HiWave: Training-Free High-Resolution Image Generation via Wavelet-Based
  Diffusion Sampling
Authors: name: Tobias Vontobel; name: Seyedmorteza Sadat; name: Farnood Salehi; name: Romann M. Weber
Published: 2025-06-25 13:58:37

Abstract: Diffusion models have emerged as the leading approach for image synthesis,
demonstrating exceptional photorealism and diversity. However, training
diffusion models at high resolutions remains computationally prohibitive, and
existing zero-shot generation techniques for synthesizing images beyond
training resolutions often produce artifacts, including object duplication and
spatial incoherence. In this paper, we introduce HiWave, a training-free,
zero-shot approach that substantially enhances visual fidelity and structural
coherence in ultra-high-resolution image synthesis using pretrained diffusion
models. Our method employs a two-stage pipeline: generating a base image from
the pretrained model followed by a patch-wise DDIM inversion step and a novel
wavelet-based detail enhancer module. Specifically, we first utilize inversion
methods to derive initial noise vectors that preserve global coherence from the
base image. Subsequently, during sampling, our wavelet-domain detail enhancer
retains low-frequency components from the base image to ensure structural
consistency, while selectively guiding high-frequency components to enrich fine
details and textures. Extensive evaluations using Stable Diffusion XL
demonstrate that HiWave effectively mitigates common visual artifacts seen in
prior methods, achieving superior perceptual quality. A user study confirmed
HiWave's performance, where it was preferred over the state-of-the-art
alternative in more than 80% of comparisons, highlighting its effectiveness for
high-quality, ultra-high-resolution image synthesis without requiring
retraining or architectural modifications.