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

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 …

Abstract Latent Diffusion Models (LDMs) enable high-quality image synthesis while avoiding
excessive compute demands by training a diffusion model in a compressed lower …

The recent success of text-to-image synthesis has taken the world by storm and captured the
general public's imagination. From a technical standpoint, it also marked a drastic change in …

Abstract We introduce Adversarial Diffusion Distillation (ADD), a novel training approach that
efficiently samples large-scale foundational image diffusion models in just 1–4 steps while …

Latent Diffusion models (LDMs) have achieved remarkable results in synthesizing high-
resolution images. However, the iterative sampling process is computationally intensive and …

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 …

Text-to-image synthesis has recently seen significant progress thanks to large pretrained
language models, large-scale training data, and the introduction of scalable model families …

Currently, applying diffusion models in pixel space of high resolution images is difficult.
Instead, existing approaches focus on diffusion in lower dimensional spaces (latent …

During image editing, existing deep generative models tend to re-synthesize the entire
output from scratch, including the unedited regions. This leads to a significant waste of …