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…
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…