As ChatGPT goes viral, generative AI (AIGC, aka AI-generated content) has made headlines
everywhere because of its ability to analyze and create text, images, and beyond. With such …

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 …

We present ControlNet, a neural network architecture to add spatial conditioning controls to
large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large …

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 …

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

While generative models produce high-quality images of concepts learned from a large-
scale database, a user often wishes to synthesize instantiations of their own concepts (for …

The incredible generative ability of large-scale text-to-image (T2I) models has demonstrated
strong power of learning complex structures and meaningful semantics. However, relying …

To replicate the success of text-to-image (T2I) generation, recent works employ large-scale
video datasets to train a text-to-video (T2V) generator. Despite their promising results, such …

Large-scale text-to-image generative models have been a revolutionary breakthrough in the
evolution of generative AI, synthesizing diverse images with highly complex visual concepts …

Recent text-to-video generation approaches rely on computationally heavy training and
require large-scale video datasets. In this paper, we introduce a new task, zero-shot text-to …