The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …

Denoising diffusion models, a class of generative models, have garnered immense interest
lately in various deep-learning problems. A diffusion probabilistic model defines a forward …

Foundation models, now powering most of the exciting applications in deep learning, are
almost universally based on the Transformer architecture and its core attention module …

Text-to-audio (TTA) system has recently gained attention for its ability to synthesize general
audio based on text descriptions. However, previous studies in TTA have limited generation …

This paper introduces ModelScopeT2V, a text-to-video synthesis model that evolves from a
text-to-image synthesis model (ie, Stable Diffusion). ModelScopeT2V incorporates spatio …

Diffusion models generate high-quality images but require dozens of forward passes. We
introduce Distribution Matching Distillation (DMD) a procedure to transform a diffusion model …

Abstract 3D Semantic Scene Completion (SSC) has emerged as a nascent and pivotal
undertaking in autonomous driving aiming to predict the voxel occupancy within volumetric …

We propose a method for editing images from human instructions: given an input image and
a written instruction that tells the model what to do, our model follows these instructions to …

The recent wave of large-scale text-to-image diffusion models has dramatically increased
our text-based image generation abilities. These models can generate realistic images for a …

Typical diffusion models are trained to accept a particular form of conditioning, most
commonly text, and cannot be conditioned on other modalities without retraining. In this …