One of the key shortcomings in current text-to-image (T2I) models is their inability to
consistently generate images which faithfully follow the spatial relationships specified in the …

The deployment of large-scale text-to-image diffusion models on mobile devices is impeded
by their substantial model size and slow inference speed. In this paper, we propose\textbf …

Modern diffusion models, particularly those utilizing a Transformer-based UNet for
denoising, rely heavily on self-attention operations to manage complex spatial relationships …

The evaluation of generative models has received significant attention in the machine
learning community. When applied to a multi-modal distribution which is common among …

Dynamic novel view synthesis aims to capture the temporal evolution of visual content within
videos. Existing methods struggle to distinguishing between motion and structure …

We study the scaling properties of latent diffusion models (LDMs) with an emphasis on their
sampling efficiency. While improved network architecture and inference algorithms have …

Sampling from diffusion probabilistic models (DPMs) is often expensive for high-quality
image generation and typically requires many steps with a large model. In this paper, we …

The deployment of large-scale text-to-image diffusion models on mobile devices is impeded
by their substantial model size and high latency. In this paper, we present MobileDiffusion …

Diffusion models have achieved remarkable progress in the field of image generation due to
their outstanding capabilities. However, these models require substantial computing …

Recent advances indicate that diffusion models hold great promise in image super-
resolution. While the latest methods are primarily based on latent diffusion models with …