Abstract Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks across different data modalities. A PFM (eg, BERT, ChatGPT, GPT-4) is …

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 …

Text-guided generative diffusion models unlock powerful image creation and editing tools.
Recent approaches that edit the content of footage while retaining structure require …

This work aims to learn a high-quality text-to-video (T2V) generative model by leveraging a
pre-trained text-to-image (T2I) model as a basis. It is a highly desirable yet challenging task …

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

Despite tremendous progress in generating high-quality images using diffusion models,
synthesizing a sequence of animated frames that are both photorealistic and temporally …

In this work, we investigate a simple and must-known conditional generative framework
based on Vector Quantised-Variational AutoEncoder (VQ-VAE) and Generative Pre-trained …

Image diffusion models, trained on massive image collections, have emerged as the most
versatile image generator model in terms of quality and diversity. They support inverting real …

The pursuit of controllability as a higher standard of visual content creation has yielded
remarkable progress in customizable image synthesis. However, achieving controllable …

A diffusion probabilistic model (DPM), which constructs a forward diffusion process by
gradually adding noise to data points and learns the reverse denoising process to generate …