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

Diffusion models have achieved unprecedented performance in generative modeling. The
commonly-adopted formulation of the latent code of diffusion models is a sequence of …

Deep generative models have unlocked another profound realm of human creativity. By
capturing and generalizing patterns within data, we have entered the epoch of all …

Diffusion models are generative models, which gradually add and remove noise to learn the
underlying distribution of training data for data generation. The components of diffusion …

Diffusion models are emerging expressive generative models, in which a large number of
time steps (inference steps) are required for a single image generation. To accelerate such …

Vision transformers (ViT) have shown promise in various vision tasks while the U-Net based
on a convolutional neural network (CNN) remains dominant in diffusion models. We design …

Diffusion-based generative models have achieved remarkable success in image generation.
Their guidance formulation allows an external model to plug-and-play control the generation …

The recent wave of AI-generated content (AIGC) has witnessed substantial success in
computer vision, with the diffusion model playing a crucial role in this achievement. Due to …

Diffusion models have shown incredible capabilities as generative models; indeed, they
power the current state-of-the-art models on text-conditioned image generation such as …

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 …