Denoising diffusion models represent a recent emerging topic in computer vision,
demonstrating remarkable results in the area of generative modeling. A diffusion model is a …

Diffusion models have emerged as a powerful new family of deep generative models with
record-breaking performance in many applications, including image synthesis, video …

Score distillation sampling (SDS) has shown great promise in text-to-3D generation by
distilling pretrained large-scale text-to-image diffusion models, but suffers from over …

A diffusion model learns to predict a vector field of gradients. We propose to apply chain rule
on the learned gradients, and back-propagate the score of a diffusion model through the …

We propose DiffusionDet, a new framework that formulates object detection as a denoising
diffusion process from noisy boxes to object boxes. During the training stage, object boxes …

Large-scale generative models are capable of producing high-quality images from detailed
prompts. However, many aspects of an image are difficult or impossible to convey through …

Diffusion models arise as a powerful generative tool recently. Despite the great progress,
existing diffusion models mainly focus on uni-modal control, ie, the diffusion process is …

A popular approach to protein design is to combine a generative model with a discriminative
model for conditional sampling. The generative model samples plausible sequences while …

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 …

Abstract Neural network-based Combinatorial Optimization (CO) methods have shown
promising results in solving various NP-complete (NPC) problems without relying on hand …