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

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.