Abstract We propose Diffusion Noise Optimization (DNO) a new method that effectively
leverages existing motion diffusion models as motion priors for a wide range of motion …

Diffusion models are a powerful generative framework, but come with expensive inference.
Existing acceleration methods often compromise image quality or fail under complex …

In text-to-speech (TTS) synthesis, diffusion models have achieved promising generation
quality. However, because of the pre-defined data-to-noise diffusion process, their prior …

Diffusion models play a pivotal role in contemporary generative modeling, claiming state-of-
the-art performance across various domains. Despite their superior sample quality …

We present Deep Compression Autoencoder (DC-AE), a new family of autoencoder models
for accelerating high-resolution diffusion models. Existing autoencoder models have …

Diffusion models have been demonstrated as strong priors for solving general inverse
problems. Most existing Diffusion model-based Inverse Problem Solvers (DIS) employ a …

Imitation learning aims to solve the problem of defining reward functions in real-world
decision-making tasks. The current popular approach is the Adversarial Imitation Learning …

Diffusion-based image super-resolution (SR) methods have shown promise in
reconstructing high-resolution images with fine details from low-resolution counterparts …

Scaling up model and data size has been quite successful for the evolution of LLMs.
However the scaling law for the diffusion based text-to-image (T2I) models is not fully …

An inherent fragility of quadrotor systems stems from model inaccuracies and external
disturbances. These factors hinder performance and compromise the stability of the system …