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

Diffusion Transformers have recently demonstrated unprecedented generative capabilities
for various tasks. The encouraging results, however, come with the cost of slow inference …

As one of the most popular and sought-after generative models in the recent years, diffusion
models have sparked the interests of many researchers and steadily shown excellent …

Diffusion probabilistic models can generate high-quality samples. Yet, their sampling
process requires numerous denoising steps, making it slow and computationally intensive …

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

Transformer-based diffusion models have achieved significant advancements across a
variety of generative tasks. However, producing high-quality outputs typically necessitates …

Diffusion Transformers (DiTs) have gained prominence for outstanding scalability and
extraordinary performance in generative tasks. However, their considerable inference costs …

Parameter generation has struggled to scale up for a long time, significantly limiting its range
of applications. In this study, we introduce\textbf {R} ecurrent diffusion for large-scale\textbf …

Diffusion-based generative models have emerged as powerful tools in the realm of
generative modeling. Despite extensive research on denoising across various timesteps …

As recent advances in large-scale Text-to-Image (T2I) diffusion models have yielded
remarkable high-quality image generation, diverse downstream Image-to-Image (I2I) …