Search Results for author:
Found 8 papers, 5 papers with code
Identity Decoupling for Multi-Subject Personalization of Text-to-Image Models
Our experiments demonstrate that MuDI can produce high-quality personalized images without identity mixing, even for highly similar subjects as shown in Figure 1.
Generative Modeling on Manifolds Through Mixture of Riemannian Diffusion Processes
Learning the distribution of data on Riemannian manifolds is crucial for modeling data from non-Euclidean space, which is required by many applications from diverse scientific fields.
DiffusionNAG: Predictor-guided Neural Architecture Generation with Diffusion Models
To tackle such limitations of existing NAS methods, we propose a paradigm shift from NAS to a novel conditional Neural Architecture Generation (NAG) framework based on diffusion models, dubbed DiffusionNAG.
Text-Conditioned Sampling Framework for Text-to-Image Generation with Masked Generative Models
Token-based masked generative models are gaining popularity for their fast inference time with parallel decoding.
Graph Generation with Diffusion Mixture
Generation of graphs is a major challenge for real-world tasks that require understanding the complex nature of their non-Euclidean structures.
Exploring Chemical Space with Score-based Out-of-distribution Generation
A well-known limitation of existing molecular generative models is that the generated molecules highly resemble those in the training set.
Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations
Specifically, we propose a new graph diffusion process that models the joint distribution of the nodes and edges through a system of stochastic differential equations (SDEs).
Edge Representation Learning with Hypergraphs
This dual hypergraph construction allows us to apply message-passing techniques for node representations to edges.
