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

Text-to-Image (T2I) generative models are becoming increasingly crucial due to their ability
to generate high-quality images, which also raises concerns about the social biases in their …

Transformer models have emerged as dominant networks for various tasks in computer
vision compared to Convolutional Neural Networks (CNNs). The transformers demonstrate …

This paper identifies significant redundancy in the query-key interactions within self-attention
mechanisms of diffusion transformer models, particularly during the early stages of …

The rise of generative neural networks has triggered an increased demand for intellectual
property (IP) protection in generated content. Deep watermarking techniques, recognized for …

Diffusion Transformer (DiT), an emerging diffusion model for image generation, has
demonstrated superior performance but suffers from substantial computational costs. Our …

This paper investigates the role of CLIP image embeddings within the Stable Video Diffusion
(SVD) framework, focusing on their impact on video generation quality and computational …

The intensive computational burden of Stable Diffusion (SD) for text-to-image generation
poses a significant hurdle for its practical application. To tackle this challenge, recent …

Understanding long text is of great demands in practice but beyond the reach of most
language-image pre-training (LIP) models. In this work, we empirically confirm that the key …

City scene generation has gained significant attention in autonomous driving, smart city
development, and traffic simulation. It helps enhance infrastructure planning and monitoring …