In this paper, we introduce PixArt-Σ, a Diffusion Transformer model (DiT) capable of directly
generating images at 4K resolution. PixArt-Σ represents a significant advancement over its …

Diffusion models are the main driver of progress in image and video synthesis, but suffer
from slow inference speed. Distillation methods, like the recently introduced adversarial …

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 …

The biological functions of proteins often depend on dynamic structural ensembles. In this
work, we develop a flow-based generative modeling approach for learning and sampling the …

The pre-trained text-to-image diffusion models have been increasingly employed to tackle
the real-world image super-resolution (Real-ISR) problem due to their powerful generative …

Image data augmentation constitutes a critical methodology in modern computer vision
tasks, since it can facilitate towards enhancing the diversity and quality of training datasets; …

In this work, we address two limitations of existing conditional diffusion models: their slow
inference speed due to the iterative denoising process and their reliance on paired data for …

Recent approaches have shown promises distilling diffusion models into efficient one-step
generators. Among them, Distribution Matching Distillation (DMD) produces one-step …

We propose a method to distill a complex multistep diffusion model into a single-step
conditional GAN student model, dramatically accelerating inference, while preserving image …

We present a new method for making diffusion models faster to sample. The method distills
many-step diffusion models into few-step models by matching conditional expectations of …