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Found 23 papers, 11 papers with code
Cosmos World Foundation Model Platform for Physical AI
We position a world foundation model as a general-purpose world model that can be fine-tuned into customized world models for downstream applications.
Edify Image: High-Quality Image Generation with Pixel Space Laplacian Diffusion Models
We introduce Edify Image, a family of diffusion models capable of generating photorealistic image content with pixel-perfect accuracy.
Edify 3D: Scalable High-Quality 3D Asset Generation
We introduce Edify 3D, an advanced solution designed for high-quality 3D asset generation.
EdgeRunner: Auto-regressive Auto-encoder for Artistic Mesh Generation
Current auto-regressive mesh generation methods suffer from issues such as incompleteness, insufficient detail, and poor generalization.
Masked Diffusion Models are Secretly Time-Agnostic Masked Models and Exploit Inaccurate Categorical Sampling
Masked diffusion models (MDMs) have emerged as a popular research topic for generative modeling of discrete data, thanks to their superior performance over other discrete diffusion models, and are rivaling the auto-regressive models (ARMs) for language modeling tasks.
RefDrop: Controllable Consistency in Image or Video Generation via Reference Feature Guidance
Motivated by this observation, we find that a rank-1 coefficient is not necessary and simplifies the controllable generation mechanism.
Condition-Aware Neural Network for Controlled Image Generation
In parallel to prior conditional control methods, CAN controls the image generation process by dynamically manipulating the weight of the neural network.
DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models
To overcome this dilemma, we observe the high similarity between the input from adjacent diffusion steps and propose displaced patch parallelism, which takes advantage of the sequential nature of the diffusion process by reusing the pre-computed feature maps from the previous timestep to provide context for the current step.
Symbolic Music Generation with Non-Differentiable Rule Guided Diffusion
We propose Stochastic Control Guidance (SCG), a novel guidance method that only requires forward evaluation of rule functions that can work with pre-trained diffusion models in a plug-and-play way, thus achieving training-free guidance for non-differentiable rules for the first time.
Improved Order Analysis and Design of Exponential Integrator for Diffusion Models Sampling
By reformulating the differential equations in DMs and capitalizing on the theory of exponential integrators, we propose refined EI solvers that fulfill all the order conditions, which we designate as Refined Exponential Solver (RES).
DiffCollage: Parallel Generation of Large Content with Diffusion Models
We present DiffCollage, a compositional diffusion model that can generate large content by leveraging diffusion models trained on generating pieces of the large content.
eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers
Therefore, in contrast to existing works, we propose to train an ensemble of text-to-image diffusion models specialized for different synthesis stages.
Ranked #14 on
Text-to-Image Generation
on MS COCO
gDDIM: Generalized denoising diffusion implicit models
In the CLD, a diffusion model by augmenting the diffusion process with velocity, our algorithm achieves an FID score of 2. 26, on CIFAR10, with only 50 number of score function evaluations~(NFEs) and an FID score of 2. 86 with only 27 NFEs.
Fast Sampling of Diffusion Models with Exponential Integrator
Our goal is to develop a fast sampling method for DMs with a much less number of steps while retaining high sample quality.
Variational Wasserstein gradient flow
Wasserstein gradient flow has emerged as a promising approach to solve optimization problems over the space of probability distributions.
Path Integral Sampler: a stochastic control approach for sampling
The PIS is built on the Schr\"odinger bridge problem which aims to recover the most likely evolution of a diffusion process given its initial distribution and terminal distribution.
Diffusion Normalizing Flow
Our method is closely related to normalizing flow and diffusion probabilistic models and can be viewed as a combination of the two.
Learning Hidden Markov Models from Aggregate Observations
This problem arises when only the population level counts of the number of individuals at each time step are available, from which one seeks to learn the individual hidden Markov model.
Filtering for Aggregate Hidden Markov Models with Continuous Observations
We consider a class of filtering problems for large populations where each individual is modeled by the same hidden Markov model (HMM).
Incremental inference of collective graphical models
We consider incremental inference problems from aggregate data for collective dynamics.
Multi-marginal optimal transport and probabilistic graphical models
We study multi-marginal optimal transport problems from a probabilistic graphical model perspective.
Improving Robustness via Risk Averse Distributional Reinforcement Learning
One major obstacle that precludes the success of reinforcement learning in real-world applications is the lack of robustness, either to model uncertainties or external disturbances, of the trained policies.
Distributional Reinforcement Learning
reinforcement-learning
+2
Inference with Aggregate Data: An Optimal Transport Approach
Consequently, the celebrated Sinkhorn/iterative scaling algorithm for multi-marginal optimal transport can be leveraged together with the standard belief propagation algorithm to establish an efficient inference scheme which we call Sinkhorn belief propagation (SBP).
