8 papers
Seed-to-Seed: Unpaired Image Translation in Diffusion Seed Space
Or Greenberg, Eran Kishon, Dani Lischinski
We introduce Seed-to-Seed Translation (StS), a novel approach that combines GANs and diffusion models (DMs) for unpaired Image-to-Image Translation. Our approach is aimed at global…
Capacity-Controlled Multi-View Stylization of 3D Gaussian Splatting
Zhihao Wen, Yixin Yang, Bojian Wu +4
While 3D Gaussian Splatting (3DGS) provides an efficient and explicit representation for novel view synthesis, enforcing stylistic coherence across viewpoints remains challenging.…
Complexity-Balanced Diffusion Splitting
Noam Issachar, Dani Lischinski, Raanan Fattal
Standard continuous-time generative models rely on monolithic architectures that must navigate vastly different signal regimes, from isotropic noise to intricate data distributions…
Cycle-Consistent Tuning for Layered Image Decomposition
Zheng Gu, Min Lu, Zhida Sun +3
Disentangling visual layers in real-world images is a persistent challenge in vision and graphics, as such layers often involve non-linear and globally coupled interactions, includ…
DyPE: Dynamic Position Extrapolation for Ultra High Resolution Diffusion
Noam Issachar, Guy Yariv, Sagie Benaim +3
Diffusion Transformer models can generate images with remarkable fidelity and detail, yet training them at ultra-high resolutions remains extremely costly due to the self-attention…
Palette Aligned Image Diffusion
Elad Aharoni, Noy Porat, Dani Lischinski +1
We introduce the Palette-Adapter, a novel method for conditioning text-to-image diffusion models on a user-specified color palette. While palettes are a compact and intuitive tool…