ASGDiffusion: Parallel High-Resolution Generation with Asynchronous Structure Guidance

TL;DR

ASGDiffusion introduces a parallel high-resolution image generation method that uses asynchronous structure guidance to improve semantic consistency, reduce pattern repetition, and accelerate the process with multi-GPU support.

Contribution

The paper presents a novel parallel HR image generation approach with asynchronous structure guidance, addressing pattern repetition and computational efficiency issues in diffusion models.

Findings

Reduces pattern repetition in HR images

Significantly accelerates generation speed with multi-GPU parallelism

Achieves state-of-the-art results in high-resolution image generation

Abstract

Training-free high-resolution (HR) image generation has garnered significant attention due to the high costs of training large diffusion models. Most existing methods begin by reconstructing the overall structure and then proceed to refine the local details. Despite their advancements, they still face issues with repetitive patterns in HR image generation. Besides, HR generation with diffusion models incurs significant computational costs. Thus, parallel generation is essential for interactive applications. To solve the above limitations, we introduce a novel method named ASGDiffusion for parallel HR generation with Asynchronous Structure Guidance (ASG) using pre-trained diffusion models. To solve the pattern repetition problem of HR image generation, ASGDiffusion leverages the low-resolution (LR) noise weighted by the attention mask as the structure guidance for the denoising step to ensure semantic consistency. The proposed structure guidance can significantly alleviate the pattern repetition problem. To enable parallel generation, we further propose a parallelism strategy, which calculates the patch noises and structure guidance asynchronously. By leveraging multi-GPU parallel acceleration, we significantly accelerate generation speed and reduce memory usage per GPU. Extensive experiments demonstrate that our method effectively and efficiently addresses common issues like pattern repetition and achieves state-of-the-art HR generation.