Image restoration (IR) has been an indispensable and challenging task in the low-level
vision field, which strives to improve the subjective quality of images distorted by various …

Text-to-image diffusion models can create stunning images from natural language
descriptions that rival the work of professional artists and photographers. However, these …

Diffusion models have recently gained unprecedented attention in the field of image
synthesis due to their remarkable generative capabilities. Notwithstanding their prowess …

Diffusion models have achieved great success in image synthesis through iterative noise
estimation using deep neural networks. However, the slow inference, high memory …

This paper presents DreamLLM, a learning framework that first achieves versatile
Multimodal Large Language Models (MLLMs) empowered with frequently overlooked …

Diffusion models have recently dominated image synthesis and other related generative
tasks. However, the iterative denoising process is expensive in computations at inference …

Denoising diffusion generative models are capable of generating high-quality data, but
suffers from the computation-costly generation process, due to a iterative noise estimation …

General world models represent a crucial pathway toward achieving Artificial General
Intelligence (AGI), serving as the cornerstone for various applications ranging from virtual …

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 …

The Diffusion model a prevalent framework for image generation encounters significant
challenges in terms of broad applicability due to its extended inference times and substantial …