PixelRush: Ultra-Fast, Training-Free High-Resolution Image Generation via One-step Diffusion

TL;DR

PixelRush is a novel, training-free framework that enables rapid high-resolution image generation by efficient patch-based denoising, significantly reducing generation time while maintaining high visual quality.

Contribution

We introduce PixelRush, the first tuning-free, high-resolution image generation method that eliminates multiple cycles, enabling 4K images in about 20 seconds with superior fidelity.

Findings

Generates 4K images in approximately 20 seconds.

Achieves 10x to 35x speedup over existing methods.

Maintains high visual fidelity in generated images.

Abstract

Pre-trained diffusion models excel at generating high-quality images but remain inherently limited by their native training resolution. Recent training-free approaches have attempted to overcome this constraint by introducing interventions during the denoising process; however, these methods incur substantial computational overhead, often requiring more than five minutes to produce a single 4K image. In this paper, we present PixelRush, the first tuning-free framework for practical high-resolution text-to-image generation. Our method builds upon the established patch-based inference paradigm but eliminates the need for multiple inversion and regeneration cycles. Instead, PixelRush enables efficient patch-based denoising within a low-step regime. To address artifacts introduced by patch blending in few-step generation, we propose a seamless blending strategy. Furthermore, we mitigate over-smoothing effects through a noise injection mechanism. PixelRush delivers exceptional efficiency, generating 4K images in approximately 20 seconds representing a 10$\times$ to 35$\times$ speedup over state-of-the-art methods while maintaining superior visual fidelity. Extensive experiments validate both the performance gains and the quality of outputs achieved by our approach.