Identity Decoupling for Multi-Subject Personalization of Text-to-Image Models

Text-to-image diffusion models have shown remarkable success in generating a personalized subject based on a few reference images. However, current methods struggle with handling multiple subjects simultaneously, often resulting in mixed identities with combined attributes from different subjects. In this work, we present MuDI, a novel framework that enables multi-subject personalization by effectively decoupling identities from multiple subjects. Our main idea is to utilize segmented subjects generated by the Segment Anything Model for both training and inference, as a form of data augmentation for training and initialization for the generation process. Our experiments demonstrate that MuDI can produce high-quality personalized images without identity mixing, even for highly similar subjects as shown in Figure 1. In human evaluation, MuDI shows twice as many successes for personalizing multiple subjects without identity mixing over existing baselines and is preferred over 70% compared to the strongest baseline. More results are available at https://mudi-t2i.github.io/.

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. Yet, existing generative models on manifolds suffer from expensive divergence computation or rely on approximations of heat kernel. These limitations restrict their applicability to simple geometries and hinder scalability to high dimensions. In this work, we introduce the Riemannian Diffusion Mixture, a principled framework for building a generative process on manifolds as a mixture of endpoint-conditioned diffusion processes instead of relying on the denoising approach of previous diffusion models, for which the generative process is characterized by its drift guiding toward the most probable endpoint with respect to the geometry of the manifold. We further propose a simple yet efficient training objective for learning the mixture process, that is readily applicable to general manifolds. Our method outperforms previous generative models on various manifolds while scaling to high dimensions and requires a dramatically reduced number of in-training simulation steps for general manifolds.

DiffusionNAG: Task-guided Neural Architecture Generation with Diffusion Models

Neural Architecture Search (NAS) has emerged as a powerful technique for automating neural architecture design. However, existing NAS methods either require an excessive amount of time for repetitive training or sampling of many task-irrelevant architectures. Moreover, they lack generalization across different tasks and usually require searching for optimal architectures for each task from scratch without reusing the knowledge from the previous NAS tasks. To tackle such limitations of existing NAS methods, we propose a novel transferable task-guided Neural Architecture Generation (NAG) framework based on diffusion models, dubbed DiffusionNAG. With the guidance of a surrogate model, such as a performance predictor for a given task, our DiffusionNAG can generate task-optimal architectures for diverse tasks, including unseen tasks. DiffusionNAG is highly efficient as it generates task-optimal neural architectures by leveraging the prior knowledge obtained from the previous tasks and neural architecture distribution. Furthermore, we introduce a score network to ensure the generation of valid architectures represented as directed acyclic graphs, unlike existing graph generative models that focus on generating undirected graphs. Extensive experiments demonstrate that DiffusionNAG significantly outperforms the state-of-the-art transferable NAG model in architecture generation quality, as well as previous NAS methods on four computer vision datasets with largely reduced computational cost.

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. While recent token-based approaches achieve competitive performance to diffusion-based models, their generation performance is still suboptimal as they sample multiple tokens simultaneously without considering the dependence among them. We empirically investigate this problem and propose a learnable sampling model, Text-Conditioned Token Selection (TCTS), to select optimal tokens via localized supervision with text information. TCTS improves not only the image quality but also the semantic alignment of the generated images with the given texts. To further improve the image quality, we introduce a cohesive sampling strategy, Frequency Adaptive Sampling (FAS), to each group of tokens divided according to the self-attention maps. We validate the efficacy of TCTS combined with FAS with various generative tasks, demonstrating that it significantly outperforms the baselines in image-text alignment and image quality. Our text-conditioned sampling framework further reduces the original inference time by more than 50% without modifying the original generative model.

Graph Generation with Destination-Driven Diffusion Mixture

Generation of graphs is a major challenge for real-world tasks that require understanding the complex nature of their non-Euclidean structures. Although diffusion models have achieved notable success in graph generation recently, they are ill-suited for modeling the structural information of graphs since learning to denoise the noisy samples does not explicitly capture the graph topology. To tackle this limitation, we propose a novel generative process that models the topology of graphs by predicting the destination of the process. Specifically, we design the generative process as a mixture of diffusion processes conditioned on the endpoint in the data distribution, which drives the process toward the probable destination. Further, we introduce new training objectives for learning to predict the destination, and discuss the advantages of our generative framework that can explicitly model the graph topology and exploit the inductive bias of the data. Through extensive experimental validation on general graph and 2D/3D molecular graph generation tasks, we show that our method outperforms previous generative models, generating graphs with correct topology with both continuous and discrete features.

Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations

Generating graph-structured data requires learning the underlying distribution of graphs. Yet, this is a challenging problem, and the previous graph generative methods either fail to capture the permutation-invariance property of graphs or cannot sufficiently model the complex dependency between nodes and edges, which is crucial for generating real-world graphs such as molecules. To overcome such limitations, we propose a novel score-based generative model for graphs with a continuous-time framework. 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). Then, we derive novel score matching objectives tailored for the proposed diffusion process to estimate the gradient of the joint log-density with respect to each component, and introduce a new solver for the system of SDEs to efficiently sample from the reverse diffusion process. We validate our graph generation method on diverse datasets, on which it either achieves significantly superior or competitive performance to the baselines. Further analysis shows that our method is able to generate molecules that lie close to the training distribution yet do not violate the chemical valency rule, demonstrating the effectiveness of the system of SDEs in modeling the node-edge relationships.

Edge Representation Learning with Hypergraphs

Graph neural networks have recently achieved remarkable success in representing graph-structured data, with rapid progress in both the node embedding and graph pooling methods. Yet, they mostly focus on capturing information from the nodes considering their connectivity, and not much work has been done in representing the edges, which are essential components of a graph. However, for tasks such as graph reconstruction and generation, as well as graph classification tasks for which the edges are important for discrimination, accurately representing edges of a given graph is crucial to the success of the graph representation learning. To this end, we propose a novel edge representation learning framework based on Dual Hypergraph Transformation (DHT), which transforms the edges of a graph into the nodes of a hypergraph. This dual hypergraph construction allows us to apply message passing techniques for node representations to edges. After obtaining edge representations from the hypergraphs, we then cluster or drop edges to obtain holistic graph-level edge representations. We validate our edge representation learning method with hypergraphs on diverse graph datasets for graph representation and generation performance, on which our method largely outperforms existing graph representation learning methods. Moreover, our edge representation learning and pooling method also largely outperforms state-of-the-art graph pooling methods on graph classification, not only because of its accurate edge representation learning, but also due to its lossless compression of the nodes and removal of irrelevant edges for effective message passing.